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Family Data (family + data)

Distribution by Scientific Domains

Life Sciences	75%

Selected Abstracts

Testing Hardy-Weinberg Equilibrium using Family Data from Complex Surveys

ANNALS OF HUMAN GENETICS, Issue 4 2009
Dewei She
Summary Genetic data collected during the second phase of the Third National Health and Nutrition Examination Survey (NHANES III) enable us to investigate the association of a wide variety of health factors with regard to genetic variation. The classic question when looking into the genetic variations in a population is whether the population is in the state of Hardy-Weinberg Equilibrium (HWE). Our objective was to develop test procedures using family data from complex surveys such as NHANES III. We developed six Pearson ,2 based tests for a diallelic locus of autosomal genes. The finite sample properties of the proposed test procedures were evaluated via Monte Carlo simulation studies and the Rao-Scott first order corrected test was recommended. Test procedures were applied to three loci from NHANES III genetic databases, i.e., ADRB2, TGFB1, and VDR. HWE was shown to hold at 0.05 level for all three loci when only families with genotypic information available for two parents and for one or more children were used in the analysis. [source]

SNP Haplotypes in the Angiotensin I-Converting Enzyme (ACE) Gene: Analysis of Nigerian Family Data Using Gamete Competition Models

ANNALS OF HUMAN GENETICS, Issue 2 2005
C. A. McKenzie
Summary Gamete competition models were used to explore the relationships between 13 ACE gene polymorphisms and plasma ACE concentration in a set of Nigerian families. Several markers in the 5, and 3, regions of the gene were significantly associated with ACE concentration (P < 10 -4). Multi-locus genotypes comprising different combinations of markers from the 5, UTR and the 3, region of the gene were also analysed; in addition to G2350A, in the 3, region, two markers from the 5, UTR (A-5466C and A-240T) were found to be associated with ACE concentration. These results are consistent with reports that have suggested the presence of at least two ACE-linked QTLs, and demonstrate the utility of gamete competition models in the exploratory investigation of the relationship between a quantitative trait and multiple variants in a small genomic region. [source]

Family data in Rett syndrome: Association with other genetic disorders

JOURNAL OF PAEDIATRICS AND CHILD HEALTH, Issue 4 2000
H Leonard
Background: Rett syndrome is a neurological disorder, almost exclusively affecting girls. Methodology: Between 1993 and 1995 pedigree data were obtained from families of girls registered with the Australian Rett syndrome database. Results: Although 21 individual disorders were reported to be present in family members of affected girls, there was no apparent clustering of the same disorder in different families. However it was certain that a geneticist had been involved in only 10.9% of cases. Conclusions: Mutations in the MECP2 gene have now been reported in a proportion of sporadic cases. Thus, it will be important to examine this phenotype,genotype correlation in the Australian cohort. Where a mutation is found, prenatal diagnosis in a subsequent pregnancy will be a possibility. Using the Australian population database and in conjunction with the clinical genetic services in each state it is planned to contact families with an affected girl to offer testing and counselling. [source]

Using evidence for population stratification bias in combined individual- and family-level genetic association analyses of quantitative traits

GENETIC EPIDEMIOLOGY, Issue 5 2010
Lucia Mirea
Abstract Genetic association studies are generally performed either by examining differences in the genotype distribution between individuals or by testing for preferential allele transmission within families. In the absence of population stratification bias (PSB), integrated analyses of individual and family data can increase power to identify susceptibility loci [Abecasis et al., 2000. Am. J. Hum. Genet. 66:279,292; Chen and Lin, 2008. Genet. Epidemiol. 32:520,527; Epstein et al., 2005. Am. J. Hum. Genet. 76:592,608]. In existing methods, the presence of PSB is initially assessed by comparing results from between-individual and within-family analyses, and then combined analyses are performed only if no significant PSB is detected. However, this strategy requires specification of an arbitrary testing level ,PSB, typically 5%, to declare PSB significance. As a novel alternative, we propose to directly use the PSB evidence in weights that combine results from between-individual and within-family analyses. The weighted approach generalizes previous methods by using a continuous weighting function that depends only on the observed P -value instead of a binary weight that depends on ,PSB. Using simulations, we demonstrate that for quantitative trait analysis, the weighted approach provides a good compromise between type I error control and power to detect association in studies with few genotyped markers and limited information regarding population structure. Genet. Epidemiol. 34: 502,511, 2010. © 2010 Wiley-Liss, Inc. [source]

Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment

GENETIC EPIDEMIOLOGY, Issue 8 2006
J. Carayol
Abstract The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood-based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype-based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy-Weinberg equilibrium. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source]

Haplotype association analysis for late onset diseases using nuclear family data

GENETIC EPIDEMIOLOGY, Issue 3 2006
Chun Li
Abstract In haplotype-based association studies for late onset diseases, one attractive design is to use available unaffected spouses as controls (Valle et al. [1998] Diab. Care 21:949,958). Given cases and spouses only, the standard expectation-maximization (EM) algorithm (Dempster et al. [1977] J. R. Stat. Soc. B 39:1,38) for case-control data can be used to estimate haplotype frequencies. But often we will have offspring for at least some of the spouse pairs, and offspring genotypes provide additional information about the haplotypes of the parents. Existing methods may either ignore the offspring information, or reconstruct haplotypes for the subjects using offspring information and discard data from those whose haplotypes cannot be reconstructed with high confidence. Neither of these approaches is efficient, and the latter approach may also be biased. For case-control data with some subjects forming spouse pairs and offspring genotypes available for some spouse pairs or individuals, we propose a unified, likelihood-based method of haplotype inference. The method makes use of available offspring genotype information to apportion ambiguous haplotypes for the subjects. For subjects without offspring genotype information, haplotypes are apportioned as in the standard EM algorithm for case-control data. Our method enables efficient haplotype frequency estimation using an EM algorithm and supports probabilistic haplotype reconstruction with the probability calculated based on the whole sample. We describe likelihood ratio and permutation tests to test for disease-haplotype association, and describe three test statistics that are potentially useful for detecting such an association. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source]

A novel method to identify gene,gene effects in nuclear families: the MDR-PDT

GENETIC EPIDEMIOLOGY, Issue 2 2006
E.R. Martin
Abstract It is now well recognized that gene,gene and gene,environment interactions are important in complex diseases, and statistical methods to detect interactions are becoming widespread. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions that occur in the absence of detectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method [Ritchie et al., 2001: Am J Hum Genet 69:138,147] was developed. The MDR is wellsuited for examining high-order interactions and detecting interactions without main effects. The MDR was originally designed to analyze balanced case-control data. The analysis can use family data, but requires a single matched pair be selected from each family. This may be a discordant sib pair, or may be constructed from triad data when parents are available. To take advantage of additional affected and unaffected siblings requires a test statistic that measures the association of genotype with disease in general nuclear families. We have developed a novel test, the MDR-PDT, by merging the MDR method with the genotype-Pedigree Disequilibrium Test (geno-PDT)[Martin et al., 2003: Genet Epidemiol 25:203,213]. MDR-PDT allows identification of single-locus effects or joint effects of multiple loci in families of diverse structure. We present simulations to demonstrate the validity of the test and evaluate its power. To examine its applicability to real data, we applied the MDR-PDT to data from candidate genes for Alzheimer disease (AD) in a large family dataset. These results show the utility of the MDR-PDT for understanding the genetics of complex diseases. Genet. Epidemiol. 2006. © 2005 Wiley-Liss, Inc. [source]

Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene

GENETIC EPIDEMIOLOGY, Issue 3 2003
Gail Gong
Abstract Many clinical decisions require accurate estimates of disease risks associated with mutations of known disease-susceptibility genes. Such risk estimation is difficult when the mutations are rare. We used computer simulations to compare the performance of estimates obtained from two types of designs based on family data. In the first (clinic-based designs), families are ascertained because they meet certain criteria concerning multiple disease occurrences among family members. In the second (population-based designs), families are sampled through a population-based registry of affected individuals called probands, with oversampling of probands whose families are more likely to segregate mutations. We generated family structures, genotypes, and phenotypes using models that reflect the frequencies and penetrances of mutations of the BRCA1/2 genes. We studied the effects of risk heterogeneity due to unmeasured, shared risk factors by including risk variation due to unmeasured genotypes of another gene. The simulations were chosen to mimic the ascertainment and selection processes commonly used in the two types of designs. We found that penetrance estimates from both designs are nearly unbiased in the absence of unmeasured shared risk factors, but are biased upward in the presence of such factors. The bias increases with increasing variation in risks across genotypes of the second gene. However, it is small compared to the standard error of the estimates. Standard errors from population-based designs are roughly twice those from clinic-based designs with the same number of families. Using the root-mean-square error as a measure of performance, we found that in all instances, the clinic-based designs gave more accurate estimates than did the population-based designs with the same numbers of families. Rough variance calculations suggest that clinic-based designs give more accurate estimates because they include more identified mutation carriers. Genet Epidemiol 24:173,180, 2003. © 2003 Wiley-Liss, Inc. [source]

Unified sampling approach for multipoint linkage disequilibrium mapping of qualitative and quantitative traits

GENETIC EPIDEMIOLOGY, Issue 4 2002
Fang-Chi Hsu
Abstract Rapid development in biotechnology has enhanced the opportunity to deal with multipoint gene mapping for complex diseases, and association studies using quantitative traits have recently generated much attention. Unlike the conventional hypothesis-testing approach for fine mapping, we propose a unified multipoint method to localize a gene controlling a quantitative trait. We first calculate the sample size needed to detect linkage and linkage disequilibrium (LD) for a quantitative trait, categorized by decile, under three different modes of inheritance. Our results show that sampling trios of offspring and their parents from either extremely low (EL) or extremely high (EH) probands provides greater statistical power than sampling in the intermediate range. We next propose a unified sampling approach for multipoint LD mapping, where the goal is to estimate the map position (,) of a trait locus and to calculate a confidence interval along with its sampling uncertainty. Our method builds upon a model for an expected preferential transmission statistic at an arbitrary locus conditional on the sampling scheme, such as sampling from EL and EH probands. This approach is valid regardless of the underlying genetic model. The one major assumption for this model is that no more than one quantitative trait locus (QTL) is linked to the region being mapped. Finally we illustrate the proposed method using family data on total serum IgE levels collected in multiplex asthmatic families from Barbados. An unobserved QTL appears to be located at ,, = 41.93 cM with 95% confidence interval of (40.84, 43.02) through the 20-cM region framed by markers D12S1052 and D12S1064 on chromosome 12. The test statistic shows strong evidence of linkage and LD (chi-square statistic = 18.39 with 2 df, P -value = 0.0001). Genet. Epidemiol. 22:298,312, 2002. © 2002 Wiley-Liss, Inc. [source]

Extension of variance components approach to incorporate temporal trends and longitudinal pedigree data analysis

GENETIC EPIDEMIOLOGY, Issue 3 2002
Mariza de Andrade
Abstract Here we present a method that permits one to evaluate genetic effects and to detect genetic linkages by using serial observations of quantitative traits in pedigrees. We developed a statistical method that incorporates longitudinal family data and genetic marker information into an estimating equations framework. With this approach, we can study changes in components over time that measure polygenic and major genetic variances as well as shared and individual-specific environmental effects. Our method provides a measure of heritability from analysis of longitudinal data. Results using longitudinal family data from the Center for Preventive Medicine (Nancy, France) are presented. The results of our analysis show that the apolipoprotein E locus has no effect on interindividual variability in systolic blood pressure. We found that the longitudinal measure of heritability of systolic blood pressure is 0.32. Genet. Epidemiol. 22:221,232, 2002. © 2002 Wiley-Liss, Inc. [source]

Tests for genetic association using family data

GENETIC EPIDEMIOLOGY, Issue 2 2002
Mei-Chiung Shih
Abstract We use likelihood-based score statistics to test for association between a disease and a diallelic polymorphism, based on data from arbitrary types of nuclear families. The Nonfounder statistic extends the transmission disequilibrium test (TDT) to accommodate affected and unaffected offspring, missing parental genotypes, phenotypes more general than qualitative traits, such as censored survival data and quantitative traits, and residual correlation of phenotypes within families. The Founder statistic compares observed or inferred parental genotypes to those expected in the general population. Here the genotypes of affected parents and those with many affected offspring are weighted more heavily than unaffected parents and those with few affected offspring. We illustrate the tests by applying them to data on a polymorphism of the SRD5A2 gene in nuclear families with multiple cases of prostate cancer. We also use simulations to compare the power of these family-based statistics to that of the score statistic based on Cox's partial likelihood for censored survival data, and find that the family-based statistics have considerably more power when there are many untyped parents. The software program FGAP for computing test statistics is available at http://www.stanford.edu/dept/HRP/epidemiology/FGAP. Genet. Epidemiol. 22:128,145, 2002. © 2002 Wiley-Liss, Inc. [source]

Testing Hardy-Weinberg Equilibrium using Family Data from Complex Surveys

Detecting linkage disequilibrium in the presence of locus heterogeneity

ANNALS OF HUMAN GENETICS, Issue 3 2006
D. Wang
Summary Locus heterogeneity is a common phenomenon in complex diseases and is one of the most important factors that affect the power of either linkage or linkage disequilibrium (LD) analysis. In linkage analysis, the heterogeneity LOD score (HLOD) rather than LOD itself is often used. However, the existing methods for detecting linkage disequilibrium, such as the TDT and many of its variants, do not take into account locus heterogeneity. We propose two novel likelihood-based methods, an LD-Het likelihood and an LD-multinomial likelihood, to test linkage disequilibrium (LD) that explicitly incorporate locus heterogeneity in the analysis. The LD-Het is applicable to general nuclear family data but requires a working penetrance model. The LD-multinomial is only applicable to affected sib-pair data but does not require specification of a trait model. For affected sib-pair data, both methods have similar power to detect LD under the recessive model, but the LD-multinomial model has greater power when the underlying model is dominant or additive. [source]

Statistical properties and performance of pairwise relatedness estimators using turbot (Scophthalmus maximus L.) family data

AQUACULTURE RESEARCH, Issue 4 2010
Ania Pino-Querido
Abstract The statistical properties and performance of four estimators of pairwise relatedness were evaluated in several scenarios using the microsatellite genotype data from a set of large known full-sibships of turbot. All estimators showed a significant negative bias for the four kinships commonly used in these studies (unrelated: UR, half-sibs, full-sibs and parent,offspring), when allele frequencies of the reference population were estimated from the individuals analysed. When these frequencies were obtained from the base population from which all families proceeded, the bias was mostly corrected. The Wang (W) and Li (L) estimators were the least sensitive to this factor, while the Lynch and Ritland (L&R estimator) was the highest one. The error (mean around 0.130) was very similar in all scenarios for W, L and Queller and Goodnight (QG) estimators, while L&R was the highest error-prone estimator. Parent,offspring kinship resulted in the lowest error, when using W, L and QG estimators, while UR resulted in the lowest error with the L&R estimator. Globally, W was the best-performing estimator, although L&R could perform better in specific sampling scenarios. In summary, pairwise estimators represent useful tools for kinship classification in aquaculture broodstock management by applying appropriate thresholds depending on the goals of the analysis. [source]

Genetic improvement in the Australian aquaculture industry

AQUACULTURE RESEARCH, Issue 1 2000
A J. Lymbery
Most aquaculture industries in Australia are at an early stage of development and would benefit from the introduction of genetic improvement programmes. Size at harvest is perceived by industry participants, managers and researchers as the trait that will most influence profitability. Although most current genetic improvement programmes in aquaculture use mass selection, inbreeding is widely regarded as an important problem, which could be overcome by the use of family data in selection decisions. The major research priority is the development of genetic markers to enable accurate pedigree determination. The major constraint upon the implementation of genetic improvement programmes by aquaculture industries is lack of available funds and resources. Industry ownership and national co-ordination of research and development is seen as the best way of addressing this constraint. [source]

Estimating Disease Prevalence Using Relatives of Case and Control Probands

BIOMETRICS, Issue 1 2010
Kristin N. Javaras
Summary We introduce a method of estimating disease prevalence from case,control family study data. Case,control family studies are performed to investigate the familial aggregation of disease; families are sampled via either a case or a control proband, and the resulting data contain information on disease status and covariates for the probands and their relatives. Here, we introduce estimators for overall prevalence and for covariate-stratum-specific (e.g., sex-specific) prevalence. These estimators combine the proportion of affected relatives of control probands with the proportion of affected relatives of case probands and are designed to yield approximately unbiased estimates of their population counterparts under certain commonly made assumptions. We also introduce corresponding confidence intervals designed to have good coverage properties even for small prevalences. Next, we describe simulation experiments where our estimators and intervals were applied to case,control family data sampled from fictional populations with various levels of familial aggregation. At all aggregation levels, the resulting estimates varied closely and symmetrically around their population counterparts, and the resulting intervals had good coverage properties, even for small sample sizes. Finally, we discuss the assumptions required for our estimators to be approximately unbiased, highlighting situations where an alternative estimator based only on relatives of control probands may perform better. [source]