A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29–39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance

Lipoprotein receptor-related protein 1 variants and dietary fatty acids: meta-analysis of European origin and African American studies

OBJECTIVE: Low-density lipoprotein-related receptor protein 1 (LRP1) is a multi-functional endocytic receptor and signaling molecule that is expressed in adipose and the hypothalamus. Evidence for a role of LRP1 in adiposity is accumulating from animal and in vitro models, but data from human studies are limited. The study objectives were to evaluate (i) relationships between LRP1 genotype and anthropometric traits, and (ii) whether these relationships were modified by dietary fatty acids. DESIGN AND METHODS: We conducted race/ethnic-specific meta-analyses using data from 14 studies of US and European whites and 4 of African Americans to evaluate associations of dietary fatty acids and LRP1 genotypes with body mass index (BMI), waist circumference and hip circumference, as well as interactions between dietary fatty acids and LRP1 genotypes. Seven single-nucleotide polymorphisms (SNPs) of LRP1 were evaluated in whites (N up to 42 000) and twelve SNPs in African Americans (N up to 5 RESULTS: After adjustment for age, sex and population substructure if relevant, for each one unit greater intake of percentage of energy from saturated fat (SFA), BMI was 0.104 kg m(-2) greater, waist was 0.305 cm larger and hip was 0.168 cm larger (all P<0.0001). Other fatty acids were not associated with outcomes. The association of SFA with outcomes varied by genotype at rs2306692 (genotyped in four studies of whites), where the magnitude of the association of SFA intake with each outcome was CONCLUSION: Dietary SFA and LRP1 genotype may interactively influence anthropometric traits. Further exploration of this, and other diet x genotype interactions, may improve understanding of interindividual variability in the relationships of dietary factors with anthropometric traits

Treating inflammation by blocking interleukin-1 in a broad spectrum of diseases.

Item does not contain fulltextInterleukin-1 (IL-1) is a highly active pro-inflammatory cytokine that lowers pain thresholds and damages tissues. Monotherapy blocking IL-1 activity in autoinflammatory syndromes results in a rapid and sustained reduction in disease severity, including reversal of inflammation-mediated loss of sight, hearing and organ function. This approach can therefore be effective in treating common conditions such as post-infarction heart failure, and trials targeting a broad spectrum of new indications are underway. So far, three IL-1-targeted agents have been approved: the IL-1 receptor antagonist anakinra, the soluble decoy receptor rilonacept and the neutralizing monoclonal anti-IL-1beta antibody canakinumab. In addition, a monoclonal antibody directed against the IL-1 receptor and a neutralizing anti-IL-1alpha antibody are in clinical trials

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology