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Dsuite - Fast D-statistics and related admixture evidence from VCF files.

ABSTRACT: Patterson's D, also known as the ABBA-BABA statistic, and related statistics such as the f₄ -ratio, are commonly used to assess evidence of gene flow between populations or closely related species. Currently available implementations often require custom file formats, implement only small subsets of the available statistics, and are impractical to evaluate all gene flow hypotheses across data sets with many populations or species due to computational inefficiencies. Here, we present a new software package Dsuite, an efficient implementation allowing genome scale calculations of the D and f₄ -ratio statistics across all combinations of tens or hundreds of populations or species directly from a variant call format (VCF) file. Our program also implements statistics suited for application to genomic windows, providing evidence of whether introgression is confined to specific loci, and it can also aid in interpretation of a system of f₄ -ratio results with the use of the "f-branch" method. Dsuite is available at https://github.com/millanek/Dsuite, is straightforward to use, substantially more computationally efficient than comparable programs, and provides a convenient suite of tools and statistics, including some not previously available in any software package. Thus, Dsuite facilitates the assessment of evidence for gene flow, especially across larger genomic data sets.

SUBMITTER: Malinsky M

PROVIDER: S-EPMC7116594 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In this opinion article, we discuss the formatting of files from (plant) genotyping studies, in particular the formatting of metadata in Variant Call Format (VCF) files. The flexibility of the VCF format specification facilitates its use as a generic interchange format across domains but can lead to inconsistency between files in the presentation of metadata. To enable fully autonomous machine actionable data flow, generic elements need to be further specified. We strongly support the merits of the FAIR principles and see the need to facilitate them also through technical implementation specifications. They form a basis for the proposed VCF extensions here. We have learned from the existing application of VCF that the definition of relevant metadata using controlled standards, vocabulary and the consistent use of cross-references via resolvable identifiers (machine-readable) are particularly necessary and propose their encoding. VCF is an established standard for the exchange and publication of genotyping data. Other data formats are also used to capture variant data (for example, the HapMap and the gVCF formats), but none currently have the reach of VCF. For the sake of simplicity, we will only discuss VCF and our recommendations for its use, but these recommendations could also be applied to gVCF. However, the part of the VCF standard relating to metadata (as opposed to the actual variant calls) defines a syntactic format but no vocabulary, unique identifier or recommended content. In practice, often only sparse descriptive metadata is included. When descriptive metadata is provided, proprietary metadata fields are frequently added that have not been agreed upon within the community which may limit long-term and comprehensive interoperability. To address this, we propose recommendations for supplying and encoding metadata, focusing on use cases from plant sciences. We expect there to be overlap, but also divergence, with the needs of other domains.

Project description:Imputation using external reference panels (e.g. 1000 Genomes) is a widely used approach for increasing power in genome-wide association studies and meta-analysis. Existing hidden Markov models (HMM)-based imputation approaches require individual-level genotypes. Here, we develop a new method for Gaussian imputation from summary association statistics, a type of data that is becoming widely available.In simulations using 1000 Genomes (1000G) data, this method recovers 84% (54%) of the effective sample size for common (>5%) and low-frequency (1-5%) variants [increasing to 87% (60%) when summary linkage disequilibrium information is available from target samples] versus the gold standard of 89% (67%) for HMM-based imputation, which cannot be applied to summary statistics. Our approach accounts for the limited sample size of the reference panel, a crucial step to eliminate false-positive associations, and it is computationally very fast. As an empirical demonstration, we apply our method to seven case-control phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) data and a study of height in the British 1958 birth cohort (1958BC). Gaussian imputation from summary statistics recovers 95% (105%) of the effective sample size (as quantified by the ratio of [Formula: see text] association statistics) compared with HMM-based imputation from individual-level genotypes at the 227 (176) published single nucleotide polymorphisms (SNPs) in the WTCCC (1958BC height) data. In addition, for publicly available summary statistics from large meta-analyses of four lipid traits, we publicly release imputed summary statistics at 1000G SNPs, which could not have been obtained using previously published methods, and demonstrate their accuracy by masking subsets of the data. We show that 1000G imputation using our approach increases the magnitude and statistical evidence of enrichment at genic versus non-genic loci for these traits, as compared with an analysis without 1000G imputation. Thus, imputation of summary statistics will be a valuable tool in future functional enrichment analyses.Publicly available software package available at http://bogdan.bioinformatics.ucla.edu/software/.bpasaniuc@mednet.ucla.edu or aprice@hsph.harvard.eduSupplementary materials are available at Bioinformatics online.

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Dsuite - Fast D-statistics and related admixture evidence from VCF files.

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