Improving imputation in disease-relevant regions: lessons from cystic fibrosis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Naim Panjwani, Bowei Xiao, Lizhen Xu, Jiafen Gong, Katherine Keenan, Fan Lin, Gengming He, Zeynep Baskurt, Sangook Kim, Lin Zhang, Mohsen Esmaeili, Scott Blackman, Stephen W. Scherer, Harriet Corvol, Mitchell Drumm, Michael Knowles, Garry Cutting, Johanna M. Rommens, Lei Sun, Lisa J. Strug

ABSTRACT

Does genotype imputation with public reference panels identify variants contributing to disease? Genotype imputation using the 1000 Genomes Project (1KG; 2504 individuals) displayed poor coverage at the causal cystic fibrosis (CF) transmembrane conductance regulator (CFTR) locus for the International CF Gene Modifier Consortium. Imputation with the larger Haplotype Reference Consortium (HRC; 32,470 individuals) displayed improved coverage but low sensitivity of variants clinically relevant for CF. A hybrid reference that combined whole genome sequencing (WGS) from 101 CF individuals with the 1KG imputed a greater number of single-nucleotide variants (SNVs) that would be analyzed in a genetic association study (r2 ≥ 0.3 and MAF ≥ 0.5%) than imputation with the HRC, while the HRC excelled in the lower frequency spectrum. Using the 1KG or HRC as reference panels missed the most common CF-causing variants or displayed low imputation accuracy. Designs that incorporate population-specific WGS can improve imputation accuracy at disease-specific loci, while imputation using public data sets can omit disease-relevant genotypes. More... »

PAGES

8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41525-018-0047-6

DOI

http://dx.doi.org/10.1038/s41525-018-0047-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101582153

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29581887


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17 schema:description Does genotype imputation with public reference panels identify variants contributing to disease? Genotype imputation using the 1000 Genomes Project (1KG; 2504 individuals) displayed poor coverage at the causal cystic fibrosis (CF) transmembrane conductance regulator (<i>CFTR</i>) locus for the International CF Gene Modifier Consortium. Imputation with the larger Haplotype Reference Consortium (HRC; 32,470 individuals) displayed improved coverage but low sensitivity of variants clinically relevant for CF. A hybrid reference that combined whole genome sequencing (WGS) from 101 CF individuals with the 1KG imputed a greater number of single-nucleotide variants (SNVs) that would be analyzed in a genetic association study (<i>r</i><sup>2</sup> ≥ 0.3 and MAF ≥ 0.5%) than imputation with the HRC, while the HRC excelled in the lower frequency spectrum. Using the 1KG or HRC as reference panels missed the most common CF-causing variants or displayed low imputation accuracy. Designs that incorporate population-specific WGS can improve imputation accuracy at disease-specific loci, while imputation using public data sets can omit disease-relevant genotypes.
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