scholarly journals Efficient analysis of large datasets and sex bias with ADMIXTURE

2016 ◽  
Author(s):  
Suyash S. Shringarpure ◽  
Carlos D. Bustamante ◽  
Kenneth L. Lange ◽  
David H. Alexander
Keyword(s):  
Large Datasets ◽  
Allele Frequencies ◽  
Sex Bias ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Males And Females ◽  
Related Individuals ◽  
Using Data ◽  
Human Ancestry ◽  
Individual Ancestry

Background: A number of large genomic datasets are being generated for studies of human ancestry and diseases. The ADMIXTURE program is commonly used to infer individual ancestry from genomic data. Results: We describe two improvements to the ADMIXTURE software. The first enables ADMIXTURE to infer ancestry for a new set of individuals using cluster allele frequencies from a reference set of individuals. Using data from the 1000 Genomes Project, we show that this allows ADMIXTURE to infer ancestry for 10,920 individuals in a few hours (a 5x speedup). This mode also allows ADMIXTURE to correctly estimate individual ancestry and allele frequencies from a set of related individuals. The second modification allows ADMIXTURE to correctly handle X-chromosome (and other haploid) data from both males and females. We demonstrate increased power to detect sex-biased admixture in African-American individuals from the 1000 Genomes project using this extension. Conclusions: These modifications make ADMIXTURE more efficient and versatile, allowing users to extract more information from large genomic datasets.

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 Open-Access STRS Database Of Populations From The 1000 Genomes Project Using High Coverage Phase 3 Data

2021 ◽  
Author(s):  
Tamara Soledad Frontanilla ◽  
Guilherme Valle Silva ◽  
Jesus Ayala ◽  
Celso Teixeira Mendes
Keyword(s):  
Open Access ◽  
Forensic Genetics ◽  
Allele Frequencies ◽  
Equilibrium Analysis ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
Principal Coordinates Analysis ◽  
High Coverage ◽  
1000 Genomes ◽  
Principal Coordinates

Accurate STR genotyping from next-generation sequencing (NGS) data has been challenging. Haplotype inference and phasing for STRs (HipSTR) was specifically developed to deal with genotyping errors and obtain reliable STR genotypes from whole-genome sequencing datasets. The objective of this investigation was to perform a comprehensive genotyping analysis of a set of STRs of broad forensic interest from the 1000 Genomes populations and release a reliable open-access STR database to the forensic genetics community. A set of 22 STR markers were analyzed using the CRAM files of the 1000 Genomes Project Phase 3 high-coverage (30x) dataset generated by the New York Genome Center (NYGC). HipSTR was used to call genotypes from 2,504 samples from 26 populations organized into five groups: African, East Asian, European, South Asian, and admixed American. The D21S11 marker could not be detected in the present study. Moreover, the Hardy-Weinberg equilibrium analysis, coupled with a comprehensive analysis of allele frequencies, revealed that HipSTR could not identify longer Penta E (and Penta D at a lesser extent) alleles. This issue is probably due to the limited length of sequencing reads available for genotype calling, resulting in heterozygote deficiency. Notwithstanding that, AMOVA, a clustering analysis using STRUCTURE, and a Principal Coordinates Analysis revealed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium (AFR, EUR, EAS, SAS). Meanwhile, the AMOVA results corroborated previous reports that most of the variance is (97.12%) observed within populations. This set of analyses revealed that except for larger Penta D and Penta E alleles, allele frequencies and genotypes defined by HipSTR from the 1000 Genomes Project phase 3 data and offered as an open-access database are consistent and highly reliable.

scholarly journals Inference of recent admixture using genotype data

2020 ◽  
Author(s):  
Peter Pfaffelhuber ◽  
Elisabeth Sester-Huss ◽  
Franz Baumdicker ◽  
Jana Naue ◽  
Sabine Lutz-Bonengel ◽  
...  
Keyword(s):  
State Of The Art ◽  
Forensic Genetics ◽  
Statistical Test ◽  
Genotype Data ◽  
1000 Genomes Project ◽  
Additional Information ◽  
1000 Genomes ◽  
Project Data ◽  
Ancestry Proportions ◽  
Individual Ancestry

AbstractThe inference of biogeographic ancestry (BGA) has become a focus of forensic genetics. Mis-inference of BGA can have profound unwanted consequences for investigations and society. We show that recent admixture can lead to misclassification and erroneous inference of ancestry proportions, using state of the art analysis tools with (i) simulations, (ii) 1000 genomes project data, and (iii) two individuals analyzed using the ForenSeq DNA Signature Prep Kit. Subsequently, we extend existing tools for estimation of individual ancestry (IA) by allowing for different IA in both parents, leading to estimates of parental individual ancestry (PIA), and a statistical test for recent admixture. Estimation of PIA outperforms IA in most scenarios of recent admixture. Furthermore, additional information about parental ancestry can be acquired with PIA that may guide casework.

scholarly journals Major sex differences in allele frequencies for X chromosome variants in the 1000 Genomes Project data

2021 ◽  
Author(s):  
Zhong Wang ◽  
Lei Sun ◽  
Andrew D Paterson
Keyword(s):  
Sex Differences ◽  
X Chromosome ◽  
Association Studies ◽  
Allele Frequencies ◽  
Whole Genome Sequence ◽  
P Value ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
High Coverage ◽  
1000 Genomes

An unexpectedly high proportion of SNPs on the X chromosome in the 1000 Genomes Project phase 3 data were identified with significant sex differences in minor allele frequencies (sdMAF). sdMAF persisted for many of these SNPs in the recently released high coverage whole genome sequence, and it was consistent between the five super-populations. Among the 245,825 common biallelic SNPs in phase 3 data presumed to be high quality, 2,039 have genome-wide significant sdMAF (p-value <5e-8). sdMAF varied by location: (NPR)=0.83%, pseudo-autosomal region (PAR1)=0.29%, PAR2=13.1%, and PAR3=0.85% of SNPs had sdMAF, and they were clustered at the NPR-PAR boundaries, among others. sdMAF at the NPR-PAR boundaries are biologically expected due to sex-linkage, but have generally been ignored in association studies. For comparison, similar analyses found only 6, 1 and 0 SNPs with significant sdMAF on chromosomes 1, 7 and 22, respectively. Future X chromosome analyses need to take sdMAF into account.

In-silico sequencing of the GPR177 bone mineral density (BMD) locus using data from the “1000 genomes” project

Bone ◽  
2010 ◽  
Vol 47 ◽  
pp. S70
Author(s):  
F. Rivadeneira⁎ ◽  
K. Estrada ◽  
R. Kraaij ◽  
A. Hofman ◽  
H.A. Pols ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Bone Mineral ◽  
In Silico ◽  
Mineral Density ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Using Data

scholarly journals Mapping Bias Overestimates Reference Allele Frequencies at theHLAGenes in the 1000 Genomes Project Phase I Data

2015 ◽  
Vol 5 (5) ◽  
pp. 931-941 ◽  
Author(s):  
Débora Y. C. Brandt ◽  
Vitor R. C. Aguiar ◽  
Bárbara D. Bitarello ◽  
Kelly Nunes ◽  
Jérôme Goudet ◽  
...  
Keyword(s):  
Phase I ◽  
Allele Frequencies ◽  
1000 Genomes Project ◽  
Reference Allele ◽  
1000 Genomes ◽  
Mapping Bias

scholarly journals The Epidemiology and Genetics of Hyperuricemia and Gout Across Major Racial Groups: A literature Review and Population Genetics Secondary Data Analysis

2021 ◽  
Author(s):  
Faven Butler ◽  
Ali Alghubayshi ◽  
Youssef Malak 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

Abstract BackgroundGout 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. MethodsEnsembl 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<0.005 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 a literature review.ResultsCompared 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.ConclusionsCompared 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 2.7-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 Performance of Genotype Imputations Using Data from the 1000 Genomes Project

2011 ◽  
Vol 73 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Yun Ju Sung ◽  
Lihua Wang ◽  
Tuomo Rankinen ◽  
Claude Bouchard ◽  
D.C. Rao
Keyword(s):  
1000 Genomes Project ◽  
1000 Genomes ◽  
Using Data

scholarly journals Country of Origin Effects on the Average Annual Values of NHL Player Contracts

2019 ◽  
Vol 7 (2) ◽  
pp. 24
Author(s):  
Aju J. Fenn ◽  
Lucas Gerdes ◽  
Samuel Rothstein
Keyword(s):  
Quantile Regression ◽  
Fixed Effects ◽  
Country Of Origin ◽  
Large Datasets ◽  
National Hockey League ◽  
Dummy Variables ◽  
Country Of Origin Effects ◽  
Performance Statistics ◽  
Career Performance ◽  
Using Data

Using data from 2005 to 2016, this paper examines if players in the National Hockey League (NHL) are being paid a positive differential for their services due to the competition from the Kontinental Hockey League (KHL) and the Swedish Hockey League (SHL). In order to control for performance, we use two different large datasets, (N = 4046) and (N = 1717). In keeping with the existing literature, we use lagged performance statistics and dummy variables to control for the type of NHL contract. The first dataset contains lagged career performance statistics, while the performance statistics are based on the statistics generated during the years under the player’s previous contract. Fixed effects least squares (FELS) and quantile regression results suggest that player production statistics, contract status, and country of origin are significant determinants of NHL player salaries.

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