Large-scale Genetic Association Studies Research Articles

Update on the Genetics of Systemic Lupus Erythematosus: Genome-Wide Association Studies and Beyond.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of complex etiology that primarily affects women of childbearing age. The development of SLE is attributed to the breach of immunological tolerance and the interaction between SLE-susceptibility genes and various environmental factors, resulting in the production of pathogenic autoantibodies. Working in concert with the innate and adaptive arms of the immune system, lupus-related autoantibodies mediate immune-complex deposition in various tissues and organs, leading to acute and chronic inflammation and consequent end-organ damage. Over the past two decades or so, the impact of genetic susceptibility on the development of SLE has been well demonstrated in a number of large-scale genetic association studies which have uncovered a large fraction of genetic heritability of SLE by recognizing about a hundred SLE-susceptibility loci. Integration of genetic variant data with various omics data such as transcriptomic and epigenomic data potentially provides a unique opportunity to further understand the roles of SLE risk variants in regulating the molecular phenotypes by various disease-relevant cell types and in shaping the immune systems with high inter-individual variances in disease susceptibility. In this review, the catalogue of SLE susceptibility loci will be updated, and biological signatures implicated by the SLE-risk variants will be critically discussed. It is optimistically hoped that identification of SLE risk variants will enable the prognostic and therapeutic biomarker armamentarium of SLE to be strengthened, a major leap towards precision medicine in the management of the condition.

PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

SummaryPhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants.Availability and implementationPhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.

Interaction between genetic ancestry and common breast cancer susceptibility variants in Colombian women

Latino women show lower incidences of breast cancer (BC) than non-Hispanic whites. Large-scale genetic association studies have identified variants robustly associated with BC risk in European women. We examine here the relevance of these variants to Colombian BC and possible interactions with genetic ancestry. Native American, European and African proportions were estimated for 1022 Colombian BC cases and 1023 controls. Logistic regression was applied to assess the association between 78 variants and BC risk and interactions between the variants and ancestry proportions. We constructed a multifactorial risk score combining established BC risk factors, associated risk variants and individual ancestry proportions. Each 1% increase in the Native American proportion translated into a 2.2% lower BC risk (95% CI: 1.4-2.9). Thirteen variants were associated with BC in Colombian women, with allele frequencies and risk effects partially different from European women. Ancestry proportions moderated the risk effects of two variants. The ability of Native American proportions to separate Colombian cases and controls (area-under-the-curve (AUC) = 0.61) was similar to the discriminative ability of family history of BC in first-degree female relatives (AUC = 0.58) or the combined effect of all 13 associated risk variants (AUC = 0.57). Our findings demonstrate ample potential for individualized BC prevention in Hispanic women taking advantage of individual Native American proportions, information on established susceptibility factors and recently identified common risk variants.

Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

Introduction: When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification. Objective: We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility. Methods: The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package (“genpwr”). Results: We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects. Conclusions: Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.

Identification of complement-related host genetic risk factors associated with influenza A(H1N1)pdm09 outcome: challenges ahead.

Influenza remains an important threat for human health, despite the extensive study of influenza viruses and the production of effective vaccines. In contrast to virus genetics determinants, host genetic factors with clinical impact remained unexplored until recently. The association between three single nucleotide polymorphisms (SNPs) and influenza outcome in a European population was investigated in the present study. All samples were collected during the influenza A(H1N1)pdm09 post-pandemic period 2010–11 and a sufficient number of severe and fatal cases was included. Host genomic DNA was isolated from pharyngeal samples of 110 patients from northern Greece with severe (n = 59) or mild (n = 51) influenza A(H1N1)pdm09 disease, at baseline, and the genotype of CD55 rs2564978, C1QBP rs3786054 and FCGR2A rs1801274 SNPs was investigated. Our findings suggest a relationship between the two complement-related SNPs, namely, the rare TT genotype of CD55 and the rare AA genotype of C1QBP with increased death risk. No significant differences were observed for FCGR2A genotypes neither with fatality nor disease severity. Additional large-scale genetic association studies are necessary for the identification of reliable host genetic risk factors associated with influenza A(H1N1)pdm09 outcome. Prophylactic intervention of additional high-risk populations, according to their genetic profile, will be a key achievement for the fight against influenza viruses.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational cost, and hence is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Genetic variation in the Hippo pathway and breast cancer risk in women of African ancestry.

Gene expression changes within the Hippo pathway were found to be associated with large tumor size and metastasis in breast cancer. The combined effect of genetic variants in genes of this pathway may have a causal role in breast cancer development. We examined 7086 SNPs that were not highly correlated (r2 < 0.8) in 35 Hippo pathway genes using data from the genome-wide association study of breast cancer from the Root Consortium, which includes 3686 participants of African ancestry from Nigeria, United States of America, and Barbados: 1657 cases (403 estrogen receptor-positive [ER+], 374 ER-) and 2029 controls. Gene-level analyses were conducted using improved AdaJoint test for large-scale genetic association studies adjusting for age, study site and the first four eigenvectors from the principal component analysis. SNP-level analyses were conducted with logistic regression. The Hippo pathway was significantly associated with risk of ER+ breast cancer (pathway-level P = 0.019), with WWC1 (Padj = 0.04) being the leading gene. The pathway-level significance was lost without WWC1 (P = 0.12). rs147106204 in the WWC1 gene was the most statistically significant SNP after gene-level adjustment for multiple comparisons (OR = 0.53, 95%CI = 0.41-0.70, Padj = 0.025). We found evidence of an association between genetic variations in the Hippo pathway and ER+ breast cancer. Moreover, WWC1 was identified as the most important genetic susceptibility locus highlighting the importance of genetic epidemiology studies of breast cancer in understudied populations.

Genetics of atrial fibrillation: an update.

Atrial fibrillation is a common cardiac arrhythmia with a high morbidity and mortality affecting 34 million worldwide. Current therapies are inadequate and often fail to directly address molecular mechanisms of the disease. In this review, we will provide an overview of recent advances in our understanding of the genetic underpinnings of atrial fibrillation. Large-scale genetic association studies have more than doubled the number of genetic loci associated with atrial fibrillation during the last year. Studies examining how genes at or near these loci can affect the pathogenesis of atrial fibrillation are ongoing in cellular, animal, and computational models. In addition, several recent clinical studies have also demonstrated that variants at these loci can aid in risk stratification of patients. There are now over 30 genetic loci associated with atrial fibrillation. A better understanding of how these loci relate to disease pathogenesis may provide insight into novel therapeutic targets and ultimately lead to improved clinical care.

Genome-wide association study in ethnic Russians suggests an association of the MHC class III genomic region with the risk of primary varicose veins

Heredity is a well-known risk factor for varicose veins, but genetic basis of this condition remains poorly studied. Our aim was to conduct a large-scale genetic association study for primary varicose veins (PVVs) in the population of ethnic Russians. An initial scan using Illumina HumanExome-12 v1.0 BeadChip was performed for 273 patients with PVVs and 250 controls without a history of chronic venous disease and other venous disorders. After quality control and removal of monomorphic markers, 25,424 common and 48,232 rare variants were included in the analysis. 42 single nucleotide polymorphisms (SNPs) were genotyped in the independent replication cohort of 447 PVVs patients and 443 controls. Association of common variants with PVVs was investigated by logistic regression, and the impact of rare variants was analyzed using sequence kernel association test. No effect of low frequency alleles has been revealed in our study. Common variant analysis identified a promising signal at chromosome 6 within classical major histocompatibility complex (MHC) class III subregion. The most strongly associated SNP in a combined analysis that reached a suggestive significance level of 3.2e−05 was polymorphism rs4151657 in the complement factor B gene. Testing for potential pleiotropy with other traits indicated that the same causal variant in this region increases the risk of rheumatoid arthritis and has a negative impact on human height. Our results provide suggestive evidence for the involvement of the MHC class III genes in the pathogenesis of PVVs. Further independent studies are needed to confirm our pilot findings.

Integrative DNA copy number detection and genotyping from sequencing and array-based platforms.

Copy number variations (CNVs) are gains and losses of DNA segments and have been associated with disease. Many large-scale genetic association studies are performing CNV analysis using whole exome sequencing (WES) and whole genome sequencing (WGS). In many of these studies, previous single-nucleotide polymorphism (SNP)-array data are available. An integrated cross-platform analysis is expected to improve resolution and accuracy, yet there is no tool for effectively combining data from sequencing and array platforms. The detection of CNVs using sequencing data alone can also be further improved by the utilization of allele-specific reads. We propose a statistical framework, integrated CNV (iCNV) detection algorithm, which can be applied to multiple study designs: WES only, WGS only, SNP array only, or any combination of SNP and sequencing data. iCNV applies platform-specific normalization, utilizes allele specific reads from sequencing and integrates matched NGS and SNP-array data by a hidden Markov model. We compare integrated two-platform CNV detection using iCNV to naïve intersection or union of platforms and show that iCNV increases sensitivity and robustness. We also assess the accuracy of iCNV on WGS data only and show that the utilization of allele-specific reads improve CNV detection accuracy compared to existing methods. https://github.com/zhouzilu/iCNV. Supplementary data are available at Bioinformatics online.

Phenotypic heterogeneity in m.3243A>G mitochondrial disease: The role of nuclear factors.

ObjectiveThe pathogenic mitochondrial DNA m.3243A>G mutation is associated with a wide range of clinical features, making disease prognosis extremely difficult to predict. We aimed to understand the cause of this heterogeneity.MethodsWe examined the phenotypic profile of 238 adult m.3243A>G carriers (patients and asymptomatic carriers) from the UK MRC Mitochondrial Disease Patient Cohort using the Newcastle Mitochondrial Disease Adult Scale. We modeled the role of risk factors for the development of specific phenotypes using proportional odds logistic regression. As mitochondria are under the dual control of their own and the nuclear genome, we examined the role of additive nuclear genetic factors in the development of these phenotypes within 46 pedigrees from the cohort.ResultsSeizures and stroke‐like episodes affect 25% and 17% of patients, respectively; more common features include hearing impairment, gastrointestinal disturbance, psychiatric involvement, and ataxia. Age, age‐adjusted blood heteroplasmy levels, and sex are poor predictors of phenotypic severity. Hearing impairment, diabetes, and encephalopathy show the strongest associations, but pseudo‐R 2 values are low (0.14–0.17). We found a high heritability estimate for psychiatric involvement (h 2=0.76, P = 0.0003) and moderate estimates for cognition (h 2=0.46, P = 0.0021), ataxia (h 2 = 0.45, P = 0.0011), migraine (h 2 = 0.41, P = 0.0138), and hearing impairment (h2 = 0.40, P = 0.0050).InterpretationOur results provide good evidence for the presence of nuclear genetic factors influencing clinical outcomes in m.3234A>G‐related disease, paving the way for future work identifying these through large‐scale genetic linkage and association studies, increasing our understanding of the pathogenicity of m.3243A>G and providing improved estimates of prognosis.

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

Finding susceptible and protective interaction patterns in large-scale genetic association study

Interaction detection in large-scale genetic association studies has attracted intensive research interest, since many diseases have complex traits. Various approaches have been developed for finding significant genetic interactions. In this article, we propose a novel framework SRMiner to detect interacting susceptible and protective genotype patterns. SRMiner can discover not only probable combination of single nucleotide polymorphisms (SNPs) causing diseases but also the corresponding SNPs suppressing their pathogenic functions, which provides a better prospective to uncover the underlying relevance between genetic variants and complex diseases. We have performed extensive experiments on several real Wellcome Trust Case Control Consortium (WTCCC) datasets. We use the pathway-based and the protein-protein interaction (PPI) network-based evaluation methods to verify the discovered patterns. The results show that SRMiner successfully identifies many disease-related genes verified by the existing work. Furthermore, SRMiner can also infer some uncomfirmed but highly possible disease-related genes.

Power comparison of Cochran-Armitage trend test against allelic and genotypic tests in large-scale case-control genetic association studies.

The Cochran-Armitage trend test (CA) has become a standard procedure for association testing in large-scale genome-wide association studies (GWAS). However, when the disease model is unknown, there is no consensus on the most powerful test to be used between CA, allelic, and genotypic tests. In this article, we tackle the question of whether CA is best suited to single-locus scanning in GWAS and propose a power comparison of CA against allelic and genotypic tests. Our approach relies on the evaluation of the Taylor decompositions of non-centrality parameters, thus allowing an analytical comparison of the power functions of the tests. Compared to simulation-based comparison, our approach offers the advantage of simultaneously accounting for the multidimensionality of the set of features involved in power functions. Although power for CA depends on the sample size, the case-to-control ratio and the minor allelic frequency (MAF), our results first show that it is largely influenced by the mode of inheritance and a deviation from Hardy-Weinberg Equilibrium (HWE). Furthermore, when compared to other tests, CA is shown to be the most powerful test under a multiplicative disease model or when the single-nucleotide polymorphism largely deviates from HWE. In all other situations, CA lacks in power and differences can be substantial, especially for the recessive mode of inheritance. Finally, our results are illustrated by the comparison of the performances of the statistics in two genome scans.

PhenoScanner: a database of human genotype-phenotype associations.

Summary: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies. This tool aims to facilitate ‘phenome scans’, the cross-referencing of genetic variants with many phenotypes, to help aid understanding of disease pathways and biology. The database currently contains over 350 million association results and over 10 million unique genetic variants, mostly single nucleotide polymorphisms. It is accompanied by a web-based tool that queries the database for associations with user-specified variants, providing results according to the same effect and non-effect alleles for each input variant. The tool provides the option of searching for trait associations with proxies of the input variants, calculated using the European samples from 1000 Genomes and Hapmap. Availability and Implementation: PhenoScanner is available at www.phenoscanner.medschl.cam.ac.uk. Contact: jrs95@medschl.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies.

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20-40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on high-density lipoprotein and low-density lipoprotein from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.

Group-combined P-values with applications to genetic association studies.

In large-scale genetic association studies with tens of hundreds of single nucleotide polymorphisms (SNPs) genotyped, the traditional statistical framework of logistic regression using maximum likelihood estimator (MLE) to infer the odds ratios of SNPs may not work appropriately. This is because a large number of odds ratios need to be estimated, and the MLEs may be not stable when some of the SNPs are in high linkage disequilibrium. Under this situation, the P-value combination procedures seem to provide good alternatives as they are constructed on the basis of single-marker analysis. The commonly used P-value combination methods (such as the Fisher's combined test, the truncated product method, the truncated tail strength and the adaptive rank truncated product) may lose power when the significance level varies across SNPs. To tackle this problem, a group combined P-value method (GCP) is proposed, where the P-values are divided into multiple groups and then are combined at the group level. With this strategy, the significance values are integrated at different levels, and the power is improved. Simulation shows that the GCP can effectively control the type I error rates and have additional power over the existing methods-the power increase can be as high as over 50% under some situations. The proposed GCP method is applied to data from the Genetic Analysis Workshop 16. Among all the methods, only the GCP and ARTP can give the significance to identify a genomic region covering gene DSC3 being associated with rheumatoid arthritis, but the GCP provides smaller P-value. http://www.statsci.amss.ac.cn/yjscy/yjy/lqz/201510/t20151027_313273.html liqz@amss.ac.cn Supplementary data are available at Bioinformatics online.

Genome-Wide Association Analysis of Young-Onset Stroke Identifies a Locus on Chromosome 10q25 Near HABP2.

Although a genetic contribution to ischemic stroke is well recognized, only a handful of stroke loci have been identified by large-scale genetic association studies to date. Hypothesizing that genetic effects might be stronger for early- versus late-onset stroke, we conducted a 2-stage meta-analysis of genome-wide association studies, focusing on stroke cases with an age of onset <60 years. The discovery stage of our genome-wide association studies included 4505 cases and 21 968 controls of European, South-Asian, and African ancestry, drawn from 6 studies. In Stage 2, we selected the lead genetic variants at loci with association P<5×10(-6) and performed in silico association analyses in an independent sample of ≤1003 cases and 7745 controls. One stroke susceptibility locus at 10q25 reached genome-wide significance in the combined analysis of all samples from the discovery and follow-up stages (rs11196288; odds ratio =1.41; P=9.5×10(-9)). The associated locus is in an intergenic region between TCF7L2 and HABP2. In a further analysis in an independent sample, we found that 2 single nucleotide polymorphisms in high linkage disequilibrium with rs11196288 were significantly associated with total plasma factor VII-activating protease levels, a product of HABP2. HABP2, which encodes an extracellular serine protease involved in coagulation, fibrinolysis, and inflammatory pathways, may be a genetic susceptibility locus for early-onset stroke.

Genetics of systemic sclerosis: recent advances.

Purpose of reviewLarge-scale and follow-up genetic association studies in systemic sclerosis (SSc) have implicated over 40 regions in disease risk, 15 of which with robust associations. Nevertheless, the causal variants and the functional mechanisms underlying the genetic associations remain elusive, and the reasons for the higher disease burden in African Americans unknown. Incorporating tools from diverse fields is beginning to unveil the role of genetic diversity and regulatory variation in SSc susceptibility. This review will summarize recent advances in SSc genetics, including autoimmune disease overlap, evidence of natural selection, and current progress towards the dissection of the functional role of associated risk variants.Recent findingsIn the past year, multiple large-scale studies reported novel strong and suggestive SSc associations. These results, coupled with the regions shared with other autoimmune diseases, emphasize the role of dysregulation of immune pathways as a key causative factor in SSc pathogenesis. Strong evidence implicates natural selection as a mechanism contributing to the maintenance of some of these SSc alleles in the population. Studies integrating genomic, transcriptomic, and epigenomic datasets in specific cell types to identify causal autoimmune disease variants are emerging.SummaryThe identification and comprehensive understanding of the factors and mechanisms contributing to SSc will contribute to improved diagnosis and disease management.