A new association test based on disease allele selection for case–control genome-wide association studies

Background

Current robust association tests for case–control genome-wide association study (GWAS) data are mainly based on the assumption of some specific genetic models. Due to the richness of the genetic models, this assumption may not be appropriate. Therefore, robust but powerful association approaches are desirable.

Results

In this paper, we propose a new approach to testing for the association between the genotype and phenotype for case–control GWAS. This method assumes a generalized genetic model and is based on the selected disease allele to obtain a p-value from the more powerful one-sided test. Through a comprehensive simulation study we assess the performance of the new test by comparing it with existing methods. Some real data applications are also used to illustrate the use of the proposed test.

Conclusions

Based on the simulation results and real data application, the proposed test is powerful and robust.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-358) contains supplementary material, which is available to authorized users.     

Exploiting Population Samples to Enhance Genome-Wide Association Studies of Disease

It is widely acknowledged that genome-wide association studies (GWAS) of complex human disease fail to explain a large portion of heritability, primarily due to lack of statistical power—a problem that is exacerbated when seeking detection of interactions of multiple genomic loci. An untapped source of information that is already widely available, and that is expected to grow in coming years, is population samples. Such samples contain genetic marker data for additional individuals, but not their relevant phenotypes. In this article we develop a highly efficient testing framework based on a constrained maximum-likelihood estimate in a case–control–population setting. We leverage the available population data and optional modeling assumptions, such as Hardy–Weinberg equilibrium (HWE) in the population and linkage equilibrium (LE) between distal loci, to substantially improve power of association and interaction tests. We demonstrate, via simulation and application to actual GWAS data sets, that our approach is substantially more powerful and robust than standard testing approaches that ignore or make naive use of the population sample. We report several novel and credible pairwise interactions, in bipolar disorder, coronary artery disease, Crohn’s disease, and rheumatoid arthritis.     

Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype–phenotype associations, 26 represented phenotypes closely related to previously known genotype–phenotype associations, and 33 represented potentially novel genotype–phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.     

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.     

Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-Wide and Candidate Gene-Based Association Mapping Approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r²; when r²<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.     

Centre–periphery approaches based on geography,ecology and historical climate stability: what explains the variation in morphological traits of Bulnesia sarmientoi?

Background and AimsThe centre–periphery hypothesis posits that higher species performance is expected in geographic and ecological centres rather than in peripheral populations. However, this is not the commonly found pattern; therefore, alternative approaches, including the historical dimension of species geographical ranges, should be explored. Morphological functional traits are fundamental determinants of species performance, commonly related to environmental stability and productivity. We tested whether or not historical processes may have shaped variations in tree and leaf traits of the Chaco tree Bulnesia sarmientoi.MethodsMorphological variation patterns were analysed from three centre–periphery approaches: geographical, ecological and historical. Tree (stem and canopy) and leaf (leaf size and specific leaf area) traits were measured in 24 populations across the species range. A principal component analysis was performed on morphological traits to obtain synthetic variables. Linear mixed-effects models were used to test which of the implemented centre–periphery approaches significantly explained trait spatial patterns.Key ResultsThe patterns retrieved from the three centre–periphery approaches were not concordant. The historical approach revealed that trees were shorter in centre populations than in the periphery. Significant differences in leaf traits were observed between the geographical centre and the periphery, mainly due to low specific leaf area values towards the geographical centre. We did not find any pattern associated with the ecological centre–periphery approach.ConclusionsThe decoupled response between leaf and tree traits suggests that these sets of traits respond differently to processes occurring at different times. The geographical and historical approaches showed centres with extreme environments in relation to their respective peripheries, but the historical centre has also been a climatically stable area since the Last Glacial Maximum. The historical approach allowed for the recovery of historical processes underlying variation in tree traits, highlighting that centre–periphery delimitations should be based on a multi-approach framework.     

Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10⁻⁴–10⁻⁸), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.     

Localization of Canine Brachycephaly Using an Across Breed Mapping Approach

The domestic dog, Canis familiaris, exhibits profound phenotypic diversity and is an ideal model organism for the genetic dissection of simple and complex traits. However, some of the most interesting phenotypes are fixed in particular breeds and are therefore less tractable to genetic analysis using classical segregation-based mapping approaches. We implemented an across breed mapping approach using a moderately dense SNP array, a low number of animals and breeds carefully selected for the phenotypes of interest to identify genetic variants responsible for breed-defining characteristics. Using a modest number of affected (10–30) and control (20–60) samples from multiple breeds, the correct chromosomal assignment was identified in a proof of concept experiment using three previously defined loci; hyperuricosuria, white spotting and chondrodysplasia. Genome-wide association was performed in a similar manner for one of the most striking morphological traits in dogs: brachycephalic head type. Although candidate gene approaches based on comparable phenotypes in mice and humans have been utilized for this trait, the causative gene has remained elusive using this method. Samples from nine affected breeds and thirteen control breeds identified strong genome-wide associations for brachycephalic head type on Cfa 1. Two independent datasets identified the same genomic region. Levels of relative heterozygosity in the associated region indicate that it has been subjected to a selective sweep, consistent with it being a breed defining morphological characteristic. Genotyping additional dogs in the region confirmed the association. To date, the genetic structure of dog breeds has primarily been exploited for genome wide association for segregating traits. These results demonstrate that non-segregating traits under strong selection are equally tractable to genetic analysis using small sample numbers.     

Integrating Milk Metabolite Profile Information for the Prediction of Traditional Milk Traits Based on SNP Information for Holstein Cows

In this study the benefit of metabolome level analysis for the prediction of genetic value of three traditional milk traits was investigated. Our proposed approach consists of three steps: First, milk metabolite profiles are used to predict three traditional milk traits of 1,305 Holstein cows. Two regression methods, both enabling variable selection, are applied to identify important milk metabolites in this step. Second, the prediction of these important milk metabolite from single nucleotide polymorphisms (SNPs) enables the detection of SNPs with significant genetic effects. Finally, these SNPs are used to predict milk traits. The observed precision of predicted genetic values was compared to the results observed for the classical genotype-phenotype prediction using all SNPs or a reduced SNP subset (reduced classical approach). To enable a comparison between SNP subsets, a special invariable evaluation design was implemented. SNPs close to or within known quantitative trait loci (QTL) were determined. This enabled us to determine if detected important SNP subsets were enriched in these regions. The results show that our approach can lead to genetic value prediction, but requires less than 1% of the total amount of (40,317) SNPs., significantly more important SNPs in known QTL regions were detected using our approach compared to the reduced classical approach. Concluding, our approach allows a deeper insight into the associations between the different levels of the genotype-phenotype map (genotype-metabolome, metabolome-phenotype, genotype-phenotype).     

The molecular basis of quantitative variation in foliar secondary metabolites in Eucalyptus globulus

Eucalyptus is characterized by high foliar concentrations of plant secondary metabolites with marked qualitative and quantitative variation within a single species. Secondary metabolites in eucalypts are important mediators of a diverse community of herbivores. We used a candidate gene approach to investigate genetic associations between 195 single nucleotide polymorphisms (SNPs) from 24 candidate genes and 33 traits related to secondary metabolites in the Tasmanian Blue Gum (Eucalyptus globulus). We discovered 37 significant associations (false discovery rate (FDR) Q < 0.05) across 11 candidate genes and 19 traits. The effects of SNPs on phenotypic variation were within the expected range (0.018 < r(2) < 0.061) for forest trees. Whereas most marker effects were nonadditive, two alleles from two consecutive genes in the methylerythritol phosphate pathway (MEP) showed additive effects. This study successfully links allelic variants to ecologically important phenotypes which can have a large impact on the entire community. It is one of very few studies to identify the genetic variants of a foundation tree that influences ecosystem function.     

ddRAD‐Seq reveals evolutionary insights into population differentiation and the cryptic phylogeography of Hyporhamphus intermedius in Mainland China

Species differentiation and local adaptation in heterogeneous environments have attracted much attention, although little is known about the mechanisms involved. Hyporhamphus intermedius is an anadromous, brackish‐water halfbeak that is widely distributed in coastal areas and hyperdiverse freshwater systems in China, making it an interesting model for research on phylogeography and local adaptation. Here, 156 individuals were sampled at eight sites from heterogeneous aquatic habitats to examine environmental and genetic contributions to phenotypic divergence. Using double‐digest restriction‐site‐associated DNA sequencing (ddRAD‐Seq) in the specimens from the different watersheds, 5498 single nucleotide polymorphisms (SNPs) were found among populations, with obvious population differentiation. We find that present‐day Mainland China populations are structured into distinct genetic clusters stretching from southern and northern ancestries, mirroring geography. Following a transplant event in Plateau Lakes, there were virtually no variations of genetic diversity occurred in two populations, despite the fact two main splits were unveiled in the demographic history. Additionally, dorsal, and anal fin traits varied widely between the southern group and the others, which highlighted previously unrecognized lineages. We then explore genotype–phenotype‐environment associations and predict candidate loci. Subgroup ranges appeared to correspond to geographic regions with heterogeneous hydrological factors, indicating that these features are likely important drivers of diversification. Accordingly, we conclude that genetic and phenotypic polymorphism and a moderate amount of genetic differentiation occurred, which might be ascribed to population subdivision, and the impact of abiotic factors.     

First Genome-Wide Association Study on Anxiety-Related Behaviours in Childhood

Background

Twin studies have shown that anxiety in a general population sample of children involves both domain-general and trait-specific genetic effects. For this reason, in an attempt to identify genes responsible for these effects, we investigated domain-general and trait-specific genetic associations in the first genome-wide association (GWA) study on anxiety-related behaviours (ARBs) in childhood.

Methods

The sample included 2810 7-year-olds drawn from the Twins Early Development Study (TEDS) with data available for parent-rated anxiety and genome-wide DNA markers. The measure was the Anxiety-Related Behaviours Questionnaire (ARBQ), which assesses four anxiety traits and also yields a general anxiety composite. Affymetrix GeneChip 6.0 DNA arrays were used to genotype nearly 700,000 single-nucleotide polymorphisms (SNPs), and IMPUTE v2 was used to impute more than 1 million SNPs. Several GWA associations from this discovery sample were followed up in another TEDS sample of 4804 children. In addition, Genome-wide Complex Trait Analysis (GCTA) was used on the discovery sample, to estimate the total amount of variance in ARBs that can be accounted for by SNPs on the array.

Results

No SNP associations met the demanding criterion of genome-wide significance that corrects for multiple testing across the genome (p<5×10⁻⁸). Attempts to replicate the top associations did not yield significant results. In contrast to the substantial twin study estimates of heritability which ranged from 0.50 (0.03) to 0.61 (0.01), the GCTA estimates of phenotypic variance accounted for by the SNPs were much lower 0.01 (0.11) to 0.19 (0.12).

Conclusions

Taken together, these GWAS and GCTA results suggest that anxiety – similar to height, weight and intelligence − is affected by many genetic variants of small effect, but unlike these other prototypical polygenic traits, genetic influence on anxiety is not well tagged by common SNPs.     

GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.     

How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (r = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.     

Identification of a novel duplication CFTRdup2 and functional impact of large rearrangements identified in the CFTR gene

The aim of this study was to investigate associations of two candidate gene SNPs of the endocannabinoid receptor type 1 gene (CNR1) with overweight, obesity and obesity-related traits in Chinese retired women. The study subjects were a subsample of the Taizhou Retiree Women Cohort, consisting of 2812 retired women aged 50-64 years recruited from Taizhou, Jiangsu, China. Neither rs2023239 nor rs806381 polymorphism was significantly associated with body mass index-defined overweight and obesity or waist-to-hip-ratio-defined obesity. For obesity-related traits, rs2023239 was significantly associated with glutamate pyruvate transaminase (GPT) (median, 18.00 vs 17.00 for TT and TC genotypes, respectively, P=0.043). The rs806381 also showed significant association with triglyceride (TG) (mean±SD, 1.46±0.20 vs 1.53±0.20 for GA and GG+AA genotypes, respectively, P=0.013) under the dominant genetic model. In conclusion, the rs2023239 and rs806381 polymorphisms of CNR1 were not associated with increased overweight and obesity risk. But the rs2023239 polymorphism was significantly associated with GPT, and the rs806381 polymorphism was significantly associated with TG.     

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.     

Challenges and opportunities for comparative studies of survival rates: An example with male pinnipeds

Survival rates are a central component of life‐history strategies of large vertebrate species. However, comparative studies seldom investigate interspecific variation in survival rates with respect to other life‐history traits, especially for males. The lack of such studies could be due to the challenges associated with obtaining reliable datasets, incorporating information on the 0–1 probability scale, or dealing with several types of measurement error in life‐history traits, which can be a computationally intensive process that is often absent in comparative studies. We present a quantitative approach using a Bayesian phylogenetically controlled regression with the flexibility to incorporate uncertainty in estimated survival rates and quantitative life‐history traits while considering genetic similarity among species and uncertainty in relatedness. As with any comparative analysis, our approach makes several assumptions regarding the generalizability and comparability of empirical data from separate studies. Our model is versatile in that it can be applied to any species group of interest and include any life‐history traits as covariates. We used an unbiased simulation framework to provide “proof of concept” for our model and applied a slightly richer model to a real data example for pinnipeds. Pinnipeds are an excellent taxonomic group for comparative analysis, but survival rate data are scarce. Our work elucidates the challenges associated with addressing important questions related to broader ecological life‐history patterns and how survival–reproduction trade‐offs might shape evolutionary histories of extant taxa. Specifically, we underscore the importance of having high‐quality estimates of age‐specific survival rates and information on other life‐history traits that reasonably characterize a species for accurately comparing across species.