Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course

Identification of heart rate–associated loci and their effects on cardiac conduction and rhythm disorders A full list of authors and affiliations appears at the end of the article. Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate–increasing and heart rate– decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.

doi:10.1093/biostatistics/kxq039

Biostatistics (2010), 0, 0, pp. 1–17 doi:10.1093/biostatistics/kxq039 Methods for testing association between uncertain genotypes and quantitative traits ZOLT ´AN KUTALIK∗

Whoever wishes to investigate medicine properly should proceed thus: in the first place to consider the seasons of the year. ~ Hippocrates, the father of medicine (circa 400 B.C.).

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Gout is a debilitating arthritis caused by elevated levels of serum urate in the blood (hyperuricemia). The prevalence of gout is on the rise worldwide with particularly high rates of the disease present in Māori and Pacific populations in New Zealand (9.3-13.9% of Māori men and 14.9 % of Pacific Island men affected). Genetic and environmental factors contribute to hyperuricemia and gout. The high rates of gout in some ethnic groups is thought to reflect genetic differences in serum urate regulation. Variation in the renal urate transporter gene, SLC2A9, has been associated with hyperuricemia and gout and recently with type 2 diabetes mellitus. A number of single nucleotide polymorphisms (SNPs) in SLC2A9 have been associated with hyperuricemia and gout, with rs11942223 found to be strongly associated across diverse ethnic populations. Previous studies have also found evidence for gene-environment interaction between SLC2A9 and sugar-sweetened beverage consumption contributing to gout. The aims of this study were to further characterise and identify causal variants within SLC2A9 and the underlying etiologic mechanisms such as gene-environment interaction using a case-control approach. Conditional analyses were carried out to test for association of SLC2A9 variants independent from the effect of rs11942223. It was also hypothesised that SNP-SNP interaction between non-synonymous SLC2A9 SNPs may be playing a role in gout. Analysis of Caucasian and Polynesian populations was performed to investigate potential molecular differences in gout pathology between ethnic groups.

Background: Microarray measurements are susceptible to a variety of experimental artifacts, some of which give rise to systematic biases that are spatially dependent in a unique way on each chip. It is likely that such artifacts affect many SNP arrays, but the normalization methods used in currently available genotyping algorithms make no attempt at spatial bias correction. Here, we propose an effective single-chip spatial bias removal procedure for Affymetrix 6.0 SNP arrays or platforms with similar design features. This procedure deals with both extreme and subtle biases and is intended to be applied before standard genotype calling algorithms. Results: Application of the spatial bias adjustments on HapMap samples resulted in higher genotype call rates with equal or even better accuracy for thousands of SNPs. Consequently the normalization procedure is expected to lead to more meaningful biological inferences and could be valuable for genome-wide SNP analysis. Conclusions: Spatial normalization can potentially rescue thousands of SNPs in a genetic study at the small cost of computational time. The approach is implemented in R and available from the authors upon request. Background Single nucleotide polymorphism (SNP) genotyping arrays of continually increasing resolution allow unprecedented

Analysis of recently identified dyslipidemia alleles reveals two loci that contribute to risk for carotid artery disease

A current approach to mapping complex-disease-susceptibility loci in genome-wide association (GWA) studies involves leveraging the information in a reference database of dense genotype data. By modeling the patterns of linkage disequilibrium in a reference panel, genotypes not directly measured in the study samples can be imputed and tested for disease association. This imputation strategy has been successful for GWA studies in populations well represented by existing reference panels. We used genotypes at 513,008 autosomal single-nucleotide polymorphism (SNP) loci in 443 unrelated individuals from 29 worldwide populations to evaluate the ''portability'' of the HapMap reference panels for imputation in studies of diverse populations. When a single HapMap panel was leveraged for imputation of randomly masked genotypes, European populations had the highest imputation accuracy, followed by populations from East Asia, Central and South Asia, the Americas, Oceania, the Middle East, and Africa. For each population, we identified ''optimal'' mixtures of reference panels that maximized imputation accuracy, and we found that in most populations, mixtures including individuals from at least two HapMap panels produced the highest imputation accuracy. From a separate survey of additional SNPs typed in the same samples, we evaluated imputation accuracy in the scenario in which all genotypes at a given SNP position were unobserved and were imputed on the basis of data from a commercial ''SNP chip,'' again finding that most populations benefited from the use of combinations of two or more HapMap reference panels. Our results can serve as a guide for selecting appropriate reference panels for imputation-based GWA analysis in diverse populations.