Effects In Genome-wide Association Studies Research Articles

A genealogy-based approach for revealing ancestry-specific structures in admixed populations.

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods for elucidating the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for elucidating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the Ancestral Recombination Graph (ARG) and local ancestry calls and computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories. Taken together, as-eGRM has the promise to better reveal the fine-scale structure within an ancestry component of admixed individuals, which can help improve the robustness and interpretation of findings from association studies of disease or complex traits for these understudied populations.

How well can we separate genetics from the environment?

A simulation study demonstrates a better method for separating genetic effects from environmental effects in genome-wide association studies, but there is still some way to go before this becomes a "solved" problem.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes.

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Parent-of-origin-environment interactions in case-parent triads with or without independent controls.

With case–parent triad data, one can frequently deduce parent of origin of the child's alleles. This allows a parent‐of‐origin (PoO) effect to be estimated as the ratio of relative risks associated with the alleles inherited from the mother and the father, respectively. A possible cause of PoO effects is DNA methylation, leading to genomic imprinting. Because environmental exposures may influence methylation patterns, gene–environment interaction studies should be extended to allow for interactions between PoO effects and environmental exposures (i.e., PoOxE). One should thus search for loci where the environmental exposure modifies the PoO effect.We have developed an extensive framework to analyze PoOxE effects in genome‐wide association studies (GWAS), based on complete or incomplete case–parent triads with or without independent control triads. The interaction approach is based on analyzing triads in each exposure stratum using maximum likelihood estimation in a log‐linear model. Interactions are then tested applying a Wald‐based posttest of parameters across strata. Our framework includes a complete setup for power calculations. We have implemented the models in the R software package Haplin.To illustrate our PoOxE test, we applied the new methodology to top hits from our previous GWAS, assessing whether smoking during the periconceptional period modifies PoO effects on cleft palate only.

Erratum to: Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.

Erratum to: Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.

Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism.

BackgroundGenome-wide association studies provide important insights to the genetic component of disease risks. However, an existing challenge is how to incorporate collective effects of interactions beyond the level of independent single nucleotide polymorphism (SNP) tests. While methods considering each SNP pair separately have provided insights, a large portion of expected heritability may reside in higher-order interaction effects.ResultsWe describe an inference approach (discrete discriminant analysis; DDA) designed to probe collective interactions while treating both genotypes and phenotypes as random variables. The genotype distributions in case and control groups are modeled separately based on empirical allele frequency and covariance data, whose differences yield disease risk parameters. We compared pairwise tests and collective inference methods, the latter based both on DDA and logistic regression. Analyses using simulated data demonstrated that significantly higher sensitivity and specificity can be achieved with collective inference in comparison to pairwise tests, and with DDA in comparison to logistic regression. Using age-related macular degeneration (AMD) data, we demonstrated two possible applications of DDA. In the first application, a genome-wide SNP set is reduced into a small number (∼100) of variants via filtering and SNP pairs with significant interactions are identified. We found that interactions between SNPs with highest AMD association were epigenetically active in the liver, adipocytes, and mesenchymal stem cells. In the other application, multiple groups of SNPs were formed from the genome-wide data and their relative strengths of association were compared using cross-validation. This analysis allowed us to discover novel collections of loci for which interactions between SNPs play significant roles in their disease association. In particular, we considered pathway-based groups of SNPs containing up to ∼10, 000 variants in each group. In addition to pathways related to complement activation, our collective inference pointed to pathway groups involved in phospholipid synthesis, oxidative stress, and apoptosis, consistent with the AMD pathogenesis mechanism where the dysfunction of retinal pigment epithelium cells plays central roles.ConclusionsThe simultaneous inference of collective interaction effects within a set of SNPs has the potential to reveal novel aspects of disease association.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2871-3) contains supplementary material, which is available to authorized users.

Multi-locus Test and Correction for Confounding Effects in Genome-Wide Association Studies.

Genome-wide association studies (GWAS) examine a large number of genetic variants, e. g., single nucleotide polymorphisms (SNP), and associate them with a disease of interest. Traditional statistical methods for GWASs can produce spurious associations, due to limited information from individual SNPs and confounding effects. This paper develops two statistical methods to enhance data analysis of GWASs. The first is a multiple-SNP association test, which is a weighted chi-square test derived for big contingency tables. The test assesses combinatorial effects of multiple SNPs and improves conventional methods of single SNP analysis. The second is a method that corrects for confounding effects, which may come from population stratification as well as other ambiguous (unknown) factors. The proposed method identifies a latent confounding factor, using a profile of whole genome SNPs, and eliminates confounding effects through matching or stratified statistical analysis. Simulations and a GWAS of rheumatoid arthritis demonstrate that the proposed methods dramatically remove the number of significant tests, or false positives, and outperforms other available methods.

CollapsABEL: an R library for detecting compound heterozygote alleles in genome-wide association studies.

BackgroundCompound Heterozygosity (CH) in classical genetics is the presence of two different recessive mutations at a particular gene locus. A relaxed form of CH alleles may account for an essential proportion of the missing heritability, i.e. heritability of phenotypes so far not accounted for by single genetic variants. Methods to detect CH-like effects in genome-wide association studies (GWAS) may facilitate explaining the missing heritability, but to our knowledge no viable software tools for this purpose are currently available.ResultsIn this work we present the Generalized Compound Double Heterozygosity (GCDH) test and its implementation in the R package CollapsABEL. Time-consuming procedures are optimized for computational efficiency using Java or C++. Intermediate results are stored either in an SQL database or in a so-called big.matrix file to achieve reasonable memory footprint. Our large scale simulation studies show that GCDH is capable of discovering genetic associations due to CH-like interactions with much higher power than a conventional single-SNP approach under various settings, whether the causal genetic variations are available or not. CollapsABEL provides a user-friendly pipeline for genotype collapsing, statistical testing, power estimation, type I error control and graphics generation in the R language.ConclusionsCollapsABEL provides a computationally efficient solution for screening general forms of CH alleles in densely imputed microarray or whole genome sequencing datasets. The GCDH test provides an improved power over single-SNP based methods in detecting the prevalence of CH in human complex phenotypes, offering an opportunity for tackling the missing heritability problem.Binary and source packages of CollapsABEL are available on CRAN (https://cran.r-project.org/web/packages/CollapsABEL) and the website of the GenABEL project (http://www.genabel.org/packages).Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1006-9) contains supplementary material, which is available to authorized users.

Assessing SNP-SNP Interactions among DNA Repair, Modification and Metabolism Related Pathway Genes in Breast Cancer Susceptibility

Genome-wide association studies (GWASs) have identified low-penetrance common variants (i.e., single nucleotide polymorphisms, SNPs) associated with breast cancer susceptibility. Although GWASs are primarily focused on single-locus effects, gene-gene interactions (i.e., epistasis) are also assumed to contribute to the genetic risks for complex diseases including breast cancer. While it has been hypothesized that moderately ranked (P value based) weak single-locus effects in GWASs could potentially harbor valuable information for evaluating epistasis, we lack systematic efforts to investigate SNPs showing consistent associations with weak statistical significance across independent discovery and replication stages. The objectives of this study were i) to select SNPs showing single-locus effects with weak statistical significance for breast cancer in a GWAS and/or candidate-gene studies; ii) to replicate these SNPs in an independent set of breast cancer cases and controls; and iii) to explore their potential SNP-SNP interactions contributing to breast cancer susceptibility. A total of 17 SNPs related to DNA repair, modification and metabolism pathway genes were selected since these pathways offer a priori knowledge for potential epistatic interactions and an overall role in breast carcinogenesis. The study design included predominantly Caucasian women (2,795 cases and 4,505 controls) from Alberta, Canada. We observed two two-way SNP-SNP interactions (APEX1-rs1130409 and RPAP1-rs2297381; MLH1-rs1799977 and MDM2-rs769412) in logistic regression that conferred elevated risks for breast cancer (P interaction<7.3×10−3). Logic regression identified an interaction involving four SNPs (MBD2-rs4041245, MLH1-rs1799977, MDM2-rs769412, BRCA2-rs1799943) (P permutation = 2.4×10−3). SNPs involved in SNP-SNP interactions also showed single-locus effects with weak statistical significance, while BRCA2-rs1799943 showed stronger statistical significance (P correlation/trend = 3.2×10−4) than the others. These single-locus effects were independent of body mass index. Our results provide a framework for evaluating SNPs showing statistically weak but reproducible single-locus effects for epistatic effects contributing to disease susceptibility.

Approaching “Phantom Heritability” in Psychiatry by Hypothesis-Driven Gene–Gene Interactions

action studies require a reduced number of multiple tests and might help reducing the “phantom heritability.”Several statistical methods for accessing epistatic effects in genome-wide association studies (GWAS) have been created or improved in the last years ( Cowper-Sal lari et al., 2011; Gyenesei et al., 2012; Pandey et al., 2012; Piriyapongsa et al., 2012; Ma et al., 2013; Pendergrass et al., 2013), but they still fail to provide biological inter -pretation. One option for this problem is to only test interactions with biological plau-sibility by implementing semi-exhaustive approaches in GWAS using experimen-tal knowledge of biological networks. By applying this method to the Wellcome Trust Case-Control Consortium data sets, Emily et al. (2009) reduced the number of SNPs pairs inserted in the analysis (1.25 11× 10 to 3.5 6× 10) and reported a significant case of epistasis between

HYST: A Hybrid Set-Based Test for Genome-wide Association Studies, with Application to Protein-Protein Interaction-Based Association Analysis

The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.

Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression.

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2. We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8 ± 2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

MI-GWAS: a SAS platform for the analysis of inherited and maternal genetic effects in genome-wide association studies using log-linear models

BackgroundSeveral platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).ResultsAn integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies (MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.ConclusionsThe MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm.

Modeling of environmental effects in genome-wide association studies identifies SLC2A2 and HP as novel loci influencing serum cholesterol levels.

Genome-wide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort (N SE = 656) was screened for candidate genes and the non-Swedish (NS) EUROSPAN cohorts (N NS = 3,282) were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts. While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol, the other two were novel findings. For the latter SNPs, the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400 (p SE,unadjusted = 3.6×10−5, p SE,adjusted = 2.2×10−6, p NS,unadjusted = 0.047) in the SLC2A2 (Glucose transporter type 2) and rs2000999 (p SE,unadjusted = 1.1×10−3, p SE,adjusted = 3.8×10−4, p NS,unadjusted = 0.035) in the HP gene (Haptoglobin-related protein precursor). Both showed evidence of association with total cholesterol. These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci.

Detecting haplotype effects in genomewide association studies

The analysis of genomewide association studies requires methods that are both computationally feasible and statistically powerful. Given the large-scale collection of single nucleotide polymorphisms (SNPs), it is desirable to explore the information contained in their interrelationships. In particular, utilizing haplotypes rather than individual SNPs and accounting for correlations of polymorphisms in adjustment for multiple testing can lead to increased power. We present a statistically powerful and numerically efficient method based on sliding windows of adjacent SNPs to detect haplotype-disease association in genomewide studies. This method consists of an efficient algorithm to calculate a proper likelihood-ratio statistic for any given window of SNPs, along with an accurate and efficient Monte Carlo procedure to adjust for multiple testing. Simulation studies using the HapMap data showed that the proposed method performs well in realistic situations. We applied the new method to a case-control study on rheumatoid arthritis and identified several loci worthy of further investigations.