iSelect BeadChip Research Articles

Genome-wide association studies of meat color indicators in the F2 chickens resource population

The aim of the study was to search for SNPs and identify candidate genes associated with color characteristics of chicken meat. The object of the research was F2 chickens of the resource population obtained through interbreeding of two breeds with contrasting meat productivity indicators - Russian White and Cornish. The obtaining resource population of chickens was phenotyped at the age of 42 days for breast and thigh meat color according to the a*, b* and L* color scale using a spectrophotometer, and genotyped using the Illumina Chicken iSelect BeadChip 60k DNA chip. The GWAS identified 30 significant SNPs (P≤0.0001) associated with meat color in F2 chickens of the resource population, including 12 SNPs associated with breast muscle color traits and 18 SNPs associated with thigh color parameters. The 214 genes were identified in the SNPs region, including 10 genes at the positions of these SNPs - BRAF, ENO2, UBE3D, RGS6, ATP13A3, RHBDD1, MB21D2, BHLHE23, PIAS4, and MLLT1, localized on chromosomes GGA1, GGA3, GGA5, GGA9, GGA20 and GGA28. The results have practical significance for understanding the molecular genetic mechanisms in the formation and manifestation of traits that determine the color characteristics of chicken meat and can be used in subsequent studies in genomic selection of chickens to improve the quality of meat.

Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population.

For genomic selection to enhance the efficiency of broiler production, finding SNPs and candidate genes that define the manifestation of main selected traits is essential. We conducted a genome-wide association study (GWAS) for growth and meat productivity traits of roosters from a chicken F2 resource population (n = 152). The population was obtained by crossing two breeds with contrasting phenotypes for performance indicators, i.e., Russian White (slow-growing) and Cornish White (fast-growing). The birds were genotyped using the Illumina Chicken 60K SNP iSelect BeadChip. After LD filtering of the data, 54,188 SNPs were employed for the GWAS analysis that allowed us to reveal significant specific associations for phenotypic traits of interest and economic importance. At the threshold value of p < 9.2 × 10-7, 83 SNPs associated with body weight at the age of 28, 42, and 63 days were identified, as well as 171 SNPs associated with meat qualities (average daily gain, slaughter yield, and dressed carcass weight and its components). Moreover, 34 SNPs were associated with a group of three or more traits, including 15 SNPs significant for a group of growth traits and 5 SNPs for a group of meat productivity indicators. Relevant to these detected SNPs, nine prioritized candidate genes associated with the studied traits were revealed, including WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. The found SNPs and candidate genes can serve as genetic markers for growth and meat performance characteristics in chicken breeding in order to achieve genetic improvement in broiler production.

Functional role of genes in ROH sites in &lt;i&gt;Czech Golden&lt;/i&gt; chickens

Modern technologies make it possible to identify homozygous chromosome regions that have arisen as a result of animal selection. The article presents the results obtained in the genotyping of chickens using the Illumina Chicken 60KSNP iSelect Bead Chip. For the first time, a genome-wide analysis of the extended homozygous SNP sequences (ROH) was performed in the genome of Czech Golden hens. The average number of ROH segments in the chicken genome was 143 ± 8. ROH segments are usually randomly distributed in chicken chromosomes. It has been proposed to prohibit the use of heterozygous SNPs in ROH segments to prevent overestimation of ROH data. The average inbreeding coefficient in chickens calculated from ROH data was 0.34 ± 0.03. ROH islands were found on chromosomes 2, 3, 9 and 22.They contain genesassociatedwithimmunity, degradation of neurons, Fabricius bursa weight, obesity, feather pigmentation, and regulation of hit-shock genes. In the Czech Golden chicken breed, selection and the associated inbreeding have therefore influenced the genes involved in several biological processes.

Novel genetic resources associated with sucrose and stachyose content through genome-wide association study in soybean (Glycine max (L.) Merr.).

The nutritional value of soybean [Glycine max (L.) Merr.] for animals is influenced by soluble carbohydrates, such as sucrose and stachyose. Although sucrose is nutritionally desirable, stachyose is an antinutrient causing diarrhea and flatulence in non-ruminant animals. We conducted a genome-wide association study of 220 soybean accessions using 21,317 single nucleotide polymorphisms (SNPs) from the SoySNP50K iSelect Beadchip data to identify significant SNPs associated with sucrose and stachyose content. Seven significant SNPs were identified for sucrose content across chromosomes (Chrs.) 2, 8, 12, 17, and 20, while thirteen significant SNPs were identified for stachyose content across Chrs. 2, 5, 8, 9, 10, 13, 14, and 15. Among those significant SNPs, three sucrose-related SNPs on Chrs. 8 and 17 were novel, while twelve stachyose-related SNPs on Chrs. 2, 5, 8, 9, 10, 13, 14, and 15 were novel. Based on Phytozome, STRING, and GO annotation, 17 and 24 candidate genes for sucrose and stachyose content, respectively, were highly associated with the carbohydrate metabolic pathway. Among these, the publicly available RNA-seq Atlas database highlighted four candidate genes associated with sucrose (Glyma.08g361200 and Glyma.17g258100) and stachyose (Glyma.05g025300 and Glyma.13g077900) content, which had higher gene expression levels in developing seed and multiple parts of the soybean plant. The results of this study will extend knowledge of the molecular mechanism and genetic basis underlying sucrose and stachyose content in soybean seed. Furthermore, the novel candidate genes and SNPs can be valuable genetic resources that soybean breeders may utilize to modify carbohydrate profiles for animal and human usage.

Genome-wide association analysis identified consistent QTL for seed yield in a soybean diversity panel tested across multiple environments.

Improving seed yield is one of the main targets of soybean [Glycine max (L.) Merr.] breeding. Identification of loci that influence productivity and understanding their genetic mechanism will help marker-assisted trait introgression. The present study evaluated a diverse panel of 541 soybean genotypes consisting of three maturity groups (MGs III-V) in four environments in Kansas, U.S. Data on seed yield, seed weight, shattering resistance, days to maturity, and plant height showed significant genotype, environmental, and genotype × environment interaction variations. Seed yield and shattering had moderate broad-sense heritability (<85%), while the rest of the traits showed high broad-sense heritability (>90%). The SoySNP50K iSelect BeadChip dataset was used to identify significantly associated loci via genome-wide association studies (GWAS). A total of 19 single-nucleotide polymorphisms (SNPs) were significantly associated with seed yield. Particularly, two stable seed yield quantitative trait loci (QTL) on chromosomes 9 and 17 were consistently detected in at least three out of four environments. Candidate gene analysis surrounding seed yield QTL on chromosome 9 showed that Glyma.09G048900, an oxygen binding protein, was the closest to the QTL peak. Similarly, Glyma.17G090200 and Glyma.17G090400 were within 20-kb region of the seed yield QTL on chromosome 17. The candidate genes warrant further analysis to determine their functional mechanisms and develop markers for seed yield improvement.

Analysis of the accumulation of homozygosity regions in chickens of the Pushkin breed using data from whole genome genotyping

BACKGROUND: The search for genetic factors influencing the formation of the productive qualities of new breeding forms and populations of chickens is an important direction in the study of animal genomes. The knowledge gained can be in demand in projects to create modern domestic highly productive poultry lines.&#x0D; AIM: To conduct a comparative analysis of the localization of homozygous regions on chicken chromosomes in representatives of four breeds from the Genetic Collection of Rare and Endangered Breeds of Chickens to identify traces of selection and search for genes associated with productive traits.&#x0D; MATERIALS AND METHODS: Based on the bioinformatic analysis of 42275 SNP markers identified using the Illumina Chicken 60K SNP iSelect BeadChip microchip, data were obtained on the distribution of homozygous regions of four populations of chickens at the Genetic Collection of Rare and Endangered Breeds of Chickens (VNIIGRZH, St. Petersburg): Pushkinskaya (n = 20), Cornish (n = 22), Black-and-White Australorp (n = 20), Russian White (n = 23).&#x0D; RESULTS AND CONCLUSIONS: In chickens of the Pushkin breed, the observed heterozygosity was 0.372 0.004, which is higher than in other populations. According to the inbreeding index, based on the analysis of homozygous regions, the maximum level was found in Cornish chickens. For the Pushkin breed, the accumulation of homozygous regions on chromosomes 1, 2, 6, 8 was noted. The genes annotated in these loci are evidence of intensive selection.

Multi-donor × elite-based populations reveal QTL for low-lodging wheat

Key messageLow-lodging high-yielding wheat germplasm and SNP-tagged novel alleles for lodging were identified in a process that involved selecting donors through functional phenotyping for underlying traits with a designed phenotypic screen, and a crossing strategy involving multiple-donor × elite populations.Lodging is a barrier to achieving high yield in wheat. As part of a study investigating the potential to breed low-lodging high-yielding wheat, populations were developed crossing four low-lodging high-yielding donors selected based on lodging related traits, with three cultivars. Lodging was evaluated in single rows in an early generation and subsequently in plots in 2 years with contrasting lodging environment. A large number of lines lodged less than their recurrent parents, and some were also higher yielding. Heritability for lodging was high, but the genetic correlation between contrasting environments was intermediate-low. Lodging genotypic rankings in single rows did not correlate well with plots. Populations from the highest lodging background were genotyped (90 K iSelect BeadChip array). Fourteen markers on nine chromosomes were associated with lodging, differing under high- versus low-lodging conditions. Of the fourteen markers, ten were found to co-locate with previously identified QTL for lodging-related traits or at homoeologous locations for previously identified lodging-related QTL, while the remaining four markers (in chromosomes 2D, 4D, 7B and 7D) appear to map to novel QTL for lodging. Lines with more favourable markers lodged less, suggesting value in these markers as a selection tool. This study demonstrates that the combination of donor functional phenotyping, screen design and crossing strategy can help identify novel alleles in germplasm without requiring extensive bi-parental populations.

Genetic variation among 481 diverse soybean accessions, inferred from genomic re-sequencing

We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions.

Assessing the effects of rare alleles and linkage disequilibrium on estimates of genetic diversity in the chicken populations

Phenotypic diversity in poultry has been mainly driven by artificial selection and genetic drift. These led to the adaptation to the environment and the development of specific phenotypic traits of chickens in response to their economic use. This study evaluated genetic diversity within and between Russian breeds and populations using Illumina Chicken 60K SNP iSelect BeadChip by analysing genetic differences between populations with Hudson's fixation index (FST statistic) and heterozygosity. We estimated the effect of rare alleles and linkage disequilibrium (LD) on these measurements. To assess the effect of LD on the genetic diversity population, we carried out the LD-based pruning (LD<0.5 and LD<0.1) for seven chicken populations combined (I) or separately (II). LD pruning was specific for different dataset groups. Because of the noticeably large sample size in the Russian White RG population, pruning was substantial for Dataset I, and FST values were only positive when LD<0.1 pruning was applied. For Dataset II, the LD pruning results were confirmed by examining heterozygosity and alleles' frequency distribution. LD between single nucleotide polymorphisms was consistent across the seven chicken populations, except the Russian White RG population with the smallest r2 values and the largest effective population size. Our findings suggest to study variability in each population LD pruning has to be carried separately not after merging to avoid bias in estimates.

Genetic characterization for lesion mimic and other traits in relation to spot blotch resistance in spring wheat

Lesion mimic (Lm) mutants display hypersensitive responses (HR) without any pathogen attack; their symptoms are similar to those produced by a pathogen and result in cell death. In wheat, such mutants have been reported to be resistant against leaf rust due to their biotrophic nature. However, Lm mutants tend to encourage spot blotch (SB) disease caused by Bipolarissorokiniana since dead cells facilitate pathogen multiplication. In this study, 289 diverse wheat germplasm lines were phenotyped in three consecutive growing seasons (2012-2015). Genotype data was generated using the Illumina iSelect beadchip assay platform for wheat germplasm lines. A total of 13,589 single-nucleotide polymorphisms (SNPs) were selected andused for further association mapping. Lm was positively associated with Area Under Disease Progress Curve (AUDPC) for SB but negatively with glaucous index (GI), leaf tip necrosis (Ltn) and latent period (LP). Ltn had a negative association with AUDPC and Lm but a positive one with LP. In a genome-wide association study (GWAS), 29 markers were significantly associated with these traits and 27 were an notated. Seven SNP markers associated with Lm were on chromosome 6A; another on 1B was found to be linked with Ltn. Like wise, seven SNP markers were associated with GI; one on chromosome 6A with the others on 6B. Five SNP markers on chromosomes 3B and 3Dwere significantly correlated with LP, while nine SNP markers on chromosomes 5A and 5B were significantly associated with AUDPC for SB. This study is the first to explore the interaction in wheat between Lm mutants and the hemibiotrophic SB pathogen B.sorokiniana.

Identification of candidate chromosome region of Sbwm1 for Soil-borne wheat mosaic virus resistance in wheat

Soil-borne wheat mosaic virus (SBWMV) causes a serious viral disease that can significantly reduce grain yield in winter wheat worldwide. Using resistant cultivars is the only feasible strategy to reduce the losses caused by SBWMV. To fine map the resistance gene Sbwm1, 205 wheat accessions was genotyped using wheat Infinium iSelect Beadchips with 90 K SNPs. Association analysis identified 35 SNPs in 12 wheat genes and one intergenic SNP in the Sbwm1 region that showed a significant association with SBWMV resistance. Those SNPs were converted into Kompetitive Allele-Specific Polymerase assays (KASP) and analyzed in two F6-derived recombinant inbred line (RIL) populations derived from the crosses between two resistant cultivars ‘Wesley’ and ‘Deliver’ and a susceptible line ‘OK03825-5403-6’. Linkage analysis mapped this gene on chromosome 5D at intervals of 5.1 cM and 3.4 cM in the two populations, respectively. The two flanking markers in both populations delimited the gene to a 620 kb region where 19 genes were annotated. Comparative analysis identified a syntenic region of 660 kb in Ae. tauschii with 18 annotated genes and a syntenic region in chromosome 1 of B. distachyon. The candidate region includes several disease resistance related genes and we identified a PTI1-like tyrosine-protein kinase 1 gene as a putative candidate gene for Sbwm1. The two flanking SNPs for Sbwm1 can effectively separate the resistant and susceptible lines in a new diversity panel of 159 wheat germplasm. The results from this study lay a solid foundation for the cloning, functional characterization and marker-assisted selection of Sbwm1.

Genome-Wide Association Analyses Reveal Genomic Regions Controlling Canopy Wilting in Soybean.

Drought stress causes the greatest soybean [Glycine max (L.) Merr.] yield losses among the abiotic stresses in rain-fed U.S. growing areas. Because less than 10% of U.S. soybean hectares are irrigated, combating this stress requires soybean plants which possess physiological mechanisms to tolerate drought for a period of time. Phenotyping for these mechanisms is challenging, and the genetic architecture for these traits is poorly understood. A morphological trait, slow or delayed canopy wilting, has been observed in a few exotic plant introductions (PIs), and may lead to yield improvement in drought stressed fields. In this study, we visually scored wilting during stress for a panel of 162 genetically diverse maturity group VI-VIII soybean lines genotyped with the SoySNP50K iSelect BeadChip. Field evaluation of canopy wilting was conducted under rain-fed conditions at two locations (Athens, GA and Salina, KS) in 2015 and 2016. Substantial variation in canopy wilting was observed among the genotypes. Using a genome-wide association mapping approach, 45 unique SNPs that tagged 44 loci were associated with canopy wilting in at least one environment with one region identified in a single environment and data from across all environments. Several new soybean accessions were identified with canopy wilting superior to those of check genotypes. The germplasm and genomic regions identified can be used to better understand the slow canopy wilting trait and be incorporated into elite germplasm to improve drought tolerance in soybean.

Identification of mixed linkage β‐glucan quantitative trait loci and evaluation of AsCslF6 homoeologs in hexaploid oat

AbstractHexaploid oat (Avena sativa L.) is a commercially important cereal crop due in part to (1‐3,1‐4)‐β‐D‐glucan (β‐glucan), a hemicellulose important to human health. Cellulose synthase‐like (Csl) genes have been shown to contribute to β‐glucan production, with CslF6 likely being of major importance. Here, we report a genome‐wide association study (GWAS) to identify quantitative trait loci (QTLs) controlling β‐glucan production in oat. Three panels of elite accessions (Spring, Winter, and World Diversity) of oat were grown in multiple North American locations and genotyped using the Oat 6K Custom Infinium iSelect BeadChip. Independent GWAS were performed on each set of accessions and locations, with a meta‐analysis identifying 58 significantly associated markers. Synteny with the barley (Hordeum vulgare L.) genome identified four major regions of interest revealing the CslF and CslH gene families along with UGPase and AGPase as candidate genes. Subgenome‐specific expression of the A, C, and D AsCslF6 homoeologs revealed that AsCslF6_C is the least expressed in all tissue types and time points, with low‐β‐glucan varieties recording the highest proportion of AsCslF6_C expression. Linkage mapping of the homoeologs placed AsCslF6_D on consensus linkage group Mrg02 overlapping with QTL 2.2 and AsCslF6_A on Mrg12 flanked by markers associated with QTL 12.2. Many QTLs identified in this study were homoeologous, representing different gene copies duplicated in ancestral genomes, suggesting that multiple homoeologous copies of β‐glucan biosynthesis genes are contributing to the overall phenotype.

Unraveling the genetic architecture for carbon and nitrogen related traits and leaf hydraulic conductance in soybean using genome-wide association analyses

BackgroundDrought stress is a major limiting factor of soybean [Glycine max (L.) Merr.] production around the world. Soybean plants can ameliorate this stress with improved water-saving, sustained N2 fixation during water deficits, and/or limited leaf hydraulic conductance. In this study, carbon isotope composition (δ13C), which can relate to variation in water-saving capability, was measured. Additionally, nitrogen isotope composition (δ15N) and nitrogen concentration that relate to nitrogen fixation were evaluated. Decrease in transpiration rate (DTR) of de-rooted soybean shoots in a silver nitrate (AgNO3) solution compared to deionized water under high vapor pressure deficit (VPD) conditions was used as a surrogate measurement for limited leaf hydraulic conductance. A panel of over 200 genetically diverse soybean accessions genotyped with the SoySNP50K iSelect BeadChips was evaluated for the carbon and nitrogen related traits in two field environments (Athens, GA in 2015 and 2016) and for transpiration response to AgNO3 in a growth chamber. A multiple loci linear mixed model was implemented in FarmCPU to perform genome-wide association analyses for these traits.ResultsThirty two, 23, 26, and nine loci for δ13C, δ15N, nitrogen concentration, and transpiration response to AgNO3, respectively, were significantly associated with these traits. Candidate genes that relate to drought stress tolerance enhancement or response were identified near certain loci that could be targets for improving and understanding these traits. Soybean accessions with favorable breeding values were also identified. Low correlations were observed between many of the traits and the genetic loci associated with each trait were largely unique, indicating that these drought tolerance related traits are governed by different genetic loci.ConclusionsThe genomic regions and germplasm identified in this study can be used by breeders to understand the genetic architecture for these traits and to improve soybean drought tolerance. Phenotyping resources needed, trait heritability, and relationship to the target environment should be considered before deciding which of these traits to ultimately employ in a specific breeding program. Potential marker-assisted selection efforts could focus on loci which explain the greatest amount of phenotypic variation for each trait, but may be challenging due to the quantitative nature of these traits.

Positional Cloning of the Flowering Time QTL qFT12-1 Reveals the Link Between the Clock Related PRR Homolog With Photoperiodic Response in Soybeans.

Flowering time and maturity are important agronomic traits for soybean cultivars to adapt to different latitudes and achieve maximal yield. Genetic studies on genes and quantitative trait loci (QTL) that control flowering time and maturity are extensive. In particular, the molecular bases of E1-E4, E6, E9, E10, and J have been deciphered. For a better understanding of regulation of flowering time gene networks, we need to understand if more molecular factors carrying different biological functions are also involved in the regulation of flowering time in soybeans. We developed a population derived from a cross between a landrace Jilincailihua (male) and a Chinese cultivar Chongnong16 (female). Both parents carry the same genotypes of E1e2E3HaE4 at E1, E2, E3, and E4 loci. Nighty-six individuals of the F2 population were genotyped with Illumina SoySNP8k iSelect BeadChip. A total of 2,407 polymorphic single nucleotide polymorphism (SNP) markers were used to construct a genetic linkage map. One major QTL, qFT12-1, was mapped to an approximately 567-kB region on chromosome 12. Genotyping and phenotyping of recombinant plant whose recombination events were occurring within the QTL region allowed us to narrow down the QTL region to 56.4 kB, in which four genes were annotated. Allelism and association analysis indicated Glyma.12G073900, a PRR7 homolog, is the strongest candidate gene for qFT12-1. The findings of this study disclosed the possible involvement of circadian clock gene in flowering time regulation of soybeans.

Identification of New QTL Contributing to Barley Yellow Dwarf Virus-PAV (BYDV-PAV) Resistance in Wheat.

Barley yellow dwarf (BYD) is a major virus disease which dramatically reduces wheat yield. Introducing BYD resistance genes into commercial varieties has been proven to be effective in reducing damage caused by barley yellow dwarf virus (BYDV). However, only one major resistance gene is readily deployable for breeding; Bdv2 derived from Thinopyrum intermedium is deployed as a chromosomal translocation. In this study, a double haploid (DH) population was developed from a cross between XuBYDV (introduced from China showing very good resistance to BYD) and H-120 (a BYD-sensitive Chinese accession), and was used to identify QTL for BYD resistance. The population was genotyped using an Infinium iSelect bead chip array targeting 90K gene-based SNPs. The disease resistance of DH lines inoculated with BYDV was assessed at the heading stage. The infections were assessed by tissue blot immunoassay (TBIA). Three new QTL were identified on chromosomes 5A, 6A, and 7A for both symptom and TBIA, with all three resistance alleles being inherited from XuBYDV. Some DH lines with the resistance alleles from all three QTL showed high level resistance to BYD. These new QTL will be useful in breeding programs for pyramiding BYD resistance genes.

Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program.

Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.

Identification of new loci for salt tolerance in soybean by high-resolution genome-wide association mapping

BackgroundSalinity is an abiotic stress that negatively affects soybean [Glycine max (L.) Merr.] seed yield. Although a major gene for salt tolerance was identified and consistently mapped to chromosome (Chr.) 3 by linkage mapping studies, it does not fully explain genetic variability for tolerance in soybean germplasm. In this study, a genome-wide association study (GWAS) was performed to map genomic regions for salt tolerance in a diverse panel of 305 soybean accessions using a single nucleotide polymorphism (SNP) dataset derived from the SoySNP50K iSelect BeadChip. A second GWAS was also conducted in a subset of 234 accessions using another 3.7 M SNP dataset derived from a whole-genome resequencing (WGRS) study. In addition, three gene-based markers (GBM) of the known gene, Glyma03g32900, on Chr. 3 were also integrated into the two datasets. Salt tolerance among soybean lines was evaluated by leaf scorch score (LSS), chlorophyll content ratio (CCR), leaf sodium content (LSC), and leaf chloride content (LCC).ResultsFor both association studies, a major locus for salt tolerance on Chr. 3 was confirmed by a number of significant SNPs, of which three gene-based SNP markers, Salt-20, Salt14056 and Salt11655, had the highest association with all four traits studied. Also, additional genomic regions on Chrs. 1, 8, and 18 were found to be associated with various traits measured in the second GWAS using the WGRS-derived SNP dataset.ConclusionsA region identified on Chr. 8 was identified to be associated with all four traits and predicted as a new minor locus for salt tolerance in soybean. The candidate genes harbored in this minor locus may help reveal the molecular mechanism involved in salt tolerance and to improve tolerance in soybean cultivars. The significant SNPs will be useful for marker-assisted selection for salt tolerance in soybean breeding programs.

Genome-Wide Association Analysis Pinpoints Additional Major Genomic Regions Conferring Resistance to Soybean Cyst Nematode (Heterodera glycines Ichinohe).

Soybean cyst nematode (Heterodera glycines Ichinohe) (SCN) is the most destructive pest affecting soybeans [Glycine max (L.) Merr.] in the U.S. To date, only two major SCN resistance alleles, rhg1 and Rhg4, identified in PI 88788 (rhg1) and Peking (rhg1/Rhg4), residing on chromosomes (Chr) 18 and 8, respectively, have been widely used to develop SCN resistant cultivars in the U.S. Thus, some SCN populations have evolved to overcome the PI 88788 and Peking derived resistance, making it a priority for breeders to identify new alleles and sources of SCN resistance. Toward that end, 461 soybean accessions from various origins were screened using a greenhouse SCN bioassay and genotyped with Illumina SoySNP50K iSelect BeadChips and three KASP SNP markers developed at the Rhg1 and Rhg4 loci to perform a genome-wide association study (GWAS) and a haplotype analysis at the Rhg1 and Rhg4 loci. In total, 35,820 SNPs were used for GWAS, which identified 12 SNPs at four genomic regions on Chrs 7, 8, 10, and 18 that were significantly associated with SCN resistance (P < 0.001). Of those, three SNPs were located at Rhg1 and Rhg4, and 24 predicted genes were found near the significant SNPs on Chrs 7 and 10. KASP SNP genotyping results of the 462 accessions at the Rhg1 and Rhg4 loci identified 30 that carried PI 88788-type resistance, 50 that carried Peking-type resistance, and 58 that carried neither the Peking-type nor the PI 88788-type resistance alleles, indicating they may possess novel SCN resistance alleles. By using two subsets of SNPs near the Rhg1 and Rhg4 loci obtained from SoySNP iSelect BeadChips, a haplotype analysis of 461 accessions grouped those 58 accessions differently from the accessions carrying Peking or PI 88788 derived resistance, thereby validating the genotyping results at Rhg1 and Rhg4. The significant SNPs, candidate genes, and newly characterized SCN resistant accessions will be beneficial for the development of DNA markers to be used for marker-assisted breeding and developing soybean cultivars carrying novel sources of SCN resistance.

Mapping Quantitative Trait Loci for Tolerance to Pythium irregulare in Soybean (Glycine max L.).

Pythium root rot is one of the significant diseases of soybean (Glycine max (L.) Merr.) in the United States. The causal agent of the disease is a soil-borne oomycete pathogen Pythium irregulare, the most prevalent and aggressive species of Pythium in North Central United States. However, few studies have been conducted in soybean for the identification of quantitative trait loci (QTL) for tolerance to P. irregulare. In this study, two recombinant inbred line (RIL) populations (designated as POP1 and POP2) were challenged with P. irregulare (isolate CMISO2-5-14) in a greenhouse assay. POP1 and POP2 were derived from ‘E09014’ × ‘E05226-T’ and ‘E05226-T’ × ‘E09088’, and contained 113 and 79 lines, respectively. Parental tests indicated that ‘E05226-T’ and ‘E09014’ were more tolerant than ‘E09088’, while ‘E09088’ was highly susceptible to the pathogen. The disease indices, root weight of inoculation (RWI) and ratio of root weight (RRW) of both populations showed near normal distributions, with transgressive segregation, suggesting the involvement of multiple QTL from both parents contributed to the tolerance. All the lines were genotyped using Illumina Infinium BARCSoySNP6K iSelect BeadChip and yielded 1373 and 1384 polymorphic markers for POP1 and POP2, respectively. Notably, despite high density, polymorphic markers coverage was incomplete in some genomic regions. As such, 28 and 37 linkage groups were obtained in POP1 and POP2, respectively corresponding to the 20 soybean chromosomes. Using RRW, one QTL was identified in POP1 on Chromosome 20 that explained 12.7–13.3% of phenotypic variation. The desirable allele of this QTL was from ‘E05226-T’. Another QTL was found in POP2 on Chromosome 11. It explained 15.4% of the phenotypic variation and the desirable allele was from ‘E09088’. However, no QTL were identified using RWI in either population. These results supported that RRW was more suitable to be used to evaluate P. irregulare tolerance in soybean.