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Hongwen Deng - One of the best experts on this subject based on the ideXlab platform.

  • tests of association for quantitative traits in nuclear families using principal components to correct for Population Stratification
    Annals of Human Genetics, 2009
    Co-Authors: Hongwen Deng, Lei Zhang, Jian Li
    Abstract:

    Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to Population Stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for Population Stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of Population Stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.

  • Tests of association for quantitative traits in nuclear families using principal components to correct for Population Stratification.
    Annals of human genetics, 2009
    Co-Authors: Lei Zhang, Jian Li, Hongwen Deng
    Abstract:

    Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to Population Stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for Population Stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within the variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of Population Stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.

  • comparison of Population based association study methods correcting for Population Stratification
    PLOS ONE, 2008
    Co-Authors: Feng Zhang, Yuping Wang, Hongwen Deng
    Abstract:

    Population Stratification can cause spurious associations in Population–based association studies. Several statistical methods have been proposed to reduce the impact of Population Stratification on Population–based association studies. We simulated a set of stratified Populations based on the real haplotype data from the HapMap ENCODE project, and compared the relative power, type I error rates, accuracy and positive prediction value of four prevailing Population–based association study methods: traditional case-control tests, structured association (SA), genomic control (GC) and principal components analysis (PCA) under various Population Stratification levels. Additionally, we evaluated the effects of sample sizes and frequencies of disease susceptible allele on the performance of the four analytical methods in the presence of Population Stratification. We found that the performance of PCA was very stable under various scenarios. Our comparison results suggest that SA and PCA have comparable performance, if sufficient ancestral informative markers are used in SA analysis. GC appeared to be strongly conservative in significantly stratified Populations. It may be better to apply GC in the stratified Populations with low Stratification level. Our study intends to provide a practical guideline for researchers to select proper study methods and make appropriate inference of the results in Population-based association studies.

Christoph Lange - One of the best experts on this subject based on the ideXlab platform.

  • effect of Population Stratification on snp by environment interaction
    Genetic Epidemiology, 2019
    Co-Authors: Jaehoon An, Christoph Lange, Sharon M Lutz, Julian Hecker
    Abstract:

    Proportions of false-positive rates in genome-wide association analysis are affected by Population Stratification, and if it is not correctly adjusted, the statistical analysis can produce the large false-negative finding. Therefore various approaches have been proposed to adjust such problems in genome-wide association studies. However, in spite of its importance, a few studies have been conducted in genome-wide single nucleotide polymorphism (SNP)-by-environment interaction studies. In this report, we illustrate in which scenarios can lead to the false-positive rates in association mapping and approach to maintaining the overall type-1 error rate.

  • differentiating Population Stratification from genotyping error using family data
    Annals of Human Genetics, 2012
    Co-Authors: Ronnie Sebro, Christoph Lange, Nan M Laird, John J Rogus, Neil Risch
    Abstract:

    Summary Identifying Population Stratification and genotyping error are important for candidate gene association studies using the Transmission Disequilibrium Test (TDT). Although the TDT retains the prespecified Type I error in the presence of Population Stratification, the test may have decreased power in the presence of Population Stratification. Genotyping error can also cause the TDT to have an elevated Type I error. Differentiating Population Stratification from genotyping error remains a challenge for geneticists. Both genotyping error and Population Stratification can result in an increase in the observed homozygosity of a sample relative to that expected assuming Hardy-Weinberg Equilibrium (HWE). We show that when family data are available, even if a limited number of markers are genotyped, evaluating the markers that show statistically significant deviation from HWE with the Mating Type Distortion Test (MTDT) – a test based on the mating type distribution – can reliably differentiate genotyping error from Population Stratification. We simulate data based on several models of genotyping error in previously published literature, and show how this method could be used in practice to assist in differentiating Population Stratification from systematic genotyping error.

  • impact of Population Stratification on family based association tests with longitudinal measurements
    Statistical Applications in Genetics and Molecular Biology, 2009
    Co-Authors: Xiao Ding, Christoph Lange, Scott T Weiss, Benjamin A Raby, Nan M Laird
    Abstract:

    Several family-based approaches for testing genetic association with traits obtained from longitudinal or repeated measurement studies have been previously proposed. These approaches utilize the multivariate data more efficiently by using estimated optimal weights to combine univariate tests. We show that these FBAT approaches are still robust against hidden Population Stratification, but their power can be heavily affected since the estimated weights might provide poor approximation of the true theoretical optimal weights with the presence of Population Stratification. We introduce a permutation-based approach FBAT-MinP and an equal combination approach FBAT-EW, both of which do not involve the use of estimated weights. Through simulation studies, FBAT-MinP and FBAT-EW are shown to be powerful even in the presence of Population Stratification, when other approaches may substantially lose their power. An application of these approaches to the Childhood Asthma Management Program (CAMP) study data for testing an association between body mass index and a previously reported candidate SNP is given as an example.

Nick Patterson - One of the best experts on this subject based on the ideXlab platform.

  • new approaches to Population Stratification in genome wide association studies
    Nature Reviews Genetics, 2010
    Co-Authors: Noah A. Zaitlen, Alkes L. Price, David Reich, Nick Patterson
    Abstract:

    This article compares the different approaches that have been developed for detecting confounding due to Population Stratification, family structure and cryptic relatedness, with an emphasis on the potential of mixed models for addressing these problems simultaneously.

  • New approaches to Population Stratification in genome-wide association studies
    Nature Reviews Genetics, 2010
    Co-Authors: Alkes L. Price, Noah A. Zaitlen, David Reich, Nick Patterson
    Abstract:

    Genome-wide association (GWA) studies are an effective approach for identifying genetic variants associated with disease risk. GWA studies can be confounded by Population Stratification--systematic ancestry differences between cases and controls--which has previously been addressed by methods that infer genetic ancestry. Those methods perform well in data sets in which Population structure is the only kind of structure present but are inadequate in data sets that also contain family structure or cryptic relatedness. Here, we review recent progress on methods that correct for Stratification while accounting for these additional complexities.

  • Assessing the impact of Population Stratification on genetic association studies
    Nature Genetics, 2004
    Co-Authors: Matthew L. Freedman, Kathryn L. Penney, Gavin J. Mcdonald, Andre A. Mignault, Stacey B. Gabriel, David Reich, Nick Patterson, Eric J Topol, Jordan W. Smoller, Carlos N. Pato
    Abstract:

    Population Stratification refers to differences in allele frequencies between cases and controls due to systematic differences in ancestry rather than association of genes with disease. It has been proposed that false positive associations due to Stratification can be controlled by genotyping a few dozen unlinked genetic markers. To assess Stratification empirically, we analyzed data from 11 case-control and case-cohort association studies. We did not detect statistically significant evidence for Stratification but did observe that assessments based on a few dozen markers lack power to rule out moderate levels of Stratification that could cause false positive associations in studies designed to detect modest genetic risk factors. After increasing the number of markers and samples in a case-cohort study (the design most immune to Stratification), we found that Stratification was in fact present. Our results suggest that modest amounts of Stratification can exist even in well designed studies.

Alkes L. Price - One of the best experts on this subject based on the ideXlab platform.

Neil Risch - One of the best experts on this subject based on the ideXlab platform.

  • A brief note on the resemblance between relatives in the presence of Population Stratification.
    Heredity, 2012
    Co-Authors: Ronnie Sebro, Neil Risch
    Abstract:

    Population Stratification occurs when a study Population is comprised of several sub-Populations, and can result in increased false positive findings in genomewide-association studies. Recently published work shows that sub-Population-specific positive assortative mating at the genotypic level results in Population Stratification. We show that if the allele frequency of a single nucleotide polymorphism responsible for a trait varies between sub-Populations and there is no dominance variance, then the heritability of the trait increases, primarily due to an increase in the additive genetic variance of the trait.

  • differentiating Population Stratification from genotyping error using family data
    Annals of Human Genetics, 2012
    Co-Authors: Ronnie Sebro, Christoph Lange, Nan M Laird, John J Rogus, Neil Risch
    Abstract:

    Summary Identifying Population Stratification and genotyping error are important for candidate gene association studies using the Transmission Disequilibrium Test (TDT). Although the TDT retains the prespecified Type I error in the presence of Population Stratification, the test may have decreased power in the presence of Population Stratification. Genotyping error can also cause the TDT to have an elevated Type I error. Differentiating Population Stratification from genotyping error remains a challenge for geneticists. Both genotyping error and Population Stratification can result in an increase in the observed homozygosity of a sample relative to that expected assuming Hardy-Weinberg Equilibrium (HWE). We show that when family data are available, even if a limited number of markers are genotyped, evaluating the markers that show statistically significant deviation from HWE with the Mating Type Distortion Test (MTDT) – a test based on the mating type distribution – can reliably differentiate genotyping error from Population Stratification. We simulate data based on several models of genotyping error in previously published literature, and show how this method could be used in practice to assist in differentiating Population Stratification from systematic genotyping error.