10.5061/DRYAD.KD58F
Field, Yair
Stanford University
Boyle, Evan A.
Stanford University
Telis, Natalie
Stanford University
Gao, Ziyue
Stanford University
Gaulton, Kyle J.
Stanford University
Golan, David
Stanford University
Yengo, Loic
University of Lille
Rocheleau, Ghislain
University of Lille
Froguel, Philippe
University of Lille
McCarthy, Mark I.
Oxford Centre for Diabetes, Endocrinology and Metabolism
Pritchard, Jonathan K.
Stanford University
Data from: Detection of human adaptation during the past 2000 years
Dryad
dataset
2017
2017-10-03T00:00:00Z
2017-10-03T00:00:00Z
en
https://doi.org/10.1126/science.aag0776
88895053 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Detection of recent natural selection is a challenging problem in
population genetics. Here we introduce the Singleton Density Score (SDS),
a method to infer very recent changes in allele frequencies from
contemporary genome sequences. Applied to data from the UK10K Project, SDS
reflects allele frequency changes in the ancestors of modern Britons
during the past ~2,000-3,000 years. We see strong signals of selection at
lactase and the MHC, and in favor of blond hair and blue eyes. For
polygenic adaptation we find that recent selection for increased height
has driven allele frequency shifts across most of the genome. Moreover, we
identify shifts associated with other complex traits, suggesting that
polygenic adaptation has played a pervasive role in shaping genotypic and
phenotypic variation in modern humans.
SDS_UK10K_n3195_release_Sep_19_2016.tabSingleton Density Score (SDS)
computed for autosomal SNPs with let least 5% minor allele frequency over
a set of 3,195 individuals from the UK10K project. This is a gzipped
tab-delimited file and the first row is a column header. Columns
correspond to: (1) chromosome; (2) genomic position (hg19); (3) SNP id
(rsid); (4) ancestral allele; (5) derived allele; (6) derived allele
frequency; (7) SDS. See our manuscript for further description of the SDS
method as well as the data quality control and SNP filtering.