10.5061/DRYAD.314B2
Berthouly-Salazar, Cécile
Institut de Recherche pour le Développement
Thuillet, Anne-Céline
Institut de Recherche pour le Développement
Rhoné, Bénédicte
Institut de Recherche pour le Développement
University of Lyon System
Mariac, Cédric
Institut de Recherche pour le Développement
Ousseini, Issaka Salia
Institut de Recherche pour le Développement
Couderc, Marie
Institut de Recherche pour le Développement
Tenaillon, Maud I.
French National Centre for Scientific Research
French National Institute for Agricultural Research
Vigouroux, Yves
Institut de Recherche pour le Développement
Data from: Genome scan reveals selection acting on genes linked to stress
response in wild pearl millet
Dryad
dataset
2016
gradient
Pennisetum glaucum
RNAseq
2016-09-23T13:25:15Z
2016-09-23T13:25:15Z
en
https://doi.org/10.1111/mec.13859
132402565 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Uncovering genomic regions involved in adaption is a major goal in
evolutionary biology. High-throughput sequencing now makes it possible to
tackle this challenge in nonmodel species. Yet, despite the increasing
number of methods targeted to specifically detect genomic footprints of
selection, the complex demography of natural populations often causes high
rates of false positive in gene discoveries. The aim of this study was to
identify climate adaptations in wild pearl millet populations, Cenchrus
americanus ssp. monodii. We focused on two climate gradients, one in Mali
and one in Niger. We used a two-step strategy to limit false-positive
outliers. First, we considered gradients as biological replicates and
performed RNA sequencing of four populations at the extremities. We
combined four methods—three based on differentiation among populations and
one based on diversity patterns within populations—to identify outlier
SNPs from a set of 87 218 high-quality SNPs. Among 11 155 contigs of pearl
millet reference transcriptome, 540 exhibited selection signals as
evidenced by at least one of the four methods. In a second step, we
genotyped 762 samples in 11 additional populations distributed along the
gradients using SNPs from the detected contigs and random SNPs as control.
We further assessed selection on this large data set using a
differentiation-based method and a method based on correlations with
environmental variables based. Four contigs displayed consistent
signatures between the four extreme and 11 additional populations, two of
which were linked to abiotic and biotic stress responses.
CommandlinesBiinformatic command lines to obtain final
VCFPearlMillet_SNPs_filteredFinal VCF filtered used for analyses. Raw
sequencing reads have been deposited on the NCBI database