Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

Extreme‐phenotype genome‐wide association study (XP‐GWAS): a method for identifying trait‐associated variants by sequencing pools of individuals selected from a diversity panel

Although approaches for performing genome‐wide association studies (GWAS) are well developed, conventional GWAS requires high‐density genotyping of large numbers of individuals from a diversity panel. Here we report a method for performing GWAS that does not require genotyping of large numbers of individuals. Instead XP‐GWAS (extreme‐phenotype GWAS) relies on genotyping pools of individuals from a diversity panel that have extreme phenotypes. This analysis measures allele frequencies in the extreme pools, enabling discovery of associations between genetic variants and traits of interest. This method was evaluated in maize (Zea mays) using the well‐characterized kernel row number trait, which was selected to enable comparisons between the results of XP‐GWAS and conventional GWAS. An exome‐sequencing strategy was used to focus sequencing resources on genes and their flanking regions. A total of 0.94 million variants were identified and served as evaluation markers; comparisons among pools showed that 145 of these variants were statistically associated with the kernel row number phenotype. These trait‐associated variants were significantly enriched in regions identified by conventional GWAS. XP‐GWAS was able to resolve several linked QTL and detect trait‐associated variants within a single gene under a QTL peak. XP‐GWAS is expected to be particularly valuable for detecting genes or alleles responsible for quantitative variation in species for which extensive genotyping resources are not available, such as wild progenitors of crops, orphan crops, and other poorly characterized species such as those of ecological interest.     

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions (“Flexible cFDR”). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.     

Searching new signals for production traits through gene‐based association analysis in three Italian cattle breeds

Genome‐wide association studies (GWAS) have been widely applied to disentangle the genetic basis of complex traits. In cattle breeds, classical GWAS approaches with medium‐density marker panels are far from conclusive, especially for complex traits. This is due to the intrinsic limitations of GWAS and the assumptions that are made to step from the association signals to the functional variations. Here, we applied a gene‐based strategy to prioritize genotype–phenotype associations found for milk production and quality traits with classical approaches in three Italian dairy cattle breeds with different sample sizes (Italian Brown n = 745; Italian Holstein n = 2058; Italian Simmental n = 477). Although classical regression on single markers revealed only a single genome‐wide significant genotype–phenotype association, for Italian Holstein, the gene‐based approach identified specific genes in each breed that are associated with milk physiology and mammary gland development. As no standard method has yet been established to step from variation to functional units (i.e., genes), the strategy proposed here may contribute to revealing new genes that play significant roles in complex traits, such as those investigated here, amplifying low association signals using a gene‐centric approach.     

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. A substantial number of recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results. We review and discuss three analytic methods to combine preliminary GWAS statistics to identify genes, alleles, and pathways for deeper investigations. Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable. Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact. Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases. Reviews of published methods with recommendations for their application are provided within the framework for each approach.     

Genome‐wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward‐related ventral striatum activity in African‐ and European‐Americans

Genetic influences on alcohol and drug dependence partially overlap, however, specific loci underlying this overlap remain unclear. We conducted a genome‐wide association study (GWAS) of a phenotype representing alcohol or illicit drug dependence (ANYDEP) among 7291 European‐Americans (EA; 2927 cases) and 3132 African‐Americans (AA: 1315 cases) participating in the family‐based Collaborative Study on the Genetics of Alcoholism. ANYDEP was heritable (h ² in EA = 0.60, AA = 0.37). The AA GWAS identified three regions with genome‐wide significant (GWS; P < 5E‐08) single nucleotide polymorphisms (SNPs) on chromosomes 3 (rs34066662, rs58801820) and 13 (rs75168521, rs78886294), and an insertion‐deletion on chromosome 5 (chr5:141988181). No polymorphisms reached GWS in the EA. One GWS region (chromosome 1: rs1890881) emerged from a trans‐ancestral meta‐analysis (EA + AA) of ANYDEP, and was attributable to alcohol dependence in both samples. Four genes (AA: CRKL, DZIP3, SBK3; EA: P2RX6) and four sets of genes were significantly enriched within biological pathways for hemostasis and signal transduction. GWS signals did not replicate in two independent samples but there was weak evidence for association between rs1890881 and alcohol intake in the UK Biobank. Among 118 AA and 481 EA individuals from the Duke Neurogenetics Study, rs75168521 and rs1890881 genotypes were associated with variability in reward‐related ventral striatum activation. This study identified novel loci for substance dependence and provides preliminary evidence that these variants are also associated with individual differences in neural reward reactivity. Gene discovery efforts in non‐European samples with distinct patterns of substance use may lead to the identification of novel ancestry‐specific genetic markers of risk.     

Evaluation of results from genome‐wide studies of language and reading in a novel independent dataset

Recent genome‐wide association scans (GWAS) for reading and language abilities have pin‐pointed promising new candidate loci. However, the potential contributions of these loci remain to be validated. In this study, we tested 17 of the most significantly associated single nucleotide polymorphisms (SNPs) from these GWAS studies (P < 10^?6 in the original studies) in a new independent population dataset from the Netherlands: known as Familial Influences on Literacy Abilities. This dataset comprised 483 children from 307 nuclear families and 505 adults (including parents of participating children), and provided adequate statistical power to detect the effects that were previously reported. The following measures of reading and language performance were collected: word reading fluency, nonword reading fluency, phonological awareness and rapid automatized naming. Two SNPs (rs12636438 and rs7187223) were associated with performance in multivariate and univariate testing, but these did not remain significant after correction for multiple testing. Another SNP (rs482700) was only nominally associated in the multivariate test. For the rest of the SNPs, we did not find supportive evidence of association. The findings may reflect differences between our study and the previous investigations with respect to the language of testing, the exact tests used and the recruitment criteria. Alternatively, most of the prior reported associations may have been false positives. A larger scale GWAS meta‐analysis than those previously performed will likely be required to obtain robust insights into the genomic architecture underlying reading and language.     

Adaptive landscape genetics and malaria across divergent island bird populations

Environmental conditions play a major role in shaping the spatial distributions of pathogens, which in turn can drive local adaptation and divergence in host genetic diversity. Haemosporidians, such as Plasmodium (malaria), are a strong selective force, impacting survival and fitness of hosts, with geographic distributions largely determined by habitat suitability for their insect vectors. Here, we have tested whether patterns of fine‐scale local adaptation to malaria are replicated across discrete, ecologically differing island populations of Berthelot's pipits Anthus berthelotii. We sequenced TLR4, an innate immunity gene that is potentially under positive selection in Berthelot's pipits, and two SNPs previously identified as being associated with malaria infection in a genome‐wide association study (GWAS) in Berthelot's pipits in the Canary Islands. We determined the environmental predictors of malaria infection, using these to estimate variation in malaria risk on Porto Santo, and found some congruence with previously identified environmental risk factors on Tenerife. We also found a negative association between malaria infection and a TLR4 variant in Tenerife. In contrast, one of the GWAS SNPs showed an association with malaria risk in Porto Santo, but in the opposite direction to that found in the Canary Islands GWAS. Together, these findings suggest that disease‐driven local adaptation may be an important factor in shaping variation among island populations.     

Genome‐wide association study of genetic factors related to confectionery intake: Potential roles of the ADIPOQ gene

Objective: The excessive consumption of confectionery might have adverse effects on human health. To screen genetic factors associated with confectionery‐intake frequency, a genome‐wide association study (GWAS) in Japan was conducted. Design and Methods: For the discovery phase (stage 1), we conducted a GWAS of 939 noncancer patients in a cancer hospital. Additive models were used to test associations between genotypes of approximately 500,000 single‐nucleotide polymorphisms (SNPs) and the confectionery‐intake score (based on intake frequency). We followed‐up association signals with P < 1 × 10^?5 and minor allele frequency >0.01 in stage 1 by genotyping the SNPs of 4,491 participants in a cross‐sectional study within a cohort (replication phase [stage 2]). Results: We identified 12 SNPs in stage 1 that were potentially related to confectionery intake. In stage 2, this association was replicated for one SNP (rs822396; P = 0.049 for stage 2 and 4.2 × 10^?5 for stage 1+2) in intron 1 of the ADIPOQ gene, which encodes the adipokine adiponectin. Conclusions: Given the biological plausibility and previous relevant findings, the association of an SNP in the ADIPOQ gene with a preference for confectionery is worthy of follow‐up and provides a good working hypothesis for experimental testing.     

A genome‐wide association study of coping behaviors suggests FBXO45 is associated with emotional expression

Individuals use coping behaviors to deal with unpleasant daily events. Such behaviors can moderate or mediate the pathway between psychosocial stress and health‐related outcomes. However, few studies have examined the associations between coping behaviors and genetic variants. We conducted a genome‐wide association study (GWAS) on coping behaviors in 14088 participants aged 35 to 69 years as part of the Japan Multi‐Institutional Collaborative Cohort Study. Five coping behaviors (emotional expression, emotional support seeking, positive reappraisal, problem solving and disengagement) were measured and analyzed. A GWAS analysis was performed using a mixed linear model adjusted for study area, age and sex. Variants with suggestive significance in the discovery phase (N = 6403) were further examined in the replication phase (N = 7685). We then combined variant‐level association evidence into gene‐level evidence using a gene‐based analysis. The results showed a significant genetic contribution to emotional expression and disengagement, with an estimation that the 19.5% and 6.6% variance in the liability‐scale was explained by common variants. In the discovery phase, 12 variants met suggestive significance (P < 1 × 10^?6) for association with the coping behaviors and perceived stress. However, none of these associations were confirmed in the replication stage. In gene‐based analysis, FBXO45, a gene with regulatory roles in synapse maturation, was significantly associated with emotional expression after multiple corrections (P < 3.1 × 10^?6). In conclusion, our results showed the existence of up to 20% genetic contribution to coping behaviors. Moreover, our gene‐based analysis using GWAS data suggests that genetic variations in FBXO45 are associated with emotional expression.     

A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data

Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into “ancestry groups” and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.     

Metabolic GWAS‐based dissection of genetic bases underlying the diversity of plant metabolism

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome‐wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass‐spectrometry‐based analytical systems and genome sequencing technologies. Metabolic genome‐wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS‐based multi‐dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS‐based multi‐dimensional analysis and emerging insights into the diversity of metabolism.     

Genome‐wide association study for the level of serum electrolytes in Italian Large White pigs

Calcium, magnesium and phosphorus are essential electrolytes involved in a large number of biological processes. Imbalance of these minerals in blood may indicate clinically relevant conditions and are important in inferring acute or chronic pathologies in humans and animals. In this work, we carried out a genome‐wide association study (GWAS) for the level of these three electrolytes in the serum of 843 performance‐tested Italian Large White pigs. All pigs were genotyped with the Illumina PorcineSNP60 BeadChip, and GWAS was carried out using genome‐wide efficient mixed‐model association. For the level of Ca²⁺, eight single nucleotide polymorphisms (SNPs) were significant, considering a false discovery rate (FDR) < 0.05, and another eight were above the moderate association threshold (P_{nominal value} < 5.00E‐05). These SNPs are distributed in four porcine chromosomes (SSC): SSC8, SSC11, SSC12 and SSC13. In particular, a few putative different signals of association detected on SSC13 and one on SSC12 were in genes or close to genes involved in calcium metabolism (P2RY1, RAP2B, SLC9A9, C3orf58, TSC22D2, PLCH1 and CACNB1). Only one SNP (on SSC7) and six SNPs (on SSC2 and SSC7) showed moderate association with the level of magnesium and phosphorus respectively. The association signals for these two latter minerals might identify genes not known thus far for playing a role in their biological functions and regulations. In conclusion, our GWAS contributed to increased knowledge on the role that calcium, magnesium and phosphorus may play in the genetically determined physiological mechanisms affecting the natural variability of mineral levels in mammalian blood.     

A genome‐wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle

A genome‐wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)‐BovineLD V3 SNP chip, which had 26 151 public SNPs in its manifest file. After data cleaning, 20 326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3 years. Two statistical models, a fixed‐effect linear regression model and a mixed‐effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome‐wide significant and suggestive thresholds were set at 2.46E‐06 and 4.95E‐05 respectively. The two statistical models concurrently identified two genome‐wide significant (P < 0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies.     

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation,gene expression,and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.     

Genetic associations with brain cortical thickness in multiple sclerosis

Multiple sclerosis (MS) is characterized by temporal and spatial dissemination of demyelinating lesions in the central nervous system. Associated neurodegenerative changes contributing to disability have been recognized even at early disease stages. Recent studies show the importance of gray matter damage for the accrual of clinical disability rather than white matter where demyelination is easily visualized by magnetic resonance imaging (MRI). The susceptibility to MS is influenced by genetic risk, but genetic factors associated with the disability are not known. We used MRI data to determine cortical thickness in 557 MS cases and 75 controls and in another cohort of 219 cases. We identified nine areas showing different thickness between cases and controls (regions of interest, ROI) (eight of them were negatively correlated with Kurtzke's expanded disability status scale, EDSS) and conducted genome‐wide association studies (GWAS) in 464 and 211 cases available from the two data sets. No marker exceeded genome‐wide significance in the discovery cohort. We next combined nominal statistical evidence of association with physical evidence of interaction from a curated human protein interaction network, and searched for subnetworks enriched with nominally associated genes and for commonalities between the two data sets. This network‐based pathway analysis of GWAS detected gene sets involved in glutamate signaling, neural development and an adjustment of intracellular calcium concentration. We report here for the first time gene sets associated with cortical thinning of MS. These genes are potentially correlated with disability of MS.     

An Efficient Stepwise Statistical Test to Identify Multiple Linked Human Genetic Variants Associated with Specific Phenotypic Traits

Recent advances in genotyping methodologies have allowed genome-wide association studies (GWAS) to accurately identify genetic variants that associate with common or pathological complex traits. Although most GWAS have focused on associations with single genetic variants, joint identification of multiple genetic variants, and how they interact, is essential for understanding the genetic architecture of complex phenotypic traits. Here, we propose an efficient stepwise method based on the Cochran-Mantel-Haenszel test (for stratified categorical data) to identify causal joint multiple genetic variants in GWAS. This method combines the CMH statistic with a stepwise procedure to detect multiple genetic variants associated with specific categorical traits, using a series of associated I × J contingency tables and a null hypothesis of no phenotype association. Through a new stratification scheme based on the sum of minor allele count criteria, we make the method more feasible for GWAS data having sample sizes of several thousands. We also examine the properties of the proposed stepwise method via simulation studies, and show that the stepwise CMH test performs better than other existing methods (e.g., logistic regression and detection of associations by Markov blanket) for identifying multiple genetic variants. Finally, we apply the proposed approach to two genomic sequencing datasets to detect linked genetic variants associated with bipolar disorder and obesity, respectively.