A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Animal-SNPAtlas: a comprehensive SNP database for multiple animals

Single-nucleotide polymorphisms (SNPs) as the most important type of genetic variation are widely used in describing population characteristics and play vital roles in animal genetics and breeding. Large amounts of population genetic variation resources and tools have been developed in human, which provided solid support for human genetic studies. However, compared with human, the development of animal genetic variation databases was relatively slow, which limits the genetic researches in these animals. To fill this gap, we systematically identified ∼ 499 million high-quality SNPs from 4784 samples of 20 types of animals. On that basis, we annotated the functions of SNPs, constructed high-density reference panels and calculated genome-wide linkage disequilibrium (LD) matrixes. We further developed Animal-SNPAtlas, a user-friendly database (http://gong_lab.hzau.edu.cn/Animal_SNPAtlas/) which includes high-quality SNP datasets and several support tools for multiple animals. In Animal-SNPAtlas, users can search the functional annotation of SNPs, perform online genotype imputation, explore and visualize LD information, browse variant information using the genome browser and download SNP datasets for each species. With the massive SNP datasets and useful tools, Animal-SNPAtlas will be an important fundamental resource for the animal genomics, genetics and breeding community.

A missense variant in SHARPIN mediates Alzheimer's disease-specific brain damages

Established genetic risk factors for Alzheimer's disease (AD) account for only a portion of AD heritability. The aim of this study was to identify novel associations between genetic variants and AD-specific brain atrophy. We conducted genome-wide association studies for brain magnetic resonance imaging measures of hippocampal volume and entorhinal cortical thickness in 2643 Koreans meeting the clinical criteria for AD (n = 209), mild cognitive impairment (n = 1449) or normal cognition (n = 985). A missense variant, rs77359862 (R274W), in the SHANK-associated RH Domain Interactor (SHARPIN) gene was associated with entorhinal cortical thickness (p = 5.0 × 10^-9) and hippocampal volume (p = 5.1 × 10^-12). It revealed an increased risk of developing AD in the mediation analyses. This variant was also associated with amyloid-β accumulation (p = 0.03) and measures of memory (p = 1.0 × 10^-4) and executive function (p = 0.04). We also found significant association of other SHARPIN variants with hippocampal volume in the Alzheimer's Disease Neuroimaging Initiative (rs3417062, p = 4.1 × 10^-6) and AddNeuroMed (rs138412600, p = 5.9 × 10^-5) cohorts. Further, molecular dynamics simulations and co-immunoprecipitation indicated that the variant significantly reduced the binding of linear ubiquitination assembly complex proteins, SHPARIN and HOIL-1 Interacting Protein (HOIP), altering the downstream NF-κB signaling pathway. These findings suggest that SHARPIN plays an important role in the pathogenesis of AD.

Minding the gap in HIV host genetics: opportunities and challenges

Genome-wide association studies (GWAS) have been successful in identifying and confirming novel genetic variants that are associated with diverse HIV phenotypes. However, these studies have predominantly focused on European cohorts. HLA molecules have been consistently associated with HIV outcomes, some of which have been found to be population specific, underscoring the need for diversity in GWAS. Recently, there has been a concerted effort to address this gap that leads to health care (disease prevention, diagnosis, treatment) disparities with marginal improvement. As precision medicine becomes more utilized, non-European individuals will be more and more disadvantaged, as the genetic variants identified in genomic research based on European populations may not accurately reflect that of non-European individuals. Leveraging pre-existing, large, multiethnic cohorts, such as the UK Biobank, 23andMe, and the National Institute of Health's All of Us Research Program, can contribute in raising genomic research in non-European populations and ultimately lead to better health outcomes.

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

Genome-wide and gene-based association studies of anxiety disorders in European and African American samples

Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.

Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

Identity by descent: variation in meiosis, across genomes, and in populations

Gene identity by descent (IBD) is a fundamental concept that underlies genetically mediated similarities among relatives. Gene IBD is traced through ancestral meioses and is defined relative to founders of a pedigree, or to some time point or mutational origin in the coalescent of a set of extant genes in a population. The random process underlying changes in the patterns of IBD across the genome is recombination, so the natural context for defining IBD is the ancestral recombination graph (ARG), which specifies the complete ancestry of a collection of chromosomes. The ARG determines both the sequence of coalescent ancestries across the chromosome and the extant segments of DNA descending unbroken by recombination from their most recent common ancestor (MRCA). DNA segments IBD from a recent common ancestor have high probability of being of the same allelic type. Non-IBD DNA is modeled as of independent allelic type, but the population frame of reference for defining allelic independence can vary. Whether of IBD, allelic similarity, or phenotypic covariance, comparisons may be made to other genomic regions of the same gametes, or to the same genomic regions in other sets of gametes or diploid individuals. In this review, I present IBD as the framework connecting evolutionary and coalescent theory with the analysis of genetic data observed on individuals. I focus on the high variance of the processes that determine IBD, its changes across the genome, and its impact on observable data.

Imputation-based meta-analysis of severe malaria in three African populations

Combining data from genome-wide association studies (GWAS) conducted at different locations, using genotype imputation and fixed-effects meta-analysis, has been a powerful approach for dissecting complex disease genetics in populations of European ancestry. Here we investigate the feasibility of applying the same approach in Africa, where genetic diversity, both within and between populations, is far more extensive. We analyse genome-wide data from approximately 5,000 individuals with severe malaria and 7,000 population controls from three different locations in Africa. Our results show that the standard approach is well powered to detect known malaria susceptibility loci when sample sizes are large, and that modern methods for association analysis can control the potential confounding effects of population structure. We show that pattern of association around the haemoglobin S allele differs substantially across populations due to differences in haplotype structure. Motivated by these observations we consider new approaches to association analysis that might prove valuable for multicentre GWAS in Africa: we relax the assumptions of SNP-based fixed effect analysis; we apply Bayesian approaches to allow for heterogeneity in the effect of an allele on risk across studies; and we introduce a region-based test to allow for heterogeneity in the location of causal alleles.

Genetic variants associated with increased risk of malignant pleural mesothelioma: a genome-wide association study

Asbestos exposure is the main risk factor for malignant pleural mesothelioma (MPM), a rare aggressive tumor. Nevertheless, only 5-17% of those exposed to asbestos develop MPM, suggesting the involvement of other environmental and genetic risk factors. To identify the genetic risk factors that may contribute to the development of MPM, we conducted a genome-wide association study (GWAS; 370,000 genotyped SNPs, 5 million imputed SNPs) in Italy, among 407 MPM cases and 389 controls with a complete history of asbestos exposure. A replication study was also undertaken and included 428 MPM cases and 1269 controls from Australia. Although no single marker reached the genome-wide significance threshold, several associations were supported by haplotype-, chromosomal region-, gene- and gene-ontology process-based analyses. Most of these SNPs were located in regions reported to harbor aberrant alterations in mesothelioma (SLC7A14, THRB, CEBP350, ADAMTS2, ETV1, PVT1 and MMP14 genes), causing at most a 2-3-fold increase in MPM risk. The Australian replication study showed significant associations in five of these chromosomal regions (3q26.2, 4q32.1, 7p22.2, 14q11.2, 15q14). Multivariate analysis suggested an independent contribution of 10 genetic variants, with an Area Under the ROC Curve (AUC) of 0.76 when only exposure and covariates were included in the model, and of 0.86 when the genetic component was also included, with a substantial increase of asbestos exposure risk estimation (odds ratio, OR: 45.28, 95% confidence interval, CI: 21.52-95.28). These results showed that genetic risk factors may play an additional role in the development of MPM, and that these should be taken into account to better estimate individual MPM risk in individuals who have been exposed to asbestos.

Genetics of obesity and type 2 diabetes in African Americans

Obesity and type 2 diabetes are highly prevalent and lead to significant morbidity and mortality. In the United States, the impact of these conditions may be worse on historically underserved minorities, particularly African Americans. Genetic ancestry and differences in physiology are unlikely to be the sole or primary determinants of these disparities. In addition, research in this area has the ethically problematic possibility of conflating race with biology. Despite these important considerations and the challenges of conducting this work, population-based approaches for investigating the etiology of obesity and T2D may yield useful information about the pathophysiology of disease, and have implications that extend to all affected individuals. The purpose of this paper is to describe what is understood about the genetic variation that underlies obesity and T2D in African Americans and other individuals of more recent African descent and to highlight several examples that illustrate how ensuring adequate minority representation in genetic research improves its quality. For a variety of reasons a number of unique insights have been possible as a result of these efforts.

A multi-SNP locus-association method reveals a substantial fraction of the missing heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.