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Significance Threshold (significance + threshold)
Selected AbstractsReplication study of candidate genes for cognitive abilities: the Lothian Birth Cohort 1936GENES, BRAIN AND BEHAVIOR, Issue 2 2009L. M. Houlihan As the proportion of older people in societies has increased, research into the determinants of cognitive ageing has risen in importance. Genetic influences account for over 50% of the variance in adult cognitive abilities. Previous studies on cognition and illnesses with cognitive impairments have identified single nucleotide polymorphisms (SNPs) within candidate genes that might influence cognition or age-related cognitive change. This study investigated 10 candidate genes in over 1000 Scots: the Lothian Birth Cohort 1936 (LBC1936). These participants were tested on general cognitive ability (Scottish Mental Survey 1947) at age 11. At mean age 70, they completed the same general cognitive ability test and a battery of diverse cognitive tests. Nineteen SNPs in 10 genes previously associated with cognition, Alzheimer's disease or autism were genotyped in 1063 individuals. The genes include BDNF, COMT, DISC1, KL, NCSTN, PPP1R1B, PRNP, SHANK3, SORL1 and WRN. Linear regression analysis investigated the additive effect of each SNP on the cognitive variables, covarying for gender and age. Childhood cognitive ability was also included as a covariate to identify associations specifically with cognitive ageing. Certain SNPs reached the conventional significance threshold for association with cognitive traits or cognitive ageing in LBC1936 (P < 0.05). No SNPs reached the Bonferroni-level of significance (all P > 0.0015). Of the 10 genes, we discuss that COMT, KL, PRNP, PPP1R1B, SORL1 and WRN especially merit further attention for association with cognitive ability and/or age-related cognitive change. All results are also presented so that they are valuable for future meta-analyses of candidate genes for cognition. [source] Upward bias in odds ratio estimates from genome-wide association studiesGENETIC EPIDEMIOLOGY, Issue 4 2007Chad Garner Abstract Genome-wide association studies are carried out to identify unknown genes for a complex trait. Polymorphisms showing the most statistically significant associations are reported and followed up in subsequent confirmatory studies. In addition to the test of association, the statistical analysis provides point estimates of the relationship between the genotype and phenotype at each polymorphism, typically an odds ratio in case-control association studies. The statistical significance of the test and the estimator of the odds ratio are completely correlated. Selecting the most extreme statistics is equivalent to selecting the most extreme odds ratios. The value of the estimator, given the value of the statistical significance depends on the standard error of the estimator and the power of the study. This report shows that when power is low, estimates of the odds ratio from a genome-wide association study, or any large-scale association study, will be upwardly biased. Genome-wide association studies are often underpowered given the low , levels required to declare statistical significance and the small individual genetic effects known to characterize complex traits. Factors such as low allele frequency, inadequate sample size and weak genetic effects contribute to large standard errors in the odds ratio estimates, low power and upwardly biased odds ratios. Studies that have high power to detect an association with the true odds ratio will have little or no bias, regardless of the statistical significance threshold. The results have implications for the interpretation of genome-wide association analysis and the planning of subsequent confirmatory stages. Genet Epidemiol. 2007. © 2007 Wiley-Liss, Inc. [source] The bootstrap and cross-validation in neuroimaging applications: Estimation of the distribution of extrema of random fields for single volume tests, with an application to ADC mapsHUMAN BRAIN MAPPING, Issue 10 2007Roberto Viviani Abstract We discuss the assessment of signal change in single magnetic resonance images (MRI) based on quantifying significant departure from a reference distribution estimated from a large sample of normal subjects. The parametric approach is to build a test based on the expected distribution of extrema in random fields. However, in conditions where the variance is not uniform across the volume and the smoothness of the images is moderate to low, this test may be rather conservative. Furthermore, parametric tests are limited to datasets for which distributional assumptions hold. This paper investigates resampling methods that improve statistical tests for signal changes in single images in such adverse conditions, and that can be used for the assessment of images taken for clinical purposes. Two methods, the bootstrap and cross-validation, are compared. It is shown that the bootstrap may fail to provide a good estimate of the distribution of extrema of parametric maps. In contrast, calibration of the significance threshold by means of cross-validation (or related sampling without replacement techniques) address three issues at once: improved power, better voxel-by-voxel estimate of variance by local pooling, and adaptation to departures from ideal distributional assumptions on the signal. We apply the cross-validated tests to apparent diffusion coefficient maps, a type of MRI capable of detecting changes in the microstructural organization of brain parenchyma. We show that deviations from parametric assumptions are strong enough to cast doubt on the correctness of parametric tests for these images. As case studies, we present parametric maps of lesions in patients suffering from stroke and glioblastoma at different stages of evolution. Hum Brain Mapp 2007. © 2007 Wiley-Liss, Inc. [source] Genome-wide pleiotropy of osteoporosis-related phenotypes: The framingham studyJOURNAL OF BONE AND MINERAL RESEARCH, Issue 7 2010David Karasik Abstract Genome-wide association studies offer an unbiased approach to identify new candidate genes for osteoporosis. We examined the Affymetrix 500K,+,50K SNP GeneChip marker sets for associations with multiple osteoporosis-related traits at various skeletal sites, including bone mineral density (BMD, hip and spine), heel ultrasound, and hip geometric indices in the Framingham Osteoporosis Study. We evaluated 433,510 single-nucleotide polymorphisms (SNPs) in 2073 women (mean age 65 years), members of two-generational families. Variance components analysis was performed to estimate phenotypic, genetic, and environmental correlations (,P, ,G, and ,E) among bone traits. Linear mixed-effects models were used to test associations between SNPs and multivariable-adjusted trait values. We evaluated the proportion of SNPs associated with pairs of the traits at a nominal significance threshold ,,=,0.01. We found substantial correlation between the proportion of associated SNPs and the ,P and ,G (r,=,0.91 and 0.84, respectively) but much lower with ,E (r,=,0.38). Thus, for example, hip and spine BMD had 6.8% associated SNPs in common, corresponding to ,P,=,0.55 and ,G,=,0.66 between them. Fewer SNPs were associated with both BMD and any of the hip geometric traits (eg, femoral neck and shaft width, section moduli, neck shaft angle, and neck length); ,G between BMD and geometric traits ranged from ,0.24 to +0.40. In conclusion, we examined relationships between osteoporosis-related traits based on genome-wide associations. Most of the similarity between the quantitative bone phenotypes may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in defining the best phenotypes to be used in genetic studies of osteoporosis. © 2010 American Society for Bone and Mineral Research [source] Source population of dispersing rock-wallabies (Petrogale lateralis) idengified by assignment tests on multilocus genotypic dataMOLECULAR ECOLOGY, Issue 12 2001M. D. B. Eldridge Abstract The ability to confidently idengify or exclude a population as the source of an individual has numerous powerful applications in molecular ecology. Several alternative assignment methods have recently been developed and are yet to be fully evaluated with empirical data. In this study we tested the efficacy of different assignment methods by using a translocated rock-wallaby (Petrogale lateralis) population, of known provenance. Specimens from the translocated population (n = 43), its known source population (n = 30) and four other nearby populations (n = 19,32) were genotyped for 11 polymorphic microsatellite loci. The results idengified Bayesian clustering, frequency and Bayesian methods as the most consistent and accurate, correctly assigning 93,100% of individuals up to a significance threshold of P = 0.01. Performance was variable among the distance-based methods, with the Cavalli-Sforza and Edwards chord distance performing best, whereas Goldstein et al.'s (,µ)2 consistently performed poorly. Using Bayesian clustering, frequency and Bayesian methods we then attempted to determine the source of rock-wallabies which have recently recolonized an outcrop (Gardners) 8 km from the nearest rock-wallaby population. Results indicate that the population at Gardners originated via a recent dispersal event from the eastern end of Mt. Caroline. This is only the second published record of dispersal by rock-wallabies between habitat patches and is the longest movement recorded to date. Molecular techniques and methods of analysis are now available to allow detailed studies of dispersal in rock-wallabies and should also be possible for many other taxa. [source] Characterization of polymorphic microsatellite markers, isolated from ginger (Zingiber officinale Rosc.)MOLECULAR ECOLOGY RESOURCES, Issue 6 2007SOK-YOUNG LEE Abstract The present study reports isolation and characterization of eight polymorphic microsatellite markers for Zingiber officinale Rosc. (Ginger). A total of 34 alleles were detected across the 20 accessions, with an average of 4.3 alleles per locus. Values for observed and expected heterozygosities ranged from 0 to 1.0 and from 0.23 to 0.67, respectively. The heterozygote deficits were observed at three loci. At the significance threshold (P < 0.05) of the eight loci, seven were found to have deviated from Hardy,Weinberg equilibrium, whereas significant linkage disequilibria were observed between 10 pairs of loci. Our data indicate the existence of moderate level of genetic diversity among the ginger accessions genotyped with eight markers. [source] Transcriptional profiling using a novel cDNA array identifies differential gene expression during porcine embryo elongationMOLECULAR REPRODUCTION & DEVELOPMENT, Issue 2 2005So Hyun Lee Abstract A novel porcine cDNA array, containing 1,015 PCR products selected for embryonic expression, was used for transcriptional profiling of conceptuses at four stages of peri-implantation development. Total conceptus RNA from small spherical, large spherical, tubular, and filamentous stages was amplified, converted to cDNA, and hybridized to membranes. Initially, normalized signal intensities obtained using cDNA from total RNA or from amplified RNA were compared. Uniform distribution of P -values associated with t -tests conducted for each gene indicated no evidence that amplification introduced bias. Analysis of data obtained by using amplified targets and the novel array identified genes differentially expressed across stages. Such genes were identified by testing for significant stage effects in gene-specific mixed models. A total of nine genes were declared differentially expressed. Six of the nine genes had P -values less than 0.001, and a false discovery rate of approximately 17% was associated with this significance threshold. Two out of six genes were significant when using the Bonferroni method to control the probability of one or more false positives. The other three genes had P -values between 0.001 and 0.01 and exhibited differences greater than twofold between stages. All four genes selected for confirmation (steroidogenic acute regulatory protein, interleukin 1 beta, transforming growth factor beta 3, and thymosin beta 10) were shown to be differentially expressed by using quantitative real time RT-PCR. Our study shows that RNA amplification is useful for transcriptional profiling with limiting porcine embryonic RNA, and that this novel targeted array can detect differential gene expression during trophoblastic elongation. Finally, our results contribute to an increased understanding of the temporal patterns of expression of known genes controlling conceptus development, as well as identify novel genes also differentially regulated during implantation. Mol. Reprod. Dev. 71: 129,139, 2005. © 2005 Wiley-Liss, Inc. [source] Identification of quantitative trait loci associated with egg quality, egg production, and body weight in an F2 resource population of chickens,ANIMAL GENETICS, Issue 2 2006M. A. Schreiweis Summary Egg production and egg quality are complex sex-limited traits that may benefit from the implementation of marker-assisted selection. The primary objective of the current study was to identify quantitative trait loci (QTL) associated with egg traits, egg production, and body weight in a chicken resource population. Layer (White Leghorn hens) and broiler (Cobb-Cobb roosters) lines were crossed to generate an F2 population of 508 hens over seven hatches. Phenotypes for 29 traits (weekly body weight from hatch to 6 weeks, egg traits including egg, albumen, yolk, and shell weight, shell thickness, shell puncture score, percentage of shell, and egg shell colour at 35 and 55 weeks of age, as well as egg production between 16 and 55 weeks of age) were measured in hens of the resource population. Genotypes of 120 microsatellite markers on 28 autosomal groups were determined, and interval mapping was conducted to identify putative QTL. Eleven QTL tests representing two regions on chromosomes 2 and 4 surpassed the 5% genome-wise significance threshold. These QTL influenced egg colour, egg and albumen weight, percent shell, body weight, and egg production. The chromosome 4 QTL region is consistent with multiple QTL studies that define chromosome 4 as a critical region significantly associated with a variety of traits across multiple resource populations. An additional 64 QTL tests surpassed the 5% chromosome-wise significance threshold. [source] How semiregular are irregular variables?ASTRONOMISCHE NACHRICHTEN, Issue 4 2009T. Lebzelter Abstract We investigate the question whether there is a real difference in the light change between stars classified as semiregular (SRV) or irregular (Lb) variables by analysing photometric light curves of 12 representatives of each class. Using Fourier analysis we try to find a periodic signal in each light curve and determine the S/N of this signal. For all stars, independent of their variability class we detect a period above the significance threshold. No difference in the measured S/N between the two classes could be found. We propose that the Lb stars can be seen as an extension of the SRVs towards shorter periods and smaller amplitudes. This is in agreement with findings from other quantities which also showed no marked difference between the two classes (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Bayesian Methods for Examining Hardy,Weinberg EquilibriumBIOMETRICS, Issue 1 2010Jon Wakefield Summary Testing for Hardy,Weinberg equilibrium is ubiquitous and has traditionally been carried out via frequentist approaches. However, the discreteness of the sample space means that uniformity of,p -values under the null cannot be assumed, with enumeration of all possible counts, conditional on the minor allele count, offering a computationally expensive way of,p -value calibration. In addition, the interpretation of the subsequent,p -values, and choice of significance threshold depends critically on sample size, because equilibrium will always be rejected at conventional levels with large sample sizes. We argue for a Bayesian approach using both Bayes factors, and the examination of posterior distributions. We describe simple conjugate approaches, and methods based on importance sampling Monte Carlo. The former are convenient because they yield closed-form expressions for Bayes factors, which allow their application to a large number of single nucleotide polymorphisms (SNPs), in particular in genome-wide contexts. We also describe straightforward direct sampling methods for examining posterior distributions of parameters of interest. For large numbers of alleles at a locus we resort to Markov chain Monte Carlo. We discuss a number of possibilities for prior specification, and apply the suggested methods to a number of real datasets. [source] Spatial dependence in agricultural land prices: does it exist?AGRICULTURAL ECONOMICS, Issue 3 2009Philip Kostov Spatial dependence; Hedonic models; Functional form Abstract Trade-offs arise between spatial dependence and choice of functional form in agricultural land price hedonic models. We discuss these trade-offs and how they can create spurious spatial dependence. Using a land sales dataset with apparent spatial dependence, we implement a semiparametric approach avoiding potential problems with the functional form. The results show that in addition to being nonlinear, the impacts are also characterized by significance thresholds that are difficult to capture in a parametric model. More importantly, we fail to detect any spatial dependence demonstrating that inappropriate functional form can indeed be responsible for finding spatial dependence in hedonic models. [source] Simple means to improve the interpretability of regression coefficientsMETHODS IN ECOLOGY AND EVOLUTION, Issue 2 2010Holger Schielzeth Summary 1. Linear regression models are an important statistical tool in evolutionary and ecological studies. Unfortunately, these models often yield some uninterpretable estimates and hypothesis tests, especially when models contain interactions or polynomial terms. Furthermore, the standard errors for treatment groups, although often of interest for including in a publication, are not directly available in a standard linear model. 2. Centring and standardization of input variables are simple means to improve the interpretability of regression coefficients. Further, refitting the model with a slightly modified model structure allows extracting the appropriate standard errors for treatment groups directly from the model. 3. Centring will make main effects biologically interpretable even when involved in interactions and thus avoids the potential misinterpretation of main effects. This also applies to the estimation of linear effects in the presence of polynomials. Categorical input variables can also be centred and this sometimes assists interpretation. 4. Standardization (z -transformation) of input variables results in the estimation of standardized slopes or standardized partial regression coefficients. Standardized slopes are comparable in magnitude within models as well as between studies. They have some advantages over partial correlation coefficients and are often the more interesting standardized effect size. 5. The thoughtful removal of intercepts or main effects allows extracting treatment means or treatment slopes and their appropriate standard errors directly from a linear model. This provides a simple alternative to the more complicated calculation of standard errors from contrasts and main effects. 6. The simple methods presented here put the focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds. They allow fitting complex, but meaningful models that can be concisely presented and interpreted. The presented methods can also be applied to generalised linear models (GLM) and linear mixed models. [source] Ratio-dependent significance thresholds in reciprocal 15N-labeling experiments as a robust tool in detection of candidate proteins responding to biological treatmentPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 7 2009Sylwia Kierszniowska Abstract Metabolic labeling of plant tissues with 15N has become widely used in plant proteomics. Here, we describe a robust experimental design and data analysis workflow implementing two parallel biological replicate experiments with reciprocal labeling and series of 1:1 control mixtures. Thereby, we are able to unambiguously distinguish (i) inherent biological variation between cultures and (ii) specific responses to a biological treatment. The data analysis workflow is based on first determining the variation between cultures based on 15N/14N ratios in independent 1:1 mixtures before biological treatment is applied. In a second step, ratio-dependent SD is used to define p -values for significant deviation of protein ratios in the biological experiment from the distribution of protein ratios in the 1:1 mixture. This approach allows defining those proteins showing significant biological response superimposed on the biological variation before treatment. The proposed workflow was applied to a series of experiments, in which changes in composition of detergent resistant membrane domains was analyzed in response to sucrose resupply after carbon starvation. Especially in experiments involving cell culture treatment (starvation) prior to the actual biological stimulus of interest (resupply), a clear distinction between culture to culture variations and biological response is of utmost importance. [source] Age-dependent quantitative trait loci affecting growth traits in Scottish Blackface sheepANIMAL GENETICS, Issue 2 2009G. Hadjipavlou Summary To dissect age-dependent quantitative trait loci (QTL) associated with growth and to examine changes in QTL effects over time, the Gompertz growth model was fitted to longitudinal live weight data on 788 Scottish Blackface lambs from nine half-sib families. QTL were mapped for model parameters and weekly live weights and growth rates using microsatellite markers on chromosomes 1, 2, 3, 5, 14, 18, 20 and 21. QTL significance (using , = 0.05 chromosome-wide significance thresholds, unless otherwise stated) varied with age, and those for growth rate occurred earlier than equivalent QTL for live weight. A chromosome 20 QTL for growth rate was significant from 4 to 9 weeks (maximum significance at 6 weeks) and for maximum growth rate. For live weight, this QTL was significant from 8 to 16 weeks (maximum significance at 12 weeks). A nominally significant chromosome 14 QTL was detected for growth rates from birth to week 2 in the same families and location as an 8-week weight QTL. In addition, at the same position on chromosome 14, a QTL was significant for growth rate for 17,28 weeks (maximum significance at 24 weeks). A chromosome 3 QTL was significant for weights at early ages (birth to week 4) and a growth rate QTL on chromosome 18 was significant from 8 to 12 weeks. Fitting growth curves allowed the combination of information from multiple measurements into a few biologically meaningful variables, and the detection of growth QTL that were not observed from analyses of raw weight data. These QTL describe distinct parts of an animal's growth curve trajectory, possibly enabling manipulation of this trajectory. [source] | |||||||||||||||||||