- Genome wide association mapping and candidate gene analysis for hundred seed weight in soybean [ Glycine max (L.) Merrill]. - Background: The hundred seed weight (HSW) is one of the yield components of soybean [ Glycine max (L.) Merrill]. - The seed weight (SW) of soybean (often denoted by hundred seed weight (HSW)) is an important yield com- ponent and positively correlates with seed yield [1, 2].. - HSW exhibits wider variation ranges [3], and the HSW of the modern elite cultivar (18–20 g) is approximately 6–7-fold greater than that of the wild soybean (3–4 g) [4]. - Full list of author information is available at the end of the article. - Yan et al. - Zhang et al. - The aim of the present study is to identify QTLs associated with HSW and to screen candidate genes located in peak SNP regions.. - Distribution of hundred seed weight in the association panel. - Distribution of SNPs and genetic characteristics of the mapping population. - A total of 24,180 SNPs distributed across all 20 chromo- somes of the soybean genome with minor allele frequen- cies >. - The number of SNPs among the 20 chromosomes was not even, and large variations in the number of SNPs (from 676 in Chr.11 to 1774 in Chr.15) were observed among different chromosomes. - Quantitative trait nucleotide (QTN) associated with hundred seed weight by GWAS. - The first three principle components (PCs) accounted for 12.48% of the total genetic variation (Fig. - In the present study, thirty-four association sig- nals were associated with HSW, which were distributed on fifteen chromosomes, including Chr.3, Chr.4, Chr.5, Chr.6, Chr.8, Chr.9, Chr.10, Chr.12, Chr.13, Chr.14,. - Chr.16, Chr.17, Chr.18, Chr.19, and Chr.20 (Fig. - Among them, five SNP loci (HSW-8-1 on Chr.8, HSW-9-1 on Chr.9, HSW-12-1 on Chr.12, HSW- 12-4 on Chr.12, and HSW-16–3 on Chr.16) were identi- fied in more than three environments in this study. - Among all 34 QTNs, fifteen signals over- lapped or located near the genomic region of the known QTL underlying HSW or soybean yield, and the other seventeen were novel for HSW (Table 2). - The HSW of the tested accessions with different alleles were evalu- ated, and the results indicated that the HSW of these. - 1 Variation of hundred seed weight of soybean in the association panel. - Prediction of candidate genes controlling hundred seed weight. - Among these identified candidate genes, Gly- ma.03G192300, a starch branching enzyme located near HSW-3-1 of Chr.3, could promote seed weight in crops [31]. - Glyma.05G005400 (located near HSW5–1 of Chr.5) and Glyma.08G317300 (located near HSW8–2 of Chr.5), a MYB-domain protein, play key roles in regulating seed development and determining seed weight in plants [32]. - Glyma.05G196400 (located near HSW5 – 2 of Chr.5), one Leucine-rich repeat protein kinase family protein, has been proven a regulator of seed weight in plants [33]. - Masatake et al. - (2013) re- ported that HS3, which has the same domain as Gly- ma.06G201700 (located near HSW6 – 1 of Chr.6), regulates seed development in plants [34]. - Bhatnagar et al. - Both OsPP2C51 and Glyma.12G109500 (located near HSW-. - 2 Linkage disequilibrium (LD) evaluation and genetic features of the mapping population. - a LD decay of the genome-wide association study (GWAS) population. - d A heatmap of the kinship matrix of the 185 soybean accessions. - 12-2 of Chr.12) are members of protein phosphatase 2C.. - Glyma.12G083500, 4 SNP;. - Glyma.13G182400, 4 SNP . - Glyma.17G178400, 4 SNP . - Glyma.20G140200, 4 SNP. - Glyma.20G140400, 4 SNP;. - Glyma.03G192300, 6 SNP. - Glyma.05G005200, 6 SNP;. - 3 Manhattan plot of association mapping of hundred seed weight in soybean. - Table 2 Single nucleotide polymorphisms (SNPs) associated with hundred seed weight of soybean and known QTL overlapped with peak SNP. - G:T Seed weight per plant. - Kuroda et al. - 860,827 (Salas et al. - 860,827(Salas et al. - A:G Seed weight per plant 3-2_. - Liu et al. - Seed weight Li et al. - 196,800 (Rossi et al. - Seed weight Rossi et al. - Seed weight Teng et al. - G:T Seed weight . - 208,429 (Yan et al. - Seed weight Liu et al. - A:G Seed weight . - 167,274 (Kim et al. - Glyma.18G282100, 6 SNP. - Glyma.05G196300, 8 SNP;. - Glyma.05G196100, 8 SNP. - Glyma.17G178300, 8 SNP;. - Glyma.18G282200, 8 SNP. - Glyma.20G139600, 8 SNP;. - Glyma.13G182600, 12 SNP. - Glyma.20G111100, 12 SNP) were significantly associated with HSW in soybean (Fig. - The more diverse of the selected association panel in genotype and phenotype, the better universal of QTNs and. - 201,427 (Mian et al. - 831,244(Han et al. - Yao et al. - 664,504 (Yao et al. - 498,552 (Yao et al. - Seed weight Han et al. - Two QTNs (HSW-3-1 of Chr.03 and HSW-13- 2 of Chr.13) were significantly associated with HSW, and the association between these two genomic regions and. - Similarly, twelve QTNs (HSW-4-1 and HSW-4-2 of Chr.04, HSW-10-1 of Chr.10, HSW-13-3 of Chr.13, HSW-14 – 1 of Chr.14, HSW-16 – 1 of Chr.16, HSW-17-1 and HSW-17-3 of Chr.17, HSW- 18–1 of Chr.18, HSW-20-1, HSW-20-2 and HSW-20-3 of Chr.20) have also been reported . - Li et al. - (2008) identified one major QTL (Seed weight 23–2 lo- cated in Chr.12) that affects HSW using a wild soybean- derived mapping population, which has a similar genomic region to HSW-12-1 in this study [42]. - Lu et al. - next to gene IDs represent the suggested significance of the t-test at p <. - Among these identi- fied candidate genes, Glyma.03G192300 (located near HSW-3-1 of Chr.3), Glyma.05G005400 (located near HSW5–1 of Chr.5), Glyma.08G317300 (located near HSW8–2 of Chr.5), Glyma.05G196400 (located near HSW5–2 of Chr.5), Glyma.06G201700 (located near HSW6–1 of Chr.6), and Glyma.12G109500 (located near HSW-12-2 of Chr.12) have been reported to play key roles in regulating seed development and determining seed weight in crops, specific mechanisms of which still need to be analysed.. - Additional file 1: Basic genetic parameter statistics for 100-seed weight in the tested soybean population ( n = 185). - Additional file 2: Genes in 100 kbp flanking regions of peak SNP associated with 100-seed weight of soybean. - Additional file 3: Gene-based association study on 100-seed weight of soybean with 281 candidate genes. - HSW: Hundred seed weight. - SW: Seed weight. - QTL location and epistatic effect analysis of 100-seed weight using wild soybean. - Seed weight in relation to environmental conditions in California.. - Seed weight of amaranthus retroflexus in relation to moisture and length of growing season. - Molecular markers associated with seed weight in two soybean populations. - Molecular-marker analysis of seed- weight: genomic locations, gene action, and evidence for orthologous evolution among three legume species. - QTL underlying developmental behaviour of 100-seed weight of soybean. - QTL analysis of soybean seed weight across multi-genetic backgrounds and environments. - Determination of the genetic architecture of seed size and shape via;linkage and association analysis in soybean (Glycine max L. - A major and stable QTL associated with seed weight in soybean across multiple environments and genetic backgrounds. - Identification of quantitative trait loci underlying soybean ( Glycine max [L.] Merr.) seed weight including main, epistatic and QTL × environment effects in different regions of Northeast China. - Identification and validation of an over-dominant QTL controlling soybean seed weight using populations derived from Glycine max × Glycine soja. - Validation and designation of quantitative trait loci for seed protein, seed oil, and seed weight from two soybean populations. - QTL analyses of seed weight during the development of soybean ( Glycine max L. - A PP2C-1 allele underlying a quantitative trait locus enhances soybean 100- seed weight. - Identification of QTL with large effect on seed weight in a selective population of soybean with genome-wide association and fixation index analyses. - Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean ( Glycine max. - Establishment of a 100-seed weight quantitative trait locus – allele matrix of the germplasm population for optimal recombination design in soybean breeding programmes
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