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Open AccessJournal ArticleDOI

A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness

TLDR
It is found that SHAPEIT2 produces much lower switch error rates in all cohorts compared to other methods, including those designed specifically for isolated populations, and a general strategy for phasing cohorts with any level of implicit or explicit relatedness between individuals is developed.
Abstract
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.

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Journal ArticleDOI

A global reference for human genetic variation.

Adam Auton, +517 more
- 01 Oct 2015 - 
TL;DR: 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.
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Genetic effects on gene expression across human tissues.

TL;DR: It is found that local genetic variation affects gene expression levels for the majority of genes, and inter-chromosomal genetic effects for 93 genes and 112 loci are identified, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
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Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

TL;DR: It is demonstrated that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable.
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Association analysis identifies 65 new breast cancer risk loci

Kyriaki Michailidou, +396 more
- 02 Nov 2017 - 
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
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Gene expression elucidates functional impact of polygenic risk for schizophrenia

TL;DR: It is shown that schizophrenia is polygenic and the utility of this resource of gene expression and its genetic regulation for mechanistic interpretations of genetic liability for brain diseases is highlighted.
References
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Journal ArticleDOI

A tutorial on hidden Markov models and selected applications in speech recognition

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI

A new statistical method for haplotype reconstruction from population data.

TL;DR: 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.
Journal ArticleDOI

A haplotype map of the human genome

John W. Belmont, +232 more
TL;DR: A public database of common variation in the human genome: more than one million single nucleotide polymorphisms for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted.
Journal ArticleDOI

A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.

TL;DR: 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.
Journal ArticleDOI

A Comparison of Bayesian Methods for Haplotype Reconstruction from Population Genotype Data

TL;DR: 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.
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