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Environmental versus geographical effects on genomic variation in wild soybean ( Glycine soja ) across its native range in northeast Asia

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ABSTRACT

A fundamental goal in evolutionary biology is to understand how various evolutionary factors interact to affect the population structure of diverse species, especially those of ecological and/or agricultural importance such as wild soybean (Glycine soja). G. soja, from which domesticated soybeans (Glycine max) were derived, is widely distributed throughout diverse habitats in East Asia (Russia, Japan, Korea, and China). Here, we utilize over 39,000 single nucleotide polymorphisms genotyped in 99 ecotypes of wild soybean sampled across their native geographic range in northeast Asia, to understand population structure and the relative contribution of environment versus geography to population differentiation in this species. A STRUCTURE analysis identified four genetic groups that largely corresponded to the geographic regions of central China, northern China, Korea, and Japan, with high levels of admixture between genetic groups. A canonical correlation and redundancy analysis showed that environmental factors contributed 23.6% to population differentiation, much more than that for geographic factors (6.6%). Precipitation variables largely explained divergence of the groups along longitudinal axes, whereas temperature variables contributed more to latitudinal divergence. This study provides a foundation for further understanding of the genetic basis of climatic adaptation in this ecologically and agriculturally important species.

No MeSH data available.


Population structure inferred by the STRUCTURE procedure generated from the 99 wild soybean ecotypes. The colors in each ecotype in A for K = 2, 3, and 4 represent the fraction of their genome that is inferred to be from each of four genetic groups. Red: GROUP 1; Blue: GROUP 2; Green: GROUP 3; Purple: GROUP 4.
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ece32351-fig-0002: Population structure inferred by the STRUCTURE procedure generated from the 99 wild soybean ecotypes. The colors in each ecotype in A for K = 2, 3, and 4 represent the fraction of their genome that is inferred to be from each of four genetic groups. Red: GROUP 1; Blue: GROUP 2; Green: GROUP 3; Purple: GROUP 4.

Mentions: The STRUCTURE procedure applied to the wild soybean SNP data identified four major genetic groups (GROUP 1‐GROUP 4). These groups primarily describe ecotypes from separate geographical regions: GROUP 1 – central China (N = 5); GROUP 2 – northern China (N = 7); GROUP 3 – Korea (N = 45); and GROUP 4 – Japan (N = 26) (Fig. 1). When K = 2, the samples from northern China were separated from all others; when K = 3, samples from northern China were further separated from all others and from those in central China; when K = 4, four separate groups were defined (Fig. 2). The membership of each individual generated in this procedure is listed in Table S2. The phylogenetic tree generated from the SNP data (Fig. S2) also clearly separates the four groups. The means of the eight (untransformed) geographic and environmental variables in each of the four genetic groups are given in Table S3.


Environmental versus geographical effects on genomic variation in wild soybean ( Glycine soja ) across its native range in northeast Asia
Population structure inferred by the STRUCTURE procedure generated from the 99 wild soybean ecotypes. The colors in each ecotype in A for K = 2, 3, and 4 represent the fraction of their genome that is inferred to be from each of four genetic groups. Red: GROUP 1; Blue: GROUP 2; Green: GROUP 3; Purple: GROUP 4.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5016653&req=5

ece32351-fig-0002: Population structure inferred by the STRUCTURE procedure generated from the 99 wild soybean ecotypes. The colors in each ecotype in A for K = 2, 3, and 4 represent the fraction of their genome that is inferred to be from each of four genetic groups. Red: GROUP 1; Blue: GROUP 2; Green: GROUP 3; Purple: GROUP 4.
Mentions: The STRUCTURE procedure applied to the wild soybean SNP data identified four major genetic groups (GROUP 1‐GROUP 4). These groups primarily describe ecotypes from separate geographical regions: GROUP 1 – central China (N = 5); GROUP 2 – northern China (N = 7); GROUP 3 – Korea (N = 45); and GROUP 4 – Japan (N = 26) (Fig. 1). When K = 2, the samples from northern China were separated from all others; when K = 3, samples from northern China were further separated from all others and from those in central China; when K = 4, four separate groups were defined (Fig. 2). The membership of each individual generated in this procedure is listed in Table S2. The phylogenetic tree generated from the SNP data (Fig. S2) also clearly separates the four groups. The means of the eight (untransformed) geographic and environmental variables in each of the four genetic groups are given in Table S3.

View Article: PubMed Central - PubMed

ABSTRACT

A fundamental goal in evolutionary biology is to understand how various evolutionary factors interact to affect the population structure of diverse species, especially those of ecological and/or agricultural importance such as wild soybean (Glycine soja). G. soja, from which domesticated soybeans (Glycine max) were derived, is widely distributed throughout diverse habitats in East Asia (Russia, Japan, Korea, and China). Here, we utilize over 39,000 single nucleotide polymorphisms genotyped in 99 ecotypes of wild soybean sampled across their native geographic range in northeast Asia, to understand population structure and the relative contribution of environment versus geography to population differentiation in this species. A STRUCTURE analysis identified four genetic groups that largely corresponded to the geographic regions of central China, northern China, Korea, and Japan, with high levels of admixture between genetic groups. A canonical correlation and redundancy analysis showed that environmental factors contributed 23.6% to population differentiation, much more than that for geographic factors (6.6%). Precipitation variables largely explained divergence of the groups along longitudinal axes, whereas temperature variables contributed more to latitudinal divergence. This study provides a foundation for further understanding of the genetic basis of climatic adaptation in this ecologically and agriculturally important species.

No MeSH data available.