• Publications
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Inference of population structure using multilocus genotype data.
TLDR
A model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations that can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
A new statistical method for haplotype reconstruction from population data.
TLDR
A new statistical method is presented, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms and performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.
A comparison of bayesian methods for haplotype reconstruction from population genotype data.
TLDR
A new algorithm is introduced that combines the modeling strategy of one method with the computational strategies of another and outperforms all three existing methods for inferring haplotypes from genotype data in a population sample.
A global reference for human genetic variation
  • Adam Gonçalo R. David M. Richard M. Gonçalo R. David R. Auton Abecasis Altshuler Durbin Abecasis Bentley C, A. Auton, Shane A. McCarthy
  • Biology
    Nature
  • 30 September 2015
TLDR
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies
TLDR
It is found that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method.
Association mapping in structured populations.
TLDR
This article describes a novel, statistically valid, method for case-control association studies in structured populations that uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations.
A new multipoint method for genome-wide association studies by imputation of genotypes
TLDR
This work proposes a coherent analysis framework that treats the genome-wide association problem as one involving missing or uncertain genotypes, and proposes a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping.
The UK Biobank resource with deep phenotyping and genomic data
TLDR
Deep phenotype and genome-wide genetic data from 500,000 individuals from the UK Biobank is described, describing population structure and relatedness in the cohort, and imputation to increase the number of testable variants to 96 million.
A second generation human haplotype map of over 3.1 million SNPs
TLDR
The Phase II HapMap is described, which characterizes over 3.1 million human single nucleotide polymorphisms genotyped in 270 individuals from four geographically diverse populations and includes 25–35% of common SNP variation in the populations surveyed, and increased differentiation at non-synonymous, compared to synonymous, SNPs is demonstrated.
Transcriptome and genome sequencing uncovers functional variation in humans
TLDR
Se sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project—the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences discover extremely widespread genetic variation affecting the regulation of most genes.
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