Frontiers in Plant Science (Aug 2016)
Characterizing two inter-specific bin maps for the exploration of the QTLs/genes that confer three soybean evolutionary traits
Abstract
Annual wild soybean (Glycine soja Sieb. & Zucc.), the wild progenitor of the cultivated soybean (G. max (L.) Merr.), is valuable for improving the later. The construction of a linkage map is crucial for studying the genetic differentiation between these species, but marker density is the main factor limiting the accuracy of such a map. Recent advances in next-generation sequencing technologies allow for the generation of high-density linkage maps. Here, two sets of inter-specific recombinant inbred line populations, named NJIRNP and NJIR4P, composed of 284 and 161 lines, respectively, were generated from the same wild male parent, PI 342618B, and genotyped by restriction-site-associated DNA sequencing (RAD-seq). Two linkage maps containing 5,728 and 4,354 bins were constructed based on 89,680 and 80,995 SNPs, spanning a total genetic distance of 2204.6 and 2136.7 cM, with an average distance of 0.4 and 0.5 cM between neighboring bins in NJRINP and NJRI4P, respectively. With the two maps, seven well-studied loci, B1 for seed bloom (SB); G and I for seed coat color (SCC); E2, E3, qDTF16.1 and two linked FLOWERING LOCUS T for days to flowering (DTF), were detected. In addition, two SB and two DTF loci were newly identified in wild soybean. Using two high-density maps, the mapping resolution was enhanced, e.g., G was narrowed to a region of 0.4 Mb on chromosome 1, encompassing 54 gene models, among which only Glyma01g40590 was predicted to be involved in anthocyanin accumulation, and its interaction with I was verified in both populations. In addition, five genes, Glyma16g03030, orthologous to Arabidopsis Phytochrome A (PHYA); Glyma13g28810, Glyma13g29920 and Glyma13g30710 predicted to encode the APETALA 2 (AP2) domain; and Glyma02g00300, involved in response to red or far red light, might be candidate DTF genes. Our results demonstrate that RAD-seq is a cost-effective approach for constructing high-density and high-quality bin maps that can be used to map QTLs/genes into such small enough regions that their candidate genes can be predicted.
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