Identification and mapping of late blight resistance QTLs in the wild tomato accession PI 224710 (Solanum pimpinellifolium).

Late blight (LB), caused by oomycete Phytophthora infestans, is one of the most destructive diseases of the cultivated tomato, Solanum lycopersicum. Since new and aggressive clonal lineages of P. infestans, many of which overcoming formerly effective fungicides or host resistance genes, have continued to emerge, it is crucial to identify, characterize, and utilize new sources of host resistance in tomato breeding. A recent screening of tomato germplasm identified Solanum pimpinellifolium accession PI 224710 with very strong resistance to several current P. infestans clonal lineages. The present study aimed to identify and characterize QTLs associated with LB resistance in PI 224710. Disease screening of a large F2 population (n = 1721), derived from a cross between PI 224710 and LB-susceptible tomato breeding line Fla. 8059, followed by F3 progeny testing, resulted in the identification of 43 highly-resistant and 27 highly-susceptible F2 individuals. A selective genotyping approach, using 469 non-identical SNP markers, resulted in the construction of a genetic linkage map and identification of three LB-resistance QTLs on chromosomes 6, 9 and 10 of PI 224710. A comparison of the QTLs genomic locations with the tomato physical map resulted in the identification of several candidate genes, which might be underpinning the LB-resistance QTLs in PI 224710. The identified markers associated with the LB-resistance QTLs can be utilized in breeding programs to transfer resistance from PI 224710 into tomato breeding lines and hybrid cultivars via marker-assisted breeding; they also can be used to develop near-isogenic lines for fine mapping of the QTLs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01498-1.

Impact and utility of shallow pedigree using single-step genomic BLUP for prediction of unbiased genomic breeding values.

In unstructured dairy programs, pedigree is usually shallow, which leads to biased prediction of breeding values using best linear unbiased prediction (BLUP). The objective of this study was to come out with a genomic prediction strategy that can utilize shallow pedigree information and predict unbiased and more accurate GEBV for sex-limited traits in a small population using single-step GBLUP (ssGBLUP). The data and models for a population under selection were simulated. Out of current 10 generations, 10th generation with 1000 candidates served as validation population. For the complete pedigree scenario, pedigree (P)BLUP estimated breeding values (EBV) were unbiased with accuracy (r) of 0.35 ± 0.02 and 0.26 ± 0.01 for 0.3 and 0.1 h2 scenario, respectively. For the shallow pedigree, biased prediction of breeding values and low accuracies were obtained with linear decline in the accuracy of EBV for removal of information on more distant pedigree. Accuracy and bias (ρ) for scenario with removing 4 distant generations from pedigree were 0.30 ± 0.02 and 0.55 ± 0.03, respectively, in moderate h2 scenario. Use of Genomic (G)BLUP, especially with "extreme phenotypic contrast selective genotyping," (TB) resulted in higher accuracy for a small reference of females; however, GEBV were highly biased. We observed that ssGBLUPF, where the numerator relationship matrix is corrected for inbreeding, resulted in more accurate and unbiased estimates of GEBV across shallow pedigree scenario, with TB all female reference (missing 4 distant generations: r = 0.50 ± 0.02; ρ = 0.96 ± 0.02). We recommend use of ssGBLUPF with two tailed selectively genotyped all female reference in shallow pedigree scenarios, to obtain unbiased and accurate GEBV for sex-limited traits, when resources are limited.

A reference-anchored oat linkage map reveals quantitative trait loci conferring adult plant resistance to crown rust (Puccinia coronata f. sp. avenae).

KEY MESSAGE: We mapped three adult plant resistance (APR) loci on oat chromosomes 4D and 6C and developed flanking KASP/PACE markers for marker-assisted selection and gene pyramiding. Using sequence orthology search and the available oat genomic and transcriptomic data, we surveyed these genomic regions for genes that may control disease resistance. Sources of durable disease resistance are needed to minimize yield losses in cultivated oat caused by crown rust (Puccinia coronata f. sp. avenae). In this study, we developed five oat recombinant inbred line mapping populations to identify sources of adult plant resistance from crosses between five APR donors and Otana, a susceptible variety. The preliminary bulk segregant mapping based on allele frequencies showed two regions in linkage group Mrg21 (Chr4D) that are associated with the APR phenotype in all five populations. Six markers from these regions in Chr4D were converted to high-throughput allele specific PCR assays and were used to genotype all individuals in each population. Simple interval mapping showed two peaks in Chr4D, named QPc.APR-4D.1 and QPc.APR-4D.2, which were detected in the OtanaA/CI4706-2 and OtanaA/CI9416-2 and in the Otana/PI189733, OtanaD/PI260616, and OtanaA/CI8000-4 populations, respectively. These results were validated by mapping two entire populations, Otana/PI189733 and OtanaA/CI9416, genotyped using Illumina HiSeq, in which polymorphisms were called against the OT3098 oat reference genome. Composite interval mapping results confirmed the presence of the two quantitative trait loci (QTL) located on oat chromosome 4D and an additional QTL with a smaller effect located on chromosome 6C. This mapping approach also narrowed down the physical intervals to between 5 and 19 Mb, and indicated that QPc.APR-4D.1, QPc.APR-4D.2, and QPc.APR-6C explained 43.4%, 38.5%, and 21.5% of the phenotypic variation, respectively. In a survey of the gene content of each QTL, several clusters of disease resistance genes that may contribute to APR were found. The allele specific PCR markers developed for these QTL regions would be beneficial for marker-assisted breeding, gene pyramiding, and future cloning of resistance genes from oat.

Identification of loci controlling timing of stem elongation in red clover using genotyping by sequencing of pooled phenotypic extremes.

MAIN CONCLUSION: Through selective genotyping of pooled phenotypic extremes, we identified a number of loci and candidate genes putatively controlling timing of stem elongation in red clover. We have identified candidate genes controlling the timing of stem elongation prior to flowering in red clover (Trifolium pratense L.). This trait is of ecological and agronomic significance, as it affects fitness, competitivity, climate adaptation, forage and seed yield, and forage quality. We genotyped replicate pools of phenotypically extreme individuals (early and late-elongating) within cultivar Lea using genotyping-by-sequencing in pools (pool-GBS). After calling and filtering SNPs and GBS locus haplotype polymorphisms, we estimated allele frequencies and searched for markers with significantly different allele frequencies in the two phenotypic groups using BayeScan, an FST-based test utilizing replicate pools, and a test based on error variance of replicate pools. Of the three methods, BayeScan was the least stringent, and the error variance-based test the most stringent. Fifteen significant markers were identified in common by all three tests. The candidate genes flanking the markers include genes with potential roles in the vernalization, autonomous, and photoperiod regulation of floral transition, hormonal regulation of stem elongation, and cell growth. These results provide a first insight into the potential genes and mechanisms controlling transition to stem elongation in a perennial legume, which lays a foundation for further functional studies of the genetic determinants regulating this important trait.

Discovery and Validation of a Recessively Inherited Major-Effect QTL Conferring Resistance to Maize Lethal Necrosis (MLN) Disease.

Maize lethal necrosis (MLN) is a viral disease with a devastating effect on maize production. Developing and deploying improved varieties with resistance to the disease is important to effectively control MLN; however, little is known about the causal genes and molecular mechanism(s) underlying MLN resistance. Screening thousands of maize inbred lines revealed KS23-5 and KS23-6 as two of the most promising donors of MLN resistance alleles. KS23-5 and KS23-6 lines were earlier developed at the University of Hawaii, United States, on the basis of a source population constituted using germplasm from Kasetsart University, Thailand. Both linkage mapping and association mapping approaches were used to discover and validate genomic regions associated with MLN resistance. Selective genotyping of resistant and susceptible individuals within large F2 populations coupled with genome-wide association study identified a major-effect QTL (qMLN06_157) on chromosome 6 for MLN disease severity score and area under the disease progress curve values in all three F2 populations involving one of the KS23 lines as a parent. The major-effect QTL (qMLN06_157) is recessively inherited and explained 55%-70% of the phenotypic variation with an approximately 6 Mb confidence interval. Linkage mapping in three F3 populations and three F2 populations involving KS23-5 or KS23-6 as one of the parents confirmed the presence of this major-effect QTL on chromosome 6, demonstrating the efficacy of the KS23 allele at qMLN06.157 in varying populations. This QTL could not be identified in population that was not derived using KS23 lines. Validation of this QTL in six F2 populations with 20 SNPs closely linked with qMLN06.157 was further confirmed its consistent expression across populations and its recessive nature of inheritance. On the basis of the consistent and effective resistance afforded by the KS23 allele at qMLN06.157, the QTL can be used in both marker-assisted forward breeding and marker-assisted backcrossing schemes to improve MLN resistance of breeding populations and key lines for eastern Africa.

Beta vulgaris ssp. vulgaris chromosome 8 shows significant association with geosmin concentration in table beet.

Geosmin, a degraded sesquiterpene molecule with earthy and musty odor, imbues table beet with its characteristic aroma. Geosmin is heritable and endogenously produced in table beet; its earthy aroma is sought by some consumers but deters others. Geosmin biosynthesis is catalyzed by a bifunctional geosmin synthase enzyme in diverse bacteria and fungi, but a mechanism for geosmin biosynthesis in plants has not been reported. This work employed association analysis and selective genotyping of a segregating F2:3 mapping population to seek QTL associated with geosmin concentration in table beet. GBS reads were aligned to sugar beet reference genome EL10.2, and association analysis revealed two QTL for geosmin concentration on Beta vulgaris ssp. vulgaris chromosome 8. QTL at EL10.2 positions 28,017,624 and 38,488,687 each show effect size 8.7 µg·kg-1 geosmin and explain 8.5% and 6.4% of total variation in geosmin concentration, respectively. Resolution was low due to large recombination bin size and imperfect alignment between the reference genome and mapping population, but population size and selection proportion were sufficient to detect moderate to large effect QTL. This study, the first molecular genetic mapping experiment in table beet, succeeded in finding QTL for geosmin concentration in table beet, and it provides the basis for fine mapping or candidate gene investigation of functional loci for this distinctive sensory trait.

Selective genotyping and phenotypic data inclusion strategies of crossbred progeny for combined crossbred and purebred selection in swine breeding.

Inclusion of crossbred (CB) data into traditionally purebred (PB) genetic evaluations has been shown to increase the response in CB performance. Currently, it is unrealistic to collect data on all CB animals in swine production systems, thus, a subset of CB animals must be selected to contribute genomic/phenotypic information. The aim of this study was to evaluate selective genotyping strategies in a simulated 3-way swine crossbreeding scheme. The swine crossbreeding scheme was simulated and produced 3-way CB animals for 6 generations with 3 distinct PB breeds each with 25 and 175 mating males and females, respectively. F1 crosses (400 mating females) produced 4,000 terminal CB progeny which were subjected to selective genotyping. The genome consisted of 18 chromosomes with 1,800 QTL and 72k SNP markers. Selection was performed using estimated breeding values (EBV) for CB performance. It was assumed that both PB and CB performance was moderately heritable (h2=0.4). Several scenarios altering the genetic correlation between PB and CB performance (rpc=0.1, 0.3, 0.5, 0.7,or 0.9) were considered. CB animals were chosen based on phenotypes to select 200, 400, or 800 CB animals to genotype per generation. Selection strategies included: (1) Random: random selection, (2) Top: highest phenotype, (3) Bottom: lowest phenotype, (4) Extreme: half highest and half lowest phenotypes, and (5) Middle: average phenotype. Each selective genotyping strategy, except for Random, was considered by selecting animals in half-sib (HS) or full-sib (FS) families. The number of PB animals with genotypes and phenotypes each generation was fixed at 1,680. Each unique genotyping strategy and rpc scenario was replicated 10 times. Selection of CB animals based on the Extreme strategy resulted in the highest (P < 0.05) rates of genetic gain in CB performance (ΔG) when rpc<0.9. For highly correlated traits (rpc=0.9) selective genotyping did not impact (P > 0.05) ΔG. No differences (P > 0.05) were observed in ΔG between top, bottom, or middle when rpc>0.1. Higher correlations between true breeding values (TBV) and EBV were observed using Extreme when rpc<0.9. In general, family sampling method did not impact ΔG or the correlation between TBV and EBV. Overall, the Extreme genotyping strategy produced the greatest genetic gain and the highest correlations between TBV and EBV, suggesting that 2-tailed sampling of CB animals is the most informative when CB performance is the selection goal.

Interactions Among Multiple Quantitative Trait Loci Underlie Rhizome Development of Perennial Rice.

Perennial crops have some advantages over annuals in soil erosion prevention, lower labor and water requirements, carbon sequestration, and maintenance of thriving soil ecosystems. Rhizome, a kind of root-like underground stem, is a critical component of perenniality, which allows many grass species to survive through harsh environment. Identification of rhizome-regulating genes will contribute to the development of perennial crops. There have been no reports on the cloning of such genes until now, which bring urgency for identification of genes controlling rhizomatousness. Using rhizomatous Oryza longistaminata and rhizome-free cultivated rice as male and female parents, respectively, genetic populations were developed to identify genes regulating rhizome. Both entire population genotyping and selective genotyping mapping methods were adopted to detect rhizome-regulating quantitative trait loci (QTL) in 4 years. Results showed that multiple genes regulated development of rhizomes, with over 10 loci related to rhizome growth. At last, five major-effect loci were identified including qRED1.2, qRED3.1, qRED3.3, qRED4.1, and qRED4.2. It has been found that the individual plant with well-developed rhizomes carried at least three major-effect loci and a certain number of minor-effect loci. Both major-effect and minor-effect loci worked together to control rhizome growth, while no one could work alone. These results will provide new understanding of genetic regulation on rhizome growth and reference to the subsequent gene isolation in rice. And the related research methods and results in this study will contribute to the research on rhizome of other species.

Phenotypically Selective Genotyping Realizes More Genetic Gains in a Rainbow Trout Breeding Program in the Presence of Genotype-by-Environment Interactions.

Selective genotyping of phenotypically superior animals may lead to bias and less accurate genomic breeding values (GEBV). Performing selective genotyping based on phenotypes measured in the breeding environment (B) is not necessarily a good strategy when the aim of a breeding program is to improve animals' performance in the commercial environment (C). Our simulation study compared different genotyping strategies for selection candidates and for fish in C in a breeding program for rainbow trout in the presence of genotype-by-environment interactions when the program had limited genotyping resources and unregistered pedigrees of individuals. For the reference population, selective genotyping of top and bottom individuals in C based on phenotypes measured in C led to the highest genetic gains, followed by random genotyping and then selective genotyping of top individuals in C. For selection candidates, selective genotyping of top individuals in B based on phenotypes measured in B led to the highest genetic gains, followed by selective genotyping of top and bottom individuals and then random genotyping. Selective genotyping led to bias in predicting GEBV. However, in scenarios that used selective genotyping of top fish in B and random genotyping of fish in C, predictions of GEBV were unbiased, with genetic correlations of 0.2 and 0.5 between traits measured in B and C. Estimates of variance components were sensitive to genotyping strategy, with an overestimation of the variance with selective genotyping of top and bottom fish and an underestimation of the variance with selective genotyping of top fish. Unbiased estimates of variance components were obtained when fish in B and C were genotyped at random. In conclusion, we recommend phenotypic genotyping of top and bottom fish in C and top fish in B for the purpose of selecting breeding animals and random genotyping of individuals in B and C for the purpose of estimating variance components when a genomic breeding program for rainbow trout aims to improve animals' performance in C.

Allele substitution and dominance effects of CD166/ALCAM gene polymorphisms for endoparasite resistance and test-day traits in a small cattle population using logistic regression analyses.

The study investigated the effects of four single-nucleotide polymorphisms (SNPs) in the activated leukocyte cell adhesion molecule (ALCAM) gene on liver fluke (Fasciola hepatica) infections (FH-INF), gastrointestinal nematode infections (GIN-INF) and disease indicator traits [e.g. somatic cell score (SCS), fat-to-protein ratio (FPR)] in German dual-purpose cattle (DSN). A genome-wide association study inferred the chip SNP ALCAMc.73+32791A>G as a candidate for F. hepatica resistance in DSN. Because of the crucial function of ALCAM in immune responses, SNPs in the gene might influence further resistance and performance traits. Causal mutations were identified in exon 9 (ALCAMc.1017T>C) and intron 9 (ALCAMc.1104+10T>A, ALCAMc.1104+85T>C) in a selective subset of 94 DSN cows. We applied logistic regression analyses for the association between SNP genotypes with residuals for endoparasite traits (rINF-FH, rGIN-INF) and estimated breeding values (EBVs) for test-day traits. The probability of the heterozygous genotype was estimated in dependency of the target trait. Allele substitution effects for rFH-INF were significant for all four loci. The T allele of the SNPs ALCAMc.1017T>C and ALCAMc.1104+85T>C was the favourable allele when improving resistance against FH-INF. Significant allele substitution for rGIN-INF was only found for the chip SNP ALCAMc.73+32791A>G. We identified significant associations between the SNPs with EBVs for milk fat%, protein% and FPR. Dominance effects for the EBVs of test-day traits ranged from 0.00 to 0.47 SD and were in the direction of improved resistance for rFH-INF. We estimated favourable dominance effects from same genotypes for rFH-INF and FPR, but dominance effects were antagonistic between rFH-INF and SCS.