Genome‐wide association mapping and candidate gene analysis for saturated fatty acid content in soybean seed

Saturated fatty acids (FA), an important component of soybean oil, plays a crucial role in the nutritional value of soybean oil through different concentration and relative proportions. In this study, an association population of 185 diverse soybean accessions was used to identify quantitative trait nucleotide (QTN) and scan candidate genes via genome‐wide association analysis (GWAS), which was based on high throughout single‐nucleotide polymorphisms (SNPs) developed via the Specific Locus Amplified Fragment Sequencing (SLAF‐seq) approach. A total of 33,149 SNPs were identified with minor allele frequencies (MAF) >4%, which covered 97% of the soybean whole genome. For the two saturated FA concentration, including palmitic acid (PA) and stearic acid (SA), up to 65 SNPs were verified via GWAS. Among them, 35 and 16 SNPs loci were the novel loci for PA and SA, respectively. There were other six loci for PA and eight loci for SA overlapped or located in the linked genomic regions reported by the previous study. Furthermore, many loci were repeated in more than two environments, and four pair of pleiotropic loci (PA‐3‐2 and SA‐3‐2, PA‐11‐2 and SA‐11‐1, PA‐12‐2 and SA‐12‐1, and PA‐17‐1 and SA‐17‐2) had similar genomic regions, which might control both PA and SA simultaneously. A total of 49 genes, which could participate in lipid biosynthesis pathway or hormone metabolism, were identified as the potential candidate genes associated with saturated FA. The identified loci with beneficial alleles and the candidate genes would be valuable for studying the molecular mechanisms of saturated FA and further for improving nutritional value of soybean oil.     

Genome‐wide association studies in elite varieties of German winter barley using single‐marker and haplotype‐based methods

Genome‐wide association studies (GWAS) became a widely used method to map qualitative and quantitative traits in plants. We compared existing single‐marker and haplotype‐based methods for GWAS with a focus on barley. Based on German winter barley cultivars, four different single‐marker and haplotype‐based methods were tested for their power to detect significant associations in a large genome with a limited number of markers. We identified significant associations for yield and quality‐related traits using the iSelect array with 3886 mapped single nucleotide polymorphism (SNP) markers in a structured population consisting of 109 genotypes. Genome simulations with different numbers of genotypes, marker densities and marker effects were used to compare different GWAS methods. Results of simulations revealed a higher power in detecting significant associations for haplotype‐ than for single‐marker approaches, but showed a higher false discovery rate for SNP detection, due to lack of correction for population structure. Our simulations revealed that a population size of about 500 individuals is required to detect QTLs explaining a small trait variance (<10%).     

Screening oat germplasm for better adaptation to cold stress in the Southern Great Plains of the United States

Oat (Avena sativa L.) is one of the most important forage crops in the Southern Great Plains of the United States. However, it is more sensitive to cold stress than other small grains. In this study, diverse oat germplasm was evaluated for winter survival across multiple years and locations in the region. Field screening started with an observation trial of 1,861 diverse genotypes in the 2012–2013 season and was followed by four seasons of replicated trials from 2013 to 2017. Selection of good winter survivors was started in 2014–2015 season. All trials were laid out in randomized complete blocks with replications of two in 2013–2014 and 2014–2015, four in 2015–2016, and three in 2016–2017. Winter survival was scored in a 1‐to‐9 scale. Data were analysed for each year and location separately. Additive main effects and multiplicative interaction (AMMI) analysis were carried out on combined data of 35 genotypes that were commonly grown in each year and location. Highly significant (p < 0.001) variations were observed among genotypes, environments and genotype‐by‐environment interaction (GEI). The first three interaction principal components (IPCs) were highly significant (p < 0.001), explaining 96% of GEI. Broad sense heritability ranged from 46% to 93%, while heritability for all environments combined was relatively low (24.6%). At the end of the two cycles (2014/2015‐to‐2016/2017) of selection, mean winter survival was improved by more than 38% per cycle compared with the base population mean. Genotypes CIav 4390, CIav 6909 and CIav 7618 showed significantly higher winter survival than the standard checks Okay and Dallas. Genotypes CIav 4390 showed 20% and 35% improvement over the standard checks Okay and Dallas, respectively. Winter survival improvement in oat will remain a difficult task because of high GEI and low heritability. The identified superior genotypes will be used as crossing parents to transfer cold tolerance genes to other elite lines.     

The Influence of Genotype–Environment Interaction on the Grain Yields of 10 Pigeonpea Cultivars Grown in Kenya

To study the importance of the effects of genotype–environment interactions on the yield of pigeonpea ( Cajanus cajan L. Millsp.), 10 early-maturing genotypes were grown in a randomized complete block design with three replications in a total of seven environments spread over five regions of Kenya between 1987 and 1988. Results indicated the presence of a substantial genotype–environment interaction effect on grain yield. The observed significant genotype–environment interaction effect is discussed in relation to its importance in pigeonpea grain yield evaluation studies. It is noted that the best genotype in one environment is not always so in other environments. Results from regression analysis indicated that this method of analysis is appropriate for describing the response of pigeonpea genotypes grown in a number of locations. Analysis of variance showed significant additive and multiplicative genotype–environment interaction effects. Only the first interaction principal component axis (IPCA) was found to be important in describing the multiplicative interaction effects. The additive main effects and multiplicative effects (AMMI) model allowed the partitioning of interaction variance into agronomically important sources (genotype groups), and the specific genotype × environment patterns that are the basis of these sources of variance were examined.     

Identification of soybean genes related to fatty acid content based on a soybean genome collinearity analysis

Seed fatty acid content is an important consideration for soybean produced for food, feed, and industrial applications. In this study, MCScanX was used to analyze the entire soybean genome to generate a collinearity block, which was then used to assess the collinearity among the soybean fatty acid quantitative trait loci (QTL) in the SoyBase database. The hub‐QTLs located in the Gm06, Gm07, and Gm10 segments were identified. The Kyoto Encyclopedia of Genes and Genomes and gene ontology databases were used to analyze the genes in hub‐QTL regions, resulting in the identification of 17 candidate genes related to soybean fatty acid content. Two lines with different fatty acid contents and a recurrent parent were selected from a chromosome segment substitution line library for a subsequent quantitative real‐time polymerase chain reaction (qRT‐PCR) assay to verify the candidate gene expression patterns. Four genes were related to the total soybean fatty acid content, while three genes were related to the content of specific fatty acid types. The results of this study may be relevant for the fine mapping of soybean fatty acid QTLs/genes.     

Growth response of 98 barley (Hordeum vulgare L.) genotypes to elevated CO2 and identification of related quantitative trait loci using genome‐wide association studies

Elevated CO₂ (eCO₂) concentrations can stimulate crop growth, but little is known about intraspecific variability in the response to eCO₂ and the underlying genetics in cereals. Field experiments over two years with 98 barley genotypes were conducted in open‐top chambers (OTCs) under ambient CO₂ (400 ppm) and eCO₂ (700 ppm) concentrations. At crop maturity, different fractions of aboveground biomass (AGB) were measured, and genome‐wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL). Averaged across all genotypes, eCO₂ significantly enhanced AGB by 15%, while the increase in culm and ear biomass alone was not significant. The AGB response to eCO₂ of the individual genotypes ranged from c. ?36% to +95% compared with ambient CO₂ (aCO₂), showing a large variability of growth responses. In GWAS, 51 associations between SNP markers and the relative changes (eCO₂/aCO₂) in biomass were detected on different chromosomes. Loci potentially involved in biomass alterations under eCO₂ were identified. The wide range of variability in responses might be exploited by marker‐based breeding for climate‐resilient barley.     

应用基因型与播期互作效应分析水稻光温敏核不育系对光周期和温度的育性敏感性

应用AMMI模型、线性回归模型和系统聚类分析方法分析了9个水稻光温敏核不育系和2个对照不育系(培矮64,浙农大11S),在9个播期环境下花粉和种子育性的变化动态。从AMMI两维图可直观看到基因型与播期的交互模式,根据最大互作效应主成分轴IPCA1值和花粉育性平均值将不育系分成3个集团。第1集团是培矮64(1),浙大21S（10）和浙大22S（11）,具有较低花粉育性平均值和较高IPCA1值,表明它们对温度变化比较敏感,且有较大的负向互作效应。第2集团是浙农大11S（2）、浙大4S（3）、浙大7S（6）、浙大8S（7）和浙大9S（8）,其花粉育种平均值和互作值相对较低,变动在0.013-0.276间,暗示花粉育性对播期敏感度低。第3集团是浙大6S（5）和浙大10S（9）,花粉育性平均值高,互作效应大,该2个不育系尚有分离,且花粉育性对播期反映敏感度高。对光敏型和温敏型不育系而言,基因型IPCA值大小主要反映它们对光周期和温度敏感性强弱。花粉和种子育性在基因型、播期及其互作效应上都存在极显著差异。本文还提出利用育性相对稳定性的定量指标D_i界定光温敏核不育的育性稳定性,分析表明,Di值与育种实践结果较为接近。基于AMMI模型的基因型主效应和互作效应分析可以明确划分不育系的不育期、育性转换期和可育期,并将水稻光敏型温敏型不育系区分开,因而该模型可为不育系应用于种子生产提供信息和依据。