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Hypertrophic cardiomyopathy (HCM) is a common, serious, genetic heart disorder. Rare pathogenic variants in sarcomere genes cause HCM, but with unexplained phenotypic heterogeneity. Moreover, most patients do not carry such variants. We report a genome-wide association study of 2,780 cases and 47,486 controls that identified 12 genome-wide-significant susceptibility loci for HCM. Single-nucleotide polymorphism heritability indicated a strong polygenic influence, especially for sarcomere-negative HCM (64% of cases; h 2 g  = 0.34 ± 0.02). A genetic risk score showed substantial influence on the odds of HCM in a validation study, halving the odds in the lowest quintile and doubling them in the highest quintile, and also influenced phenotypic severity in sarcomere variant carriers. Mendelian randomization identified diastolic blood pressure (DBP) as a key modifiable risk factor for sarcomere-negative HCM, with a one standard deviation increase in DBP increasing the HCM risk fourfold. Common variants and modifiable risk factors have important roles in HCM that we suggest will be clinically actionable. Genome-wide association analyses identify 12 susceptibility loci for hypertrophic cardiomyopathy (HCM). A genetic risk score for HCM was associated with disease status in a validation study and influenced phenotypic severity in carriers of risk variants in sarcomere genes.

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5-5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10⁻⁸) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10⁻¹¹⁷). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10⁻⁴), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.

Background Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p -value from a locus with multiple variants which appear to be in a haplotype block having very similar p -values. Results Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page. Conclusions We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.

Understanding the complex genetic basis of coronary artery disease — now the world's biggest killer — will provide a vital boost towards developing new prognostic and therapeutic strategies. Recent progress towards this goal has been made by using large-scale, systematic, genome-wide approaches. Key Points Family history is a predictor of risk of coronary artery disease (CAD) even after correction for known risk factors, such as cholesterol levels and blood pressure. This indicates that unknown susceptibility genes exist, as well as those that influence these risk factors. Rare Mendelian traits can cause premature CAD, often through alterations in levels of low density lipoprotein and high density lipoprotein cholesterol, but these account for a small proportion of susceptibility at the population level. Mouse models have been informative for some components of atherosclerosis, but no convincing animal model exists for the crucial events that trigger myocardial infarction — that is, plaque rupture and thrombotic occlusion. Data from genome-wide linkage scans indicate that although there are no genetic loci for CAD risk that have large effects, there are mappable loci with effect sizes that are comparable to those that have been successfully identified for other complex diseases. Candidate-gene association studies have been plagued by poor reproducibility. Genome-wide association studies using hundreds of thousands of SNPs are now becoming feasible, and offer the best prospects for the identification of novel CAD genes. Large studies are needed to provide robust proof of novel susceptibility genes in CAD because effect sizes are modest and gene–environment interactions are inevitable. However, even a gene of modest effect can be of enormous significance if it identifies a new therapeutic target or a novel biological pathway. Many of the CAD-susceptibility genes that have been identified so far are involved in innate or adaptive immunity. These mirror the growing recognition of the role of inflammation in atherosclerosis and CAD and might point to new approaches for prevention and intervention. Combined genetic and genomic approaches — for example, mapping of expression QTLs — show promise for studies of complex diseases. However, these approaches have still to be validated in human populations and tend to be limited to genomic analyses that are carried out on blood or blood cells, so it is not clear how informative these will be for atherosclerosis in humans. Family history is an important independent risk factor for coronary artery disease (CAD), and identification of susceptibility genes for this common, complex disease is a vital goal. Although there has been considerable success in identifying genetic variants that influence well-known risk factors, such as cholesterol levels, progress in unearthing novel CAD genes has been slow. However, advances are now being made through the application of large-scale, systematic, genome-wide approaches. Recent findings particularly highlight the link between CAD and inflammation and immunity, and highlight the biological insights to be gained from a genetic understanding of the world's biggest killer.

Low circulating vitamin D levels have been associated with risk of asthma, atopic dermatitis, and elevated total immunoglobulin E (IgE). These epidemiological associations, if true, would have public health importance, since vitamin D insufficiency is common and correctable. We aimed to test whether genetically lowered vitamin D levels were associated with risk of asthma, atopic dermatitis, or elevated serum IgE levels, using Mendelian randomization (MR) methodology to control bias owing to confounding and reverse causation. The study employed data from the UK Biobank resource and from the SUNLIGHT, GABRIEL and EAGLE eczema consortia. Using four single-nucleotide polymorphisms (SNPs) strongly associated with 25-hydroxyvitamin D (25OHD) levels in 33,996 individuals, we conducted MR studies to estimate the effect of lowered 25OHD on the risk of asthma (n = 146,761), childhood onset asthma (n = 15,008), atopic dermatitis (n = 40,835), and elevated IgE level (n = 12,853) and tested MR assumptions in sensitivity analyses. None of the four 25OHD-lowering alleles were associated with asthma, atopic dermatitis, or elevated IgE levels (p ≥ 0.2). The MR odds ratio per standard deviation decrease in log-transformed 25OHD was 1.03 (95% confidence interval [CI] 0.90-1.19, p = 0.63) for asthma, 0.95 (95% CI 0.69-1.31, p = 0.76) for childhood-onset asthma, and 1.12 (95% CI 0.92-1.37, p = 0.27) for atopic dermatitis, and the effect size on log-transformed IgE levels was -0.40 (95% CI -1.65 to 0.85, p = 0.54). These results persisted in sensitivity analyses assessing population stratification and pleiotropy and vitamin D synthesis and metabolism pathways. The main limitations of this study are that the findings do not exclude an association between the studied outcomes and 1,25-dihydoxyvitamin D, the active form of vitamin D, the study was underpowered to detect effects smaller than an OR of 1.33 for childhood asthma, and the analyses were restricted to white populations of European ancestry. This research has been conducted using the UK Biobank Resource and data from the SUNLIGHT, GABRIEL and EAGLE Eczema consortia. In this study, we found no evidence that genetically determined reduction in 25OHD levels conferred an increased risk of asthma, atopic dermatitis, or elevated total serum IgE, suggesting that efforts to increase vitamin D are unlikely to reduce risks of atopic disease.

Childhood asthma genes Rates of childhood asthma diagnosis are rising: 6% of children in the United States are sufferers. Both genetic and environmental factors are clearly important. To discover more about the genetic element, Moffatt et al . looked for genes linked to asthma in a genome-wide association scan. More than a third of children with asthma of onset below the age of seven showed variations in expression of the ORMDL3 gene on chromosome 17. Similar genes are found in yeast and other primitive organisms, suggesting that they may be components of an ancient and conserved immune mechanism. Variations in expression of the gene ORMDL3 were found to be associated with development of childhood asthma, suggesting this gene should be examined in more patient groups. Asthma is caused by a combination of poorly understood genetic and environmental factors 1 , 2 . We have systematically mapped the effects of single nucleotide polymorphisms (SNPs) on the presence of childhood onset asthma by genome-wide association. We characterized more than 317,000 SNPs in DNA from 994 patients with childhood onset asthma and 1,243 non-asthmatics, using family and case-referent panels. Here we show multiple markers on chromosome 17q21 to be strongly and reproducibly associated with childhood onset asthma in family and case-referent panels with a combined P  value of P  < 10 -12 . In independent replication studies the 17q21 locus showed strong association with diagnosis of childhood asthma in 2,320 subjects from a cohort of German children ( P = 0.0003) and in 3,301 subjects from the British 1958 Birth Cohort ( P = 0.0005). We systematically evaluated the relationships between markers of the 17q21 locus and transcript levels of genes in Epstein–Barr virus (EBV)-transformed lymphoblastoid cell lines from children in the asthma family panel used in our association study. The SNPs associated with childhood asthma were consistently and strongly associated ( P  < 10 -22 ) in cis with transcript levels of ORMDL3 , a member of a gene family that encodes transmembrane proteins anchored in the endoplasmic reticulum 3 . The results indicate that genetic variants regulating ORMDL3 expression are determinants of susceptibility to childhood asthma.

F cells measure the presence of fetal hemoglobin, a heritable quantitative trait in adults that accounts for substantial phenotypic diversity of sickle cell disease and β thalassemia. We applied a genome-wide association mapping strategy to individuals with contrasting extreme trait values and mapped a new F cell quantitative trait locus to BCL11A , which encodes a zinc-finger protein, on chromosome 2p15. The 2p15 BCL11A quantitative trait locus accounts for 15.1% of the trait variance.

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-9)). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-54), respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies, assembled through collaborative networks of researchers. Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype, ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis. Heterogeneity tests based on individual genetic variants (e.g. Q, I2) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity. We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants, to reveal systematic patterns of heterogeneity that elude conventional single variant analysis. Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity, which could be partly explained by age-of-disease onset, family-history of disease and ancestry. Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations.

Many aspects of physiology and behavior follow a circadian rhythm. Brain and muscle Arnt-like protein-1 (BMAL1) is a key component of the mammalian molecular clock, which controls circadian oscillations. In the rat, the gene encoding Bmal1 is located within hypertension susceptibility loci. We analyzed the SNP distribution pattern in a congenic interval associated with hypertension in the spontaneously hypertensive rat (SHR), and we show that Bmal1 maps close to a region genetically divergent between SHR and its normotensive (Wistar-Kyoto) counterpart. Bmal1 sequencing in rat strains identified 19 polymorphisms, including an SHR promoter variant that significantly affects Gata-4 activation of transcription in transient transfection experiments. A genetic association study designed to test the relevance of these findings in 1,304 individuals from 424 families primarily selected for type 2 diabetes showed that two BMAL1 haplotypes are associated with type 2 diabetes and hypertension. This comparative genetics finding translated from mouse and rat models to human provides evidence of a causative role of Bmal1 variants in pathological components of the metabolic syndrome.