Dataset Information

A human genome-wide library of local phylogeny predictions for whole-genome inference problems.

ABSTRACT: BACKGROUND: Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences. Detailed predictions of the full phylogenies are therefore of value in improving our ability to make further inferences about population history and sources of genetic variation. Making phylogenetic predictions on the scale needed for whole-genome analysis is, however, extremely computationally demanding. RESULTS: In order to facilitate phylogeny-based predictions on a genomic scale, we develop a library of maximum parsimony phylogenies within local regions spanning all autosomal human chromosomes based on Haplotype Map variation data. We demonstrate the utility of this library for population genetic inferences by examining a tree statistic we call 'imperfection,' which measures the reuse of variant sites within a phylogeny. This statistic is significantly predictive of recombination rate, shows additional regional and population-specific conservation, and allows us to identify outlier genes likely to have experienced unusual amounts of variation in recent human history. CONCLUSION: Recent theoretical advances in algorithms for phylogenetic tree reconstruction have made it possible to perform large-scale inferences of local maximum parsimony phylogenies from single nucleotide polymorphism (SNP) data. As results from the imperfection statistic demonstrate, phylogeny predictions encode substantial information useful for detecting genomic features and population history. This data set should serve as a platform for many kinds of inferences one may wish to make about human population history and genetic variation.

SUBMITTER: Sridhar S

PROVIDER: S-EPMC2556685 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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A human genome-wide library of local phylogeny predictions for whole-genome inference problems.

Sridhar Srinath S Schwartz Russell R

BMC genomics 20080818

<h4>Background</h4>Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences. Detailed predictions of the full phylogenies are therefore of value in improving our ability to make further inferences about population history and sources of genetic variation. Making phylogenetic predictions on the scale needed for whole-genome analysis is, however, extremely computationally demanding.<h4>Re ...[more]

PMID: 18710563

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Project description:Sex-biased demographic events ("sex-bias") involve unequal numbers of females and males. These events are typically inferred from the relative amount of X-chromosomal to autosomal genetic variation and have led to conflicting conclusions about human demographic history. Though population size changes alter the relative amount of X-chromosomal to autosomal genetic diversity even in the absence of sex-bias, this has generally not been accounted for in sex-bias estimators to date. Here, we present a novel method to identify sex-bias from genetic sequence data that models population size changes and estimates the female fraction of the effective population size during each time epoch. Compared to recent sex-bias inference methods, our approach can detect sex-bias that changes on a single population branch without requiring data from an outgroup or knowledge of divergence events. When applied to simulated data, conventional sex-bias estimators are biased by population size changes, especially recent growth or bottlenecks, while our estimator is unbiased. We next apply our method to high-coverage exome data from the 1000 Genomes Project and estimate a male bias in Yorubans (47% female) and Europeans (44%), possibly due to stronger background selection on the X chromosome than on the autosomes. Finally, we apply our method to the 1000 Genomes Project Phase 3 high-coverage Complete Genomics whole-genome data and estimate a female bias in Yorubans (63% female), Europeans (84%), Punjabis (82%), as well as Peruvians (56%), and a male bias in the Southern Han Chinese (45%). Our method additionally identifies a male-biased migration out of Africa based on data from Europeans (20% female). Our results demonstrate that modeling population size change is necessary to estimate sex-bias parameters accurately. Our approach gives insight into signatures of sex-bias in sexual species, and the demographic models it produces can serve as more accurate null models for tests of selection.

Project description:IntroductionThe outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a global pandemic resulting in over 1 million deaths worldwide. In the Department of Defense (DoD), over 129,000 personnel (civilians, dependents, and active duty) have been infected with the virus to date. Rapid estimations of transmission and mutational patterns of virus outbreaks can be accomplished using whole-genome viral sequencing. Deriving interpretable and actionable results from pathogen sequence data is accomplished by the construction of phylogenetic trees (from local and global virus sequences) and by the creation of protein maps, to visualize and predict the effects of structural protein amino acid mutations.Materials and methodsWe developed a sequencing and bioinformatics workflow for molecular epidemiological SARS-CoV-2 surveillance using excess clinical specimens collected under an institutional review board exempt protocol at Joint Base San Antonio, Lackland AFB. This workflow includes viral RNA isolation, viral load quantification, tiling-based next-generation sequencing, sequencing and bioinformatics analysis, and data visualization via phylogenetic trees and protein mapping.ResultsSequencing of 37 clinical specimens collected at JBSA/Lackland revealed that by June 2020, SAR-CoV-2 strains carrying the 614G mutation were the predominant cause of local coronavirus disease 2019 infections. We identified 109 nucleotide changes in the coding region of the SARS-CoV-2 genome (which lead to 63 unique, non-synonymous amino acid mutations), one mutation in the 5'-untranslated region (UTR), and two mutations in the 3'UTR. Furthermore, we identified and mapped six additional spike protein amino acid changes-information which could potentially aid vaccine design.ConclusionThe workflow presented here is designed to enable DoD public health officials to track viral evolution and conduct near real-time evaluation of future outbreaks. The generation of molecular epidemiological sequence data is critical for the development of disease intervention strategies-most notably, vaccine design. Overall, we present a streamlined sequencing and bioinformatics methodology aimed at improving long-term readiness efforts in the DoD.

Dataset Information

A human genome-wide library of local phylogeny predictions for whole-genome inference problems.

Publications

A human genome-wide library of local phylogeny predictions for whole-genome inference problems.

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