Genome-wide Significant Single Nucleotide Polymorphisms Research Articles

Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.).

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

Pathway-Specific Genetic Risk for Alzheimer's Disease Differentiates Regional Patterns of Cortical Atrophy in Older Adults.

Brain aging is highly variable and represents a challenge to delimit aging from disease processes. Moreover, genetic factors may influence both aging and disease. Here we focused on this issue and investigated effects of multiple genetic loci previously identified to be associated with late-onset Alzheimer's disease (AD) on brain structure of older adults from a population sample. We calculated a genetic risk score (GRS) using genome-wide significant single-nucleotide polymorphisms from genome-wide association studies of AD and tested its effect on cortical thickness (CT). We observed a common pattern of cortical thinning (right inferior frontal, left posterior temporal, medial occipital cortex). To identify CT changes by specific biological processes, we subdivided the GRS effect according to AD-associated pathways and performed follow-up analyses. The common pattern from the main analysis was further differentiated by pathway-specific effects yielding a more bilateral pattern. Further findings were located in the superior parietal and mid/anterior cingulate regions representing 2 unique pathway-specific patterns. All patterns, except the superior parietal pattern, were influenced by apolipoprotein E. Our step-wise approach revealed atrophy patterns that partially resembled imaging findings in early stages of AD. Our study provides evidence that genetic burden for AD contributes to structural brain variability in normal aging.

A post-GWAS confirming effects of PRKG1 gene on milk fatty acids in a Chinese Holstein dairy population

BackgroundWe previously conducted a genome-wide association study (GWAS) strategy for milk fatty acids in Chinese Holstein, and identified 83 genome-wide significant single nucleotide polymorphisms (SNPs) and 314 suggestive significant SNPs. Among them, two SNPs, BTB-01077939 and BTA-11275-no-rs associated with C10:0, C12:0, and C14 index (P = 0.000014 ~ 0.000024), were within and close to (0.85 Mb) protein kinase, cGMP-dependent, type І (PRKG1) gene on BTA26, respectively. PRKG1 gene plays a key role in lipolysis to release fatty acids and glycerol through the hydrolysis of triacyglycerol in adipocytes. We herein considered it as a promising candidate for milk fatty acids. The purpose of this study was to investigate whether PRKG1 had effects on milk fatty acids.ResultsBy direct sequencing the PCR products of pooled DNA, we identified a total of six SNPs, including one in 5′ flanking region, four in 3′ untranslated region (UTR), and one in 3′ flanking region. The single-locus association analysis was carried out, and showed that the six SNPs mainly had significant associations with C6:0, C8:0 and C17:1 (P < 0.0001 ~ 0.0035). In addition, we observed a haplotype block formed by g.6903810G > A and g.6904047G > T with Haploview 4.1, and it was strongly associated with C8:0, C10:0, C16:1, C17:1, C20:0 and C16 index (P = < 0.0001 ~ 0.0123). The SNP, g.8344262A > T, was predicted to alter the binding site (BS) of transcription factor (TF) GAGA box with Genomatix software, and the subsequent luciferase assay verified that it really changed the transcriptional activity of PRKG1 gene (P = 0.0009).ConclusionIn conclusion, to our best of knowledge, we are the first who identified the significant effects of PRKG1 on milk fatty acids in dairy cattle.

Contribution of Genome-Wide Significant Single Nucleotide Polymorphisms in Myopia Prediction: Findings from a 10-year Cohort of Chinese Twin Children

To determine the added predictive ability of genome-wide significant single nucleotide polymorphisms (SNPs) in refraction prediction in children and investigate the earliest age threshold for an accurate prediction of high myopia. Prospective longitudinal study. A total of 1063 first-born twins followed annually between 2006 and 2015 in China. The exposures were genetic factors (parental myopia, SNPs) and environmental factors (near work, outdoor activity). Five linear mixed-effect models, consisting of different combinations of age, gender, genetic, and environmental factors, were built to predict myopia development. All predictions were performed on the basis of spherical equivalent (SE) at baseline and the measurements on the second and third visits. The primary outcome measure was SE at the last visit among all subjects, and the secondary outcome measure was the presence of high myopia at the age of 18 years. Mean age of the study population was 10.5±2.2 years (range, 7-15 years) at baseline, and 48.6% were male. In linear mixed-effect models, age, age square, gender, paternal SE, maternal SE, and genetic risk scores (GRSs) showed a significant fixed effect, whereas outdoor and near-work time were not significant to SE at the last visit. Incorporating more follow-up data into the model showed better performance across all models. In the prediction of the presence of high myopia at 18 years of age, the model consisting of only age and gender showed a good performance (area under the curve [AUC]= 0.95), whereas the addition of SNPs did not enhance the model performance significantly. The AUC for predicting high myopia was >0.95 after the age of 13 years for participants with a single visit and after the age of 12 years for those with 1 more visit data. A simple model incorporating age, sex, and relevant refraction data is sufficient to accurately predict high myopia; there was limited improvement in the prediction model after adding genetic information. Furthermore, this prediction on the outcome at 18 years is possible when the child is aged 12 to 13 years.

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.

Population-based genome-wide association study of cognitive decline in older adults free of dementia: identification of a novel locus for the attention domain

To identify novel loci that affect cognitive decline in older adults free of dementia, we conducted genome-wide and gene-based meta-analyses on longitudinal slopes of 5 cognitive domains (memory, executive function, language, attention/processing speed, and visuospatial ability) derived from 2 population-based cohorts. For decline over time in each cognitive domain, we normalized intraindividual slopes within each cohort, accounting for baseline age, sex, and years of education. Normalized slope for each domain was used in cohort-specific genome-wide analyses after including top principal components as covariates followed by genome-wide and gene-based meta-analyses. Both analyses revealed a novel WDFY2 locus at genome-wide (p = 3.37E-08) and gene-wide (p = 7.10E-07) significance levels for the attention/processing speed domain. In the GTEx eQTL analysis, genome-wide significant single-nucleotide polymorphism was associated with RNA expression levels of WDFY2 in several brain regions: cerebellar hemisphere (p = 1.07E-04), cerebellum (p = 6.92E-04), hippocampus (p = 2.18E-03) and cortex (p = 2.29E-02), and in whole blood (p = 4.41E-05). Our results suggest that WDFY2 genetic variation may affect individual differences in decline over time on tests of attention/processing speed.

Genome-Wide Association Scan of Serum Urea in European Populations Identifies Two Novel Loci

Background: Serum urea level is a heritable trait, commonly used as a diagnostic marker for kidney function. Genome-wide association studies (GWAS) in East-Asian populations identified a number of genetic loci related to serum urea, however there is a paucity of data for European populations. Methods: We performed a two-stage meta-analysis of GWASs on serum urea in 13,312 participants, with independent replication in 7,379 participants of European ancestry. Results: We identified 6 genome-wide significant single nucleotide polymorphisms (SNPs) in or near 6 loci, of which 2 were novel (POU2AF1 and ADAMTS9-AS2). Replication of East-Asian and Scottish data provided evidence for an additional 8 loci. SNPs tag regions previously associated with anthropometric traits, serum magnesium, and urinary albumin-to-creatinine ratio, as well as expression quantitative trait loci for genes preferentially expressed in kidney and gastro-intestinal tissues. Conclusions: Our findings provide insights into the genetic underpinnings of urea metabolism, with potential relevance to kidney function.

Determination of Genetic Effects of LIPK and LIPJ Genes on Milk Fatty Acids in Dairy Cattle.

In our previous genome-wide association study (GWAS) on milk fatty acids (FAs) in Chinese Holstein, we discovered 83 genome-wide significant single nucleotide polymorphisms (SNPs) associated with milk FAs. Two of them were close to lipase family member K (LIPK) and lipase family member J (LIPJ), respectively. Hence, this study is a follow-up to verify whether the LIPK and LIPJ have significant genetic effects on milk FAs in dairy cattle. By re-sequencing the entire exons, and 3 kb of 5′ and 3′ flanking regions, two and seven SNPs were identified in LIPK and LIPJ, respectively, including a novel SNP, ss158213049726. With the Haploview 4.1 software, we found that five of the SNPs in LIPJ formed a haplotype block (D′ = 0.96 ~ 1.00). Single-locus association analyses revealed that each SNP in LIPK and LIPJ was significantly associated with at least one milk FA (p = < 1.00 × 10−4 ~ 4.88 × 10−2), and the haplotype-based association analyses showed significant genetic effects on nine milk FAs (p = < 1.00 × 10−4 ~ 3.98 × 10−2). Out of these SNPs, the missense mutation in LIPK gene, rs42774527, could change the protein secondary structure and function predicted by SOPMA, SIFT, and PROVEAN softwares. With the Genomatix software, we predicted that two SNPs, rs110322221 in LIPK and rs211373799 in LIPJ, altered the transcription factors binding sites (TFBSs), indicating their potential regulation on promoter activity of the genes. Furthermore, we found that both LIPK and LIPJ had relatively high expressions in the mammary gland. In conclusion, our research is the first to demonstrate that LIPK and LIPJ genes have significant associations with milk FAs, and the identified SNPs might be served as genetic markers to optimize breeding programs for milk FAs in dairy cattle. This research deserves in-depth verification.

Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score.

Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS. We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5). African-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts. Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.

Genome-wide association study for longevity in the Holstein cattle population

Longevity is one of the most important traits determining dairy cow profitability. In the last decades dairy cows suffered a lowering in the age at culling. With the aim to identify the genes involved in longevity, dates of birth, yields, dates of calving during lifespan and culling dates were collected for 946 culled cows which had been genotyped with the Bovine High Density panel. Using the GenABEL package in R, genome-wide association analysis was performed on three potential traits of longevity: (1) ‘days in production,’ (2) ‘days in herd,’ (3) number of calvings over lifespan.’ Five genome-wide significant single nucleotide polymorphisms (SNPs) associated with all three longevity traits were detected. Several consecutive SNPs identified on chromosomes 16 and 30 indicated the presence of two suggestive quantitative trait loci (QTL). The genes comprised in the QTL regions had biological functions related to fertility, reproductive disorders, heat stress and welfare of cows. These findings might contribute to improving breeding strategies to improve longevity.

SU90 - GENOME-WIDE ASSOCIATION STUDY (GWAS) AND ANALYSIS OF VIOLENCE IN SCHIZOPHRENIA

Background Schizophrenia patients are at higher risk of engaging in violent behaviours than the general population. Schizophrenia is also regarded as a highly heritable disorder, and in 2014, a large genetic study by the Schizophrenia Working Group (SWG) of the Psychiatric Genomics Consortium (PGC) identified 108 risk loci for SCZ. The relationship between violence and these risk loci has not been studied, neither has there been any GWAS where Single-Nucleotide Polymorphisms (SNPs) were correlated with violence. This study aims to analyze the specific effect of genome-wide significant schizophrenia SNPs on violence in schizophrenia. Methods We recruited 87 subjects between the age of 18 to 75 from the Centre for Addiction and Mental Health (CAMH), who had a diagnosis of schizophrenia or schizoaffective disorder. We recorded physical violence scores indicating any violent actions to inflict pain, bodily harm, or death on another individual from the standardized scale, Modified Overt Aggression Scale (MOAS). We genotyped 2.5 million SNPs for each participant, and the DNA was analyzed using the whole genome analysis tool-set, PLINK. Results We found 13 SNPs which were associated with violence, and the highest correlation was with the SNP kgp11155273 on chromosome 10 (p=2.051E-7). Discussion This study found an association between violence and the kgp11155273 SNP on chromosome 10. Further studies using larger subject pools are required to probe further into the nature of this association, and determine the genetic basis of violence in schizophrenia.

Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach.

Genetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of testing association between one trait and one genetic variant at a time in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data. Many existing GWAS summary data-based association test methods have relied on ad hoc approach or crude Monte Carlo approximation. In this article, we develop rigorous statistical methods for efficient and powerful multi-trait association test. We develop robust and efficient methods to accurately estimate the marginal trait correlation matrix using only GWAS summary data. We construct the principal component (PC)-based association test from the summary statistics. PC-based test has optimal power when the underlying multi-trait signal can be captured by the first PC, and otherwise it will have suboptimal performance. We develop an adaptive test by optimally weighting the PC-based test and the omnibus chi-square test to achieve robust performance under various scenarios. We develop efficient numerical algorithms to compute the analytical P-values for all the proposed tests without the need of Monte Carlo sampling. We illustrate the utility of proposed methods through application to the GWAS meta-analysis summary data for multiple lipids and glycemic traits. We identify multiple novel loci that were missed by individual trait-based association test. All the proposed methods are implemented in an R package available at http://www.github.com/baolinwu/MTAR. The developed R programs are extremely efficient: it takes less than 2 min to compute the list of genome-wide significant single nucleotide polymorphisms (SNPs) for all proposed multi-trait tests for the lipids GWAS summary data with 2.5 million SNPs on a single Linux desktop. Supplementary data are available at Bioinformatics online.

Genome-wide association study reveals novel genetic locus associated with intra-individual variability in response time

Intra-individual response time variability (IIRTV) is proposed as a viable endophenotype for many psychiatric disorders, particularly attention-deficit hyperactivity disorder (ADHD). Here we assessed whether IIRTV was associated with common DNA variation genome-wide and whether IIRTV mediated the relationship between any associated loci and self-reported ADHD symptoms. A final data set from 857 Australian young adults (489 females and 368 males; Mage = 22.14 years, SDage = 4.82 years) who completed five response time tasks and self-reported symptoms of ADHD using the Conners’ Adult ADHD Rating Scale was used. Principal components analysis (PCA) on these response time measures (standard deviation of reaction times and the intra-individual coefficient of variation) produced two variability factors (labelled response selection and selective attention). To understand the genetic drivers of IIRTV we performed a genome-wide association analysis (GWAS) on these PCA-derived indices of IIRTV. For the selective attention variability factor, we identified one single-nucleotide polymorphism (SNP) attaining genome-wide significance; rs62182100 in the HDAC4 gene located on chromosome 2q37. A bootstrapping mediation analysis demonstrated that the selective attention variability factor mediated the relationship between rs62182100 and self-reported ADHD symptoms. Our findings provide the first evidence of a genome-wide significant SNP association with IIRTV and support the potential utility of IIRTV as a valid endophenotype for ADHD symptoms. However, limitations of this study suggest that these observations should be interpreted with caution until replication samples become available.

Genetically Determined Serum Uric Acid and Alzheimer's Disease Risk.

To evaluate whether genetically increased serum uric acid levels influence the risk of Alzheimer's disease (AD), we used genome-wide significant single nucleotide polymorphisms for uric acid as the instrumental variables, and undertook a Mendelian randomization (MR) study to estimate the effect of uric acid on the risk of AD. The MR method prevents bias due to reverse causation (e.g., uric acid changes because of AD) and minimizes bias due to confounding of both measured and unmeasured confounders. We used the summary statistics from The International Genomics of Alzheimer's Project Consortium that is the largest AD genome-wide association study of 74,046 individuals of European ethnicity including 25,580 AD cases. We further performed sensitivity analyses to evaluate the assumptions of the MR method. The MR analyses did not support a causal role of genetically elevated serum uric acid on AD risk (odds ratio: 1.02, 95% confidence interval: 0.93-1.12, p = 0.65). Sensitivity analyses, including MR-Egger regression, suggested no strong evidence of bias due to pleiotropy. In conclusion, lifelong genetically increased serum uric acid levels have no protective effect on the risk of AD.

GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P<1×10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

Genome-wide association study of nocturnal blood pressure dipping in hypertensive patients

BackgroundReduced nocturnal fall (non-dipping) of blood pressure (BP) is a predictor of cardiovascular target organ damage. No genome-wide association studies (GWAS) on BP dipping have been previously reported.MethodsTo study genetic variation affecting BP dipping, we conducted a GWAS in Genetics of Drug Responsiveness in Essential Hypertension (GENRES) cohort (n = 204) using the mean night-to-day BP ratio from up to four ambulatory BP recordings conducted on placebo. Associations with P < 1 × 10− 5 were further tested in two independent cohorts: Haemodynamics in Primary and Secondary Hypertension (DYNAMIC) (n = 183) and Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic Syndrome (DILGOM) (n = 180). We also tested the genome-wide significant single nucleotide polymorphism (SNP) for association with left ventricular hypertrophy in GENRES.ResultsIn GENRES GWAS, rs4905794 near BCL11B achieved genome-wide significance (β = − 4.8%, P = 9.6 × 10− 9 for systolic and β = − 4.3%, P = 2.2 × 10− 6 for diastolic night-to-day BP ratio). Seven additional SNPs in five loci had P values < 1 × 10− 5. The association of rs4905794 did not significantly replicate, even though in DYNAMIC the effect was in the same direction (β = − 0.8%, P = 0.4 for systolic and β = − 1.6%, P = 0.13 for diastolic night-to-day BP ratio). In GENRES, the associations remained significant even during administration of four different antihypertensive drugs. In separate analysis in GENRES, rs4905794 was associated with echocardiographic left ventricular mass (β = − 7.6 g/m2, P = 0.02).Conclusionsrs4905794 near BCL11B showed evidence for association with nocturnal BP dipping. It also associated with left ventricular mass in GENRES. Combined with earlier data, our results provide support to the idea that BCL11B could play a role in cardiovascular pathophysiology.

Sample Size for Successful Genome-Wide Association Study of Major Depressive Disorder.

Major depressive disorder (MDD) is a complex, heritable psychiatric disorder. Advanced statistical genetics for genome-wide association studies (GWASs) have suggested that the heritability of MDD is largely explained by common single nucleotide polymorphisms (SNPs). However, until recently, there has been little success in identifying MDD-associated SNPs. Here, based on an empirical Bayes estimation of a semi-parametric hierarchical mixture model using summary statistics from GWASs, we show that MDD has a distinctive polygenic architecture consisting of a relatively small number of risk variants (~17%), e.g., compared to schizophrenia (~42%). In addition, these risk variants were estimated to have very small effects (genotypic odds ratio ≤ 1.04 under the additive model). Based on the estimated architecture, the required sample size for detecting significant SNPs in a future GWAS was predicted to be exceptionally large. It is noteworthy that the number of genome-wide significant MDD-associated SNPs would rapidly increase when collecting 50,000 or more MDD-cases (and the same number of controls); it can reach as much as 100 SNPs out of nearly independent (linkage disequilibrium pruned) 100,000 SNPs for ~120,000 MDD-cases.

Trans-eQTLs identified in whole blood have limited influence on complex disease biology.

Trans-eQTLs have been implicated in complex traits and common diseases, but many were initially identified on the basis of having an effect in cis, and there has been no assessment of the significance of the overlap in relation to chance expectations. Here, we investigated whether trans-expression quantitative trait loci (eQTL) associations identified in whole blood contribute to variance in complex traits by determining (1) whether genome-wide significant (GWS) single-nucleotide polymorphisms (SNPs) were enriched for trans-eQTL (including trans-only eQTL), and (2) whether the genomic regions surrounding associated trans-genes were enriched for statistical associations in the relevant GWAS. On average for a given phenotype, we identify 4.8% of GWS SNPs overlapping with trans-eQTL present in blood, and show that for the majority of these phenotypes, this observation does not exceed that expected by chance. Likewise, we observe no enrichment for genetic associations with the GWAS phenotype in the regions surrounding the linked trans-genes, with the exception of rheumatoid arthritis. Interestingly, the GWS SNPs for each phenotype were consistently more enriched for unique trans-eQTL SNPs than trans-eQTL SNP-probe pairs (p = 4 × 10-7), with schizophrenia the only exception. This relative enrichment for trans-eQTL SNPs over trans-eQTL SNP-probe pairs implies that trait-associated trans-eQTL SNPs in whole blood are less likely to be 'master regulators' than random trans-eQTL SNPs. Taken together, these results suggest little evidence for the role of blood-based trans-eQTL in complex traits and disease, although this may reflect the finite size of currently available data sets and our findings may not hold for trans-eQTLs in more trait-relevant tissues. All software is publically available at https://github.com/IMB-Computational-Genomics-Lab/eqtlOverlapper .

Genome-wide association studies identified variants for taurine concentration in Japanese Black beef.

We performed genome-wide association studies (GWAS) using the BovineSNP50 array to detect significant single nucleotide polymorphisms (SNPs) that may affect the concentration of 22 free amino acids and three peptides in Japanese Black beef cattle. A total of 574 Japanese Black cattle and 40,657 SNPs from the array were used for this study. Genome-wide significant SNPs were detected for β-alanine (three SNPs on chromosomes 22 and 29) and taurine (26 SNPs on chromosome 22). Importantly, the top two SNPs for taurine were highly significant (p=6.2×10-21 ), and the frequency of the increase-concentration allele (Q) for taurine was found to be 0.73. The Q allele frequency of this population was similar to that of the other unrelated Japanese Black cattle, but different from that of the other breeds. In addition, the significant SNPs were not associated with carcass traits or fatty acid compositions. Interestingly, the top three of the four most significant SNPs for taurine were located near solute carrier family 6, member 6 (SLC6A6), which is a membrane transporter for taurine. We also found two associated variants in the 5'-upstream region of SLC6A6; however, they were less significantly associated than the SNPs from the BovineSNP50 array.