The effect of cycles of genomic selection on the wheat (T. aestivum) genome.

KEY MESSAGE: We documented changes in the wheat genome attributed to genomic selection including loss of diversity, and changes in population structure and linkage disequilibrium patterns. We conclude that training and prediction populations need to co-evolve instead of the use of a static training population. Genomic selection (GS) is widely used in plant breeding to shorten breeding cycles. Our objective was to assess the impact of rapid cycling GS on the wheat genome. We used 3927 markers to genotype a training population (YTP) and individuals from five cycles (YC1-YC5) of GS for grain yield. We assessed changes of allele frequency, genetic distance, population structure, and linkage disequilibrium (LD). We found 27.3% of all markers had a significant allele frequency change by YC5, 18% experienced a significant change attributed to selection, and 9.3% had a significant change due to either drift or selection. A total of 725 of 3927 markers were fixed by YC5 with selection fixing 7.3% of the 725 markers. The genetic distance between cycles increased over time. The Fst value of 0.224 between YTP and YC5 indicates their relationship was low. The number LD blocks decreased over time and the correlation between LD matrices also decreased over time. Overall, we found a reduction in genetic diversity, increased genetic differentiation of cycles from the training population, and restructuring of the LD patterns over cycles. The accuracy of GS depends on the genomic similarity of the training population and the prediction populations. Our results show that the similarity can decline rapidly over cycles of GS and compromise the predictive ability of the YTP-based model. Our results support implementing a GS scheme where the training and prediction populations co-evolve instead of the use of a static training population.

Joint linkage QTL analyses for partial resistance to Phytophthora sojae in soybean using six nested inbred populations with heterogeneous conditions.

Partial resistance to Phytophthora sojae in soybean is controlled by multiple quantitative trait loci (QTL). With traditional QTL mapping approaches, power to detect such QTL, frequently of small effect, can be limited by population size. Joint linkage QTL analysis of nested recombinant inbred line (RIL) populations provides improved power to detect QTL through increased population size, recombination, and allelic diversity. However, uniform development and phenotyping of multiple RIL populations can prove difficult. In this study, the effectiveness of joint linkage QTL analysis was evaluated on combinations of two to six nested RIL populations differing in inbreeding generation, phenotypic assay method, and/or marker set used in genotyping. In comparison to linkage analysis in a single population, identification of QTL by joint linkage analysis was only minimally affected by different phenotypic methods used among populations once phenotypic data were standardized. In contrast, genotyping of populations with only partially overlapping sets of markers had a marked negative effect on QTL detection by joint linkage analysis. In total, 16 genetic regions with QTL for partial resistance against P. sojae were identified, including four novel QTL on chromosomes 4, 9, 12, and 16, as well as significant genotype-by-isolate interactions. Resistance alleles from PI 427106 or PI 427105B contributed to a major QTL on chromosome 18, explaining 10-45% of the phenotypic variance. This case study provides guidance on the application of joint linkage QTL analysis of data collected from populations with heterogeneous assay conditions and a genetic framework for partial resistance to P. sojae.

Novel quantitative trait loci for partial resistance to Phytophthora sojae in soybean PI 398841.

Phytophthora root and stem rot caused by Phytophthora sojae Kaufmann and Gerdemann is one of the most severe soybean [Glycine max (L.) Merr] diseases in the USA. Partial resistance is as effective in managing this disease as single-gene (Rps gene)-mediated resistance and is more durable. The objective of this study was to identify quantitative trait loci (QTL) associated with partial resistance to P. sojae in PI 398841, which originated from South Korea. A population of 305 F7:8 recombinant inbred lines derived from a cross of OX20-8 × PI 398841 was used to evaluate partial resistance against P. sojae isolate C2S1 using a tray test. Composite interval mapping using a genome-wide logarithm of odd (LOD) threshold detected three QTL on chromosomes 1, 13, and 18, which individually explained 4-16 % of the phenotypic variance. Seven additional QTL, accounting for 2-3 % of phenotypic variance each, were identified using chromosome-wide LOD thresholds. Seven of the ten QTL for resistance to P. sojae were contributed by PI 398841. Seven QTL co-localized with known Rps genes and previously reported QTL for soil-borne root pathogens, isoflavone, and seed oil. Three QTL on chromosomes 3, 13, and 18 co-localized with known Rps genes, but PI 398841 did not exhibit an Rps gene-mediated resistance response following inoculation with 48 different isolates of P. sojae. PI 398841 is potentially a source of novel genes for improving soybean cultivars for partial resistance to P. sojae.

Genome-Wide Association Study Reveals Novel Genetic Loci for Quantitative Resistance to Septoria Tritici Blotch in Wheat (Triticum aestivum L.).

Septoria tritici blotch, caused by the fungus Zymoseptoria titici, poses serious and persistent challenges to wheat cultivation in Ethiopia and worldwide. Deploying resistant cultivars is a major component of controlling septoria tritici blotch (STB). Thus, the objective of this study was to elucidate the genomic architecture of STB resistance in an association panel of 178 bread wheat genotypes. The association panel was phenotyped for STB resistance, phenology, yield, and yield-related traits in three locations for 2 years. The panel was also genotyped for single nucleotide polymorphism (SNP) markers using the genotyping-by-sequencing (GBS) method, and a total of 7,776 polymorphic SNPs were used in the subsequent analyses. Marker-trait associations were also computed using a genome association and prediction integrated tool (GAPIT). The study then found that the broad-sense heritability for STB resistance ranged from 0.58 to 0.97 and 0.72 to 0.81 at the individual and across-environment levels, respectively, indicating the presence of STB resistance alleles in the association panel. Population structure and principal component analyses detected two sub-groups with greater degrees of admixture. A linkage disequilibrium (LD) analysis in 338,125 marker pairs also detected the existence of significant (p ≤ 0.01) linkage in 27.6% of the marker pairs. Specifically, in all chromosomes, the LD between SNPs declined within 2.26-105.62 Mbp, with an overall mean of 31.44 Mbp. Furthermore, the association analysis identified 53 loci that were significantly (false discovery rate, FDR, <0.05) associated with STB resistance, further pointing to 33 putative quantitative trait loci (QTLs). Most of these shared similar chromosomes with already published Septoria resistance genes, which were distributed across chromosomes 1B, 1D, 2A, 2B, 2D, 3A,3 B, 3D, 4A, 5A, 5B, 6A, 7A, 7B, and 7D. However, five of the putative QTLs identified on chromosomes 1A, 5D, and 6B appeared to be novel. Dissecting the detected loci on IWGSC RefSeq Annotation v2.1 revealed the existence of disease resistance-associated genes in the identified QTL regions that are involved in plant defense responses. These putative QTLs explained 2.7-13.2% of the total phenotypic variation. Seven of the QTLs (R 2 = 2.7-10.8%) for STB resistance also co-localized with marker-trait associations (MTAs) for agronomic traits. Overall, this analysis reported on putative QTLs for adult plant resistance to STB and some important agronomic traits. The reported and novel QTLs have been identified previously, indicating the potential to improve STB resistance by pyramiding QTLs by marker-assisted selection.

Polygenic inheritance of canopy wilting in soybean [Glycine max (L.) Merr.].

As water demand for agriculture exceeds water availability, cropping systems need to become more efficient in water usage, such as deployment of cultivars that sustain yield under drought conditions. Soybean cultivars differ in how quickly they wilt during water-deficit stress, and this trait may lead to yield improvement during drought. The objective of this study was to determine the genetic mechanism of canopy wilting in soybean using a mapping population of recombinant inbred lines (RILs) derived from a cross between KS4895 and Jackson. Canopy wilting was rated in three environments using a rating scale of 0 (no wilting) to 100 (severe wilting and plant death). Transgressive segregation was observed for the RIL population with the parents expressing intermediate wilting scores. Using multiple-loci analysis, four quantitative trait loci (QTLs) on molecular linkage groups (MLGs) A2, B2, D2, and F were detected (P

Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia.

Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.

Crop transformation and the challenge to increase yield potential.

Molecular transformation is commonly offered as a hope to overcome the apparent stagnation in crop yield potential. A basic understanding of the resource limits imposed on crops and the yield hierarchy going from gene expression to harvestable yield leads to a rather negative view that transformations of a few, or even of a complex of genes will result directly in major yield increases. Forty years of biochemical and physiological research illustrate the great difficulty in translating research at the basic level into improvements in crop yield. However, there are a few cases where physiological research has led to improved crop cultivars with increased yield. These successes are instructive in highlighting key elements required to achieve success in developing crop cultivars for increased yield.