Association Studies Of Common Variants Research Articles

Pathway analysis with next-generation sequencing data.

Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.

Genome-Wide Association Scan for Variants Associated with Early-Onset Prostate Cancer

Prostate cancer is the most common non-skin cancer and the second leading cause of cancer related mortality for men in the United States. There is strong empirical and epidemiological evidence supporting a stronger role of genetics in early-onset prostate cancer. We performed a genome-wide association scan for early-onset prostate cancer. Novel aspects of this study include the focus on early-onset disease (defined as men with prostate cancer diagnosed before age 56 years) and use of publically available control genotype data from previous genome-wide association studies. We found genome-wide significant (p<5×10−8) evidence for variants at 8q24 and 11p15 and strong supportive evidence for a number of previously reported loci. We found little evidence for individual or systematic inflated association findings resulting from using public controls, demonstrating the utility of using public control data in large-scale genetic association studies of common variants. Taken together, these results demonstrate the importance of established common genetic variants for early-onset prostate cancer and the power of including early-onset prostate cancer cases in genetic association studies.

PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants

Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership; control for missing covariate data; remove related individuals, duplicate samples and individuals with incorrect sex specification; recode primary traits and covariates; transform data; remove or winsorize outliers; select covariates for analysis; and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data. Phenoman is freeware that provides approaches for efficient exploration and management of phenotype data. Proper QC of phenotypes before proceeding to the association analysis is critical to ensure control of type I and II errors, reliable effect estimates and consistent results between studies. PhenoMan is highly beneficial for the preparation of qualitative and quantitative trait data for association studies using new datasets as well as those obtained from public repositories. code.google.com/p/phenoman

Association study of common variants within the G protein‐coupled receptor kinase 6 gene and schizophrenia susceptibility in Han Chinese

In this study, we examined whether common variants in the G protein-coupled receptor kinase 6 gene (GRK6) confers susceptibility to schizophrenia in Chinese. We genotyped two common variants in 697 schizophrenia patients and 563 healthy control subjects. No significant difference in either allele or genotype comparisons between the case and control groups was found. Our results imply that GRK6 may not play a role in the pathophysiology of schizophrenia among Han Chinese.

GEE-based SNP set association test for continuous and discrete traits in family-based association studies.

Family-based genetic association studies of related individuals provide opportunities to detect genetic variants that complement studies of unrelated individuals. Most statistical methods for family association studies for common variants are single marker based, which test one SNP a time. In this paper, we consider testing the effect of an SNP set, e.g., SNPs in a gene, in family studies, for both continuous and discrete traits. Specifically, we propose a generalized estimating equations (GEEs) based kernel association test, a variance component based testing method, to test for the association between a phenotype and multiple variants in an SNP set jointly using family samples. The proposed approach allows for both continuous and discrete traits, where the correlation among family members is taken into account through the use of an empirical covariance estimator. We derive the theoretical distribution of the proposed statistic under the null and develop analytical methods to calculate the P-values. We also propose an efficient resampling method for correcting for small sample size bias in family studies. The proposed method allows for easily incorporating covariates and SNP-SNP interactions. Simulation studies show that the proposed method properly controls for type I error rates under both random and ascertained sampling schemes in family studies. We demonstrate through simulation studies that our approach has superior performance for association mapping compared to the single marker based minimum P-value GEE test for an SNP-set effect over a range of scenarios. We illustrate the application of the proposed method using data from the Cleveland Family GWAS Study.

Association Study of Common Genetic Variants and HIV-1 Acquisition in 6,300 Infected Cases and 7,200 Controls

Multiple genome-wide association studies (GWAS) have been performed in HIV-1 infected individuals, identifying common genetic influences on viral control and disease course. Similarly, common genetic correlates of acquisition of HIV-1 after exposure have been interrogated using GWAS, although in generally small samples. Under the auspices of the International Collaboration for the Genomics of HIV, we have combined the genome-wide single nucleotide polymorphism (SNP) data collected by 25 cohorts, studies, or institutions on HIV-1 infected individuals and compared them to carefully matched population-level data sets (a list of all collaborators appears in Note S1 in Text S1). After imputation using the 1,000 Genomes Project reference panel, we tested approximately 8 million common DNA variants (SNPs and indels) for association with HIV-1 acquisition in 6,334 infected patients and 7,247 population samples of European ancestry. Initial association testing identified the SNP rs4418214, the C allele of which is known to tag the HLA-B*57:01 and B*27:05 alleles, as genome-wide significant (p = 3.6×10−11). However, restricting analysis to individuals with a known date of seroconversion suggested that this association was due to the frailty bias in studies of lethal diseases. Further analyses including testing recessive genetic models, testing for bulk effects of non-genome-wide significant variants, stratifying by sexual or parenteral transmission risk and testing previously reported associations showed no evidence for genetic influence on HIV-1 acquisition (with the exception of CCR5Δ32 homozygosity). Thus, these data suggest that genetic influences on HIV acquisition are either rare or have smaller effects than can be detected by this sample size.

Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: Genome‐wide association study of both common and rare variants

Attention-deficit hyperactivity disorder (ADHD) is a complex polygenic disorder. This study aimed to discover common and rare DNA variants associated with ADHD in a large homogeneous Han Chinese ADHD case-control sample. The sample comprised 1,040 cases and 963 controls. All cases met DSM-IV ADHD diagnostic criteria. We used the Affymetrix6.0 array to assay both single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). Genome-wide association analyses were performed using PLINK. SNP-heritability and SNP-genetic correlations with ADHD in Caucasians were estimated with genome-wide complex trait analysis (GCTA). Pathway analyses were performed using the Interval enRICHment Test (INRICH), the Disease Association Protein-Protein Link Evaluator (DAPPLE), and the Genomic Regions Enrichment of Annotations Tool (GREAT). We did not find genome-wide significance for single SNPs but did find an increased burden of large, rare CNVs in the ADHD sample (P = 0.038). SNP-heritability was estimated to be 0.42 (standard error, 0.13, P = 0.0017) and the SNP-genetic correlation with European Ancestry ADHD samples was 0.39 (SE 0.15, P = 0.0072). The INRICH, DAPPLE, and GREAT analyses implicated several gene ontology cellular components, including neuron projections and synaptic components, which are consistent with a neurodevelopmental pathophysiology for ADHD. This study suggested the genetic architecture of ADHD comprises both common and rare variants. Some common causal variants are likely to be shared between Han Chinese and Caucasians. Complex neurodevelopmental networks may underlie ADHD's etiology.

Genome-Wide Association Study Identifies Three Common Variants Associated with Serologic Response to Vitamin E Supplementation in Men1–4

Vitamin E inhibits lipid peroxidation in cell membranes, prevents oxidative damage to DNA by scavenging free radicals, and reduces carcinogen production. No study to our knowledge, however, has examined the association between genetic variants and response to long-term vitamin E supplementation. We conducted a genome-wide association study (GWAS) of common variants associated with circulating a-tocopherol concentrations following 3 y of controlled supplementation. The study population included 2112 middle-aged, male smokers in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort who received a trial supplementation of a-tocopherol (50 mg/d) and had fasting serum a-tocopherol concentrations measured after 3 y. Serum concentrations were log-transformed for statistical analysis and general linear models adjusted for age, BMI, serum total cholesterol, and cancer case status. Associations with serum response to α-tocopherol supplementation achieved genome-wide significance for 2 single nucleotide polymorphisms (SNP): rs964184 on 11q23.3 (P = 2.6 × 10-12) and rs2108622 on 19pter-p13.11 (P = 2.2 × 10-7), and approached genome-wide significance for one SNP, rs7834588 on 8q12.3 (P = 6.2 × 10-7). Combined, these SNP explain 3.4% of the residual variance in serum a-tocopherol concentrations during controlled vitamin E supplementation. A GWAS has identified 3 genetic variants at different loci that appear associated with serum concentrations after vitamin E supplementation in men. Identifying genetic variants that influence serum nutrient biochemical status (e.g., α-tocopherol) under supplementation conditions improves our understanding of the biological determinants of these nutritional exposures and their associations with cancer etiology.

A Genetic Signature of Spina Bifida Risk from Pathway-Informed Comprehensive Gene-Variant Analysis

Despite compelling epidemiological evidence that folic acid supplements reduce the frequency of neural tube defects (NTDs) in newborns, common variant association studies with folate metabolism genes have failed to explain the majority of NTD risk. The contribution of rare alleles as well as genetic interactions within the folate pathway have not been extensively studied in the context of NTDs. Thus, we sequenced the exons in 31 folate-related genes in a 480-member NTD case-control population to identify the full spectrum of allelic variation and determine whether rare alleles or obvious genetic interactions within this pathway affect NTD risk. We constructed a pathway model, predetermined independent of the data, which grouped genes into coherent sets reflecting the distinct metabolic compartments in the folate/one-carbon pathway (purine synthesis, pyrimidine synthesis, and homocysteine recycling to methionine). By integrating multiple variants based on these groupings, we uncovered two provocative, complex genetic risk signatures. Interestingly, these signatures differed by race/ethnicity: a Hispanic risk profile pointed to alterations in purine biosynthesis, whereas that in non-Hispanic whites implicated homocysteine metabolism. In contrast, parallel analyses that focused on individual alleles, or individual genes, as the units by which to assign risk revealed no compelling associations. These results suggest that the ability to layer pathway relationships onto clinical variant data can be uniquely informative for identifying genetic risk as well as for generating mechanistic hypotheses. Furthermore, the identification of ethnic-specific risk signatures for spina bifida resonated with epidemiological data suggesting that the underlying pathogenesis may differ between Hispanic and non-Hispanic groups.

Effect of population stratification analysis on false-positive rates for common and rare variants

Principal components analysis (PCA) has been successfully used to correct for population stratification in genome-wide association studies of common variants. However, rare variants also have a role in common disease etiology. Whether PCA successfully controls population stratification for rare variants has not been addressed. Thus we evaluate the effect of population stratification analysis on false-positive rates for common and rare variants at the single-nucleotide polymorphism (SNP) and gene level. We use the simulation data from Genetic Analysis Workshop 17 and compare false-positive rates with and without PCA at the SNP and gene level. We found that SNPs’ minor allele frequency (MAF) influenced the ability of PCA to effectively control false discovery. Specifically, PCA reduced false-positive rates more effectively in common SNPs (MAF > 0.05) than in rare SNPs (MAF < 0.01). Furthermore, at the gene level, although false-positive rates were reduced, power to detect true associations was also reduced using PCA. Taken together, these results suggest that sequence-level data should be interpreted with caution, because extremely rare SNPs may exhibit sporadic association that is not controlled using PCA.

Association study of common variants in the sFRP1 gene region and parameters of bone strength and body composition in two independent healthy Caucasian male cohorts

Bone mineral density (BMD) and bone strength are predictive parameters for the development of osteoporosis and related fracture later in life. Although it is well known that BMD and bone strength have a high heritability, not much of the variation is already explained. Mice models showed that sFRP1 has an influence on bone formation. Therefore this study aimed to investigate the effect of common genetic variation on BMD and bone strength in Caucasian men of different ages.Using HapMap we selected 13 tagSNPs which tag most common genetic variation in and around sFRP1 and we genotyped these SNPs in the young cohort of the Odense Androgen Study (OAS). The OAS includes a total of 1383 Danish men from two different age groups ([20–29years]: N=783; [60–74years]: N=600) and is well characterised. The subjects were phenotyped for BMD at several sites, and additionally for body composition and hip geometry parameters. Based on the results of the young cohort we selected three SNPs for further analysis in the complete OAS population. To conclude we tried to replicate the results of two SNPs in an independent population of 994 Belgian men.We found a strong association for rs9694405 with BMI as well in both cohorts separately as in the whole OAS population. Further we found rs4736965 associated with several hip geometry parameters in the same population. However we were not able to replicate those results in the Belgian population. At last we found in the OAS population age specific effects for rs10106678 with whole body BMD and waist to hip ratio.

Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease

More than a thousand disease susceptibility loci have been identified via genome-wide association studies (GWAS) of common variants; however, the specific genes and full allelic spectrum of causal variants underlying these findings generally remain to be defined. We utilize pooled next-generation sequencing to study 56 genes in regions associated to Crohn’s Disease in 350 cases and 350 controls. Follow up genotyping of 70 rare and low-frequency protein-altering variants (MAF ~ .001-.05) in nine independent case-control series (16054 CD patients, 12153 UC patients, 17575 healthy controls) identifies four additional independent risk factors in NOD2, two additional protective variants in IL23R, a highly significant association to a novel, protective splice variant in CARD9 (p < 1e-16, OR ~ 0.29), as well as additional associations to coding variants in IL18RAP, CUL2, C1orf106, PTPN22 and MUC19. We extend the results of successful GWAS by providing novel, rare, and likely functional variants that will empower functional experiments and predictive models.

Human genetics, natriuretic peptides and hypertension

Results We used a candidate gene association study of common variants across the NPPA-NPPB locus to identify genetic variants that influence atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) [1]. The minor alleles of three noncoding SNPs rs5068 (MAF 0.06), rs198358 (MAF 0.19) and rs632793 (MAF 0.38) were associated with higher ANP (+0.42 SD p=8x10-70; +0.20 p=8x10-30; +0.08 p=2x10-10, respectively) and higher BNP (+0.17 SD p=3x10-12, +0.18 p=9x10-25, +0.21 p=2x10-68) in 14,515 individuals of European ancestry. The alleles of rs5068 and rs198358 associated with higher ANP/BNP were associated with lower SBP (p=2x10-6, p=6x10-5, respectively) and lower DBP (p=1x10-6, p=5x10-5) as well as lower odds of HTN (OR 0.85 p=4x10-5, OR 0.90 p=2x10-4) in 29,717 individuals. The association of rs5068 was replicated in the Global BPgen GWAS [2]. Recently, Kato et al reported the association of a common variant downstream of NPR3 encoding the natriuretic peptide clearance receptor [3]. Murine NPR3 knockout is associated with lower BP, consistent with higher levels of circulating natriuretic peptides [4]. The minor allele of a SNP highly correlated to the BP NPR3 SNP has recently been reported to be associated with taller stature in humans [5], consistent with the effect of murine knockout [4] or apparent loss-of-function mutations [6] in NPR3.

Comparison of statistical tests for disease association with rare variants

In anticipation of the availability of next-generation sequencing data, there is increasing interest in investigating association between complex traits and rare variants (RVs). In contrast to association studies for common variants (CVs), due to the low frequencies of RVs, common wisdom suggests that existing statistical tests for CVs might not work, motivating the recent development of several new tests for analyzing RVs, most of which are based on the idea of pooling/collapsing RVs. However, there is a lack of evaluations of, and thus guidance on the use of, existing tests. Here we provide a comprehensive comparison of various statistical tests using simulated data. We consider both independent and correlated rare mutations, and representative tests for both CVs and RVs. As expected, if there are no or few non-causal (i.e. neutral or non-associated) RVs in a locus of interest while the effects of causal RVs on the trait are all (or mostly) in the same direction (i.e. either protective or deleterious, but not both), then the simple pooled association tests (without selecting RVs and their association directions) and a new test called kernel-based adaptive clustering (KBAC) perform similarly and are most powerful; KBAC is more robust than simple pooled association tests in the presence of non-causal RVs; however, as the number of non-causal CVs increases and/or in the presence of opposite association directions, the winners are two methods originally proposed for CVs and a new test called C-alpha test proposed for RVs, each of which can be regarded as testing on a variance component in a random-effects model. Interestingly, several methods based on sequential model selection (i.e. selecting causal RVs and their association directions), including two new methods proposed here, perform robustly and often have statistical power between those of the above two classes.

Genome-wide association study identifies common variants associated with circulating vitamin E levels.

In genome-wide association studies (GWAS) of common genetic variants associated with circulating alpha- and gamma-tocopherol concentrations in two adult cohorts comprising 5006 men of European descent, we observed three loci associated with alpha-tocopherol levels, two novel single-nucleotide polymorphisms (SNPs), rs2108622 on 19pter-p13.11 (P= 1.7 × 10−8) and rs11057830 on 12q24.31 (P= 2.0 × 10−8) and confirmed a previously reported locus marked by rs964184 on 11q23.3 (P= 2.7 × 10−10). The three SNPs have been reported to be associated with lipid metabolism and/or regulation. We replicated these findings in a combined meta-analysis with two independent samples, P= 7.8 × 10−12 (rs964184 on 11q23.3 near BUD13, ZNF259 and APOA1/C3/A4/A5), P= 1.4 × 10−10 (rs2108622 on 19pter-p13.11 near CYP4F2) and P= 8.2 × 10−9 (rs11057830 on 12q24.31 near SCARB1). Combined, these SNPs explain 1.7% of the residual variance in log alpha-tocopherol levels. In one of the two male GWAS cohorts (n= 992), no SNPs were significantly associated with gamma-tocopherol concentrations after including data from the replication sample for 71 independent SNPs with P< 1 × 10−4 identified.

Genetic association study of common mitochondrial DNA variants in osteoporosis

Genetic association study of common mitochondrial DNA variants in osteoporosis

Familial mesial temporal lobe epilepsy: a benign epilepsy syndrome showing complex inheritance

Temporal lobe epilepsy is the commonest partial epilepsy of adulthood. Although generally perceived as an acquired disorder, several forms of familial temporal lobe epilepsy, with mesial or lateral seizure semiology, have been described. Descriptions of familial mesial temporal lobe epilepsy have varied widely from a benign epilepsy syndrome with prominent déjà vu and without antecedent febrile seizures or magnetic resonance imaging abnormalities, to heterogeneous, but generally more refractory epilepsies, often with a history of febrile seizures and with frequent hippocampal atrophy and high T₂ signal on magnetic resonance imaging. Compelling evidence of a genetic aetiology (rather than chance aggregation) in familial mesial temporal lobe epilepsy has come from twin studies. Dominant inheritance has been reported in two large families, though the usual mode of inheritance is not known. Here, we describe clinical and neurophysiological features of 20 new mesial temporal lobe epilepsy families including 51 affected individuals. The epilepsies in these families were generally benign, and febrile seizure history was infrequent (9.8%). No evidence of hippocampal sclerosis or dysplasia was present on brain imaging. A single individual underwent anterior temporal lobectomy, with subsequent seizure freedom and histopathological evidence of hippocampal sclerosis was not found. Inheritance patterns in probands' relatives were analysed in these families, together with 19 other temporal lobe epilepsy families previously reported by us. Observed frequencies of epilepsies in relatives were lower than predicted by dominant Mendelian models, while only a minority (8/39) of families could be compatible with recessive inheritance. These findings strongly suggest that complex inheritance, similar to that widely accepted in the idiopathic generalized epilepsies, is the usual mode of inheritance in familial mesial temporal lobe epilepsy. This disorder, which appears to be relatively common, and not typically associated with hippocampal sclerosis, is an appropriate target for contemporary approaches to complex disorders such as genome-wide association studies for common genetic variants or deep sequencing for rare variants.

Uncovering the roles of rare variants in common disease through whole-genome sequencing

Although genome-wide association (GWA) studies for common variants have thus far succeeded in explaining only a modest fraction of the genetic components of human common diseases, recent advances in next-generation sequencing technologies could rapidly facilitate substantial progress. This outcome is expected if much of the missing genetic control is due to gene variants that are too rare to be picked up by GWA studies and have relatively large effects on risk. Here, we evaluate the evidence for an important role of rare gene variants of major effect in common diseases and outline discovery strategies for their identification.

Anti-Müllerian hormone (AMH): Regulator and marker of ovarian function

In this review, the role of anti-Müllerian hormone (AMH) as a regulator and marker of ovarian function is described. Studies in mice showed that AMH is one of the intra-ovarian growth factors regulating primordial follicle recruitment and follicle-stimulating hormone (FSH) sensitivity of growing follicles in an inhibitory manner. Association studies of common variants of the AMH and AMHRII gene suggest that AMH may have a similar role in the human ovary. When it was discovered that serum levels AMH are correlated with the number of growing follicles, AMH gained further clinical interest as a marker for the quantitative aspect of ovarian reserve and as a diagnostic marker for polycystic ovary syndrome (PCOS).

Genetics in psychiatry: common variant association studies

Many psychiatric conditions and traits are associated with significant heritability. Genetic risk for psychiatric conditions encompass rare variants, identified due to major effect, as well as common variants, the latter analyzed by association analyses. We review guidelines for common variant association analyses, undertaking after assessing evidence of heritability. We highlight the importance of: suitably large sample sizes; an experimental design that controls for ancestry; careful data cleaning; correction for multiple testing; small P values for positive findings; assessment of effect size for positive findings; and, inclusion of an independent replication sample. We also note the importance of a critical discussion of any prior findings, biological follow-up where possible, and a means of accessing the raw data.