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

2016 ◽  
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.

scholarly journals Ancestry inference using reference labeled clusters of haplotypes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yong Wang ◽  
Shiya Song ◽  
Joshua G. Schraiber ◽  
Alisa Sedghifar ◽  
Jake K. Byrnes ◽  
...  
Keyword(s):  
Haplotype Diversity ◽  
Population Admixture ◽  
Genome Diversity ◽  
1000 Genomes Project ◽  
Local Ancestry ◽  
1000 Genomes ◽  
Public Data ◽  
Population Information ◽  
Test Sets ◽  
Global And Local

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.

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

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.

scholarly journals Genome-Wide Analysis of Wild-Type Epstein–Barr Virus Genomes Derived from Healthy Individuals of the 1000 Genomes Project

2014 ◽  
Vol 6 (4) ◽  
pp. 846-860 ◽  
Author(s):  
Gabriel Santpere ◽  
Fleur Darre ◽  
Soledad Blanco ◽  
Antonio Alcami ◽  
Pablo Villoslada ◽  
...  
Keyword(s):  
Epstein Barr Virus ◽  
Healthy Individuals ◽  
Wild Type ◽  
1000 Genomes Project ◽  
Genome Wide Analysis ◽  
Barr Virus ◽  
1000 Genomes ◽  
Genome Wide ◽  
Epstein Barr ◽  
Virus Genomes

scholarly journals Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project

2015 ◽  
Vol 32 (9) ◽  
pp. 1366-1372 ◽  
Author(s):  
Dmitry Prokopenko ◽  
Julian Hecker ◽  
Edwin K. Silverman ◽  
Marcello Pagano ◽  
Markus M. Nöthen ◽  
...  
Keyword(s):  
Simulation Study ◽  
Population Stratification ◽  
Jaccard Index ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
1000 Genomes

scholarly journals Evaluation of serverless computing for scalable execution of a joint variant calling workflow

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254363
Author(s):  
Aji John ◽  
Kathleen Muenzen ◽  
Kristiina Ausmees
Keyword(s):  
Genetic Information ◽  
Best Practice ◽  
Workflow Management ◽  
Variant Calling ◽  
Phase Iii ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Genomics Research ◽  
The Cost ◽  
Analysis Of Performance

Advances in whole-genome sequencing have greatly reduced the cost and time of obtaining raw genetic information, but the computational requirements of analysis remain a challenge. Serverless computing has emerged as an alternative to using dedicated compute resources, but its utility has not been widely evaluated for standardized genomic workflows. In this study, we define and execute a best-practice joint variant calling workflow using the SWEEP workflow management system. We present an analysis of performance and scalability, and discuss the utility of the serverless paradigm for executing workflows in the field of genomics research. The GATK best-practice short germline joint variant calling pipeline was implemented as a SWEEP workflow comprising 18 tasks. The workflow was executed on Illumina paired-end read samples from the European and African super populations of the 1000 Genomes project phase III. Cost and runtime increased linearly with increasing sample size, although runtime was driven primarily by a single task for larger problem sizes. Execution took a minimum of around 3 hours for 2 samples, up to nearly 13 hours for 62 samples, with costs ranging from $2 to $70.

scholarly journals The Decay of Disease Association with Declining Linkage Disequilibrium: A Fine Mapping Theorem

2016 ◽  
Author(s):  
Mehdi Maadooliat ◽  
Naveen K. Bansal ◽  
Jiblal Upadhya ◽  
Manzur R. Farazi ◽  
Zhan Ye ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Linkage Disequilibrium ◽  
Fine Mapping ◽  
Disease Model ◽  
Disease Association ◽  
Simple Relationship ◽  
Locus System ◽  
Using Data ◽  
Relationship Of ◽  
The Relationship

AbstractSeveral important and fundamental aspects of disease genetics models have yet to be described. One such property is the relationship of disease association statistics at a marker site closely linked to a disease causing site. A complete description of this two-locus system is of particular importance to experimental efforts to fine map association signals for complex diseases. Here, we present a simple relationship between disease association statistics and the decline of linkage disequilibrium from a causal site. A complete derivation of this relationship from a general disease model is shown for very large sample sizes. Quite interestingly, this relationship holds across all modes of inheritance. Extensive Monte Carlo simulations using a disease genetics model applied to chromosomes subjected to a standard model of recombination are employed to better understand the variation around this fine mapping theorem due to sampling effects. We also use this relationship to provide a framework for estimating properties of a non-interrogated causal site using data at closely linked markers. We anticipate that understanding the patterns of disease association decay with declining linkage disequilibrium from a causal site will enable more powerful fine mapping methods.

scholarly journals The projection of a test genome onto a reference population and applications to humans and archaic hominins

2014 ◽  
Author(s):  
Melinda A Yang ◽  
Kelley Harris ◽  
Montgomery Slatkin
Keyword(s):  
Numerical Analysis ◽  
Population Size ◽  
Demographic History ◽  
Reference Population ◽  
Average Weight ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Test Genome ◽  
History Of ◽  
Past Population

We introduce a method for comparing a test genome with numerous genomes from a reference population. Sites in the test genome are given a weight w that depends on the allele frequency x in the reference population. The projection of the test genome onto the reference population is the average weight for each x, w(x). The weight is assigned in such a way that if the test genome is a random sample from the reference population, w(x)=1. Using analytic theory, numerical analysis, and simulations, we show how the projection depends on the time of population splitting, the history of admixture and changes in past population size. The projection is sensitive to small amounts of past admixture, the direction of admixture and admixture from a population not sampled (a ghost population). We compute the projection of several human and two archaic genomes onto three reference populations from the 1000 Genomes project, Europeans (CEU), Han Chinese (CHB) and Yoruba (YRI) and discuss the consistency of our analysis with previously published results for European and Yoruba demographic history. Including higher amounts of admixture between Europeans and Yoruba soon after their separation and low amounts of admixture more recently can resolve discrepancies between the projections and demographic inferences from some previous studies.

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