Home About us Contact
|
Home About us Contact | ||
|
|
||
Trait Loci (trait + locus)
Kinds of Trait Loci Terms modified by Trait Loci Selected AbstractsFine mapping and detection of the causative mutation underlying Quantitative Trait LociJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2010E. Uleberg Summary The effect on power and precision of including the causative SNP amongst the investigated markers in Quantitative Trait Loci (QTL) mapping experiments was investigated. Three fine mapping methods were tested to see which was most efficient in finding the causative mutation: combined linkage and linkage disequilibrium mapping (LLD); association mapping (MARK); a combination of LLD and association mapping (LLDMARK). Two simulated data sets were analysed: in one set, the causative SNP was included amongst the markers, while in the other set the causative SNP was masked between markers. Including the causative SNP amongst the markers increased both precision and power in the analyses. For the LLD method the number of correctly positioned QTL increased from 17 for the analysis without the causative SNP to 77 for the analysis including the causative SNP. The likelihood of the data analysis increased from 3.4 to 13.3 likelihood units for the MARK method when the causative SNP was included. When the causative SNP was masked between the analysed markers, the LLD method was most efficient in detecting the correct QTL position, while the MARK method was most efficient when the causative SNP was included as a marker in the analysis. The LLDMARK method, combining association mapping and LLD, assumes a QTL as the null hypothesis (using LLD method) and tests whether the ,putative causative SNP' explains significantly more variance than a QTL in the region. Thus, if the putative causative SNP does not only give an Identical-By-Descent (IBD) signal, but also an Alike-In-State (AIS) signal, LLDMARK gives a positive likelihood ratio. LLDMARK detected less than half as many causative SNPs as the other methods, and also had a relatively high false discovery rate when the QTL effect was large. LLDMARK may however be more robust against spurious associations, because the regional IBD is largely corrected for by fitting a QTL effect in the null hypothesis model. [source] Linkage and QTL mapping for Sus scrofa chromosome 11JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2003M. Dragos-Wendrich Summary Linkage and QTL maps of Sus scrofa chromosome 11 (SSC11) have been produced based on four microsatellite markers genotyped in three F2 families from Wild Boar (W), Meishan (M) and Pietrain (P) crosses. The maps were similar across the families and in agreement with already published maps. Quantitative Trait Loci (QTLs) were identified in the W × M family and not in the M × P and W × P families. The QTLs affected live weight, loin and neck meat weight, back-fat depth and food consumption, and explained up to 4.7% of the F2 phenotypic variance. Additive and dominance effects were similar in size. The Wild Boar QTL alleles led to higher trait values in comparison with Meishan alleles. Zusammenfassung Kopplungskarten für Chromosom 11 (SSC11), die durch Analyse von vier Mikrosatelliten-Markern in drei F2 -Familien aus Kreuzungen von Wildschwein (W), Meishan (M) und Pietrain (P) erstellt wurden, zeigten eine gute Übereinstimmung zwischen den Familien sowie mit Literaturergebnissen. Quantitative Trait Loci (QTLs) waren in der Familie W × M nachzuweisen, jedoch nicht in den Familien M × P und W × P. Sie beeinflussten Lebendgewicht, Kotelettstranggewicht, Rückenspeckdicke und Futteraufnahme und erklärten bis zu 4,7% der phänotypischen Varianz in der F2 -Generation. Additiv- und Dominanzeffekte waren ähnlich groß. Wildschwein-QTL-Allele führten im Vergleich zu Meishan-Allelen zu höheren Merkmalswerten. [source] Linkage and QTL mapping for Sus scrofa chromosome 12JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 2003G. Yue Summary The SSC12 (Sus scrofa chromosome 12) linkage and QTL maps were generated using 11 markers, of which seven to 10 have been used in the three F2 families based on Wild Boar (W), Meishan (M) and Pietrain (P) crosses. Linkage maps showed identical marker order among the families, but differed in total lengths. They were in agreement with the already published maps, except for the order SWR1021,SW605. Most quantitative trait loci (QTLs) affected fat or meat content in carcass, but were also found for some other traits (heart weight, CK20 values and teat number). They explained up to 5.4% of F2 phenotypic variance. Meishan alleles had stimulating effects on fat deposition and decreasing effects on lean content and CK20 value. The QTL profiles differed between families, with QTL effects in the vicinity of the GH1 locus found solely in the M × P family. Zusammenfassung Auf der Basis von elf Markern wurden Kopplungs- und QTL-Karten für Chromosom 12 (SSC12) in drei F2 -Familien aus Kreuzungen von Wildschwein (W), Meishan (M) und Pietrain (P) erstellt. Hierbei wurden sieben bis zehn Marker pro F2 -Familie benutzt. Die Kopplungskarten zeigten eine gleichartige Anordnung der Loci für alle Familien, jedoch mit verschiedenen Kartenlängen. Sie stimmen, außer in der Anordnung SWR1021,SW605, mit bereits publizierten Karten überein. Quantitative Trait Loci (QTLs) waren hauptsächlich für Merkmale des Fett-oder Fleischanteils im Schlachtkörper festzustellen, daneben aber auch für weitere Merkmale (Herzgewicht, CK20 -Wert, Zitzenzahl). Sie erklärten bis zu 5,4% der phänotypischen Varianz in der F2 -Generation. Meishan-Allele waren assoziiert mit einer Steigerung des Fettansatzes sowie einer Reduktion der Anteile wertvoller Teilstücke und der CK20 -Werte. Die QTL-Profile unterschieden sich zwischen den Familien und ließen Assoziationen mit dem GH1 -Locus nur in der Familie M × P erkennen. [source] Generalized marker regression and interval QTL mapping methods for binary traits in half-sib family designsJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 5 2001H. N. Kadarmideen A Generalized Marker Regression Mapping (GMR) approach was developed for mapping Quantitative Trait Loci (QTL) affecting binary polygenic traits in a single-family half-sib design. The GMR is based on threshold-liability model theory and regression of offspring phenotype on expected marker genotypes at flanking marker loci. Using simulation, statistical power and bias of QTL mapping for binary traits by GMR was compared with full QTL interval mapping based on a threshold model (GIM) and with a linear marker regression mapping method (LMR). Empirical significance threshold values, power and estimates of QTL location and effect were identical for GIM and GMR when QTL mapping was restricted to within the marker interval. These results show that the theory of the marker regression method for QTL mapping is also applicable to binary traits and possibly for traits with other non-normal distributions. The linear and threshold models based on marker regression (LMR and GMR) also resulted in similar estimates and power for large progeny group sizes, indicating that LMR can be used for binary data for balanced designs with large families, as this method is computationally simpler than GMR. GMR may have a greater potential than LMR for QTL mapping for binary traits in complex situations such as QTL mapping with complex pedigrees, random models and models with interactions. Generalisierte Marker Regression und Intervall QTL Kartierungsmethoden für binäre Merkmale in einem Halbgeschwisterdesign Es wurde ein Ansatz zur generalisierten Marker Regressions Kartierung (GMR) entwickelt, um quantitative Merkmalsloci (QTL) zu kartieren, die binäre polygenetische Merkmale in einem Einfamilien-Halbgeschwisterdesign beeinflussen. Das GMR basiert auf der Theorie eines Schwellenwertmodells und auf der Regression des Nachkommenphänotyps auf den erwarteten Markergenotyp der flankierenden Markerloci. Mittels Simulation wurde die statistische Power und Schiefe der QTL Kartierung für binäre Merkmale nach GMR verglichen mit vollständiger QTL Intervallkartierung, die auf einem Schwellenmodell (GIM) basiert, und mit einer Methode zur linearen Marker Regressions Kartierung (LMR). Empirische Signifikanzschwellenwerte, Power und Schätzer für die QTL Lokation und der Effekt waren für GIM und GMR identisch, so lange die QTL Kartierung innerhalb des Markerintervalls definiert war. Diese Ergebnisse zeigen, dass die Theorie der Marker Regressions-Methode zur QTL Kartierung auch für binäre Merkmale und möglicherweise auch für Merkmale, die keiner Normalverteilung folgen, geeignet ist. Die linearen und Schwellenmodelle, die auf Marker Regression (LMR und GMR) basieren, ergaben ebenfalls ähnliche Schätzer und Power bei großen Nachkommengruppen, was schlussfolgern lässt, dass LMR für binäre Daten in einem balancierten Design mit großen Familien genutzt werden kann. Schließlich ist diese Methode computertechnisch einfacher als GMR. GMR mag für die QTL Kartierung bei binären Merkmalen in komplexen Situationen ein größeres Potential haben als LMR. Ein Beispiel dafür ist die QTL Kartierung mit komplexen Pedigrees, zufälligen Modellen und Interaktionsmodellen. [source] Quantitative Trait Loci for BMD in an SM/J by NZB/BlNJ Intercross Population and Identification of Trps1 as a Probable Candidate Gene,,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 9 2008Naoki Ishimori Abstract Identification of genes that regulate BMD will enhance our understanding of osteoporosis and could provide novel molecular targets for treatment or prevention. We generated a mouse intercross population and carried out a quantitative trait locus (QTL) analysis of 143 female and 124 male F2 progeny from progenitor strains SM/J and NZB/BlNJ using whole body and vertebral areal BMD (aBMD) as measured by DXA. We found that both whole body and vertebral aBMD was affected by two loci on chromosome 9: one with a significant epistatic interaction on distal chromosome 8 and the other with a sex-specific effect. Two additional significant QTLs were identified on chromosome 12, and several suggestive ones were identified on chromosomes 5, 8, 15, and 19. The chromosome 9, 12, and 15 loci have been previously identified in other crosses. SNP-based haplotype analysis of the progenitor strains identified blocks within the QTL region that distinguish the low allele strains from the high allele strains, significantly narrowing the QTL region and reducing the possible candidate genes to 98 for chromosome 9, 31 for chromosome 12, and only 2 for chromosome 15. Trps1 is the most probable candidate gene for the chromosome 15 QTL. The sex-specific effects may help to elucidate the BMD differences between males and females. This study shows the power of statistical modeling to resolve linked QTLs and the use of haplotype analysis in narrowing the list of candidates. [source] Mapping Quantitative Trait Loci for Vertebral Trabecular Bone Volume Fraction and Microarchitecture in Mice,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 4 2004Mary L Bouxsein Abstract BMD, which reflects both cortical and cancellous bone, has been shown to be highly heritable; however, little is known about the specific genetic factors regulating trabecular bone. Genome-wide linkage analysis of vertebral trabecular bone traits in 914 adult female mice from the F2 intercross of C57BL/6J and C3H/HeJ inbred strains revealed a pattern of genetic regulation derived from 13 autosomes, with 5,13 QTLs associated with each of the traits. Ultimately, identification of genes that regulate trabecular bone traits may yield important information regarding mechanisms that regulate mechanical integrity of the skeleton. Introduction: Both cortical and cancellous bone influence the mechanical integrity of the skeleton, with the relative contribution of each varying with skeletal site. Whereas areal BMD, which reflects both cortical and cancellous bone, has been shown to be highly heritable, little is known about the genetic determinants of trabecular bone density and architecture. Materials and Methods: To identify heritable determinants of vertebral trabecular bone traits, we evaluated the fifth lumbar vertebra from 914 adult female mice from the F2 intercross of C57BL/6J (B6) and C3H/HeJ (C3H) progenitor strains. High-resolution ,CT was used to assess total volume (TV), bone volume (BV), bone volume fraction (BV/TV), trabecular thickness (Tb.Th), separation (Tb.Sp), and number (Tb.N) of the trabecular bone in the vertebral body in the progenitors (n = 8/strain) and female B6C3H-F2 progeny (n = 914). Genomic DNA from F2 progeny was screened for 118 PCR-based markers discriminating B6 and C3H alleles on all 19 autosomes. Results and Conclusions: Despite having a slightly larger trabecular bone compartment, C3H progenitors had dramatically lower vertebral trabecular BV/TV (,53%) and Tb.N (,40%) and higher Tb.Sp (71%) compared with B6 progenitors (p < 0.001 for all). Genome-wide quantitative trait analysis revealed a pattern of genetic regulation derived from 13 autosomes, with 5,13 quantitative trait loci (QTLs) associated with each of the vertebral trabecular bone traits, exhibiting adjusted LOD scores ranging from 3.1 to 14.4. The variance explained in the F2 population by each of the individual QTL after adjusting for contributions from other QTLs ranged from 0.8% to 5.9%. Taken together, the QTLs explained 22,33% of the variance of the vertebral traits in the F2 population. In conclusion, we observed a complex pattern of genetic regulation for vertebral trabecular bone volume fraction and microarchitecture using the F2 intercross of the C57BL/6J and C3H/HeJ inbred mouse strains and identified a number of QTLs, some of which are distinct from those that were previously identified for total femoral and vertebral BMD. Identification of genes that regulate trabecular bone traits may ultimately yield important information regarding the mechanisms that regulate the acquisition and maintenance of mechanical integrity of the skeleton. [source] Quantitative Trait Loci on Chromosomes 2p, 4p, and 13q Influence Bone Mineral Density of the Forearm and Hip in Mexican Americans,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 12 2003Candace M Kammerer Abstract We performed a genome scan using BMD data of the forearm and hip on 664 individuals in 29 Mexican-American families. We obtained evidence for QTL on chromosome 4p, affecting forearm BMD overall, and on chromosomes 2p and 13q, affecting hip BMD in men. Introduction: The San Antonio Family Osteoporosis Study (SAFOS) was designed to identify genes and environmental factors that influence bone mineral density (BMD) using data from large Mexican-American families. Materials and Methods: We performed a genome-wide linkage analysis using 416 highly polymorphic microsatellite markers spaced approximately 9.5 cM apart to locate and identify quantitative trait loci (QTL) that affect BMD of the forearm and hip. Multipoint variance components linkage analyses were done using data on all 664 subjects, as well as two subgroups of 259 men and 261 premenopausal women, from 29 families for which genotypic and phenotypic data were available. Results: We obtained significant evidence for a QTL affecting forearm (radius midpoint) BMD in men and women combined on chromosome 4p near D4S2639 (maximum LOD = 4.33, genomic p = 0.006) and suggestive evidence for a QTL on chromosome 12q near locus D12S2070 (maximum conditional LOD = 2.35). We found suggestive evidence for a QTL influencing trochanter BMD on chromosome 6 (maximum LOD = 2.27), but no evidence for QTL affecting the femoral neck in men and women combined. In men, we obtained evidence for QTL affecting neck and trochanter BMD on chromosomes 2p near D2S1780 (maximum LOD = 3.98, genomic p = 0.013) and 13q near D13S788 (maximum LOD = 3.46, genomic p = 0.039), respectively. We found no evidence for QTL affecting forearm or hip BMD in premenopausal women. Conclusion: These results provide strong evidence that a QTL on chromosome 4p affects radius BMD in Mexican-American men and women, as well as evidence that QTL on chromosomes 2p and 13q affect hip BMD in men. Our results are consistent with some reports in humans and mice. [source] Congenic Strains of Mice for Verification and Genetic Decomposition of Quantitative Trait Loci for Femoral Bone Mineral Density,JOURNAL OF BONE AND MINERAL RESEARCH, Issue 2 2003Kathryn L Shultz Abstract Peak femoral volumetric bone mineral density (femoral bone mineral density) in C57BL/6J (B6) 4-month-old female mice is 50% lower than in C3H/HeJ (C3H) and 34% lower than in CAST/EiJ (CAST) females. Genome-wide analyses of (B6 × C3H)F2 and (B6 × CAST)F2 4-month-old female progeny demonstrated that peak femoral bone mineral density is a complex quantitative trait associated with genetic loci (QTL) on numerous chromosomes (Chrs) and with trait heritabilities of 83% (C3H) and 57% (CAST). To test the effect of each QTL on femoral bone mineral density, two sets of loci (six each from C3H and CAST) were selected to make congenic strains by repeated backcrossing of donor mice carrying a given QTL-containing chromosomal region to recipient mice of the B6 progenitor strain. At the N6F1 generation, each B6.C3H and B6.CAST congenic strain (statistically 98% B6-like in genomic composition) was intercrossed to obtain N6F2 progeny for testing the effect of each QTL on femoral bone mineral density. In addition, the femoral bone mineral density QTL region on Chr 1 of C3H was selected for congenic subline development to facilitate fine mapping of this strong femoral bone mineral density locus. In 11 of 12 congenic strains, 6 B6.C3H and 5 B6.CAST, femoral bone mineral density in mice carrying c3h or cast alleles in the QTL regions was significantly different from that of littermates carrying b6 alleles. Differences also were observed in body weight, femoral length, and mid-diaphyseal periosteal circumference among these 11 congenic strains when compared with control littermates; however, these latter three phenotypes were not consistently correlated with femoral bone mineral density. Analyses of eight sublines derived from the B6.C3H-1T congenic region revealed two QTLs: one located between 36.9 and 49.7 centiMorgans (cM) and the other located between 73.2 and 100.0 cM distal to the centromere. In conclusion, these congenic strains provide proof of principle that many QTLs identified in the F2 analyses for femoral bone mineral density exert independent effects when transferred and expressed in a common genetic background. Furthermore, significant differences in femoral bone mineral density among the congenic strains were not consistently accompanied by changes in body weight, femur length, or periosteal circumference. Finally, decomposition of QTL regions by congenic sublines can reveal additional loci for phenotypes assigned to a QTL region and can markedly refine genomic locations of quantitative trait loci, providing the opportunity for candidate gene testing. [source] Quantitative Trait Loci for Panicle Layer Uniformity Identified in Doubled Haploid Lines of Rice in Two EnvironmentsJOURNAL OF INTEGRATIVE PLANT BIOLOGY, Issue 9 2009Liangyong Ma Abstract Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid (DH) population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus (QTL) for three related traits of panicle-layer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniformity in two locations: Hangzhou (HZ) and Hainan (HN). A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH -4 and qTPH -8 were co-located with the QTLs for qLPH -4 and qLPH -8, which were only significant in the HZ environment, whereas the qTPH -6 and qLPH -6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD -10-1 and qPLD -10-2, were closely linked to qTPH-10, and they might have been at the same locus. One QTL, qPLD -3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD -3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD -3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice. [source] Molecular Tagging and Mapping of Quantitative Trait Loci for Lint Percentage and Morphological Marker Genes in Upland CottonJOURNAL OF INTEGRATIVE PLANT BIOLOGY, Issue 3 2006Wang-Zhen Guo Abstract Using 219 F2 individuals developed by crossing the genetic standard line TM-1 and the multiple dominant marker line T586 in Gossypium hirsutum L., a genetic linkage map with 19 linkage groups was constructed based on simple sequence repeat (SSR) markers. Compared with our tetraploid backboned molecular genetic map from a(TM-1 × Hai 7124) × TM-1 BC1 population, 17 of the 19 linkage groups were combined and anchored to 12 chromosomes (sub-genomes). Of these groups, four morphological marker genes in T586 had been mapped into the molecular linkage map. Meanwhile, three quantitative trait loci for lint percentage were tagged and mapped separately on the A03 linkage group and chromosome 6. (Managing editor: Li-Hui Zhao) [source] Genomewide SNP Screen to Detect Quantitative Trait Loci for Alcohol Preference in the High Alcohol Preferring and Low Alcohol Preferring MiceALCOHOLISM, Issue 3 2009Paula Bice Background:, The high and low alcohol preferring (HAP1 and LAP1) mouse lines were selectively bred for differences in alcohol intake. The HAP1 and LAP1 mice are essentially noninbred lines that originated from the outbred colony of HS/Ibg mice, a heterogeneous stock developed from intercrossing 8 inbred strains of mice. Methods:, A total of 867 informative SNPs were genotyped in 989 HAP1 × LAP1 F2, 68 F1s, 14 parents (6 LAP1, 8 HAP1), as well as the 8 inbred strains of mice crossed to generate the HS/Ibg colony. Multipoint genome wide analyses were performed to simultaneously detect linked QTLs and also fine map these regions using the ancestral haplotypes. Results:, QTL analysis detected significant evidence of association on 4 chromosomes: 1, 3, 5, and 9. The region on chromosome 9 was previously found linked in a subset of these F2 animals using a whole genome microsatellite screen. Conclusions:, We have detected strong evidence of association to multiple chromosomal regions in the mouse. Several of these regions include candidate genes previously associated with alcohol dependence in humans or other animal models. [source] Alcohol Effects on Central Nervous System Gene Expression in Genetic Animal ModelsALCOHOLISM, Issue 2 2005William J. McBride This article summarizes the proceedings of a symposium presented at the 2004 annual meeting of the Research Society on Alcoholism in Vancouver, British Columbia, Canada. The organizers and chairs were William J. McBride and Michael F. Miles. The presentations were (1) Molecular Triangulation on Gene Expression Patterns in Behavioral Responses to Acute Ethanol, by Robnet T. Kerns; (2) Gene Expression in Limbic Regions After Ethanol Self-Infusion Into the Posterior Ventral Tegmental Area, by Zachary A. Rodd; (3) Microarray Analysis of CNS Limbic Regions of Inbred Alcohol-Preferring and -Nonpreferring rats and Effects of Alcohol Drinking, by Wendy N. Strother and Howard J. Edenberg; and (4) Microarray Analysis of Mouse Lines Selected for Chronic Ethanol Withdrawal Severity: The Convergence of Basal, Ethanol Regulated, and Proximity to Ethanol Quantitative Trait Loci to Identify Candidate Genes, by Joel G. Hashimoto and Kristine M. Wiren. [source] Investigation of Quantitative Trait Loci in the CCKAR Gene With Susceptibility to AlcoholismALCOHOLISM, Issue 2002Takehito Okubo Background Cholecystokinin (CCK) plays an important role in the function of the central nervous system by interacting with dopamine and other neurotransmitters. We previously reported genetic variations in the promoter and coding regions of the CCKA receptor (CCKAR), CCKBR, and CCK genes and a possible association between polymorphisms of the CCKAR gene and alcoholism. In this study, association analyses were re-examined between the polymorphisms of the promoter region of the CCKAR gene and patients with alcohol withdrawal symptoms, in addition to patients with alcoholic liver injury. Methods A total of 131 Japanese male patients with alcohol withdrawal symptoms, 70 Japanese patients with alcoholic liver injury, and 98 age-matched Japanese male controls (nonhabitual drinkers) were examined using polymerase chain reaction-based single strand conformational polymorphism and sequencing analyses. Results Significant differences between patients with hallucination and controls were found in the allele frequencies at the ,388 and ,85 loci of the CCKAR gene (p= 0.0095, p= 0.0087, respectively), but these differences were not significant after Bonferroni correction for multiple testing. In contrast, the frequency of the homozygous genotype ,85 CC was significantly higher in hallucination-positive patients than in controls (p= 0.0031) and in patients with hallucination accompanying delirium tremens than in controls (p= 0.0022), and these differences were significant after Bonferroni correction. Conclusions The data from the case control suggest that polymorphisms of the promoter region of the CCKAR gene do not play a major role in the pathogenesis of alcohol withdrawal symptoms or alcoholic liver injury. However, a significant association was found between polymorphism at the ,85 locus of the CCKAR gene and patients with hallucination, and especially patients with hallucination accompanying delirium tremens. [source] The genetics of adaptation to novel environments: selection on germination timing in Arabidopsis thalianaMOLECULAR ECOLOGY, Issue 7 2010BROOK T. MOYERS When studying selection during adaptation to novel environments, researchers have often paid little attention to an organism's earliest developmental stages. Despite this lack of attention, early life history traits may be under strong selection during colonization, as the expression of adaptive phenotypes at later points is contingent upon early survival. Moreover, the timing of early developmental transitions can constrain the timing of later transitions, with potentially large effects on fitness. In this issue, Huang et al. (2010) underscore the importance of early life history traits in the adaptation of Arabidopsis thaliana to old-field sites in North America. Using a new population of mapped recombinant inbred lines, the authors examined germination timing and total lifetime fitness of A. thaliana while varying site latitude, dispersal season, and maternal photoperiod. Huang et al. (2010) discovered several Quantitative Trait Loci (QTL) with large effects on fitness that colocalized with QTL for field germination timing and seed dormancy,demonstrating that fitness is genetically associated with these early life history traits, and that these loci are likely under strong selection during adaptation to novel environments. In the epistatic interactions of some loci, recombinant genotypes outperformed parental genotypes, supporting the potentially adaptive role of recombination. This study provides elegant evidence that traits expressed early in an organism's development can play an important role during adaptive evolution. [source] European Mathematical Genetics Meeting, Heidelberg, Germany, 12th,13th April 2007ANNALS OF HUMAN GENETICS, Issue 4 2007Article first published online: 28 MAY 200 Saurabh Ghosh 11 Indian Statistical Institute, Kolkata, India High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the contribution of a common QTL to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1 , trait 2 of sib 2 and conversely) given the identity-by-descent sharing at the marker locus. The null hypothesis cannot be rejected unless there exists a common QTL. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from the Collaborative Study On The Genetics Of Alcoholism project. Rémi Kazma 1 , Catherine Bonaïti-Pellié 1 , Emmanuelle Génin 12 INSERM UMR-S535 and Université Paris Sud, Villejuif, 94817, France Keywords: Gene-environment interaction, sibling recurrence risk, exposure correlation Gene-environment interactions may play important roles in complex disease susceptibility but their detection is often difficult. Here we show how gene-environment interactions can be detected by investigating the degree of familial aggregation according to the exposure of the probands. In case of gene-environment interaction, the distribution of genotypes of affected individuals, and consequently the risk in relatives, depends on their exposure. We developed a test comparing the risks in sibs according to the proband exposure. To evaluate the properties of this new test, we derived the formulas for calculating the expected risks in sibs according to the exposure of probands for various values of exposure frequency, relative risk due to exposure alone, frequencies of latent susceptibility genotypes, genetic relative risks and interaction coefficients. We find that the ratio of risks when the proband is exposed and not exposed is a good indicator of the interaction effect. We evaluate the power of the test for various sample sizes of affected individuals. We conclude that this test is valuable for diseases with moderate familial aggregation, only when the role of the exposure has been clearly evidenced. Since a correlation for exposure among sibs might lead to a difference in risks among sibs in the different proband exposure strata, we also add an exposure correlation coefficient in the model. Interestingly, we find that when this correlation is correctly accounted for, the power of the test is not decreased and might even be significantly increased. Andrea Callegaro 1 , Hans J.C. Van Houwelingen 1 , Jeanine Houwing-Duistermaat 13 Dept. of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands Keywords: Survival analysis, age at onset, score test, linkage analysis Non parametric linkage (NPL) analysis compares the identical by descent (IBD) sharing in sibling pairs to the expected IBD sharing under the hypothesis of no linkage. Often information is available on the marginal cumulative hazards (for example breast cancer incidence curves). Our aim is to extend the NPL methods by taking into account the age at onset of selected sibling pairs using these known marginal hazards. Li and Zhong (2002) proposed a (retrospective) likelihood ratio test based on an additive frailty model for genetic linkage analysis. From their model we derive a score statistic for selected samples which turns out to be a weighed NPL method. The weights depend on the marginal cumulative hazards and on the frailty parameter. A second approach is based on a simple gamma shared frailty model. Here, we simply test whether the score function of the frailty parameter depends on the excess IBD. We compare the performance of these methods using simulated data. Céline Bellenguez 1 , Carole Ober 2 , Catherine Bourgain 14 INSERM U535 and University Paris Sud, Villejuif, France 5 Department of Human Genetics, The University of Chicago, USA Keywords: Linkage analysis, linkage disequilibrium, high density SNP data Compared with microsatellite markers, high density SNP maps should be more informative for linkage analyses. However, because they are much closer, SNPs present important linkage disequilibrium (LD), which biases classical nonparametric multipoint analyses. This problem is even stronger in population isolates where LD extends over larger regions with a more stochastic pattern. We investigate the issue of linkage analysis with a 500K SNP map in a large and inbred 1840-member Hutterite pedigree, phenotyped for asthma. Using an efficient pedigree breaking strategy, we first identified linked regions with a 5cM microsatellite map, on which we focused to evaluate the SNP map. The only method that models LD in the NPL analysis is limited in both the pedigree size and the number of markers (Abecasis and Wigginton, 2005) and therefore could not be used. Instead, we studied methods that identify sets of SNPs with maximum linkage information content in our pedigree and no LD-driven bias. Both algorithms that directly remove pairs of SNPs in high LD and clustering methods were evaluated. Null simulations were performed to control that Zlr calculated with the SNP sets were not falsely inflated. Preliminary results suggest that although LD is strong in such populations, linkage information content slightly better than that of microsatellite maps can be extracted from dense SNP maps, provided that a careful marker selection is conducted. In particular, we show that the specific LD pattern requires considering LD between a wide range of marker pairs rather than only in predefined blocks. Peter Van Loo 1,2,3 , Stein Aerts 1,2 , Diether Lambrechts 4,5 , Bernard Thienpont 2 , Sunit Maity 4,5 , Bert Coessens 3 , Frederik De Smet 4,5 , Leon-Charles Tranchevent 3 , Bart De Moor 2 , Koen Devriendt 3 , Peter Marynen 1,2 , Bassem Hassan 1,2 , Peter Carmeliet 4,5 , Yves Moreau 36 Department of Molecular and Developmental Genetics, VIB, Belgium 7 Department of Human Genetics, University of Leuven, Belgium 8 Bioinformatics group, Department of Electrical Engineering, University of Leuven, Belgium 9 Department of Transgene Technology and Gene Therapy, VIB, Belgium 10 Center for Transgene Technology and Gene Therapy, University of Leuven, Belgium Keywords: Bioinformatics, gene prioritization, data fusion The identification of genes involved in health and disease remains a formidable challenge. Here, we describe a novel bioinformatics method to prioritize candidate genes underlying pathways or diseases, based on their similarity to genes known to be involved in these processes. It is freely accessible as an interactive software tool, ENDEAVOUR, at http://www.esat.kuleuven.be/endeavour. Unlike previous methods, ENDEAVOUR generates distinct prioritizations from multiple heterogeneous data sources, which are then integrated, or fused, into one global ranking using order statistics. ENDEAVOUR prioritizes candidate genes in a three-step process. First, information about a disease or pathway is gathered from a set of known "training" genes by consulting multiple data sources. Next, the candidate genes are ranked based on similarity with the training properties obtained in the first step, resulting in one prioritized list for each data source. Finally, ENDEAVOUR fuses each of these rankings into a single global ranking, providing an overall prioritization of the candidate genes. Validation of ENDEAVOUR revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified YPEL1 as a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. Finally, we are currently evaluating a pipeline combining array-CGH, ENDEAVOUR and in vivo validation in zebrafish to identify novel genes involved in congenital heart defects. Mark Broom 1 , Graeme Ruxton 2 , Rebecca Kilner 311 Mathematics Dept., University of Sussex, UK 12 Division of Environmental and Evolutionary Biology, University of Glasgow, UK 13 Department of Zoology, University of Cambridge, UK Keywords: Evolutionarily stable strategy, parasitism, asymmetric game Brood parasites chicks vary in the harm that they do to their companions in the nest. In this presentation we use game-theoretic methods to model this variation. Our model considers hosts which potentially abandon single nestlings and instead choose to re-allocate their reproductive effort to future breeding, irrespective of whether the abandoned chick is the host's young or a brood parasite's. The parasite chick must decide whether or not to kill host young by balancing the benefits from reduced competition in the nest against the risk of desertion by host parents. The model predicts that three different types of evolutionarily stable strategies can exist. (1) Hosts routinely rear depleted broods, the brood parasite always kills host young and the host never then abandons the nest. (2) When adult survival after deserting single offspring is very high, hosts always abandon broods of a single nestling and the parasite never kills host offspring, effectively holding them as hostages to prevent nest desertion. (3) Intermediate strategies, in which parasites sometimes kill their nest-mates and host parents sometimes desert nests that contain only a single chick, can also be evolutionarily stable. We provide quantitative descriptions of how the values given to ecological and behavioral parameters of the host-parasite system influence the likelihood of each strategy and compare our results with real host-brood parasite associations in nature. Martin Harrison 114 Mathematics Dept, University of Sussex, UK Keywords: Brood parasitism, games, host, parasite The interaction between hosts and parasites in bird populations has been studied extensively. Game theoretical methods have been used to model this interaction previously, but this has not been studied extensively taking into account the sequential nature of this game. We consider a model allowing the host and parasite to make a number of decisions, which depend on a number of natural factors. The host lays an egg, a parasite bird will arrive at the nest with a certain probability and then chooses to destroy a number of the host eggs and lay one of it's own. With some destruction occurring, either natural or through the actions of the parasite, the host chooses to continue, eject an egg (hoping to eject the parasite) or abandon the nest. Once the eggs have hatched the game then falls to the parasite chick versus the host. The chick chooses to destroy or eject a number of eggs. The final decision is made by the host, choosing whether to raise or abandon the chicks that are in the nest. We consider various natural parameters and probabilities which influence these decisions. We then use this model to look at real-world situations of the interactions of the Reed Warbler and two different parasites, the Common Cuckoo and the Brown-Headed Cowbird. These two parasites have different methods in the way that they parasitize the nests of their hosts. The hosts in turn have a different reaction to these parasites. Arne Jochens 1 , Amke Caliebe 2 , Uwe Roesler 1 , Michael Krawczak 215 Mathematical Seminar, University of Kiel, Germany 16 Institute of Medical Informatics and Statistics, University of Kiel, Germany Keywords: Stepwise mutation model, microsatellite, recursion equation, temporal behaviour We consider the stepwise mutation model which occurs, e.g., in microsatellite loci. Let X(t,i) denote the allelic state of individual i at time t. We compute expectation, variance and covariance of X(t,i), i=1,,,N, and provide a recursion equation for P(X(t,i)=z). Because the variance of X(t,i) goes to infinity as t grows, for the description of the temporal behaviour, we regard the scaled process X(t,i)-X(t,1). The results furnish a better understanding of the behaviour of the stepwise mutation model and may in future be used to derive tests for neutrality under this model. Paul O'Reilly 1 , Ewan Birney 2 , David Balding 117 Statistical Genetics, Department of Epidemiology and Public Health, Imperial, College London, UK 18 European Bioinformatics Institute, EMBL, Cambridge, UK Keywords: Positive selection, Recombination rate, LD, Genome-wide, Natural Selection In recent years efforts to develop population genetics methods that estimate rates of recombination and levels of natural selection in the human genome have intensified. However, since the two processes have an intimately related impact on genetic variation their inference is vulnerable to confounding. Genomic regions subject to recent selection are likely to have a relatively recent common ancestor and consequently less opportunity for historical recombinations that are detectable in contemporary populations. Here we show that selection can reduce the population-based recombination rate estimate substantially. In genome-wide studies for detecting selection we observe a tendency to highlight loci that are subject to low levels of recombination. We find that the outlier approach commonly adopted in such studies may have low power unless variable recombination is accounted for. We introduce a new genome-wide method for detecting selection that exploits the sensitivity to recent selection of methods for estimating recombination rates, while accounting for variable recombination using pedigree data. Through simulations we demonstrate the high power of the Ped/Pop approach to discriminate between neutral and adaptive evolution, particularly in the context of choosing outliers from a genome-wide distribution. Although methods have been developed showing good power to detect selection ,in action', the corresponding window of opportunity is small. In contrast, the power of the Ped/Pop method is maintained for many generations after the fixation of an advantageous variant Sarah Griffiths 1 , Frank Dudbridge 120 MRC Biostatistics Unit, Cambridge, UK Keywords: Genetic association, multimarker tag, haplotype, likelihood analysis In association studies it is generally too expensive to genotype all variants in all subjects. We can exploit linkage disequilibrium between SNPs to select a subset that captures the variation in a training data set obtained either through direct resequencing or a public resource such as the HapMap. These ,tag SNPs' are then genotyped in the whole sample. Multimarker tagging is a more aggressive adaptation of pairwise tagging that allows for combinations of two or more tag SNPs to predict an untyped SNP. Here we describe a new method for directly testing the association of an untyped SNP using a multimarker tag. Previously, other investigators have suggested testing a specific tag haplotype, or performing a weighted analysis using weights derived from the training data. However these approaches do not properly account for the imperfect correlation between the tag haplotype and the untyped SNP. Here we describe a straightforward approach to testing untyped SNPs using a missing-data likelihood analysis, including the tag markers as nuisance parameters. The training data is stacked on top of the main body of genotype data so there is information on how the tag markers predict the genotype of the untyped SNP. The uncertainty in this prediction is automatically taken into account in the likelihood analysis. This approach yields more power and also a more accurate prediction of the odds ratio of the untyped SNP. Anke Schulz 1 , Christine Fischer 2 , Jenny Chang-Claude 1 , Lars Beckmann 121 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany 22 Institute of Human Genetics, University of Heidelberg, Germany Keywords: Haplotype, haplotype sharing, entropy, Mantel statistics, marker selection We previously introduced a new method to map genes involved in complex diseases, using haplotype sharing-based Mantel statistics to correlate genetic and phenotypic similarity. Although the Mantel statistic is powerful in narrowing down candidate regions, the precise localization of a gene is hampered in genomic regions where linkage disequilibrium is so high that neighboring markers are found to be significant at similar magnitude and we are not able to discriminate between them. Here, we present a new approach to localize susceptibility genes by combining haplotype sharing-based Mantel statistics with an iterative entropy-based marker selection algorithm. For each marker at which the Mantel statistic is evaluated, the algorithm selects a subset of surrounding markers. The subset is chosen to maximize multilocus linkage disequilibrium, which is measured by the normalized entropy difference introduced by Nothnagel et al. (2002). We evaluated the algorithm with respect to type I error and power. Its ability to localize the disease variant was compared to the localization (i) without marker selection and (ii) considering haplotype block structure. Case-control samples were simulated from a set of 18 haplotypes, consisting of 15 SNPs in two haplotype blocks. The new algorithm gave correct type I error and yielded similar power to detect the disease locus compared to the alternative approaches. The neighboring markers were clearly less often significant than the causal locus, and also less often significant compared to the alternative approaches. Thus the new algorithm improved the precision of the localization of susceptibility genes. Mark M. Iles 123 Section of Epidemiology and Biostatistics, LIMM, University of Leeds, UK Keywords: tSNP, tagging, association, HapMap Tagging SNPs (tSNPs) are commonly used to capture genetic diversity cost-effectively. However, it is important that the efficacy of tSNPs is correctly estimated, otherwise coverage may be insufficient. If the pilot sample from which tSNPs are chosen is too small or the initial marker map too sparse, tSNP efficacy may be overestimated. An existing estimation method based on bootstrapping goes some way to correct for insufficient sample size and overfitting, but does not completely solve the problem. We describe a novel method, based on exclusion of haplotypes, that improves on the bootstrap approach. Using simulated data, the extent of the sample size problem is investigated and the performance of the bootstrap and the novel method are compared. We incorporate an existing method adjusting for marker density by ,SNP-dropping'. We find that insufficient sample size can cause large overestimates in tSNP efficacy, even with as many as 100 individuals, and the problem worsens as the region studied increases in size. Both the bootstrap and novel method correct much of this overestimate, with our novel method consistently outperforming the bootstrap method. We conclude that a combination of insufficient sample size and overfitting may lead to overestimation of tSNP efficacy and underpowering of studies based on tSNPs. Our novel approach corrects for much of this bias and is superior to the previous method. Sample sizes larger than previously suggested may still be required for accurate estimation of tSNP efficacy. This has obvious ramifications for the selection of tSNPs from HapMap data. Claudio Verzilli 1 , Juliet Chapman 1 , Aroon Hingorani 2 , Juan Pablo-Casas 1 , Tina Shah 2 , Liam Smeeth 1 , John Whittaker 124 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK 25 Division of Medicine, University College London, UK Keywords: Meta-analysis, Genetic association studies We present a Bayesian hierarchical model for the meta-analysis of candidate gene studies with a continuous outcome. Such studies often report results from association tests for different, possibly study-specific and non-overlapping markers (typically SNPs) in the same genetic region. Meta analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequlibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker-wise meta analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the effect of each variant is corrected for the effect of other variants, as in multiple regression. Here we develop a Bayesian hierarchical linear regression that models the observed genotype group means and uses pairwise LD measurements between markers as prior information to make posterior inference on adjusted effects. The approach is applied to the meta analysis of 24 studies assessing the effect of 7 variants in the C-reactive protein (CRP) gene region on plasma CRP levels, an inflammatory biomarker shown in observational studies to be positively associated with cardiovascular disease. Cathryn M. Lewis 1 , Christopher G. Mathew 1 , Theresa M. Marteau 226 Dept. of Medical and Molecular Genetics, King's College London, UK 27 Department of Psychology, King's College London, UK Keywords: Risk, genetics, CARD15, smoking, model Recently progress has been made in identifying mutations that confer susceptibility to complex diseases, with the potential to use these mutations in determining disease risk. We developed methods to estimate disease risk based on genotype relative risks (for a gene G), exposure to an environmental factor (E), and family history (with recurrence risk ,R for a relative of type R). ,R must be partitioned into the risk due to G (which is modelled independently) and the residual risk. The risk model was then applied to Crohn's disease (CD), a severe gastrointestinal disease for which smoking increases disease risk approximately 2-fold, and mutations in CARD15 confer increased risks of 2.25 (for carriers of a single mutation) and 9.3 (for carriers of two mutations). CARD15 accounts for only a small proportion of the genetic component of CD, with a gene-specific ,S, CARD15 of 1.16, from a total sibling relative risk of ,S= 27. CD risks were estimated for high-risk individuals who are siblings of a CD case, and who also smoke. The CD risk to such individuals who carry two CARD15 mutations is approximately 0.34, and for those carrying a single CARD15 mutation the risk is 0.08, compared to a population prevalence of approximately 0.001. These results imply that complex disease genes may be valuable in estimating with greater precision than has hitherto been possible disease risks in specific, easily identified subgroups of the population with a view to prevention. Yurii Aulchenko 128 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Compression, information, bzip2, genome-wide SNP data, statistical genetics With advances in molecular technology, studies accessing millions of genetic polymorphisms in thousands of study subjects will soon become common. Such studies generate large amounts of data, whose effective storage and management is a challenge to the modern statistical genetics. Standard file compression utilities, such as Zip, Gzip and Bzip2, may be helpful to minimise the storage requirements. Less obvious is the fact that the data compression techniques may be also used in the analysis of genetic data. It is known that the efficiency of a particular compression algorithm depends on the probability structure of the data. In this work, we compared different standard and customised tools using the data from human HapMap project. Secondly, we investigate the potential uses of data compression techniques for the analysis of linkage, association and linkage disequilibrium Suzanne Leal 1 , Bingshan Li 129 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, USA Keywords: Consanguineous pedigrees, missing genotype data Missing genotype data can increase false-positive evidence for linkage when either parametric or nonparametric analysis is carried out ignoring intermarker linkage disequilibrium (LD). Previously it was demonstrated by Huang et al (2005) that no bias occurs in this situation for affected sib-pairs with unrelated parents when either both parents are genotyped or genotype data is available for two additional unaffected siblings when parental genotypes are missing. However, this is not the case for consanguineous pedigrees, where missing genotype data for any pedigree member within a consanguinity loop can increase false-positive evidence of linkage. The false-positive evidence for linkage is further increased when cryptic consanguinity is present. The amount of false-positive evidence for linkage is highly dependent on which family members are genotyped. When parental genotype data is available, the false-positive evidence for linkage is usually not as strong as when parental genotype data is unavailable. Which family members will aid in the reduction of false-positive evidence of linkage is highly dependent on which other family members are genotyped. For a pedigree with an affected proband whose first-cousin parents have been genotyped, further reduction in the false-positive evidence of linkage can be obtained by including genotype data from additional affected siblings of the proband or genotype data from the proband's sibling-grandparents. When parental genotypes are not available, false-positive evidence for linkage can be reduced by including in the analysis genotype data from either unaffected siblings of the proband or the proband's married-in-grandparents. Najaf Amin 1 , Yurii Aulchenko 130 Department of Epidemiology & Biostatistics, Erasmus Medical Centre Rotterdam, The Netherlands Keywords: Genomic Control, pedigree structure, quantitative traits The Genomic Control (GC) method was originally developed to control for population stratification and cryptic relatedness in association studies. This method assumes that the effect of population substructure on the test statistics is essentially constant across the genome, and therefore unassociated markers can be used to estimate the effect of confounding onto the test statistic. The properties of GC method were extensively investigated for different stratification scenarios, and compared to alternative methods, such as the transmission-disequilibrium test. The potential of this method to correct not for occasional cryptic relations, but for regular pedigree structure, however, was not investigated before. In this work we investigate the potential of the GC method for pedigree-based association analysis of quantitative traits. The power and type one error of the method was compared to standard methods, such as the measured genotype (MG) approach and quantitative trait transmission-disequilibrium test. In human pedigrees, with trait heritability varying from 30 to 80%, the power of MG and GC approach was always higher than that of TDT. GC had correct type 1 error and its power was close to that of MG under moderate heritability (30%), but decreased with higher heritability. William Astle 1 , Chris Holmes 2 , David Balding 131 Department of Epidemiology and Public Health, Imperial College London, UK 32 Department of Statistics, University of Oxford, UK Keywords: Population structure, association studies, genetic epidemiology, statistical genetics In the analysis of population association studies, Genomic Control (Devlin & Roeder, 1999) (GC) adjusts the Armitage test statistic to correct the type I error for the effects of population substructure, but its power is often sub-optimal. Turbo Genomic Control (TGC) generalises GC to incorporate co-variation of relatedness and phenotype, retaining control over type I error while improving power. TGC is similar to the method of Yu et al. (2006), but we extend it to binary (case-control) in addition to quantitative phenotypes, we implement improved estimation of relatedness coefficients, and we derive an explicit statistic that generalizes the Armitage test statistic and is fast to compute. TGC also has similarities to EIGENSTRAT (Price et al., 2006) which is a new method based on principle components analysis. The problems of population structure(Clayton et al., 2005) and cryptic relatedness (Voight & Pritchard, 2005) are essentially the same: if patterns of shared ancestry differ between cases and controls, whether distant (coancestry) or recent (cryptic relatedness), false positives can arise and power can be diminished. With large numbers of widely-spaced genetic markers, coancestry can now be measured accurately for each pair of individuals via patterns of allele-sharing. Instead of modelling subpopulations, we work instead with a coancestry coefficient for each pair of individuals in the study. We explain the relationships between TGC, GC and EIGENSTRAT. We present simulation studies and real data analyses to illustrate the power advantage of TGC in a range of scenarios incorporating both substructure and cryptic relatedness. References Clayton, D. G.et al. (2005) Population structure, differential bias and genomic control in a large-scale case-control association study. Nature Genetics37(11) November 2005. Devlin, B. & Roeder, K. (1999) Genomic control for association studies. Biometics55(4) December 1999. Price, A. L.et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics38(8) (August 2006). Voight, B. J. & Pritchard, J. K. (2005) Confounding from cryptic relatedness in case-control association studies. Public Library of Science Genetics1(3) September 2005. Yu, J.et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics38(2) February 2006. Hervé Perdry 1 , Marie-Claude Babron 1 , Françoise Clerget-Darpoux 133 INSERM U535 and Univ. Paris Sud, UMR-S 535, Villejuif, France Keywords: Modifier genes, case-parents trios, ordered transmission disequilibrium test A modifying locus is a polymorphic locus, distinct from the disease locus, which leads to differences in the disease phenotype, either by modifying the penetrance of the disease allele, or by modifying the expression of the disease. The effect of such a locus is a clinical heterogeneity that can be reflected by the values of an appropriate covariate, such as the age of onset, or the severity of the disease. We designed the Ordered Transmission Disequilibrium Test (OTDT) to test for a relation between the clinical heterogeneity, expressed by the covariate, and marker genotypes of a candidate gene. The method applies to trio families with one affected child and his parents. Each family member is genotyped at a bi-allelic marker M of a candidate gene. To each of the families is associated a covariate value; the families are ordered on the values of this covariate. As the TDT (Spielman et al. 1993), the OTDT is based on the observation of the transmission rate T of a given allele at M. The OTDT aims to find a critical value of the covariate which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions. Acknowledgments H Perdry is funded by ARSEP. Pascal Croiseau 1 , Heather Cordell 2 , Emmanuelle Génin 134 INSERM U535 and University Paris Sud, UMR-S535, Villejuif, France 35 Institute of Human Genetics, Newcastle University, UK Keywords: Association, missing data, conditionnal logistic regression Missing data is an important problem in association studies. Several methods used to test for association need that individuals be genotyped at the full set of markers. Individuals with missing data need to be excluded from the analysis. This could involve an important decrease in sample size and a loss of information. If the disease susceptibility locus (DSL) is poorly typed, it is also possible that a marker in linkage disequilibrium gives a stronger association signal than the DSL. One may then falsely conclude that the marker is more likely to be the DSL. We recently developed a Multiple Imputation method to infer missing data on case-parent trios Starting from the observed data, a few number of complete data sets are generated by Markov-Chain Monte Carlo approach. These complete datasets are analysed using standard statistical package and the results are combined as described in Little & Rubin (2002). Here we report the results of simulations performed to examine, for different patterns of missing data, how often the true DSL gives the highest association score among different loci in LD. We found that multiple imputation usually correctly detect the DSL site even if the percentage of missing data is high. This is not the case for the naïve approach that consists in discarding trios with missing data. In conclusion, Multiple imputation presents the advantage of being easy to use and flexible and is therefore a promising tool in the search for DSL involved in complex diseases. Salma Kotti 1 , Heike Bickeböller 2 , Françoise Clerget-Darpoux 136 University Paris Sud, UMR-S535, Villejuif, France 37 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany Keywords: Genotype relative risk, internal controls, Family based analyses Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRRs. We will analytically derive the GRR estimators for the 1:1 and 1:3 matching and will present the results at the meeting. Family based analyses using internal controls are very popular both for detecting the effect of a genetic factor and for estimating the relative disease risk on the corresponding genotypes. Two different procedures are often applied to reconstitute internal controls. The first one considers one pseudocontrol genotype formed by the parental non-transmitted alleles called also 1:1 matching of alleles, while the second corresponds to three pseudocontrols corresponding to all genotypes formed by the parental alleles except the one of the case (1:3 matching). Many studies have compared between the two procedures in terms of the power and have concluded that the difference depends on the underlying genetic model and the allele frequencies. However, the estimation of the Genotype Relative Risk (GRR) under the two procedures has not been studied. Based on the fact that on the 1:1 matching, the control group is composed of the alleles untransmitted to the affected child and on the 1:3 matching, the control group is composed amongst alleles already transmitted to the affected child, we expect a difference on the GRR estimation. In fact, we suspect that the second procedure leads to biased estimation of the GRR. We will analytically derive the GRR estimator for the 1:1 and 1:3 matching and will present the results at the meeting. Luigi Palla 1 , David Siegmund 239 Department of Mathematics,Free University Amsterdam, The Netherlands 40 Department of Statistics, Stanford University, California, USA Keywords: TDT, assortative mating, inbreeding, statistical power A substantial amount of Assortative Mating (AM) is often recorded on physical and psychological, dichotomous as well as quantitative traits that are supposed to have a multifactorial genetic component. In particular AM has the effect of increasing the genetic variance, even more than inbreeding because it acts across loci beside within loci, when the trait has a multifactorial origin. Under the assumption of a polygenic model for AM dating back to Wright (1921) and refined by Crow and Felsenstein (1968,1982), the effect of assortative mating on the power to detect genetic association in the Transmission Disequilibrium Test (TDT) is explored as parameters, such as the effective number of genes and the allelic frequency vary. The power is reflected by the non centrality parameter of the TDT and is expressed as a function of the number of trios, the relative risk of the heterozygous genotype and the allele frequency (Siegmund and Yakir, 2007). The noncentrality parameter of the relevant score statistic is updated considering the effect of AM which is expressed in terms of an ,effective' inbreeding coefficient. In particular, for dichotomous traits it is apparent that the higher the number of genes involved in the trait, the lower the loss in power due to AM. Finally an attempt is made to extend this relation to the Q-TDT (Rabinowitz, 1997), which involves considering the effect of AM also on the phenotypic variance of the trait of interest, under the assumption that AM affects only its additive genetic component. References Crow, & Felsenstein, (1968). The effect of assortative mating on the genetic composition of a population. Eugen.Quart.15, 87,97. Rabinowitz,, 1997. A Transmission Disequilibrium Test for Quantitative Trait Loci. Human Heredity47, 342,350. Siegmund, & Yakir, (2007) Statistics of gene mapping, Springer. Wright, (1921). System of mating.III. Assortative mating based on somatic resemblance. Genetics6, 144,161. Jérémie Nsengimana 1 , Ben D Brown 2 , Alistair S Hall 2 , Jenny H Barrett 141 Leeds Institute of Molecular Medicine, University of Leeds, UK 42 Leeds Institute for Genetics, Health and Therapeutics, University of Leeds, UK Keywords: Inflammatory genes, haplotype, coronary artery disease Genetic Risk of Acute Coronary Events (GRACE) is an initiative to collect cases of coronary artery disease (CAD) and their unaffected siblings in the UK and to use them to map genetic variants increasing disease risk. The aim of the present study was to test the association between CAD and 51 single nucleotide polymorphisms (SNPs) and their haplotypes from 35 inflammatory genes. Genotype data were available for 1154 persons affected before age 66 (including 48% before age 50) and their 1545 unaffected siblings (891 discordant families). Each SNP was tested for association to CAD, and haplotypes within genes or gene clusters were tested using FBAT (Rabinowitz & Laird, 2000). For the most significant results, genetic effect size was estimated using conditional logistic regression (CLR) within STATA adjusting for other risk factors. Haplotypes were assigned using HAPLORE (Zhang et al., 2005), which considers all parental mating types consistent with offspring genotypes and assigns them a probability of occurence. This probability was used in CLR to weight the haplotypes. In the single SNP analysis, several SNPs showed some evidence for association, including one SNP in the interleukin-1A gene. Analysing haplotypes in the interleukin-1 gene cluster, a common 3-SNP haplotype was found to increase the risk of CAD (P = 0.009). In an additive genetic model adjusting for covariates the odds ratio (OR) for this haplotype is 1.56 (95% CI: 1.16-2.10, p = 0.004) for early-onset CAD (before age 50). This study illustrates the utility of haplotype analysis in family-based association studies to investigate candidate genes. References Rabinowitz, D. & Laird, N. M. (2000) Hum Hered50, 211,223. Zhang, K., Sun, F. & Zhao, H. (2005) Bioinformatics21, 90,103. Andrea Foulkes 1 , Recai Yucel 1 , Xiaohong Li 143 Division of Biostatistics, University of Massachusetts, USA Keywords: Haploytpe, high-dimensional, mixed modeling The explosion of molecular level information coupled with large epidemiological studies presents an exciting opportunity to uncover the genetic underpinnings of complex diseases; however, several analytical challenges remain to be addressed. Characterizing the components to complex diseases inevitably requires consideration of synergies across multiple genetic loci and environmental and demographic factors. In addition, it is critical to capture information on allelic phase, that is whether alleles within a gene are in cis (on the same chromosome) or in trans (on different chromosomes.) In associations studies of unrelated individuals, this alignment of alleles within a chromosomal copy is generally not observed. We address the potential ambiguity in allelic phase in this high dimensional data setting using mixed effects models. Both a semi-parametric and fully likelihood-based approach to estimation are considered to account for missingness in cluster identifiers. In the first case, we apply a multiple imputation procedure coupled with a first stage expectation maximization algorithm for parameter estimation. A bootstrap approach is employed to assess sensitivity to variability induced by parameter estimation. Secondly, a fully likelihood-based approach using an expectation conditional maximization algorithm is described. Notably, these models allow for characterizing high-order gene-gene interactions while providing a flexible statistical framework to account for the confounding or mediating role of person specific covariates. The proposed method is applied to data arising from a cohort of human immunodeficiency virus type-1 (HIV-1) infected individuals at risk for therapy associated dyslipidemia. Simulation studies demonstrate reasonable power and control of family-wise type 1 error rates. Vivien Marquard 1 , Lars Beckmann 1 , Jenny Chang-Claude 144 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Genotyping errors, type I error, haplotype-based association methods It has been shown in several simulation studies that genotyping errors may have a great impact on the type I error of statistical methods used in genetic association analysis of complex diseases. Our aim was to investigate type I error rates in a case-control study, when differential and non-differential genotyping errors were introduced in realistic scenarios. We simulated case-control data sets, where individual genotypes were drawn from a haplotype distribution of 18 haplotypes with 15 markers in the APM1 gene. Genotyping errors were introduced following the unrestricted and symmetric with 0 edges error models described by Heid et al. (2006). In six scenarios, errors resulted from changes of one allele to another with predefined probabilities of 1%, 2.5% or 10%, respectively. A multiple number of errors per haplotype was possible and could vary between 0 and 15, the number of markers investigated. We examined three association methods: Mantel statistics using haplotype-sharing; a haplotype-specific score test; and Armitage trend test for single markers. The type I error rates were not influenced for any of all the three methods for a genotyping error rate of less than 1%. For higher error rates and differential errors, the type I error of the Mantel statistic was only slightly and of the Armitage trend test moderately increased. The type I error rates of the score test were highly increased. The type I error rates were correct for all three methods for non-differential errors. Further investigations will be carried out with different frequencies of differential error rates and focus on power. Arne Neumann 1 , Dörthe Malzahn 1 , Martina Müller 2 , Heike Bickeböller 145 Department of Genetic Epidemiology, Medical School, University of Göttingen, Germany 46 GSF-National Research Center for Environment and Health, Neuherberg & IBE-Institute of Epidemiology, Ludwig-Maximilians University München, Germany Keywords: Interaction, longitudinal, nonparametric Longitudinal data show the time dependent course of phenotypic traits. In this contribution, we consider longitudinal cohort studies and investigate the association between two candidate genes and a dependent quantitative longitudinal phenotype. The set-up defines a factorial design which allows us to test simultaneously for the overall gene effect of the loci as well as for possible gene-gene and gene time interaction. The latter would induce genetically based time-profile differences in the longitudinal phenotype. We adopt a non-parametric statistical test to genetic epidemiological cohort studies and investigate its performance by simulation studies. The statistical test was originally developed for longitudinal clinical studies (Brunner, Munzel, Puri, 1999 J Multivariate Anal 70:286-317). It is non-parametric in the sense that no assumptions are made about the underlying distribution of the quantitative phenotype. Longitudinal observations belonging to the same individual can be arbitrarily dependent on one another for the different time points whereas trait observations of different individuals are independent. The two loci are assumed to be statistically independent. Our simulations show that the nonparametric test is comparable with ANOVA in terms of power of detecting gene-gene and gene-time interaction in an ANOVA favourable setting. Rebecca Hein 1 , Lars Beckmann 1 , Jenny Chang-Claude 147 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ) Heidelberg, Germany Keywords: Indirect association studies, interaction effects, linkage disequilibrium, marker allele frequency Association studies accounting for gene-environment interactions (GxE) may be useful for detecting genetic effects and identifying important environmental effect modifiers. Current technology facilitates very dense marker spacing in genetic association studies; however, the true disease variant(s) may not be genotyped. In this situation, an association between a gene and a phenotype may still be detectable, using genetic markers associated with the true disease variant(s) (indirect association). Zondervan and Cardon [2004] showed that the odds ratios (OR) of markers which are associated with the disease variant depend highly on the linkage disequilibrium (LD) between the variant and the markers, and whether the allele frequencies match and thereby influence the sample size needed to detect genetic association. We examined the influence of LD and allele frequencies on the sample size needed to detect GxE in indirect association studies, and provide tables for sample size estimation. For discordant allele frequencies and incomplete LD, sample sizes can be unfeasibly large. The influence of both factors is stronger for disease loci with small rather than moderate to high disease allele frequencies. A decline in D' of e.g. 5% has less impact on sample size than increasing the difference in allele frequencies by the same percentage. Assuming 80% power, large interaction effects can be detected using smaller sample sizes than those needed for the detection of main effects. The detection of interaction effects involving rare alleles may not be possible. Focussing only on marker density can be a limited strategy in indirect association studies for GxE. Cyril Dalmasso 1 , Emmanuelle Génin 2 , Catherine Bourgain 2 , Philippe Broët 148 JE 2492 , Univ. Paris-Sud, France 49 INSERM UMR-S 535 and University Paris Sud, Villejuif, France Keywords: Linkage analysis, Multiple testing, False Discovery Rate, Mixture model In the context of genome-wide linkage analyses, where a large number of statistical tests are simultaneously performed, the False Discovery Rate (FDR) that is defined as the expected proportion of false discoveries among all discoveries is nowadays widely used for taking into account the multiple testing problem. Other related criteria have been considered such as the local False Discovery Rate (lFDR) that is a variant of the FDR giving to each test its own measure of significance. The lFDR is defined as the posterior probability that a null hypothesis is true. Most of the proposed methods for estimating the lFDR or the FDR rely on distributional assumption under the null hypothesis. However, in observational studies, the empirical null distribution may be very different from the theoretical one. In this work, we propose a mixture model based approach that provides estimates of the lFDR and the FDR in the context of large-scale variance component linkage analyses. In particular, this approach allows estimating the empirical null distribution, this latter being a key quantity for any simultaneous inference procedure. The proposed method is applied on a real dataset. Arief Gusnanto 1 , Frank Dudbridge 150 MRC Biostatistics Unit, Cambridge UK Keywords: Significance, genome-wide, association, permutation, multiplicity Genome-wide association scans have introduced statistical challenges, mainly in the multiplicity of thousands of tests. The question of what constitutes a significant finding remains somewhat unresolved. Permutation testing is very time-consuming, whereas Bayesian arguments struggle to distinguish direct from indirect association. It seems attractive to summarise the multiplicity in a simple form that allows users to avoid time-consuming permutations. A standard significance level would facilitate reporting of results and reduce the need for permutation tests. This is potentially important because current scans do not have full coverage of the whole genome, and yet, the implicit multiplicity is genome-wide. We discuss some proposed summaries, with reference to the empirical null distribution of the multiple tests, approximated through a large number of random permutations. Using genome-wide data from the Wellcome Trust Case-Control Consortium, we use a sub-sampling approach with increasing density to estimate the nominal p-value to obtain family-wise significance of 5%. The results indicate that the significance level is converging to about 1e-7 as the marker spacing becomes infinitely dense. We considered the concept of an effective number of independent tests, and showed that when used in a Bonferroni correction, the number varies with the overall significance level, but is roughly constant in the region of interest. We compared several estimators of the effective number of tests, and showed that in the region of significance of interest, Patterson's eigenvalue based estimator gives approximately the right family-wise error rate. Michael Nothnagel 1 , Amke Caliebe 1 , Michael Krawczak 151 Institute of Medical Informatics and Statistics, University Clinic Schleswig-Holstein, University of Kiel, Germany Keywords: Association scans, Bayesian framework, posterior odds, genetic risk, multiplicative model Whole-genome association scans have been suggested to be a cost-efficient way to survey genetic variation and to map genetic disease factors. We used a Bayesian framework to investigate the posterior odds of a genuine association under multiplicative disease models. We demonstrate that the p value alone is not a sufficient means to evaluate the findings in association studies. We suggest that likelihood ratios should accompany p values in association reports. We argue, that, given the reported results of whole-genome scans, more associations should have been successfully replicated if the consistently made assumptions about considerable genetic risks were correct. We conclude that it is very likely that the vast majority of relative genetic risks are only of the order of 1.2 or lower. Clive Hoggart 1 , Maria De Iorio 1 , John Whittakker 2 , David Balding 152 Department of Epidemiology and Public Health, Imperial College London, UK 53 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: Genome-wide association analyses, shrinkage priors, Lasso Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants of small effect, which is a plausible scenario for many complex diseases. Moreover, many simulation studies assume a single causal variant and so more complex realities are ignored. Analysing large numbers of variants simultaneously is now becoming feasible, thanks to developments in Bayesian stochastic search methods. We pose the problem of SNP selection as variable selection in a regression model. In contrast to single SNP tests this approach simultaneously models the effect of all SNPs. SNPs are selected by a Bayesian interpretation of the lasso (Tibshirani, 1996); the maximum a posterior (MAP) estimate of the regression coefficients, which have been given independent, double exponential prior distributions. The double exponential distribution is an example of a shrinkage prior, MAP estimates with shrinkage priors can be zero, thus all SNPs with non zero regression coefficients are selected. In addition to the commonly-used double exponential (Laplace) prior, we also implement the normal exponential gamma prior distribution. We show that use of the Laplace prior improves SNP selection in comparison with single -SNP tests, and that the normal exponential gamma prior leads to a further improvement. Our method is fast and can handle very large numbers of SNPs: we demonstrate its performance using both simulated and real genome-wide data sets with 500 K SNPs, which can be analysed in 2 hours on a desktop workstation. Mickael Guedj 1,2 , Jerome Wojcik 2 , Gregory Nuel 154 Laboratoire Statistique et Génome, Université d'Evry, Evry France 55 Serono Pharmaceutical Research Institute, Plan-les-Ouates, Switzerland Keywords: Local Replication, Local Score, Association In gene-mapping, replication of initial findings has been put forwards as the approach of choice for filtering false-positives from true signals for underlying loci. In practice, such replications are however too poorly observed. Besides the statistical and technical-related factors (lack of power, multiple-testing, stratification, quality control,) inconsistent conclusions obtained from independent populations might result from real biological differences. In particular, the high degree of variation in the strength of LD among populations of different origins is a major challenge to the discovery of genes. Seeking for Local Replications (defined as the presence of a signal of association in a same genomic region among populations) instead of strict replications (same locus, same risk allele) may lead to more reliable results. Recently, a multi-markers approach based on the Local Score statistic has been proposed as a simple and efficient way to select candidate genomic regions at the first stage of genome-wide association studies. Here we propose an extension of this approach adapted to replicated association studies. Based on simulations, this method appears promising. In particular it outperforms classical simple-marker strategies to detect modest-effect genes. Additionally it constitutes, to our knowledge, a first framework dedicated to the detection of such Local Replications. Juliet Chapman 1 , Claudio Verzilli 1 , John Whittaker 156 Department of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, UK Keywords: FDR, Association studies, Bayesian model selection As genomewide association studies become commonplace there is debate as to how such studies might be analysed and what we might hope to gain from the data. It is clear that standard single locus approaches are limited in that they do not adjust for the effects of other loci and problematic since it is not obvious how to adjust for multiple comparisons. False discovery rates have been suggested, but it is unclear how well these will cope with highly correlated genetic data. We consider the validity of standard false discovery rates in large scale association studies. We also show that a Bayesian procedure has advantages in detecting causal loci amongst a large number of dependant SNPs and investigate properties of a Bayesian FDR. Peter Kraft 157 Harvard School of Public Health, Boston USA Keywords: Gene-environment interaction, genome-wide association scans Appropriately analyzed two-stage designs,where a subset of available subjects are genotyped on a genome-wide panel of markers at the first stage and then a much smaller subset of the most promising markers are genotyped on the remaining subjects,can have nearly as much power as a single-stage study where all subjects are genotyped on the genome-wide panel yet can be much less expensive. Typically, the "most promising" markers are selected based on evidence for a marginal association between genotypes and disease. Subsequently, the few markers found to be associated with disease at the end of the second stage are interrogated for evidence of gene-environment interaction, mainly to understand their impact on disease etiology and public health impact. However, this approach may miss variants which have a sizeable effect restricted to one exposure stratum and therefore only a modest marginal effect. We have proposed to use information on the joint effects of genes and a discrete list of environmental exposures at the initial screening stage to select promising markers for the second stage [Kraft et al Hum Hered 2007]. This approach optimizes power to detect variants that have a sizeable marginal effect and variants that have a small marginal effect but a sizeable effect in a stratum defined by an environmental exposure. As an example, I discuss a proposed genome-wide association scan for Type II diabetes susceptibility variants based in several large nested case-control studies. Beate Glaser 1 , Peter Holmans 158 Biostatistics and Bioinformatics Unit, Cardiff University, School of Medicine, Heath Park, Cardiff, UK Keywords: Combined case-control and trios analysis, Power, False-positive rate, Simulation, Association studies The statistical power of genetic association studies can be enhanced by combining the analysis of case-control with parent-offspring trio samples. Various combined analysis techniques have been recently developed; as yet, there have been no comparisons of their power. This work was performed with the aim of identifying the most powerful method among available combined techniques including test statistics developed by Kazeem and Farrall (2005), Nagelkerke and colleagues (2004) and Dudbridge (2006), as well as a simple combination of ,2-statistics from single samples. Simulation studies were performed to investigate their power under different additive, multiplicative, dominant and recessive disease models. False-positive rates were determined by studying the type I error rates under null models including models with unequal allele frequencies between the single case-control and trios samples. We identified three techniques with equivalent power and false-positive rates, which included modifications of the three main approaches: 1) the unmodified combined Odds ratio estimate by Kazeem & Farrall (2005), 2) a modified approach of the combined risk ratio estimate by Nagelkerke & colleagues (2004) and 3) a modified technique for a combined risk ratio estimate by Dudbridge (2006). Our work highlights the importance of studies investigating test performance criteria of novel methods, as they will help users to select the optimal approach within a range of available analysis techniques. David Almorza 1 , M.V. Kandus 2 , Juan Carlos Salerno 2 , Rafael Boggio 359 Facultad de Ciencias del Trabajo, University of Cádiz, Spain 60 Instituto de Genética IGEAF, Buenos Aires, Argentina 61 Universidad Nacional de La Plata, Buenos Aires, Argentina Keywords: Principal component analysis, maize, ear weight, inbred lines The objective of this work was to evaluate the relationship among different traits of the ear of maize inbred lines and to group genotypes according to its performance. Ten inbred lines developed at IGEAF (INTA Castelar) and five public inbred lines as checks were used. A field trial was carried out in Castelar, Buenos Aires (34° 36' S , 58° 39' W) using a complete randomize design with three replications. At harvest, individual weight (P.E.), diameter (D.E.), row number (N.H.) and length (L.E.) of the ear were assessed. A principal component analysis, PCA, (Infostat 2005) was used, and the variability of the data was depicted with a biplot. Principal components 1 and 2 (CP1 and CP2) explained 90% of the data variability. CP1 was correlated with P.E., L.E. and D.E., meanwhile CP2 was correlated with N.H. We found that individual weight (P.E.) was more correlated with diameter of the ear (D.E.) than with length (L.E). Five groups of inbred lines were distinguished: with high P.E. and mean N.H. (04-70, 04-73, 04-101 and MO17), with high P.E. but less N.H. (04-61 and B14), with mean P.E. and N.H. (B73, 04-123 and 04-96), with high N.H. but less P.E. (LP109, 04-8, 04-91 and 04-76) and with low P.E. and low N.H. (LP521 and 04-104). The use of PCA showed which variables had more incidence in ear weight and how is the correlation among them. Moreover, the different groups found with this analysis allow the evaluation of inbred lines by several traits simultaneously. Sven Knüppel 1 , Anja Bauerfeind 1 , Klaus Rohde 162 Department of Bioinformatics, MDC Berlin, Germany Keywords: Haplotypes, association studies, case-control, nuclear families The area of gene chip technology provides a plethora of phase-unknown SNP genotypes in order to find significant association to some genetic trait. To circumvent possibly low information content of a single SNP one groups successive SNPs and estimates haplotypes. Haplotype estimation, however, may reveal ambiguous haplotype pairs and bias the application of statistical methods. Zaykin et al. (Hum Hered, 53:79-91, 2002) proposed the construction of a design matrix to take this ambiguity into account. Here we present a set of functions written for the Statistical package R, which carries out haplotype estimation on the basis of the EM-algorithm for individuals (case-control) or nuclear families. The construction of a design matrix on basis of estimated haplotypes or haplotype pairs allows application of standard methods for association studies (linear, logistic regression), as well as statistical methods as haplotype sharing statistics and TDT-Test. Applications of these methods to genome-wide association screens will be demonstrated. Manuela Zucknick 1 , Chris Holmes 2 , Sylvia Richardson 163 Department of Epidemiology and Public Health, Imperial College London, UK 64 Department of Statistics, Oxford Center for Gene Function, University of Oxford, UK Keywords: Bayesian, variable selection, MCMC, large p, small n, structured dependence In large-scale genomic applications vast numbers of markers or genes are scanned to find a few candidates which are linked to a particular phenotype. Statistically, this is a variable selection problem in the "large p, small n" situation where many more variables than samples are available. An additional feature is the complex dependence structure which is often observed among the markers/genes due to linkage disequilibrium or their joint involvement in biological processes. Bayesian variable selection methods using indicator variables are well suited to the problem. Binary phenotypes like disease status are common and both Bayesian probit and logistic regression can be applied in this context. We argue that logistic regression models are both easier to tune and to interpret than probit models and implement the approach by Holmes & Held (2006). Because the model space is vast, MCMC methods are used as stochastic search algorithms with the aim to quickly find regions of high posterior probability. In a trade-off between fast-updating but slow-moving single-gene Metropolis-Hastings samplers and computationally expensive full Gibbs sampling, we propose to employ the dependence structure among the genes/markers to help decide which variables to update together. Also, parallel tempering methods are used to aid bold moves and help avoid getting trapped in local optima. Mixing and convergence of the resulting Markov chains are evaluated and compared to standard samplers in both a simulation study and in an application to a gene expression data set. Reference Holmes, C. C. & Held, L. (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis1, 145,168. Dawn Teare 165 MMGE, University of Sheffield, UK Keywords: CNP, family-based analysis, MCMC Evidence is accumulating that segmental copy number polymorphisms (CNPs) may represent a significant portion of human genetic variation. These highly polymorphic systems require handling as phenotypes rather than co-dominant markers, placing new demands on family-based analyses. We present an integrated approach to meet these challenges in the form of a graphical model, where the underlying discrete CNP phenotype is inferred from the (single or replicate) quantitative measure within the analysis, whilst assuming an allele based system segregating through the pedigree. [source] Mixture Generalized Linear Models for Multiple Interval Mapping of Quantitative Trait Loci in Experimental CrossesBIOMETRICS, Issue 2 2009Zehua Chen Summary Quantitative trait loci mapping in experimental organisms is of great scientific and economic importance. There has been a rapid advancement in statistical methods for quantitative trait loci mapping. Various methods for normally distributed traits have been well established. Some of them have also been adapted for other types of traits such as binary, count, and categorical traits. In this article, we consider a unified mixture generalized linear model (GLIM) for multiple interval mapping in experimental crosses. The multiple interval mapping approach was proposed by Kao, Zeng, and Teasdale (1999, Genetics152, 1203,1216) for normally distributed traits. However, its application to nonnormally distributed traits has been hindered largely by the lack of an efficient computation algorithm and an appropriate mapping procedure. In this article, an effective expectation,maximization algorithm for the computation of the mixture GLIM and an epistasis-effect-adjusted multiple interval mapping procedure is developed. A real data set, Radiata Pine data, is analyzed and the data structure is used in simulation studies to demonstrate the desirable features of the developed method. [source] Nonparametric Functional Mapping of Quantitative Trait LociBIOMETRICS, Issue 1 2009Jie Yang Summary Functional mapping is a useful tool for mapping quantitative trait loci (QTL) that control dynamic traits. It incorporates mathematical aspects of biological processes into the mixture model-based likelihood setting for QTL mapping, thus increasing the power of QTL detection and the precision of parameter estimation. However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal. Here we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing QTL. Using the representation of a nonparametric regression as a mixed model, the final test statistic is a likelihood ratio test. We consider two types of genetic maps: dense maps and general maps, and the power of nonparametric functional mapping is investigated through simulation studies and demonstrated by examples. [source] An Empirical Bayes Method for Estimating Epistatic Effects of Quantitative Trait LociBIOMETRICS, Issue 2 2007Shizhong Xu Summary The genetic variance of a quantitative trait is often controlled by the segregation of multiple interacting loci. Linear model regression analysis is usually applied to estimating and testing effects of these quantitative trait loci (QTL). Including all the main effects and the effects of interaction (epistatic effects), the dimension of the linear model can be extremely high. Variable selection via stepwise regression or stochastic search variable selection (SSVS) is the common procedure for epistatic effect QTL analysis. These methods are computationally intensive, yet they may not be optimal. The LASSO (least absolute shrinkage and selection operator) method is computationally more efficient than the above methods. As a result, it has been widely used in regression analysis for large models. However, LASSO has never been applied to genetic mapping for epistatic QTL, where the number of model effects is typically many times larger than the sample size. In this study, we developed an empirical Bayes method (E-BAYES) to map epistatic QTL under the mixed model framework. We also tested the feasibility of using LASSO to estimate epistatic effects, examined the fully Bayesian SSVS, and reevaluated the penalized likelihood (PENAL) methods in mapping epistatic QTL. Simulation studies showed that all the above methods performed satisfactorily well. However, E-BAYES appears to outperform all other methods in terms of minimizing the mean-squared error (MSE) with relatively short computing time. Application of the new method to real data was demonstrated using a barley dataset. [source] Identification of chromosomal regions associated with growth and carcass traits in an F3 full sib intercross line originating from a cross of chicken lines divergently selected on body weightANIMAL GENETICS, Issue 5 2009D. Ter Summary An F3 resource population originating from a cross between two divergently selected lines for high (D+ line) or low (D, line) body weight at 8-weeks of age (BW55) was generated and used for Quantitative Trait Locus (QTL) mapping. From an initial cross of two founder F0 animals from D(+) and D(,) lines, progeny were randomly intercrossed over two generations following a full sib intercross line (FSIL) design. One hundred and seventy-five genome-wide polymorphic markers were employed in the DNA pooling and selective genotyping of F3 to identify markers with significant effects on BW55. Fifty-three markers on GGA2, 5 and 11 were then genotyped in the whole F3 population of 503 birds, where interval mapping with GridQTL software was employed. Eighteen QTL for body weight, carcass traits and some internal organ weights were identified. On GGA2, a comparison between 2-QTL vs. 1-QTL analysis revealed two separate QTL regions for body, feet, breast muscle and carcass weight. Given co-localization of QTL for some highly correlated traits, we concluded that there were 11 distinct QTL mapped. Four QTL localized to already mapped QTL from other studies, but seven QTL have not been previously reported and are hence novel and unique to our selection line. This study provides a low resolution QTL map for various traits and establishes a genetic resource for future fine-mapping and positional cloning in the advanced FSIL generations. [source] Power of Linkage Disequilibrium Mapping to Detect a Quantitative Trait Locus (QTL) in Selected Samples of Unrelated IndividualsANNALS OF HUMAN GENETICS, Issue 6 2003A. Tenesa Summary We considered a strategy to map quantitative trait loci (QTLs) using linkage disequilibrium (LD) when the QTL and marker locus were multiallelic. The strategy involved phenotyping a large number of unrelated individuals and genotyping only selected individuals from the two tails of the trait distribution. Power to detect trait-marker association was assessed as a function of the number of QTL and marker alleles. Two patterns of LD were used to study their influence on power. When the frequency of the QTL allele with the largest effect and that of the marker allele linked in coupling were equal, power was maximum. In this case, increasing the number of QTL alleles reduced the power. The maximum difference in power between the two LD patterns studied was ,30%. For low QTL heritabilities (h2QTL < 0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution. [source] From age correction to genome-wide associationACTA PSYCHIATRICA SCANDINAVICA, Issue 5 2009S. Cohen-Woods Objective:, Eric Strömgren was one of the pioneers of psychiatric genetics and family studies. There has now been an explosion of interest in this field and research progress, including linkage and association studies, whole genome genotyping, copy number variants and epigenetics is reviewed here. Method:, An overview of this area of psychiatric research is presented and discussed based on the relevant literature aiming at giving a recent status of the progress. Results:, Broadly speaking linkage and association are complementary approaches used to locate genes contributing to the genetic aetiology of psychopathology. Linkage can be detected over comparatively large distances, however power is problematic when searching for quantitative trait loci with small effect sizes. In contrast, association studies can detect small effects but only over very small distances. Therefore, while several genome-wide linkage studies in psychiatric disorders have been performed, the majority of association studies have investigated specific functional candidate genes. Conclusion:, Due to very recent technological advancements, genome-wide association studies have now become possible and have identified some completely novel susceptibility loci. Other recent advances include the discovery of epigenetic phenomena and copy number variants. [source] Polygenic Control of Idiopathic Generalized Epilepsy Phenotypes in the Genetic Absence Rats from Strasbourg (GAERS)EPILEPSIA, Issue 4 2004Gabrielle Rudolf Summary: Purpose: Generalized nonconvulsive absence seizures are characterized by the occurrence of synchronous and bilateral spike-and-wave discharges (SWDs) on electroencephalographic recordings, concomitant with behavioral arrest. The GAERS (genetic absence rats from Strasbourg) strain, a well-characterized inbred model for idiopathic generalized epilepsy, spontaneously develops EEG paroxysms that resemble those of typical absence seizures. The purpose of this study was to investigate the genetic control of SWD variables by using a combination of genetic analyses and electrophysiological measurements in an experimental cross derived from GAERS and Brown Norway (BN) rats. Methods: SWD subphenotypes were quantified on EEG recordings performed at both 3 and 6 months in a cohort of 118 GAERS × BN F2 animals. A genome-wide scan of the F2 progenies was carried out with 146 microsatellite markers that were used to test each marker locus for evidence of genetic linkage to the SWD quantitative traits. Results: We identified three quantitative trait loci (QTLs) in chromosomes 4, 7, and 8 controlling specific SWD variables in the cross, including frequency, amplitude, and severity of SWDs. Age was a major factor influencing the detection of genetic linkage to the various components of the SWDs. Conclusions: The identification of these QTLs demonstrates the polygenic control of SWDs in the GAERS strain. Genetic linkages to specific SWD features underline the complex mechanisms contributing to SWD development in idiopathic generalized epilepsy. [source] REVIEW: A comparison of selected quantitative trait loci associated with alcohol use phenotypes in humans and mouse modelsADDICTION BIOLOGY, Issue 2 2010Cindy L. Ehlers ABSTRACT Evidence for genetic linkage to alcohol and other substance dependence phenotypes in areas of the human and mouse genome have now been reported with some consistency across studies. However, the question remains as to whether the genes that underlie the alcohol-related behaviors seen in mice are the same as those that underlie the behaviors observed in human alcoholics. The aims of the current set of analyses were to identify a small set of alcohol-related phenotypes in human and in mouse by which to compare quantitative trait locus (QTL) data between the species using syntenic mapping. These analyses identified that QTLs for alcohol consumption and acute and chronic alcohol withdrawal on distal mouse chromosome 1 are syntenic to a region on human chromosome 1q where a number of studies have identified QTLs for alcohol-related phenotypes. Additionally, a QTL on human chromosome 15 for alcohol dependence severity/withdrawal identified in two human studies was found to be largely syntenic with a region on mouse chromosome 9, where two groups have found QTLs for alcohol preference. In both of these cases, while the QTLs were found to be syntenic, the exact phenotypes between humans and mice did not necessarily overlap. These studies demonstrate how this technique might be useful in the search for genes underlying alcohol-related phenotypes in multiple species. However, these findings also suggest that trying to match exact phenotypes in humans and mice may not be necessary or even optimal for determining whether similar genes influence a range of alcohol-related behaviors between the two species. [source] Implication of allelic polymorphism of osteopontin in the development of lupus nephritis in MRL/lpr miceEUROPEAN JOURNAL OF IMMUNOLOGY, Issue 5 2005Tatsuhiko Miyazaki Abstract Potentially, autoimmune diseases develop from a combination of multiple genes with allelic polymorphisms. An MRL/Mp-Faslpr/lpr (MRL/lpr) strain of mice develops autoimmune diseases, including lupus nephritis, but another lpr strain, C3H/HeJ-Faslpr/lpr (C3H/lpr) does not. This indicates that MRL polymorphic genes are involved in the development of the diseases. By quantitative trait loci (QTL) analysis using 527 of the (MRL/lpr × C3H/lpr)F2 mice, we identified a novel locus for susceptibility to lupus nephritis at map position D5Mit115 on chromosome 5, the same alias of the osteopontin (Opn) gene (LOD score =4.0), susceptible in the MRL allele. In functional analyses of the MRL and C3H Opn alleles using synthetic osteopontin (OPN) made with a new method "cell-free system" with wheat germ ribosomes, the MRL-OPN induced higher expression and production of immunoglobulins as well as cytokines including TNF-,, IL-1, and IFN-, in splenocytes and/or macrophages than that of the C3H allele. These findings suggest that allelic polymorphism of OPN causes the functional differences in antibody production and macrophage activation between MRL and C3H strains, possibly involved in the development of lupus nephritis. [source] RELATIVE CONTRIBUTION OF ADDITIVE, DOMINANCE, AND IMPRINTING EFFECTS TO PHENOTYPIC VARIATION IN BODY SIZE AND GROWTH BETWEEN DIVERGENT SELECTION LINES OF MICEEVOLUTION, Issue 5 2009Reinmar Hager Epigenetic effects attributed to genomic imprinting are increasingly recognized as an important source of variation in quantitative traits. However, little is known about their relative contribution to phenotypic variation compared to those of additive and dominance effects, and almost nothing about their role in phenotypic evolution. Here we address these questions by investigating the relative contribution of additive, dominance, and imprinting effects of quantitative trait loci (QTL) to variation in "early" and "late" body weight in an intercross of mice selected for divergent adult body weight. We identified 18 loci on 13 chromosomes; additive effects accounted for most of the phenotypic variation throughout development, and imprinting effects were always small. Genetic effects on early weight showed more dominance, less additive, and, surprisingly, less imprinting variation than that of late weight. The predominance of additivity of QTL effects on body weight follows the expectation that additive effects account for the evolutionary divergence between selection lines. We hypothesize that the appearance of more imprinting effects on late body weight may be a consequence of divergent selection on adult body weight, which may have indirectly selected for alleles showing partial imprinting effects due to their associated additive effects, highlighting a potential role of genomic imprinting in the response to selection. [source] A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICSEVOLUTION, Issue 5 2007Derek A. Roff Quantitative genetics is at or is fast approaching its centennial. In this perspective I consider five current issues pertinent to the application of quantitative genetics to evolutionary theory. First, I discuss the utility of a quantitative genetic perspective in describing genetic variation at two very different levels of resolution, (1) in natural, free-ranging populations and (2) to describe variation at the level of DNA transcription. Whereas quantitative genetics can serve as a very useful descriptor of genetic variation, its greater usefulness is in predicting evolutionary change, particularly when used in the first instance (wild populations). Second, I review the contributions of Quantitative trait loci (QLT) analysis in determining the number of loci and distribution of their genetic effects, the possible importance of identifying specific genes, and the ability of the multivariate breeder's equation to predict the results of bivariate selection experiments. QLT analyses appear to indicate that genetic effects are skewed, that at least 20 loci are generally involved, with an unknown number of alleles, and that a few loci have major effects. However, epistatic effects are common, which means that such loci might not have population-wide major effects: this question waits upon (QTL) analyses conducted on more than a few inbred lines. Third, I examine the importance of research into the action of specific genes on traits. Although great progress has been made in identifying specific genes contributing to trait variation, the high level of gene interactions underlying quantitative traits makes it unlikely that in the near future we will have mechanistic models for such traits, or that these would have greater predictive power than quantitative genetic models. In the fourth section I present evidence that the results of bivariate selection experiments when selection is antagonistic to the genetic covariance are frequently not well predicted by the multivariate breeder's equation. Bivariate experiments that combine both selection and functional analyses are urgently needed. Finally, I discuss the importance of gaining more insight, both theoretical and empirical, on the evolution of the G and P matrices. [source] Variation in fiber number of a male-specific muscle between Drosophila species: a genetic and developmental analysisEVOLUTION AND DEVELOPMENT, Issue 4 2007Virginie Orgogozo SUMMARY We characterize a newly discovered morphological difference between species of the Drosophila melanogaster subgroup. The muscle of Lawrence (MOL) contains about four to five fibers in D. melanogaster and Drosophila simulans and six to seven fibers in Drosophila mauritiana and Drosophila sechellia. The same number of nuclei per fiber is present in these species but their total number of MOL nuclei differs. This suggests that the number of muscle precursor cells has changed during evolution. Our comparison of MOL development indicates that the species difference appears during metamorphosis. We mapped the quantitative trait loci responsible for the change in muscle fiber number between D. sechellia and D. simulans to two genomic regions on chromosome 2. Our data eliminate the possibility of evolving mutations in the fruitless gene and suggest that a change in the twist might be partly responsible for this evolutionary change. [source] A genome-wide quantitative trait loci scan of neurocognitive performances in families with schizophreniaGENES, BRAIN AND BEHAVIOR, Issue 7 2010Y.-J. Lien Patients with schizophrenia frequently display neurocognitive dysfunction, and genetic studies suggest it to be an endophenotype for schizophrenia. Genetic studies of such traits may thus help elucidate the biological pathways underlying genetic susceptibility to schizophrenia. This study aimed to identify loci influencing neurocognitive performance in schizophrenia. The sample comprised of 1207 affected individuals and 1035 unaffected individuals of Han Chinese ethnicity from 557 sib-pair families co-affected with DSM-IV (Diagnostic and Statistical Manual, Fourth Edition) schizophrenia. Subjects completed a face-to-face semi-structured interview, the continuous performance test (CPT) and the Wisconsin card sorting test (WCST), and were genotyped with 386 microsatellite markers across the genome. A series of autosomal genome-wide multipoint nonparametric quantitative trait loci (QTL) linkage analysis were performed in affected individuals only. Determination of genome-wide empirical significance was performed using 1000 simulated genome scans. One linkage peak attaining genome-wide significance was identified: 12q24.32 for undegraded CPT hit rate [nonparametric linkage z (NPL-Z) scores = 3.32, genome-wide empirical P = 0.03]. This result was higher than the peak linkage signal obtained in the previous genome-wide scan using a dichotomous diagnosis of schizophrenia. The identification of 12q24.32 as a QTL has not been consistently implicated in previous linkage studies on schizophrenia, which suggests that the analysis of endophenotypes provides additional information from what is seen in analyses that rely on diagnoses. This region with linkage to a particular neurocognitive feature may inform functional hypotheses for further genetic studies for schizophrenia. [source] Marker-assisted dissection of genetic influences on motor and neuroendocrine sensitization to cocaine in ratsGENES, BRAIN AND BEHAVIOR, Issue 3 2009L. F. Vendruscolo This study investigated genetic influences on behavioral and neuroendocrine responses to cocaine sensitization. We used male and female rats of the inbred strains Lewis (LEW) and spontaneously hypertensive rats (SHR), which display genetic differences in stress-related responses. The influence of two quantitative trait loci (QTL; Ofil1 and Ofil2 on chromosomes 4 and 7), which modulate stress reactivity in rats, on the effects of cocaine was also investigated through the use of recombinant lines (derived from a LEW × SHR intercross) selected by their genotype at Ofil1 and Ofil2. Animals were given repeated cocaine or saline injections and tested for locomotion (induction of sensitization). Two weeks later, all animals were challenged with cocaine, and locomotion and corticosterone levels were measured (expression of sensitization). Results indicated that male SHR rats showed more behavioral sensitization than LEW rats, whereas no strain differences in sensitization were seen among females. When challenged with cocaine, LEW and SHR rats of both sexes pretreated with cocaine showed behavioral sensitization compared with saline pretreated animals; however, only LEW rats displayed an increase in the corticosterone levels. Ofil1 was found to influence the induction of sensitization in males and Ofil2 modulated the locomotor effect of cocaine in females. This study provides evidence of a genotype-dependent relationship between the induction and expression of cocaine sensitization, and between the behavioral and neuroendocrine responses induced by cocaine. Moreover, the Ofil1 and Ofil2 loci may contain one or more genes that control the behavioral effects of cocaine in rats. [source] Calcium taste preferences: genetic analysis and genome screen of C57BL/6J × PWK/PhJ hybrid miceGENES, BRAIN AND BEHAVIOR, Issue 6 2008M. G. Tordoff To characterize the genetic basis of voluntary calcium consumption, we tested C57BL/6J mice (B6; with low avidity for calcium), PWK/PhJ mice (PWK; with high avidity for calcium) and their F1 and F2 hybrids. All mice received a series of 96-h two-bottle preference tests with a choice between water and the following: 50 mm CaCl2, 50 mm calcium lactate, 50 mm MgCl2, 100 mm KCl, 100 mm NH4Cl, 100 mm NaCl, 5 mm citric acid, 30 ,m quinine hydrochloride and 2 mm saccharin. Most frequency distributions of the parental and F1 but not F2 groups were normally distributed, and there were few sex differences. Reciprocal cross analysis showed that B6 × PWK F1 mice had a non-specific elevation of fluid intake relative to PWK × B6 F1 mice. In the F2 mice, trait correlations were clustered among the divalent salts and the monovalent chlorides. A genome screen involving 116 markers showed 30 quantitative trait loci (QTLs), of which six involved consumption of calcium chloride or lactate. The results show pleiotropic controls of calcium and magnesium consumption that are distinct from those controlling consumption of monovalent chlorides or exemplars of the primary taste qualities. [source] | |||||||||||||||||||||||||||||||||||||