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.

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.

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.

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 genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.

A decrease in the incidence of bovine mastitis, the costliest disease in the dairy industry, can be facilitated through genetic marker-assisted selective breeding programs. Identification of genomic variants associated with mastitis resistance is an ongoing endeavor for which genome-wide association studies (GWAS) using high-density arrays provide a valuable tool. We identified single nucleotide polymorphisms (SNPs) in Holstein dairy cattle associated with mastitis resistance in a GWAS by using a high-density SNP array. Mastitis-resistant (15) and mastitis-susceptible (28) phenotypic extremes were identified from 224 lactating dairy cows on commercial dairy farm located in Utah based on multiple criteria of mastitis resistance over an 8-month period. Twenty-seven quantitative trait loci (QTLs) for mastitis resistance were identified based on 117 SNPs suggestive of genome-wide significance for mastitis resistance (p ≤ 1 × 10-4), including 10 novel QTLs. Seventeen QTLs overlapped previously reported QTLs of traits relevant to mastitis, including four QTLs for teat length. One QTL includes the RAS guanyl-releasing protein 1 gene (RASGRP1), a candidate gene for mastitis resistance. This GWAS identifies 117 candidate SNPs and 27 QTLs for mastitis resistance using a selective genotyping approach, including 10 novel QTLs. Based on overlap with previously identified QTLs, teat length appears to be an important trait in mastitis resistance. RASGRP1, overlapped by one QTL, is a candidate gene for mastitis resistance.

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.

Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets.

The objective of this study was to model differences in pedigree accuracy caused by selective genotyping. As genotypes are used to correct pedigree errors, some pedigree relationships are more accurate than others. These accuracy differences can be modeled with uncertain parentage models that distribute the paternal (maternal) contribution across multiple sires (dams). In our case, the parents were the parent on record and an unknown parent group to account for pedigree relationships that were not confirmed through genotypes. Pedigree accuracy was addressed through simulation and through North American Holstein data. Data were simulated to be representative of the dairy industry with heterogeneous pedigree depth, pedigree accuracy, and genotyping. Holstein data were obtained from the official evaluation for milk, fat, and protein. Two models were compared: the traditional approach, assuming accurate pedigrees, and uncertain parentage, assuming variable pedigree accuracy. The uncertain parentage model was used to add pedigree relationships for alternative parents when pedigree relationships were not certain. The uncertain parentage model included 2 possible sires (dams) when the sire (dam) could not be confirmed with genotypes. The 2 sires (dams) were the sire (dam) on record with probability 0.90 (0.95) and the unknown parent group for the birth year of the sire (dam) with probability 0.10 (0.05). An additional set of assumptions was tested in simulation to mimic an extensive dairy production system by using a sire probability of 0.75, a dam probability of 0.85, and the remainder attributed to the unknown parent groups. In the simulation, small bias differences occurred between models based on pedigree accuracy and genotype status. Rank correlations were strong between traditional and uncertain parentage models in simulation (≥0.99) and in Holstein (≥0.99). For Holsteins, the estimated breeding value differences between models were small for most animals. Thus, traditional models can continue to be used for dairy genomic prediction despite using genotypes to improve pedigree accuracy. Those genotypes can also be used to discover maternal parentage, specifically maternal grandsires and great grandsires when the dam is not known. More research is needed to understand how to use discovered maternal pedigrees in genetic prediction.

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction.

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree-based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single-Step approach (SSGBLUP) using both. For a scenario with no-selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single-Step approach to obtain accurate and unbiased prediction of GEBV.

Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies.

We investigated the effects of different strategies for genotyping populations on variance components and heritabilities estimated with an animal model under restricted maximum likelihood (REML), genomic REML (GREML), and single-step GREML (ssGREML). A population with 10 generations was simulated. Animals from the last one, two or three generations were genotyped with 45,116 SNP evenly distributed on 27 chromosomes. Animals to be genotyped were chosen randomly or based on EBV. Each scenario was replicated five times. A single trait was simulated with three heritability levels (low, moderate, high). Phenotypes were simulated for only females to mimic dairy sheep and also for both sexes to mimic meat sheep. Variance component estimates from genomic data and phenotypes for one or two generations were more biased than from three generations. Estimates in the scenario without selection were the most accurate across heritability levels and methods. When selection was present in the simulations, the best option was to use genotypes of randomly selected animals. For selective genotyping, heritabilities from GREML were more biased compared to those estimated by ssGREML, because ssGREML was less affected by selective or limited genotyping.

Identification of SNP markers associated with tolerance to ammonia toxicity by selective genotyping from de novo assembled transcriptome in Litopenaeus vannamei.

The high concentration of ammonia from deteriorated aquaculture environments and the intensive culture system could increase the susceptibility to pathogens and even cause high mortality in Litopenaeus vannamei. In addition, we have revealed that the ammonia-tolerant shrimp also have high disease resistance in L. vannamei. In the present study, in order to identify SNP markers associated with tolerance to ammonia toxicity, we developed and characterized SNPs from our previous transcriptome sequencing data of ammonia-stressed and control groups, and a marker-trait association analysis was performed for marker-assisted selection (MAS) to increase production in L. vannamei. A total of 318,919 SNPs were identified from the transcriptome sequences, and 25,772 SNPs were found from the 1826 ammonia-responsive genes with functional annotation. We selected 49 SNPs from 26 ammonia-responsive genes that had strong homologies to known genes in the shrimp and probably involved in immune function as candidate markers for genotyping, among which 39 SNPs were polymorphic for further marker-trait association analysis with the ammonia-tolerant (AT) and ammonia-sensitive (AS) groups. Finally, 12 out of the 49 SNP markers were identified to be associated with ammonia tolerance, containing 10 loci with significantly different allele frequencies and 10 loci with significantly different genotyping frequencies between the AT and AS groups. Among the associated markers, the G allele of TSP-1 (the first locus from the thrombospondin gene), the A allele of TSP-3, and the C allele of XBP1-5 (the fifth locus from X-box binding protein 1) only presented in the AT groups, but they were absent from the AS groups, which would be the preference of the MAS for the ammonia-tolerant shrimp. In addition, when the 12 associated SNP markers were used for analysis, the genetic diversity of the AT groups were significantly higher than that of the AS groups, but when the 39 loci were used there was no difference. This is the first report for the markers associated with ammonia tolerance in this species, indirectly with disease resistance, which provided important potential for genetic selection to increase survival rate and production in shrimp farming.

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies.

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20-40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on high-density lipoprotein and low-density lipoprotein from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.

Development of Vibrio spp. infection resistance related SNP markers using multiplex SNaPshot genotyping method in the clam Meretrix meretrix.

The clam Meretrix meretrix is a commercially important mollusc species in the coastal areas of South and Southeast Asia. In the present study, large-scale SNPs were genotyped by the Multiplex SNaPshot genotyping method among the stocks of M. meretrix with different Vibrio spp. infection resistance profile. Firstly, the AUTOSNP software was applied to mine SNPs from M. meretrix transcriptome, and 323 SNP loci (including 120 indels) located on 64 contigs were selected based on Uniprot-GO associations. Then, 38 polymorphic SNP loci located on 15 contigs were genotyped successfully in the clam stocks with different resistance to Vibrio parahaemolyticus infection (11-R and 11-S groups). Pearson's Chi-square test was applied to compare the allele and genotype frequency distributions of the SNPs between the different stocks, and seven SNP markers located on three contigs were found to be associated with V. parahaemolyticus infection resistance trait. Haplotype-association analysis showed that six haplotypes had significantly different frequency distributions in 11-S and 11-R (P < 0.05). With selective genotyping between 09-R and 09-C populations, which had different resistance to Vibrio harveyi infection, four out of the seven selected SNPs had significantly different distributions (P < 0.05) and therefore they were considered to be associated with Vibrio spp. infection resistance. Sequence alignments and annotations indicated that the contigs containing the associated SNPs had high similarity to the immune related genes. All these results would be useful for the future marker-assisted selection of M. meretrix strains with high Vibrio spp. infection resistance.

Association Analysis for Bacterial Spot Resistance in a Directionally Selected Complex Breeding Population of Tomato.

Bacterial spot of tomato is caused by at least four species of Xanthomonas with multiple physiological races. We developed a complex breeding population for simultaneous discovery of marker-trait linkage, validation of existing quantitative trait loci (QTL), and pyramiding of resistance. Six advanced accessions with resistance from distinct sources were crossed in all combinations and their F1 hybrids were intercrossed. Over 1,100 segregating progeny were evaluated in the field following inoculation with X. euvesicatoria race T1 strains. We selected 5% of the most resistant and 5% of the most susceptible progeny for evaluation as plots in two subsequent replicated field trials inoculated with T1 and T3 (X. perforans) strains. The estimated heritability of T1 resistance was 0.32. In order to detect previously reported resistance genes, as well as novel QTL, we explored methods to correct for population structure and analysis based on single markers or haplotypes. Both single-point and haplotype analyses identified strong associations in the genomic regions known to carry Rx-3 (chromosome 5) and Rx-4/Xv3 (chromosome 11). Accounting for kinship and structure generally improved the fit of statistical models. Detection of known loci was improved by adding kinship or a combination of kinship and structure using a Q matrix from model-based clustering. Additional QTL were detected on chromosomes 1, 4, 6, and 7 for T1 resistance and chromosomes 2, 4, and 6 for T3 resistance (P < 0.01). Haplotype analysis improved our ability to trace the origin of positive alleles. These results demonstrate that both known and novel associations can be identified using complex breeding populations that have experienced directional selection.

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.

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.

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.

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.

SSR markers associated to early leaf spot disease resistance through selective genotyping and single marker analysis in groundnut (Arachis hypogaea L.).

Groundnut (Arachis hypogaea L.) is an important oilseed and food crop of the world. Breeding for disease resistance is one of major objectives in groundnut breeding. Early leaf spot (ELS) is one of the major destructive diseases worldwide and in West Africa, particularly in Burkina Faso causing significant yield losses. Conventional breeding approaches have been employed to develop improved varieties resistant to ELS. Molecular dissection of resistance traits using QTL analysis can improve the efficiency of resistance breeding. In the present study, an ELS susceptible genotype QH243C and an ELS resistant genotype NAMA were crossed and the F2 population genotypic and F3 progenies phenotypic data were used for marker-trait association analysis. Parents were surveyed with 179 simple sequence repeat (SSR) markers out of which 103 SSR markers were found to be polymorphic between the parents. These polymorphic markers were utilized to genotype the F2 population followed by marker-trait analysis through single marker analysis (SMA) and selective genotyping of the population using 23 resistant and 23 susceptible genotypes. The SMA revealed 13 markers while the selective genotyping method identified 8 markers associated with ELS resistance. Four markers (GM1911, GM1883, GM1000 and Seq13E09) were found common between the two trait mapping methods. These four markers could be employed in genomics-assisted breeding for selection of ELS resistant genotypes in groundnut breeding.

Analysis of Allelic Imbalance in Rice Hybrids Under Water Stress and Association of Asymmetrically Expressed Genes with Drought-Response QTLs.

BACKGROUND: Information on the effect of stress on the allele-specific expression (ASE) profile of rice hybrids is limited. More so, the association of allelically imbalanced genes to important traits is yet to be understood. Here we assessed allelic imbalance (AI) in the heterozygote state of rice under non- and water-stress treatments and determined association of asymmetrically expressed genes with grain yield (GY) under drought stress by in-silico co-localization analysis and selective genotyping. The genotypes IR64, Apo and their F1 hybrid (IR64 × Apo) were grown under normal and water-limiting conditions. We sequenced the total RNA transcripts for all genotypes then reconstructed the two chromosomes in the heterozygote. RESULTS: We are able to estimate the transcript abundance of and the differential expression (DE) between the two parent-specific alleles in the rice hybrids. The magnitude and direction of AI are classified into two categories: (1) symmetrical or biallelic and (2) asymmetrical. The latter can be further classified as either IR64- or Apo-favoring gene. Analysis showed that in the hybrids grown under non-stress conditions, 179 and 183 favor Apo- and IR64-specific alleles, respectively. Hence, the number of IR64- and Apo-favoring genes is relatively equal. Under water-stress conditions, 179 and 255 favor Apo- and IR64-specific alleles, respectively, indicating that the number of allelically imbalanced genes is skewed towards IR64. This is nearly 40-60 % preference for Apo and IR64 alleles, respectively, to the hybrid transcriptome. We also observed genes which exhibit allele preference switching when exposed to water-stress conditions. Results of in-silico co-localization procedure and selective genotyping of Apo/IR64 F3:5 progenies revealed significant association of several asymmetrically expressed genes with GY under drought stress conditions. CONCLUSION: Our data suggest that water stress skews AI on a genome-wide scale towards the IR64 allele, the cross-specific maternal allele. Several asymmetrically expressed genes are strongly associated with GY under drought stress which may shed hints that genes associated with important traits are allelically imbalanced. Our approach of integrating hybrid expression analysis and QTL mapping analysis may be an efficient strategy for shortlisting candidate genes for gene discovery.