Joint Linkage Association Mapping Research Articles

Genomic loci associated with grain yield under well-watered and water-stressed conditions in multiple bi-parental maize populations

Smallholder maize farming systems in sub-Saharan Africa (SSA) are vulnerable to drought-induced yield losses, which significantly impact food security and livelihoods within these communities. Mapping and characterizing genomic regions associated with water stress tolerance in tropical maize is essential for future breeding initiatives targeting this region. In this study, three biparental F3 populations composed of 753 families were evaluated in Kenya and Zimbabwe and genotyped with high-density single nucleotide polymorphism (SNP) markers. Quantitative trait loci maping was performed on these genotypes to dissect the genetic architecture for grain yield (GY), plant height (PH), ear height (EH) and anthesis-silking interval (ASI) under well-watered (WW) and water-stressed (WS) conditions. Across the studied maize populations, mean GY exhibited a range of 4.55–8.55 t/ha under WW and 1.29–5.59 t/ha under WS, reflecting a 31–59% reduction range under WS conditions. Genotypic and genotype-by-environment (G × E) variances were significant for all traits except ASI. Overall broad sense heritabilities for GY were low to high (0.25–0.60). For GY, these genetic parameters were decreased under WS conditions. Linkage mapping revealed a significant difference in the number of QTLs detected, with 93 identified under WW conditions and 41 under WS conditions. These QTLs were distributed across all maize chromosomes. For GY, eight and two major effect QTLs (>10% phenotypic variation explained) were detected under WW and WS conditions, respectively. Under WS conditions, Joint Linkage Association Mapping (JLAM) identified several QTLs with minor effects for GY and revealed genomic region overlaps in the studied populations. Across the studied water regimes, five-fold cross-validation showed moderate to high prediction accuracies (−0.15–0.90) for GY and other agronomic traits. Our findings demonstrate the polygenic nature of WS tolerance and highlights the immense potential of using genomic selection in improving genetic gain in maize breeding.

Genetic Dissection of Resistance to Gray Leaf Spot by Combining Genome-Wide Association, Linkage Mapping, and Genomic Prediction in Tropical Maize Germplasm.

Gray leaf spot (GLS) is one of the major maize foliar diseases in sub-Saharan Africa. Resistance to GLS is controlled by multiple genes with additive effect and is influenced by both genotype and environment. The objectives of the study were to dissect the genetic architecture of GLS resistance through linkage mapping and genome-wide association study (GWAS) and assessing the potential of genomic prediction (GP). We used both biparental populations and an association mapping panel of 410 diverse tropical/subtropical inbred lines that were genotyped using genotype by sequencing. Phenotypic evaluation in two to four environments revealed significant genotypic variation and moderate to high heritability estimates ranging from 0.43 to 0.69. GLS was negatively and significantly correlated with grain yield, anthesis date, and plant height. Linkage mapping in five populations revealed 22 quantitative trait loci (QTLs) for GLS resistance. A QTL on chromosome 7 (qGLS7-105) is a major-effect QTL that explained 28.2% of phenotypic variance. Together, all the detected QTLs explained 10.50, 49.70, 23.67, 18.05, and 28.71% of phenotypic variance in doubled haploid (DH) populations 1, 2, 3, and F3 populations 4 and 5, respectively. Joint linkage association mapping across three DH populations detected 14 QTLs that individually explained 0.10–15.7% of phenotypic variance. GWAS revealed 10 significantly (p < 9.5 × 10–6) associated SNPs distributed on chromosomes 1, 2, 6, 7, and 8, which individually explained 6–8% of phenotypic variance. A set of nine candidate genes co-located or in physical proximity to the significant SNPs with roles in plant defense against pathogens were identified. GP revealed low to moderate prediction correlations of 0.39, 0.37, 0.56, 0.30, 0.29, and 0.38 for within IMAS association panel, DH pop1, DH pop2, DH pop3, F3 pop4, and F3 po5, respectively, and accuracy was increased substantially to 0.84 for prediction across three DH populations. When the diversity panel was used as training set to predict the accuracy of GLS resistance in biparental population, there was 20–50% reduction compared to prediction within populations. Overall, the study revealed that resistance to GLS is quantitative in nature and is controlled by many loci with a few major and many minor effects. The SNPs/QTLs identified by GWAS and linkage mapping can be potential targets in improving GLS resistance in breeding programs, while GP further consolidates the development of high GLS-resistant lines by incorporating most of the major- and minor-effect genes.

Combination of Linkage Mapping, GWAS, and GP to Dissect the Genetic Basis of Common Rust Resistance in Tropical Maize Germplasm

Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F3 biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h2) estimates were moderate with 0.37–0.45 for the individual F3 populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6–10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14–40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F3 populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.

Principles and approaches of association mapping in plant breeding

Association mapping (AM) is an approach that accounts for thousands of polymorphisms to evaluate the effects of quantitative trait loci (QTL). It is an important instrument for identification of alleles and new genes as well as dissection of complex characters. AM is more advantageous than linkage analysis due to the comparatively high-resolution provided, which is based on the structure of linkage disequilibrium (LD). Marker density, population, sample sizes and population structure are among the critical factors that should be considered when AM is used. It is necessary to note that, the choice of germplasm, genotypic and phenotypic data quality, the use of appropriate statistical analysis for marker-phenotype association detections and verifications are key to association analysis. Great potentials to enhance crop genetic improvement are offered by AM. However, to understand its application, extensive research is needed, such as improvements in computational and statistical methods and its integration with gene annotation data or functional analysis. Statistical apparatuses that are user-friendly and genetic resources are also needed and must be enhanced. Rare allele/variant analysis is an important area to be considered to enhance AM studies. Joint linkage association mapping has now been proposed to improve linkage-based QTL mapping and AM limitations. In the future, new candidate genes and QTL can be easily identified if genome-wide association studies (GWAS) are combined with functional genomics. As such, this review describes association mapping, its utilization in plant breeding, limitations as well as advantages over linkage mapping.

Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations.

Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F3 progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F3 progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.

Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm

Key messageAnalysis of the genetic architecture of MCMV and MLN resistance in maize doubled-haploid populations revealed QTLs with major effects on chromosomes 3 and 6 that were consistent across genetic backgrounds and environments. Two major-effect QTLs, qMCMV3-108/qMLN3-108 and qMCMV6-17/qMLN6-17, were identified as conferring resistance to both MCMV and MLN.Maize lethal necrosis (MLN) is a serious threat to the food security of maize-growing smallholders in sub-Saharan Africa. The ability of the maize chlorotic mottle virus (MCMV) to interact with other members of the Potyviridae causes severe yield losses in the form of MLN. The objective of the present study was to gain insights and validate the genetic architecture of resistance to MCMV and MLN in maize. We applied linkage mapping to three doubled-haploid populations and a genome-wide association study (GWAS) on 380 diverse maize lines. For all the populations, phenotypic variation for MCMV and MLN was significant, and heritability was moderate to high. Linkage mapping revealed 13 quantitative trait loci (QTLs) for MCMV resistance and 12 QTLs conferring MLN resistance. One major-effect QTL, qMCMV3-108/qMLN3-108, was consistent across populations for both MCMV and MLN resistance. Joint linkage association mapping (JLAM) revealed 18 and 21 main-effect QTLs for MCMV and MLN resistance, respectively. Another major-effect QTL, qMCMV6-17/qMLN6-17, was detected for both MCMV and MLN resistance. The GWAS revealed a total of 54 SNPs (MCMV-13 and MLN-41) significantly associated (P ≤ 5.60 × 10−05) with MCMV and MLN resistance. Most of the GWAS-identified SNPs were within or adjacent to the QTLs detected through linkage mapping. The prediction accuracy for within populations as well as the combined populations is promising; however, the accuracy was low across populations. Overall, MCMV resistance is controlled by a few major and many minor-effect loci and seems more complex than the genetic architecture for MLN resistance.

Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.

Dissecting the genetic architecture of agronomic traits in multiple segregating populations in rapeseed (Brassica napus L.)

Detection of QTL in multiple segregating populations is of high interest as it includes more alleles than mapping in a single biparental population. In addition, such populations are routinely generated in applied plant breeding programs and can thus be used to identify QTL which are of direct relevance for a marker-assisted improvement of elite germplasm. Multiple-line cross QTL mapping and joint linkage association mapping were used for QTL detection. We empirically compared these two different biometrical approaches with regard to QTL detection for important agronomic traits in nine segregating populations of elite rapeseed lines. The plants were intensively phenotyped in multi-location field trials and genotyped with 253 SNP markers. Both approaches detected several additive QTL for diverse traits, including flowering time, plant height, protein content, oil content, glucosinolate content, and grain yield. In addition, we identified one epistatic QTL for flowering time. Consequently, both approaches appear suited for QTL detection in multiple segregating populations.

Comparison of biometrical models for joint linkage association mapping

Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping

Epistatic interactions may contribute substantially to the hybrid performance of sugar beet. The main goal of our study was to dissect the genetic basis of eight important physiological and agronomic traits using two different biometrical models for joint linkage association mapping. A total of 197 genotypes of an elite breeding population were evaluated in multi-location trials and fingerprinted with 194 SNP markers. Two different statistical models were used for the genome-wide scan for marker-trait associations: Model A, which corrects for the genetic background with markers as cofactors and Model B, which additionally models a population effect. Based on the extent of linkage disequilibrium in the parental population, we estimated that for a genome-wide scan at least 100 equally spaced markers are necessary. We mapped across the eight traits 39 QTL for Model A and 22 for Model B. Only 11% of the total number of QTL were identified based on Models A and B, which indicates that both models are complementary. Epistasis was detected only for two out of the eight traits, and contributed only to a minor extent to the genotypic variance. This low relevance of epistasis implies that in sugar beet breeding the prediction of performance of three-way hybrids is feasible with high accuracy based on the means of their single crosses.