scholarly journals Fine Mapping Analysis of the MHC Region to Identify Variants Associated With Chinese Vitiligo and SLE and Association Across These Diseases

2022 ◽  
Vol 12 ◽  
Author(s):  
Lu Cao ◽  
Ruixue Zhang ◽  
Yirui Wang ◽  
Xia Hu ◽  
Liang Yong ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Fine Mapping ◽  
Epidemiological Studies ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
1000 Genomes ◽  
Mapping Analysis ◽  
Healthy Control ◽  
New Perspective

The important role of MHC in the pathogenesis of vitiligo and SLE has been confirmed in various populations. To map the most significant MHC variants associated with the risk of vitiligo and SLE, we conducted fine mapping analysis using 1117 vitiligo cases, 1046 SLE cases and 1693 healthy control subjects in the Han-MHC reference panel and 1000 Genomes Project phase 3. rs113465897 (P=1.03×10-13, OR=1.64, 95%CI =1.44–1.87) and rs3129898 (P=4.21×10-17, OR=1.93, 95%CI=1.66–2.25) were identified as being most strongly associated with vitiligo and SLE, respectively. Stepwise conditional analysis revealed additional independent signals at rs3130969(p=1.48×10-7, OR=0.69, 95%CI=0.60–0.79), HLA-DPB1*03:01 (p=1.07×10-6, OR=1.94, 95%CI=1.49–2.53) being linked to vitiligo and HLA-DQB1*0301 (P=4.53×10-7, OR=0.62, 95%CI=0.52-0.75) to SLE. Considering that epidemiological studies have confirmed comorbidities of vitiligo and SLE, we used the GCTA tool to analyse the genetic correlation between these two diseases in the HLA region, the correlation coefficient was 0.79 (P=5.99×10-10, SE=0.07), confirming their similar genetic backgrounds. Our findings highlight the value of the MHC region in vitiligo and SLE and provide a new perspective for comorbidities among autoimmune diseases.

scholarly journals High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

2021 ◽  
Author(s):  
Marta Byrska-Bishop ◽  
Uday S. Evani ◽  
Xuefang Zhao ◽  
Anna O. Basile ◽  
Haley J. Abel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
High Coverage ◽  
Entire Cohort ◽  
1000 Genomes ◽  
Low Coverage

ABSTRACTThe 1000 Genomes Project (1kGP), launched in 2008, is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final (phase 3) 2015 release of 1kGP included 2,504 unrelated samples from 26 populations, representing five continental regions of the world and was based on a combination of technologies including low coverage WGS (mean depth 7.4X), high coverage whole exome sequencing (mean depth 65.7X), and microarray genotyping. Here, we present a new, high coverage WGS resource encompassing the original 2,504 1kGP samples, as well as an additional 698 related samples that result in 602 complete trios in the 1kGP cohort. We sequenced this expanded 1kGP cohort of 3,202 samples to a targeted depth of 30X using Illumina NovaSeq 6000 instruments. We performed SNV/INDEL calling against the GRCh38 reference using GATK’s HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model, upgrading the 1kGP dataset to current state-of-the-art standards. Using this strategy, we defined over 111 million SNVs, 14 million INDELs, and ∼170 thousand SVs across the entire cohort of 3,202 samples with estimated false discovery rate (FDR) of 0.3%, 1.0%, and 1.8%, respectively. By comparison to the low-coverage phase 3 callset, we observed substantial improvements in variant discovery and estimated FDR that were facilitated by high coverage re-sequencing and expansion of the cohort. Specifically, we called 7% more SNVs, 59% more INDELs, and 170% more SVs per genome than the phase 3 callset. Moreover, we leveraged the presence of families in the cohort to achieve superior haplotype phasing accuracy and we demonstrate improvements that the high coverage panel brings especially for INDEL imputation. We make all the data generated as part of this project publicly available and we envision this updated version of the 1kGP callset to become the new de facto public resource for the worldwide scientific community working on genomics and genetics.

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 Detecting shared independent selection

2020 ◽  
Author(s):  
Nathan S. Harris ◽  
Alan R. Rogers
Keyword(s):  
1000 Genomes Project ◽  
Phase 3 ◽  
1000 Genomes ◽  
Different Populations ◽  
Genomic Regions ◽  
Two Populations

AbstractSignals of selection are not often shared between populations. When a mutual signal is detected, it is often not known if selection occurred before or after populations split. Here we develop a method to detect genomic regions at which selection has favored different haplotypes in two populations. This method is verified through simulations and tested on small regions of the genome. This method was then expanded to scan the phase 3 genomes of the 1000 Genomes Project populations for regions in which the evidence for independent selection is strongest. We identify several genes which likely underwent selection independently in different populations.

Genetic polymorphisms of pharmacogenomic VIP variants in Chinese Lisu population

2021 ◽  
Author(s):  
Hongyan Lu ◽  
Yuliang Wang ◽  
Zhanhao Zhang ◽  
Shishi Xing ◽  
Dandan Li ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Drug Therapy ◽  
Precision Medicine ◽  
Theoretical Basis ◽  
Yunnan Province ◽  
Nucleotide Polymorphism ◽  
1000 Genomes Project ◽  
Single Nucleotide ◽  
1000 Genomes

Abstract IntroductionThe specificity of drug therapy in individuals and races has promoted the development and improvement of pharmacogenomics and precision medicine. While there is a few cognition on the minorities in China, especially in Lisu nationality from the Yunnan Province. Therefore, we performed the research to improve the role of pharmacogenomics in the Lisu population from the Yunnan province of China.Materials and MethodsIn our study, 54 variants of very important pharmacogenes (VIPs) selected from the PharmGKB database were genotyped in 199 unrelated and healthy Lisu adults from the Yunnan province of China, and then, genotyping data wtih χ2 test were analyzed.ResultsWe compared our date with those of other 26 populations from the 1000 Genomes Project, and acquired that the Lisu ethnicity is similar with the CDX(Chinese Dai in Xishuangbanna, China) and CHS(Southern Han Chinese, China). Furthermore, rs776746 (CYP3A5), rs1805123 (KCNH2), rs4291 (ACE), rs1051298 (SLC19A1) and rs1065852 (CYP2D6) were deemed as the most varying loci. The MAF of “G” at rs1805123 (KCNH2) in the Lisu population was the largest with the value of 51.0%.ConclusionsOur results show that there are significant differences in SNP (single nucleotide polymorphism) loci, supplementing the pharmacogenomic information of the Lisu population in Yunnan province, China, and can provide a theoretical basis for individualized medication in the future.

scholarly journals Identifying Y-chromosome haplogroups in arbitrarily large samples of sequenced or genotyped men

2016 ◽  
Author(s):  
G. David Poznik
Keyword(s):  
Missing Data ◽  
Y Chromosome ◽  
Software Package ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
Array Data ◽  
Large Samples ◽  
1000 Genomes ◽  
Reasonable Number

AbstractWe have developed an algorithm to rapidly and accurately identify the Y-chromosome haplogroup of each male in a sample of one to millions. The algorithm, implemented in the yHaplo* software package (yHaplo), does not rely on any particular genotyping modality or platform. Full sequences yield the most granular haplogroup classifications, but genotyping arrays can yield reliable calls, provided a reasonable number of phylogenetically informative variants has been assayed. The algorithm is robust to missing data, genotype errors, mutation recurrence, and other complications. We have tested the software on full sequences from phase 3 of the 1000 Genomes Project and on subsets thereof constructed by downsampling to SNPs present on each of four genotyping arrays. We have also run the software on array data from more than 600,000 males.

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.

Fine-mapping of IgE-associated loci 1q23, 5q31, and 12q13 using 1000 Genomes Project data

Allergy ◽  
2014 ◽  
Vol 69 (8) ◽  
pp. 1077-1084 ◽  
Author(s):  
V. Sharma ◽  
S. Michel ◽  
V. Gaertner ◽  
A. Franke ◽  
C. Vogelberg ◽  
...  
Keyword(s):  
Fine Mapping ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Project Data

scholarly journals The quiescent X, the replicative Y and the Autosomes

2018 ◽  
Author(s):  
Guillaume Achaz ◽  
Serge Gangloff ◽  
Benoit Arcangioli
Keyword(s):  
De Novo ◽  
Yeast Cells ◽  
Simple Pattern ◽  
Maternal Lineage ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
X Chromosomes ◽  
1000 Genomes ◽  
Y Chromosomes ◽  
Human Genomes

AbstractFrom the analysis of the mutation spectrum in the 2,504 sequenced human genomes from the 1000 genomes project (phase 3), we show that sexual chromosomes (X and Y) exhibit a different proportion of indel mutations than autosomes (A), ranking them X>A>Y. We further show that X chromosomes exhibit a higher ratio of deletion/insertion when compared to autosomes. This simple pattern shows that the recent report that non-dividing quiescent yeast cells accumulate relatively more indels (and particularly deletions) than replicating ones also applies to metazoan cells, including humans. Indeed, the X chromosomes display more indels than the autosomes, having spent more time in quiescent oocytes, whereas the Y chromosomes are solely present in the replicating spermatocytes. From the proportion of indels, we have inferred that de novo mutations arising in the maternal lineage are twice more likely to be indels than mutations from the paternal lineage. Our observation, consistent with a recent trio analysis of the spectrum of mutations inherited from the maternal lineage, is likely a major component in our understanding of the origin of anisogamy.

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