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An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

ABSTRACT: Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.

SUBMITTER: Kang EY

PROVIDER: S-EPMC6028251 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

Kang Eun Yong EY Lee Cue Hyunkyu CH Furlotte Nicholas A NA Joo Jong Wha J JWJ Kostem Emrah E Zaitlen Noah N Eskin Eleazar E Han Buhm B

Genetics 20180511 3

Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. Howev ...[more]

PMID: 29752291

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Project description:BACKGROUND:In microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for individual association, followed by the Benjamini-Hochberg (BH) procedure to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. The taxonomic tree of microbiome data represents alignment from phylum to species rank and characterizes evolutionary relationships across microbial taxa. Taxa that are closer on the tree usually have similar responses to the exposure (environment). The statistical power in microbial association tests can be enhanced by efficiently employing the prior evolutionary information via the taxonomic tree. METHODS:We propose a two-stage microbial association mapping framework (massMap) which uses grouping information from the taxonomic tree to strengthen statistical power in association tests at the target rank. massMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. The method then proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH (HBH) and selected subset testing (SST) procedures are evaluated to control the FDR for the two-stage structured tests. RESULTS:Our simulations show that massMap incorporating OMiAT and the advanced FDR controlling methodologies largely alleviates the multiplicity issue. It is statistically more powerful than the traditional association mapping directly at the target rank while controlling the FDR at desired levels under most scenarios. In our real data analyses, massMap detects more or the same amount of associated species with smaller adjusted p values compared to the traditional method, which further illustrates the efficiency of the proposed framework. The R package of massMap is publicly available at https://sites.google.com/site/huilinli09/software and https://github.com/JiyuanHu/ . CONCLUSIONS:massMap is a novel microbial association mapping framework and achieves additional efficiency by utilizing the intrinsic taxonomic structure of microbiome data.

Project description:The absence or 'missingness' of single nucleotide polymorphism (SNP) assay values because of genotype or related factors of interest may bias association and other studies. Missingness was determined for the Type 1 Diabetes Genetics Consortium (T1DGC) Major Histocompatibility Complex (MHC) data and was found to vary across the region, ranging up to 11.1% of the non-null proband SNPs, with a median of 0.3%. We consider factors related to missingness in the T1DGC data and briefly assess its possible influence on association studies.We assessed associations of missingness in the SNP assay data with human leucocyte antigen (HLA) genotype of the individual and with SNP genotypes of the parents. Within-cohort analyses were combined (over all cohorts) using (i) Mantel-Haenszel tests for two-by-two tables or (ii) by combining test statistics for larger tables and regression models. Mixed effect regression models were used to assess association of the SNP genotypes with affected status of the offspring after adjustment for parental SNP genotypes, cohort membership and HLA genotypes. Log-linear models were used to assess association of missingness in the unaffected sib assays with SNP genotypes of the probands.Missingness of SNP values near the HLA class I (A, B and C) and class II (DR, DQ and DP) loci is strongly associated with carriage of corresponding HLA genotypes within these groups. Similar associations pertain to missing values among the microsatellite data. In at least some of these cases, regions of missingness coincided with known deletion regions corresponding to the associated HLA haplotype. We conjecture that other regions of associated missingness may point to similar haplotypic deletions. Analysis of association patterns of SNP genotypes with affected status of offspring does not indicate strong informative missingness. However, association of missingness in proband data with parental SNP genotypes may impact transmission disequilibrium test (TDT)-type analyses. Comparisons of affected and unaffected siblings point to possible susceptibility regions additional to the classical HLA-DR3/4 alleles near BAT4-LY6G5B-BAT5 and NOTCH4.Potentially informative missingness in SNP assay values in the MHC region may impact on association and related analyses based on the T1DGC data. These results suggest that it would be prudent to assess the degree to which missingness may abrogate assessed SNP disease markers in such studies. Initial analyses based on comparison of affected and unaffected status in offspring suggest that at least these may be little affected.

Dataset Information

An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

Publications

An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

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