A reference panel of 64,976 haplotypes for genotype imputation. - McCarthy, Shane; Das, Sayantan; Kretzschmar, Warren; Delaneau, Olivier; Wood, Andrew R; Teumer, Alexander; Kang, Hyun Min; Fuchsberger, Christian; Danecek, Petr; Sharp, Kevin; Luo, Yang; Sidore, Carlo; et al. (Nat Genet, 2016)
We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Genetic studies of body mass index yield new insights for obesity biology. - Locke, Adam E; Kahali, Bratati; Berndt, Sonja I; Justice, Anne E; Pers, Tune H; Day, Felix R; Powell, Corey; Vedantam, Sailaja; Buchkovich, Martin L; Yang, Jian; Croteau-Chonka, Damien C; Esko, Tõnu; et al. (Nature, 2015)
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
Keywords: Adipogenesis; Adiposity; Age Factors; Body Mass Index; Continental Population Groups; Energy Metabolism; Europe; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Glutamic Acid; Humans; Insulin; Male; Obesity; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Synapses

A general approach for haplotype phasing across the full spectrum of relatedness. - O'Connell, Jared; Gurdasani, Deepti; Delaneau, Olivier; Pirastu, Nicola; Ulivi, Sheila; Cocca, Massimiliano; Traglia, Michela; Huang, Jie; Huffman, Jennifer E; Rudan, Igor; McQuillan, Ruth; Fraser, Ross M; et al. (PLoS Genet, 2014)
Many existing cohorts contain a range of relatedness between genotyped individuals, either by design or by chance. Haplotype estimation in such cohorts is a central step in many downstream analyses. Using genotypes from six cohorts from isolated populations and two cohorts from non-isolated populations, we have investigated the performance of different phasing methods designed for nominally 'unrelated' individuals. We find that SHAPEIT2 produces much lower switch error rates in all cohorts compared to other methods, including those designed specifically for isolated populations. In particular, when large amounts of IBD sharing is present, SHAPEIT2 infers close to perfect haplotypes. Based on these results we have developed a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals. First SHAPEIT2 is run ignoring all explicit family information. We then apply a novel HMM method (duoHMM) to combine the SHAPEIT2 haplotypes with any family information to infer the inheritance pattern of each meiosis at all sites across each chromosome. This allows the correction of switch errors, detection of recombination events and genotyping errors. We show that the method detects numbers of recombination events that align very well with expectations based on genetic maps, and that it infers far fewer spurious recombination events than Merlin. The method can also detect genotyping errors and infer recombination events in otherwise uninformative families, such as trios and duos. The detected recombination events can be used in association scans for recombination phenotypes. The method provides a simple and unified approach to haplotype estimation, that will be of interest to researchers in the fields of human, animal and plant genetics.
Keywords: Chromosome Mapping; Cohort Effect; Family; Genotype; Haplotypes; Humans; Models, Genetic; Pedigree; Phenotype; Recombination, Genetic