Ancestry inference using reference labeled clusters of haplotypes

Abstract Background We present ARCHes, a fast and accurate haplotype-based approach for inferring an individual’s ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. Results The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and reused to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations using 10 CPU). We test ARCHes on public data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP) as well as simulated examples of known admixture. Conclusions Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at finer population scales regardless of the amount of population admixture.

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Ancestry Inference Using Reference Labeled Clusters of Haplotypes

10.1101/2020.09.23.310698 ◽

2020 ◽

Author(s):

Keith Noto ◽

Yong Wang ◽

Shiya Song ◽

Joshua G. Schraiber ◽

Alisa Sedghifar ◽

...

Keyword(s):

Haplotype Diversity ◽

Reference Panel ◽

Population Admixture ◽

Running Time ◽

Local Ancestry ◽

Public Data ◽

Population Information ◽

Test Sets ◽

Ancestry Inference ◽

Global And Local

AbstractWe present ARCHes, a fast and accurate haplotype-based approach for inferring an individual’s ancestry composition. Our approach works by modeling haplotype diversity from a large, admixed cohort of hundreds of thousands, then annotating those models with population information from reference panels of known ancestry. The running time of ARCHes does not depend on the size of a reference panel because training and testing are separate processes, and the inferred population-annotated haplotype models can be written to disk and used to label large test sets in parallel (in our experiments, it averages less than one minute to assign ancestry from 32 populations to 1,001 sections of a genotype using 10 CPU). We test ARCHes on public data from the 1,000 Genomes Project and HGDP as well as simulated examples of known admixture. Our results demonstrate that ARCHes outperforms RFMix at correctly assigning both global and local ancestry at regional levels regardless of the amount of population admixture.

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Estimating the timing of multiple admixture events using 3-locus Linkage Disequilibrium

10.1101/078378 ◽

2016 ◽

Cited By ~ 4

Author(s):

Mason Liang ◽

Rasmus Nielsen

Keyword(s):

Linkage Disequilibrium ◽

Spatial Information ◽

1000 Genomes Project ◽

Local Ancestry ◽

1000 Genomes ◽

Locus System ◽

Event Based ◽

Admixture Linkage Disequilibrium ◽

Analytical Expressions ◽

Admixture Event

AbstractEstimating admixture histories is crucial for understanding the genetic diversity we see in present-day populations. Existing allele frequency or phylogeny-based methods are excellent for inferring the existence of admixture or its proportions, but have less power for estimating admixture times. Recently introduced approaches for estimating these times use spatial information from admixed chromosomes, such as the local ancestry or the decay of admixture linkage disequilibrium (ALD). One popular method, implemented in the programs ALDER and ROLLOFF, uses two-locus ALD to infer the time of a single admixture event, but is only able to estimate the time of the most recent admixture event based on this summary statistic. We derive analytical expressions for the expected ALD in a three-locus system and provide a new statistical method based on these results that is able to resolve more complicated admixture histories. Using simulations, we show how this new statistic behaves on a range of admixture histories. As an example, we also apply our method to the Colombian and Mexican samples from the 1000 Genomes project.

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The Simons Genome Diversity Project: A Global Analysis of Mobile Element Diversity

Genome Biology and Evolution ◽

10.1093/gbe/evaa086 ◽

2020 ◽

Vol 12 (6) ◽

pp. 779-794 ◽

Cited By ~ 1

Author(s):

W Scott Watkins ◽

Julie E Feusier ◽

Jainy Thomas ◽

Clement Goubert ◽

Swapon Mallick ◽

...

Keyword(s):

Native American ◽

Global Analysis ◽

General Pattern ◽

Mobile Element ◽

Human Populations ◽

Single Individual ◽

Genome Diversity ◽

1000 Genomes Project ◽

Alu Elements ◽

1000 Genomes

Abstract Ongoing retrotransposition of Alu, LINE-1, and SINE–VNTR–Alu elements generates diversity and variation among human populations. Previous analyses investigating the population genetics of mobile element insertions (MEIs) have been limited by population ascertainment bias or by relatively small numbers of populations and low sequencing coverage. Here, we use 296 individuals representing 142 global populations from the Simons Genome Diversity Project (SGDP) to discover and characterize MEI diversity from deeply sequenced whole-genome data. We report 5,742 MEIs not originally reported by the 1000 Genomes Project and show that high sampling diversity leads to a 4- to 7-fold increase in MEI discovery rates over the original 1000 Genomes Project data. As a result of negative selection, nonreference polymorphic MEIs are underrepresented within genes, and MEIs within genes are often found in the transcriptional orientation opposite that of the gene. Globally, 80% of Alu subfamilies predate the expansion of modern humans from Africa. Polymorphic MEIs show heterozygosity gradients that decrease from Africa to Eurasia to the Americas, and the number of MEIs found uniquely in a single individual are also distributed in this general pattern. The maximum fraction of MEI diversity partitioned among the seven major SGDP population groups (FST) is 7.4%, similar to, but slightly lower than, previous estimates and likely attributable to the diverse sampling strategy of the SGDP. Finally, we utilize these MEIs to extrapolate the primary Native American shared ancestry component to back to Asia and provide new evidence from genome-wide identical-by-descent genetic markers that add additional support for a southeastern Siberian origin for most Native Americans.

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Improving variant calling using population data and deep learning

10.1101/2021.01.06.425550 ◽

2021 ◽

Author(s):

Nae-Chyun Chen ◽

Alexey Kolesnikov ◽

Sidharth Goel ◽

Taedong Yun ◽

Pi-Chuan Chang ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Variant Calling ◽

Population Data ◽

Training Data ◽

1000 Genomes Project ◽

1000 Genomes ◽

Population Information ◽

Scale Population ◽

The Impact

Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we modify DeepVariant to add a new channel encoding population allele frequencies from the 1000 Genomes Project. We show that this model reduces variant calling errors, improving both precision and recall. We assess the impact of using population-specific or diverse reference panels. We achieve the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

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The analysis of APOL1 genetic variation and haplotype diversity provided by 1000 Genomes project

BMC Nephrology ◽

10.1186/s12882-017-0675-6 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 2

Author(s):

Ting Peng ◽

Li Wang ◽

Guisen Li

Keyword(s):

Genetic Variation ◽

Haplotype Diversity ◽

1000 Genomes Project ◽

1000 Genomes

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Discovery of unfixed endogenous retrovirus insertions in diverse human populations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1602336113 ◽

2016 ◽

Vol 113 (16) ◽

pp. E2326-E2334 ◽

Cited By ~ 123

Author(s):

Julia Halo Wildschutte ◽

Zachary H. Williams ◽

Meagan Montesion ◽

Ravi P. Subramanian ◽

Jeffrey M. Kidd ◽

...

Keyword(s):

Human Genome ◽

Endogenous Retrovirus ◽

Endogenous Retroviruses ◽

Human Populations ◽

Genome Diversity ◽

1000 Genomes Project ◽

1000 Genomes ◽

Targeted Screening ◽

Report Analysis ◽

Reading Frames

Endogenous retroviruses (ERVs) have contributed to more than 8% of the human genome. The majority of these elements lack function due to accumulated mutations or internal recombination resulting in a solitary (solo) LTR, although members of one group of human ERVs (HERVs), HERV-K, were recently active with members that remain nearly intact, a subset of which is present as insertionally polymorphic loci that include approximately full-length (2-LTR) and solo-LTR alleles in addition to the unoccupied site. Several 2-LTR insertions have intact reading frames in some or all genes that are expressed as functional proteins. These properties reflect the activity of HERV-K and suggest the existence of additional unique loci within humans. We sought to determine the extent to which other polymorphic insertions are present in humans, using sequenced genomes from the 1000 Genomes Project and a subset of the Human Genome Diversity Project panel. We report analysis of a total of 36 nonreference polymorphic HERV-K proviruses, including 19 newly reported loci, with insertion frequencies ranging from <0.0005 to >0.75 that varied by population. Targeted screening of individual loci identified three new unfixed 2-LTR proviruses within our set, including an intact provirus present at Xq21.33 in some individuals, with the potential for retained infectivity.

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1000 Genomes Project reveals human variation

Nature ◽

10.1038/news.2010.567 ◽

2010 ◽

Cited By ~ 3

Author(s):

Alla Katsnelson

Keyword(s):

Human Variation ◽

1000 Genomes Project ◽

1000 Genomes

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The Epidemiology and Genetics of Hyperuricemia and Gout across Major Racial Groups: A Literature Review and Population Genetics Secondary Database Analysis

Journal of Personalized Medicine ◽

10.3390/jpm11030231 ◽

2021 ◽

Vol 11 (3) ◽

pp. 231

Author(s):

Faven Butler ◽

Ali Alghubayshi ◽

Youssef Roman

Keyword(s):

Literature Review ◽

Risk Allele ◽

Statistical Significance ◽

Elevated Serum ◽

The United States ◽

Allele Frequencies ◽

Racial Groups ◽

1000 Genomes Project ◽

1000 Genomes ◽

Risk Alleles

Gout is an inflammatory condition caused by elevated serum urate (SU), a condition known as hyperuricemia (HU). Genetic variations, including single nucleotide polymorphisms (SNPs), can alter the function of urate transporters, leading to differential HU and gout prevalence across different populations. In the United States (U.S.), gout prevalence differentially affects certain racial groups. The objective of this proposed analysis is to compare the frequency of urate-related genetic risk alleles between Europeans (EUR) and the following major racial groups: Africans in Southwest U.S. (ASW), Han-Chinese (CHS), Japanese (JPT), and Mexican (MXL) from the 1000 Genomes Project. The Ensembl genome browser of the 1000 Genomes Project was used to conduct cross-population allele frequency comparisons of 11 SNPs across 11 genes, physiologically involved and significantly associated with SU levels and gout risk. Gene/SNP pairs included: ABCG2 (rs2231142), SLC2A9 (rs734553), SLC17A1 (rs1183201), SLC16A9 (rs1171614), GCKR (rs1260326), SLC22A11 (rs2078267), SLC22A12 (rs505802), INHBC (rs3741414), RREB1 (rs675209), PDZK1 (rs12129861), and NRXN2 (rs478607). Allele frequencies were compared to EUR using Chi-Square or Fisher’s Exact test, when appropriate. Bonferroni correction for multiple comparisons was used, with p < 0.0045 for statistical significance. Risk alleles were defined as the allele that is associated with baseline or higher HU and gout risks. The cumulative HU or gout risk allele index of the 11 SNPs was estimated for each population. The prevalence of HU and gout in U.S. and non-US populations was evaluated using published epidemiological data and literature review. Compared with EUR, the SNP frequencies of 7/11 in ASW, 9/11 in MXL, 9/11 JPT, and 11/11 CHS were significantly different. HU or gout risk allele indices were 5, 6, 9, and 11 in ASW, MXL, CHS, and JPT, respectively. Out of the 11 SNPs, the percentage of risk alleles in CHS and JPT was 100%. Compared to non-US populations, the prevalence of HU and gout appear to be higher in western world countries. Compared with EUR, CHS and JPT populations had the highest HU or gout risk allele frequencies, followed by MXL and ASW. These results suggest that individuals of Asian descent are at higher HU and gout risk, which may partly explain the nearly three-fold higher gout prevalence among Asians versus Caucasians in ambulatory care settings. Furthermore, gout remains a disease of developed countries with a marked global rising.

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