Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r²) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs.

Combined QTL mapping on bi-parental immortalized heterozygous populations to detect the genetic architecture on heterosis

From bi-parental pure-inbred lines (PIL), immortalized backcross (i.e., IB₁ and IB₂, representing the two directions of backcrossing) and F₂ (i.e., IF₂) populations can be developed. These populations are suitable for genetic studies on heterosis, due to the present of both homozygous and heterozygous genotypes, and in the meantime allow repeated phenotyping trials across multiple locations and years. In this study, we developed a combined approach of quantitative trait locus (QTL) mapping, when some or all of the four immortalized populations (i.e., PIL, IB₁, IB₂, and IF₂) are available. To estimate the additive and dominant effects simultaneously and accurately, suitable transformations are made on phenotypic values from different populations. When IB₁ and IB₂ are present, summation and subtraction are used. When IF₂ and PIL are available, mid-parental values and mid-parental heterosis are used. One-dimensional genomic scanning is performed to detect the additive and dominant QTLs, based on the algorithm of inclusive composite interval mapping (ICIM). The proposed approach was applied to one IF₂ population together with PIL in maize, and identified ten QTLs on ear length, showing varied degrees of dominance. Simulation studies indicated the proposed approach is similar to or better than individual population mapping by QTL detection power, false discovery rate (FDR), and estimated QTL position and effects.

Blib is a multi-module simulation platform for genetics studies and intelligent breeding

Simulation is an efficient approach for the investigation of theoretical and applied issues in population and quantitative genetics, and animal and plant breeding. In this study, we report a multi-module simulation platform called Blib, that is able to handle more complicated genetic effects and models than existing tools. Two derived data types are first defined in Blib, one to hold the required information on genetic models, and the other one to represent the genetics and breeding populations. A number of subroutines are then developed to perform specific tasks. Four case studies are present as examples to show the applications of Blib, i.e., genetic drift of multiple alleles in randomly mating populations, joint effects of neutral mutation and genetic drift, comparison of mass versus family selection, and choice of testers in hybrid breeding. Blib together with its application modules, has great potential to benefit theoretical genetic studies and intelligent breeding by simulating and predicting outcomes in a large number of scenarios, and identifying the best optimum selection and crossing schemes.

GAHP: An integrated software package on genetic analysis with bi-parental immortalized heterozygous populations

GAHP is a freely available software package for genetic analysis with bi-parental immortalized heterozygous and pure-line populations. The package is project-based and integrated with multiple functions. All operations and running results are properly saved in a project, which can be recovered when the project is re-open by the package. Four functionalities have been implemented in the current version of GAHP, i.e., 1) MHP: visualization of genetic linkage maps; 2) VHP: analysis of variance (ANOVA) and estimation of heritability on phenotypic data; 3) QHP: quantitative trait locus (QTL) mapping on both genotypic and phenotypic data; 4) SHP: simulation of bi-parental immortalized heterozygous and pure-line populations, and power analysis of QTL mapping. VHP and QHP can be conducted in individual populations, as well as in multiple populations by the combined analysis. Input files are arranged either in the plain text format with an extension name same as the functionality or in the MS Excel formats. Output files have the same prefix name as the input file, but with different extensions to indicate their contents. Three characters before the extension names stand for the types of populations used in analysis. In the interface of the software package, input files are grouped by functionality, and output files are grouped by individual or combined mapping populations. In addition to the text-format outputs, the constructed linkage map can be visualized per chromosome or for a number of selected chromosomes; line plots and bi-plots can be drawn from QTL mapping results and phenotypic data. Functionalities and analysis methods available in GAHP help the investigation of genetic architectures of complex traits and the mechanism of heterosis in plants.

An IBD-based mixed model approach for QTL mapping in multiparental populations

The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.

Mapping QTL/QTN and mining candidate genes for plant height and its response to planting densities in soybean [Glycine max (L.) Merr.] through a FW-RIL population

Plant height (PH) determines the morphology and seed yield of soybean, so it is an important breeding target, which is controlled by multiple genes and affected by plant density. In this research, it was used about a four-way recombinant inbred lines (FW-RIL) with 144 families constructed by double cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19) as experimental materials, with the purpose to map QTL/QTN associated with PH under densities of 2.2×105 plant/ha (D1) and 3×105 plant/ha (D2) in five environments. The results showed that response of PH to densities varied in accordance to genotypes among environments. A total of 26 QTLs and 13 QTNs were identified specifically in D1; 20 QTLs and 21 QTNs were identified specifically in D2. Nine QTLs and one QTN were discovered commonly in two densities. Fifteen QTLs and 9 QTNs were repeatedly detected by multiple statistical methods, densities, or environments, which could be considered stable. Eighteen QTLs were detected, as well as 7 QTNs underlying responses of PH to density increment. Six QTNs, co-located in the interval of QTL, were detected in more than two environments or methods with a longer genome length over 3000 kb. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, five genes were predicted as candidates, which were likely to be involved in growth and development of PH. The results will help elucidate the genetic basis and improve molecular assistant selection of PH.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01209-0.

Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

Quantitative Trait Locus Analysis of Protein and Oil Content in Response to Planting Density in Soybean (Glycine max [L.] Merri.) Seeds Based on SNP Linkage Mapping

Soybean varieties suitable for high planting density allow greater yields. However, the seed protein and oil contents, which determine the value of this crop, can be influenced by planting density. Thus, it is important to understand the genetic basis of the responses of different soybean genotypes to planting density. In this study, we quantified the protein and oil contents in a four-way recombinant inbred line (FW-RIL) soybean population under two planting densities and the response to density. We performed quantitative trait locus (QTL) mapping using a single nucleotide polymorphism (SNP) linkage map generated by inclusive composite interval mapping. We identified 14 QTLs for protein content and 17 for oil content at a planting density of 2.15 × 10⁵ plant/ha (D1) and 14 QTLs for protein content and 20 for oil content at a planting density 3.0 × 10⁵ plant/ha (D2). Among the QTLs detected, two oil-content QTLs was detected at both plant densities. In addition, we identified 38 QTLs for the responses of protein and oil contents to planting density. Of the QTLs detected, 70 were identified in previous studies, while 33 were newly identified. Fourty-five QTLs accounted for over 10% of the phenotypic variation of the corresponding trait, based on 23 QTLs at a marker interval distance of ~600 kb detected under different densities and with the responses to density difference. Pathway analysis revealed four candidate genes involved in protein and oil biosynthesis/metabolism. These results improve our understanding of the genetic underpinnings of protein and oil biosynthesis in soybean, laying the foundation for enhancing protein and oil contents and increasing yields in soybean.

Construction and integration of genetic linkage maps from three multi-parent advanced generation inter-cross populations in rice

BACKGROUND

The construction of genetic maps based on molecular markers is a crucial step in rice genetic and genomic studies. Pure lines derived from multiple parents provide more abundant genetic variation than those from bi-parent populations. Two four-parent pure-line populations (4PL1 and 4PL2) and one eight-parent pure-line population (8PL) were developed from eight homozygous indica varieties of rice by the International Rice Research Institute (IRRI). To the best of our knowledge, there have been no reports on linkage map construction and their integration in multi-parent populations of rice.

RESULTS

We constructed linkage maps for the three multi-parent populations and conducted quantitative trait locus (QTL) mapping for heading date (HD) and plant height (PH) based on the three maps by inclusive composite interval mapping (ICIM). An integrated map was built from the three individual maps and used for QTL projection and meta-analysis. QTL mapping of the three populations was also conducted based on the integrated map, and the mapping results were compared with those from meta-analysis. The three linkage maps developed for 8PL, 4PL1 and 4PL2 had 5905, 4354 and 5464 bins and were 1290.16, 1720.01 and 1560.30 cM in length, respectively. The integrated map was 3022.08 cM in length and contained 10,033 bins. Based on the three linkage maps, 3, 7 and 9 QTLs were detected for HD while 6, 9 and 10 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. In contrast, 19 and 25 QTLs were identified for HD and PH by meta-analysis using the integrated map, respectively. Based on the integrated map, 5, 9, and 10 QTLs were detected for HD while 3, 10, and 12 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. Eleven of these 49 QTLs coincided with those from the meta-analysis.

CONCLUSIONS

In this study, we reported the first rice linkage map constructed from one eight-parent recombinant inbred line (RIL) population and the first integrated map from three multi-parent populations, which provide essential information for QTL linkage mapping, meta-analysis, and map-based cloning in rice genetics and breeding.

Mapping developmental QTL for plant height in soybean [Glycine max (L.) Merr.] using a four-way recombinant inbred line population

Plant height (PH) is an important trait in soybean, as taller plants may have higher yields but may also be at risk for lodging. Many genes act jointly to influence PH throughout development. To map the quantitative trait loci (QTL) controlling PH, we used the unconditional variable method (UVM) and conditional variable method (CVM) to analyze PH data for a four-way recombinant inbred line (FW-RIL) population derived from the cross of (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19). We identified 7, 8, 16, 19, 15, 27, 17, 27, 22, and 24 QTL associated with PH at 10 developmental stages, respectively. These QTL mapped to 95 genomic regions. Among these QTL, 9 were detected using UVM and CVM, and 89 and 66 were only detected by UVM or CVM, respectively. In total, 36 QTL controlling PH were detected at multiple developmental stages and these made unequal contributions to genetic variation throughout development. Among 19 novel regions discovered in our study, 7 could explain over 10% of the phenotypic variation and contained only one single QTL. The unconditional and conditional QTL detected here could be used in molecular design breeding across the whole developmental procedure.

Genetic Dissection of Resistance to the Three Fungal Plant Pathogens Blumeria graminis, Zymoseptoria tritici, and Pyrenophora tritici-repentis Using a Multiparental Winter Wheat Population

Bread wheat (Triticum aestivum L.) is one of the world’s most important crop species. The development of new varieties resistant to multiple pathogens is an ongoing task in wheat breeding, especially in times of increasing demand for sustainable agricultural practices. Despite this, little is known about the relations between various fungal disease resistances at the genetic level, and the possible consequences for wheat breeding strategies. As a first step to fill this gap, we analyzed the genetic relations of resistance to the three fungal diseases – powdery mildew (PM), septoria tritici blotch (STB), and tan spot (TS) – using a winter wheat multiparent advanced generation intercross population. Six, seven, and nine QTL for resistance to PM, STB, and TS, respectively, were genetically mapped. Additionally, 15 QTL were identified for the three agro-morphological traits plant height, ear emergence time, and leaf angle distribution. Our results suggest that resistance to STB and TS on chromosome 2B is conferred by the same genetic region. Furthermore, we identified two genetic regions on chromosome 1AS and 7AL, which are associated with all three diseases, but not always in a synchronal manner. Based on our results, we conclude that parallel marker-assisted breeding for resistance to the fungal diseases PM, STB, and TS appears feasible. Knowledge of the genetic co-localization of alleles with contrasting effects for different diseases, such as on chromosome 7AL, allows the trade-offs of selection of these regions to be better understood, and ultimately determined at the genic level.

Genetic variations for egg quality of chickens at late laying period revealed by genome-wide association study

With the extension of the egg-laying cycle, the rapid decline in egg quality at late laying period has aroused great concern in the poultry industry. Herein, we performed a genome-wide association study (GWAS) to identify genomic variations associated with egg quality, employing chicken 600 K high-density SNP arrays in a population of 1078 hens at 72 and 80 weeks of age. The results indicated that a genomic region spanning from 8.95 to 9.31 Mb (~0.36 Mb) on GGA13 was significantly associated with the albumen height (AH) and the haugh unit (HU), and the two most significant SNPs accounted for 3.12 ~ 5.75% of the phenotypic variance. Two promising genes, MSX2 and DRD1, were mapped to the narrow significant region, which was involved in embryonic and ovary development and found to be related to egg production, respectively. Moreover, three interesting genes, RHOA, SDF4 and TNFRSF4, identified from three significant loci, were considered to be candidate genes for egg shell colour. Findings in our study could provide worthy theoretical basis and technological support to improve late-stage egg quality for breeders.

Four Parent Maize (FPM) Population: Effects of Mating Designs on Linkage Disequilibrium and Mapping Quantitative Traits

Multiparent advanced generation inter-cross (MAGIC) populations can provide improved genetic mapping resolution by increasing allelic diversity and effective recombination. The Four Parent Maize (FPM; L.) population implemented five different mating designs used in MAGIC and bi-parental populations to compare empirical effects on genetic resolution and power of quantitative trait locus (QTL) detection; the combined population here comprised of 1149 individuals with 118,509 genetic markers. Measurements were recorded for plant height (PH), ear height (EH), days to anthesis (DTA) and silking (DTS) in seven environments, spanning three years. Linkage disequilibrium (LD) analysis of subpopulations indicated MAGIC population designs should incorporate generations of intermating to overcome initial LD increase caused by population admixture in a non-intermated four parent population (4way0sib). A 3- to 4-fold increase in genetic resolution (<0.8) and a 2.5-fold decrease in the extent of LD decay (<0.2) compared to the biparental populations was found for the four parent cross at the third generation of intermating (4way3sib). Power of QTL detection was affected to a greater extent by sample size rather than by mating designs. The FPM power simulations indicated that MAGIC populations have the ability to meet or exceed the mapping power of nested association panels with fewer individuals and diversity inputs. Using association mapping software we identified 2, 5, 7, and 6 QTL for PH, EH, DTA, and DTS, respectively. The FPM population is a valuable resource for quantifying empirical improvements of parent number, intermating, and the number of progeny for QTL linkage mapping.

Genome-Wide Association Analysis of Age-Dependent Egg Weights in Chickens

Egg weight (EW) is an economically-important trait and displays a consecutive increase with the hen's age. Because extremely large eggs cause a range of problems in the poultry industry, we performed a genome-wide association study (GWAS) in order to identify genomic variations that are associated with EW. We utilized the Affymetrix 600 K high density SNP array in a population of 1,078 hens at seven time points from day at first egg to 80 weeks age (EW80). Results reveal that a 90 Kb genomic region (169.42 Mb ~ 169.51 Mb) in GGA1 is significantly associated with EW36 and is also potentially associated with egg weight at 28, 56, and 66 week of age. The leading SNP could account for 3.66% of the phenotypic variation, while two promising genes (DLEU7 and MIR15A) can be mapped to this narrow significant region and may affect EW in a pleiotropic manner. In addition, one gene (CECR2 on GGA1) and two genes (MEIS1 and SPRED2 on GGA3), which involved in the processes of embryogenesis and organogenesis, were also considered to be candidates related to first egg weight (FEW) and EW56, respectively. Findings in our study could provide worthy theoretical basis to generate eggs of ideal size based on marker assisted breeding selection.