Population stratification is a growing concern in genetic-association studies. Averaged ancestry at the genome level (global ancestry) is insufficient for detecting the population substructures and correcting population stratifications in association studies. Local and phase stratification are needed for human genetic studies, but current technologies cannot be applied on the entire genome data due to various technical caveats. Here we developed a novel approach (aMAP, ancestry of Modern Admixed Populations) for inferring local phased ancestry. It took about 3 seconds on a desktop computer to finish a local ancestry analysis for each human genome with 1.4-million SNPs. This method also exhibits the scalability to larger datasets with respect to the number of SNPs, the number of samples and the size of reference panels. It can detect the lack of the proxy of reference panels. The accuracy was 99.4%. The aMAP software has a capacity for analyzing 6-way admixed individuals. As the biomedical community continues to expand its efforts to increase the representation of diverse populations and as the number of large whole-genome sequence datasets continues to grow rapidly, there is an increasing demand on rapid and accurate local ancestry analysis in genetics, pharmacogenomics, population genetics and clinical diagnosis.
机构:
Univ Los Andes, Lab Genet Humana, Bogota, ColombiaPontificia Univ Javeriana, Fac Ciencias, Bogota, Colombia
Claudia Lattig, Maria
Groot, Helena
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Univ Los Andes, Lab Genet Humana, Bogota, ColombiaPontificia Univ Javeriana, Fac Ciencias, Bogota, Colombia
Groot, Helena
de Carvalho, Elizeu Fagundes
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机构:
State Univ Rio de Janeiro UERJ, DNA Diagnost Lab, Rio De Janeiro, BrazilPontificia Univ Javeriana, Fac Ciencias, Bogota, Colombia
de Carvalho, Elizeu Fagundes
Gusmao, Leonor
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机构:
State Univ Rio de Janeiro UERJ, DNA Diagnost Lab, Rio De Janeiro, Brazil
Univ Porto, IPATIMUP Inst Patol Imunol Mol, Oporto, PortugalPontificia Univ Javeriana, Fac Ciencias, Bogota, Colombia
机构:
Univ New South Wales UNSW, Sch Biotechnol & Biomol Sci, Sydney, NSW, Australia
Univ New South Wales UNSW, UNSW Data Sci Hub, Sydney, NSW, AustraliaBabol Noshirvani Univ Technol, Fac Elect & Comp Engn, Babol, Iran
Vafaee, F.
INTERNATIONAL JOURNAL OF ENGINEERING,
2024,
37
(02):
: 412
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424
机构:
Stanford Univ, Dept Biol, Stanford, CA 94305 USA
Ctr Invest Cient Huastecas Aguazarca, Calnali, Hidalgo, Mexico
Howard Hughes Med Inst, Stanford, CA USAStanford Univ, Dept Biol, Stanford, CA 94305 USA
Schumer, Molly
Powell, Daniel L.
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机构:
Stanford Univ, Dept Biol, Stanford, CA 94305 USA
Ctr Invest Cient Huastecas Aguazarca, Calnali, Hidalgo, Mexico
Texas A&M Univ, Dept Biol, College Stn, TX 77843 USAStanford Univ, Dept Biol, Stanford, CA 94305 USA
Powell, Daniel L.
Corbett-Detig, Russ
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机构:
Univ Calif Santa Cruz, Genom Inst, Santa Cruz, CA 95064 USA
Univ Calif Santa Cruz, Dept Biomol Engn, Santa Cruz, CA 95064 USAStanford Univ, Dept Biol, Stanford, CA 94305 USA