High Coverage Whole Genome Sequencing of the Expanded 1000 Genomes Project Cohort Including 602 Trios

Byrska-Bishop, Marta; Evani, Uday S.; Zhao, Xuefang; Basile, Anna O.; Abel, Haley J.; Regier, Allison; Corvelo, André; Clarke, Wayne E.; Musunuri, Rajeeva; Nagulapalli, Kshithija; Fairley, Susan; Runnels, Alexi; Winterkorn, Lara; Lowy, Ernesto; Consortium, The Human Genome Structural Variation; Flicek, Paul; Germer, Søren; Brand, Harrison; Hall, Ira M.; Talkowski, Michael E.; Narzisi, Giuseppe; Zody, Michael C.

doi:10.2139/ssrn.3967671

Cited by 37 publications

(60 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This over-merging means that sequence differences between individuals are getting lost. Previous publications are indeed hinting towards a potential over-merge, but further investigations are needed as the number of studies investigating insertions, particularly those using long-reads, are small but rising [26][27][28][29][30]. Related to this is the overall number of SVs that one might assume in healthy human genomes (Figure 3a, Supplementary Figure 5).…”

Section: Discussionmentioning

confidence: 97%

“…Truvari anno trf incorporates a wrapper around tandem-repeat finder (TRF) [36]. We ran Truvari anno trf to annotate all SVs on GRCh38 that intersected the SimpleRepeats track procured from UCSC Table Browser [29]. Each intersecting variant is used to alter the SimpleRepeat reference region to reconstruct the sample's haplotype.…”

Section: Tandem Repeat Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Truvari: Refined Structural Variant Comparison Preserves Allelic Diversity

English

Menon

Gibbs

et al. 2022

Preprint

View full text Add to dashboard Cite

For multi-sample structural variant analyses like merging, benchmarking, and annotation, the fundamental operation is to identify when two SVs are the same. Commonly applied approaches for comparing SVs were developed alongside technologies which produce ill-defined boundaries. As SV detection becomes more exact, algorithms to preserve this refined signal are needed. Here we present Truvari - a SV comparison, annotation and analysis toolkit - and demonstrate the effect of SV comparison choices by building population-level VCFs from 36 haplotype-resolved long-read assemblies. We observe over-merging from other SV merging approaches which causes up to a 2.2x inflation of allele frequency relative to Truvari.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Tandem Repeat Experimentsmentioning

confidence: 99%

Truvari: Refined Structural Variant Comparison Preserves Allelic Diversity

English

Menon

Gibbs

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We accessed VCFs from the 1000 Genomes Project ( (accessed on 10 December 2021)) [ 56 ] for our natural human population sample ( Table S1 ). These data include 2504 individuals (i.e., 5008 COL1A1 allele copies) with no known bone abnormalities (i.e., a random sample with respect to phenotypic diversity), representing 26 geographically and ethnically diverse populations (“1000G”, hereafter).…”

Section: Methodsmentioning

confidence: 99%

Evolutionary Genetic Signatures of Selection on Bone-Related Variation within Human and Chimpanzee Populations

Stover

Housman

Stone

et al. 2022

Genes

View full text Add to dashboard Cite

Bone strength and the incidence and severity of skeletal disorders vary significantly among human populations, due in part to underlying genetic differentiation. While clinical models predict that this variation is largely deleterious, natural population variation unrelated to disease can go unnoticed, altering our perception of how natural selection has shaped bone morphologies over deep and recent time periods. Here, we conduct the first comparative population-based genetic analysis of the main bone structural protein gene, collagen type I α 1 (COL1A1), in clinical and 1000 Genomes Project datasets in humans, and in natural populations of chimpanzees. Contrary to predictions from clinical studies, we reveal abundant COL1A1 amino acid variation, predicted to have little association with disease in the natural population. We also find signatures of positive selection associated with intron haplotype structure, linkage disequilibrium, and population differentiation in regions of known gene expression regulation in humans and chimpanzees. These results recall how recent and deep evolutionary regimes can be linked, in that bone morphology differences that developed among vertebrates over 450 million years of evolution are the result of positive selection on subtle type I collagen functional variation segregating within populations over time.

show abstract

“…We ran ImmunoTyper-SR on 12 publicly available high-coverage WGS of 1kGP samples, sequenced at ∼30X on the Illumina NovaSeq 6000 Platform [29]. These samples have had independent de novo IGH haplotyping performed by Rodriguez et al (unpublished) using a targeted long read sequencing and assembly protocol called IGenotyper [8] tailor-made for the IGH region.…”

Section: Validation Using 1kgp Samplesmentioning

confidence: 99%

“…We have validated ImmunoTyper-SR on 12 individuals with diverse genetic backgrounds from the 1000 Genomes Project [28] (1kGP), by comparing ImmunoTyper-SR genotype calls on Illumina WGS data from the NYGC [29] against targeted long read-based IGH assemblies generated using IGenotyper [8]. We then applied ImmunoTyper-SR to WGS data from a cohort of 585 individuals from the NIAID COVID Consortium (from here on the "NIAID cohort") to investigate associations between IGHV genotypes, type I IFN autoantibodies and COVID-19 disease severity.…”

Section: Introductionmentioning

confidence: 99%

ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes using Short Read Data

Ford¹,

Hari²,

Rodriguez³

et al. 2022

Preprint

View full text Add to dashboard Cite

Human immunoglobulin heavy chain (IGH) locus on chromosome 14 includes more than 40 functional copies of the variable gene (IGHV), which, together with the joining genes (IGHJ), diversity genes (IGHD), constant genes (IGHC) and immunoglobulin light chains, code for antibodies that identify and neutralize pathogenic invaders as a part of the adaptive immune system. Because of its highly repetitive sequence composition, the IGH locus has been particularly difficult to assemble or genotype through the use of standard short read sequencing technologies. Here we introduce ImmunoTyper-SR, an algorithmic method for genotype and CNV analysis of the germline IGHV genes using Illumina whole genome sequencing (WGS) data. ImmunoTyper-SR is based on a novel combinatorial optimization formulation that aims to minimize the total edit distance between reads and their assigned IGHV alleles from a given database, with constraints on the number and distribution of reads across each called allele. We have validated ImmunoTyper-SR on 12 individuals with Illumina WGS data from the 1000 Genomes Project, whose IGHV allele composition have been studied extensively through the use of long read and targeted sequencing platforms, as well as nine individuals from the NIAID COVID Consortium who have been subjected to WGS twice. We have then applied ImmunoTyper-SR on 585 samples from the NIAID COVID Consortium to investigate associations between distinct IGHV alleles and anti-type I IFN autoantibodies which have been linked to COVID-19 severity.

show abstract

High Coverage Whole Genome Sequencing of the Expanded 1000 Genomes Project Cohort Including 602 Trios

Cited by 37 publications

References 46 publications

Truvari: Refined Structural Variant Comparison Preserves Allelic Diversity

Truvari: Refined Structural Variant Comparison Preserves Allelic Diversity

Evolutionary Genetic Signatures of Selection on Bone-Related Variation within Human and Chimpanzee Populations

ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes using Short Read Data

Contact Info

Product

Resources

About