In the present study, we used latent factor mixed models (LFMMs) and Moran spectral outlier detection/randomization (MSOD-MSR) to identify candidate loci for adaptation to 10 environmental variables (climatic, soil and atmospheric) among 43515 single nucleotide polymorphisms (SNPs) from 202 accessions of the model legume Medicago truncatula.
Spatial differences in environmental selective pressures interact with the genomes of organisms, ultimately leading to local adaptation. Landscape genomics is an emergent research area that uncovers genome-environment associations, thus allowing researchers to identify candidate loci for adaptation to specific environmental variables.
Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics
Andrello Marco;
2018
Abstract
Spatial differences in environmental selective pressures interact with the genomes of organisms, ultimately leading to local adaptation. Landscape genomics is an emergent research area that uncovers genome-environment associations, thus allowing researchers to identify candidate loci for adaptation to specific environmental variables.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
