BACKGROUND: Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. RESULTS: To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. CONCLUSIONS: These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.
BACKGROUND: Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. RESULTS: To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 0.90, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 0.96. CONCLUSIONS: These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for association studies when compared to the PMRA and should be considered as a competitive alternative to genotyping arrays for trait mapping in pharmacogenetics.
Authors: Bo Zhou; Steve S Ho; Xianglong Zhang; Reenal Pattni; Rajini R Haraksingh; Alexander E Urban Journal: J Med Genet Date: 2018-07-30 Impact factor: 6.318
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Authors: Alan R Shuldiner; Jeffrey R O'Connell; Kevin P Bliden; Amish Gandhi; Kathleen Ryan; Richard B Horenstein; Coleen M Damcott; Ruth Pakyz; Udaya S Tantry; Quince Gibson; Toni I Pollin; Wendy Post; Afshin Parsa; Braxton D Mitchell; Nauder Faraday; William Herzog; Paul A Gurbel Journal: JAMA Date: 2009-08-26 Impact factor: 56.272
Authors: Adam Auton; Lisa D Brooks; Richard M Durbin; Erik P Garrison; Hyun Min Kang; Jan O Korbel; Jonathan L Marchini; Shane McCarthy; Gil A McVean; Gonçalo R Abecasis Journal: Nature Date: 2015-10-01 Impact factor: 49.962
Authors: Daniel H Hovelson; Zhengyu Xue; Matthew Zawistowski; Margaret G Ehm; Elizabeth C Harris; Sophie L Stocker; Annette S Gross; In-Jin Jang; Ichiro Ieiri; Jong-Eun Lee; Lon R Cardon; Stephanie L Chissoe; Gonçalo Abecasis; Matthew R Nelson Journal: Pharmacogenet Genomics Date: 2017-03 Impact factor: 2.089
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Authors: Alicia R Martin; Elizabeth G Atkinson; Sinéad B Chapman; Anne Stevenson; Rocky E Stroud; Tamrat Abebe; Dickens Akena; Melkam Alemayehu; Fred K Ashaba; Lukoye Atwoli; Tera Bowers; Lori B Chibnik; Mark J Daly; Timothy DeSmet; Sheila Dodge; Abebaw Fekadu; Steven Ferriera; Bizu Gelaye; Stella Gichuru; Wilfred E Injera; Roxanne James; Symon M Kariuki; Gabriel Kigen; Karestan C Koenen; Edith Kwobah; Joseph Kyebuzibwa; Lerato Majara; Henry Musinguzi; Rehema M Mwema; Benjamin M Neale; Carter P Newman; Charles R J C Newton; Joseph K Pickrell; Raj Ramesar; Welelta Shiferaw; Dan J Stein; Solomon Teferra; Celia van der Merwe; Zukiswa Zingela Journal: Am J Hum Genet Date: 2021-03-25 Impact factor: 11.025
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