Simultaneous Genotype Calling and Haplotype Phasing Improves Genotype Accuracy and Reduces False-Positive Associations for Genome-wide Association Studies

Browning, Brian L.; Yu, Zhaoxia

doi:10.1016/j.ajhg.2009.11.004

Cited by 197 publications

(182 citation statements)

References 37 publications

(60 reference statements)

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“…As expected, we have found that excluding sites with low estimated r 2 results in higher quality sets of SNP calls (Browning and Yu 2009;Li et al 2010b). For example, in the original calls for the 1000 Genomes CEU sample, focusing on sites with r 2 greater than 0.0, 0.3, and 0.5 increases the ratio of transition to transversion SNPs from 1.86 to 1.88 and then to 1.97, excluding 0%, 5%, and 17% of the initial set of SNPs.…”

Section: Design Of Sequence-based Association Studiessupporting

confidence: 53%

“…Using simulations, we evaluate the trade-offs involved in deep sequencing of a few individuals and shallower sequencing of larger numbers of individuals. We also discuss several other existing methods that can perform genotype calling from low-coverage sequence data (Browning and Yu 2009;Le and Durbin 2010;McKenna et al 2010). More importantly, we systematically compare study designs based on genotyping of tagSNPs, sequencing of many individuals at depths ranging between 23 and 303 and imputation of variants discovered by sequencing in a subset of individuals into the remainder of the sample.…”

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confidence: 99%

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Low-coverage sequencing: Implications for design of complex trait association studies

Sidore

Kang³

et al. 2011

Genome Res.

278

279

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New sequencing technologies allow genomic variation to be surveyed in much greater detail than previously possible. While detailed analysis of a single individual typically requires deep sequencing, when many individuals are sequenced it is possible to combine shallow sequence data across individuals to generate accurate calls in shared stretches of chromosome. Here, we show that, as progressively larger numbers of individuals are sequenced, increasingly accurate genotype calls can be generated for a given sequence depth. We evaluate the implications of low-coverage sequencing for complex trait association studies. We systematically compare study designs based on genotyping of tagSNPs, sequencing of many individuals at depths ranging between 23 and 303, and imputation of variants discovered by sequencing a subset of individuals into the remainder of the sample. We show that sequencing many individuals at low depth is an attractive strategy for studies of complex trait genetics. For example, for disease-associated variants with frequency >0.2%, sequencing 3000 individuals at 43 depth provides similar power to deep sequencing of >2000 individuals at 303 depth but requires only~20% of the sequencing effort. We also show low-coverage sequencing can be used to build a reference panel that can drive imputation into additional samples to increase power further. We provide guidance for investigators wishing to combine results from sequenced, genotyped, and imputed samples.[Supplemental material is available for this article. Software implementing the methods is available at http://genome. sph.umich.edu/wiki/Thunder.]Genomewide association studies (GWAS), which examine hundreds of thousands of common genetic variants in thousands of individuals, have resulted in the association of >1000 genetic loci with specific traits and diseases ; www.genome .gov/gwastudies/). In the next few years, improved genotyping chip designs and next generation sequencing technologies will allow these studies to extend beyond common variants and systematically evaluate rarer variants, insertion deletion polymorphisms, and larger copy number variants-potentially expanding our understanding of complex trait architecture (Maher 2008;Manolio et al. 2009).Emerging (Ng et al. 2009;Lupski et al. 2010;Ng et al. 2010;Nikopoulos et al. 2010;Roach et al. 2010), their application to complex trait studies-which may require sequencing hundreds or thousands of individuals-remains challenging due to high sequencing costs and limits of existing sequencing capacity.We have previously outlined a Hidden Markov Model (HMM)-based approach for the analysis of shotgun sequence data across many individuals (Li et al. 2010b). The approach identifies stretches of chromosome shared among individuals and uses the information to call genotypes from low-coverage sequence data more effectively. The underlying principle is that pairs of chromosomes which share a series of alleles flanking a site of interest are likely to also exhibit identical alleles at that site. In this p...

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Section: Design Of Sequence-based Association Studiessupporting

confidence: 53%

mentioning

confidence: 99%

Low-coverage sequencing: Implications for design of complex trait association studies

Sidore

Kang³

et al. 2011

Genome Res.

278

279

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“…For example, the LD model can be incorporated into simulation methods used to generate empirical p-values in, for instance, methods to detect identity by descent from dense SNP data (Thomas, 2007;Thomas et al, 2008). They can also be used for imputation across SNP genotyping platforms, similar to the BeagleCall software (Browning and Yu, 2009). A final advantage of our general graphical modeling approach is its straightforward extension to include discrete covariates, such as an individual's population of origin, and outcome variables, facilitating, for instance, the detection of phenotype-haplotype associations, while controlling for population stratification.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and Computational Efficiency of a Graphical Modeling Approach to Linkage Disequilibrium Estimation

Abel¹,

Thomas²

2011

Statistical Applications in Genetics and Molecular Biology

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We develop recent work on using graphical models for linkage disequilibrium to provide efficient programs for model fitting, phasing, and imputation of missing data in large data sets. Two important features contribute to the computational efficiency: the separation of the model fitting and phasing-imputation processes into different programs, and holding in memory only the data within a moving window of loci during model fitting. Optimal parameter values were chosen by cross-validation to maximize the probability of correctly imputing masked genotypes. The best accuracy obtained is slightly below than that from the Beagle program of Browning and Browning, and our fitting program is slower. However, for large data sets, it uses less storage. For a reference set of n individuals genotyped at m markers, the time and storage required for fitting a graphical model are approximately O(nm) and O(n+m), respectively. To impute the phases and missing data on n individuals using an already fitted graphical model requires O(nm) time and O(m) storage. While the times for fitting and imputation are both O(nm), the imputation process is considerably faster; thus, once a model is estimated from a reference data set, the marginal cost of phasing and imputing further samples is very low.

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“…GotCloud uses two tools for genotype refinement: Beagle (Browning and Yu 2009) and ThunderVCF . Beagle is computationally efficient, but the resulting haplotypes can be made more accurate by additionally running ThunderVCF, which is based on a model used by MaCH (Li et al 2010).…”

Section: Haplotype-aware Genotype Refinementmentioning

confidence: 99%

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data

et al. 2015

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The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive volume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high-quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype information. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies.

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Simultaneous Genotype Calling and Haplotype Phasing Improves Genotype Accuracy and Reduces False-Positive Associations for Genome-wide Association Studies

Cited by 197 publications

References 37 publications

Low-coverage sequencing: Implications for design of complex trait association studies

Low-coverage sequencing: Implications for design of complex trait association studies

Accuracy and Computational Efficiency of a Graphical Modeling Approach to Linkage Disequilibrium Estimation

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data

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