Multi-locus Genome-wide Association Studies Research Articles

Optimized permutation testing for information theoretic measures of multi-gene interactions

BackgroundPermutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large.ResultsIn this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples.ConclusionsThe proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.

Genetic analysis of grain protein content and dough quality traits in elite spring bread wheat (Triticum aestivum) lines through association study

Wheat quality parameters are complex traits predominantly influenced by the genetic background of varieties. A total of 802 spring bread wheat breeding lines were tested at Marchouch station, Morocco in 2018 aimed at mining invaluable QTNs for grain protein content (PR), gluten content (GL), alveograph strength (W) and zeleny sedimentation test (Z). A single-locus (MLM) and multi-locus (FarmCPU) GWAS models were implemented using 5,095 SNP markers generated from the genotyping-by-sequencing (GBS) platform. A total of 36 and 92 QTNs were detected for the four quality traits from the MLM and FarmCPU GWAS models, respectively. The FarmCPU model detected markers on homoeologous chromosome group one having highly significant QTNs with PR, GL, W and Z traits. The two implemented GWAS models detected significant QTNs on the short arm of homoeologous chromosome groups 2 and 6 for GL, PR, W and Z traits. FarmCPU model detected SNP_615302 marker on chromosome 3B with highly significant MTAs and concurrent effect for PR, GL and W traits. Moreover, significant MTAs were detected on chromosomes 3D, 4A, 4B, 5A, 5B, 7A, 7B and 7D for the four studied quality traits across the two GWAS models. The in-silico gene annotation for QTNs using the high confidence gene models detected genes involved in numerous functions including regulating plant growth, development and tolerance to biotic and abiotic stresses. The identified markers could potentially be useful tool for quality improvement through marker-assisted selection in future breeding programs.

A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies.

The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today’s big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets.

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

Wheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome.

Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population.

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.

Genome-Wide Association Study on Resistance to Rice Black-Streaked Dwarf Disease Caused by Rice black-streaked dwarf virus.

Rice black-streaked dwarf disease caused by Rice black-streaked dwarf virus (RBSDV) is one of the most destructive viral diseases of rice. Thus, it is imperative that resistant rice germplasms are screened for novel RBSDV-resistant genes. RBSDV resistance of a diverse global collection comprising 1,953 rice accessions was evaluated under natural conditions across 3 years. The average disease incidences of the Xian/indica (XI) subgroup were significantly lower than those of the Geng/japonica (GJ) subgroup. Interestingly, most XI-1A accessions in the Xian subgroup were significantly more susceptible than XI-1B accessions, even though XI-1A and XI-1B have a close phylogenetic relationship. Four Xian accessions stably and highly resistant to RBSDV were consistently identified in 2 years. Ten genomic regions (GRs) with 147 single nucleotide polymorphisms associated with RBSDV resistance were detected by a single-locus genome-wide association study (GWAS), of which five were repeatedly identified in a multilocus GWAS. Two previously reported GRs, grRBSDV-6.1 and grRBSDV-6.3, which were repeatedly detected as stably and highly associated with RBSDV resistance, contained 17 and seven genes, respectively, with significant differences of resistance among haplotypes. Haplotype analyses of the candidate genes LOC_Os06g03150 in grRBSDV-6.1 and LOC_Os06g31190 in grRBSDV-6.3 suggested that the former gene is mainly associated with the differentiation of resistance within the Xian subgroup and the latter gene mainly explains the difference in the resistance between Xian and Geng. Another three novel resistance GRs (grRBSDV-1.1, grRBSDV-7.1, and grRBSDV-9.1) were identified. Our findings may enhance the application of disease-resistant rice germplasms for breeding RBSDV-resistant varieties.

Detection of QTNs for kernel moisture concentration and kernel dehydration rate before physiological maturity in maize using multi-locus GWAS

Maize is China’s largest grain crop. Mechanical grain harvesting is the key technology in maize production, and the kernel moisture concentration (KMC) is the main controlling factor in mechanical maize harvesting in China. The kernel dehydration rate (KDR) is closely related to the KMC. Thus, it is important to conduct genome-wide association studies (GWAS) of the KMC and KDR in maize, detect relevant quantitative trait nucleotides (QTNs), and mine relevant candidate genes. Here, 132 maize inbred lines were used to measure the KMC every 5 days from 10 to 40 days after pollination (DAP) in order to calculate the KDR. These lines were genotyped using a maize 55K single-nucleotide polymorphism array. QTNs for the KMC and KDR were detected based on five methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO) in the package mrMLM. A total of 334 significant QTNs were found for both the KMC and KDR, including 175 QTNs unique to the KMC and 178 QTNs unique to the KDR; 116 and 58 QTNs were detected among the 334 QTNs by two and more than two methods, respectively; and 9 and 5 QTNs among 58 QTNs were detected in 2 and 3 years, respectively. A significant enrichment in cellular component was revealed by Gene Ontology enrichment analysis of candidate genes in the intervals adjacent to the 14 QTNs and this category contained five genes. The information provided in this study may be useful for further mining of genes associated with the KMC and KDR in maize.

Evolutionary QTL-allele changes in main stem node number among geographic and seasonal subpopulations of Chinese cultivated soybeans.

The main stem node number (MSN) is a trait related to geographic adaptation, plant architecture and yield potential of soybean. The QTL-allele constitution of the Chinese Cultivated Soybean Population (CCSP) was identified using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) procedure, from which a QTL-allele matrix was established and then separated into submatrices to explore the genetic structure, evolutionary differentiation, breeding potential and candidate genes of MSN in CCSP. The MSN of 821 accessions varied from 8.8 to 31.1, with an average of 16.3 in Nanjing, China (32.07° N, 118.62° E), where the MSNs of all the materials could be evaluated in a standardized manner. Among the six geo-seasonal subpopulations, the MSN varied from 21.7 in a southern summer-autumn-sowing subpopulation (SA-IV) down to 13.5 in a northeastern spring-sowing subpopulation (SP-I). The materials were genotyped with restriction site-associated DNA-sequencing. Totally 142 main-effect QTLs (73.24% new) with 560 alleles contributing 72.98% to the phenotypic variance were identified. The evolutionary QTL-allele changes in MSN from SA-IV through SP-I showed that inheritance (78.93% of alleles) was the primary factor influencing the evolution of this trait, followed by allele emergence (19.64% alleles), allele exclusion (1.43% alleles), and recombination among retained alleles. In the evolutionary changes, 70 QTLs, including 12 newly emerged QTLs, with 118 alleles were involved. An increase potential of 2-8 nodes was predicted and 112 candidate genes were annotated and preliminarily verified with χ2-tests in the CCSP. The RTM-GWAS showed powerful in detecting QTL-allele system, assessing evolution factors, predicting optimal crosses and identifying candidate genes in a germplasm population.

Detecting QTL system of root hydraulic stress tolerance index at seedling stage in soybean

<p indent=0mm>Soybean is an important source of plant protein and vegetable oil in the world. Drought is one of the important environmental stress factors affecting soybean yield. To explore the genetic base of drought tolerance in soybean, a nested association mapping population composed of two sets of recombinant inbred lines with a common parent in a total of 429 lines was investigated for leaf proline content under PEG simulated drought stress. The genetic system of root hydraulic stress tolerance index (RHSTI) was analyzed using the RTM-GWAS (restrictive two-stage multilocus genome-wide association study). The results showed that there were significant differences in RHSTI among the three parents under two different environments in spring and summer, and among the nested association mapping population with the variation range of 0.11–2.94 and 0.03–1.93, respectively. The heritability values of Line and Line×Environment were 37.9% and 60.1%, respectively, indicating that the variation of RHSTI was greatly affected by the environment. Using the RHSTI data and 6137 SNPLDB markers, a total of 45 main effect QTLs were detected on 18 chromosomes, which could explain a total of 37.58% phenotypic variation, including 7 large contribution QTLs with <italic>R</italic>² more than 1%. Among them, 34 QTLs with QTL×Environment effect explained 12.50% of the phenotypic variation. Combined with the transcriptome data under PEG stress, totally 38 differentially expressed genes were identified within a QTL ± 500 kb, which can be grouped into different biological categories, including ABA responders, stress responders, transcription factors, development factors, protein metabolism factors, unknown functions and others, with stress responses, transcription factors and development factors as the major parts. In summary, the results indicated that the drought tolerance of soybean was a complex quantitative trait, with the complex genetic basis controlling by multiple loci, multiple genes and interaction with the environment. The present results can lay the foundation of molecular breeding for drought tolerance in soybean.

Decryption of favourable haplotypes and potential candidate genes for five fibre quality properties using a relatively novel genome-wide association study procedure in upland cotton

To understand the genetic basis of crop economic characteristics, identifying whole-genome quantitative trait loci (QTLs) in breeding resources via genome-wide association studies (GWASs) is important, but conventional GWAS procedures have mainly concentrated on identifying regions of major QTLs. In this study, to reveal whole-genome QTLs controlling cotton fibre quality traits, a total of 1,236,418 single-nucleotide polymorphism (SNP) markers were explored in a natural population of 315 upland cotton accessions using the specific-locus amplified fragment sequencing (SLAF-seq) method, and 13,391 high-quality SNPs were organized into 9244 SNP linkage disequilibrium blocks (SNPLDBs) to develop molecular markers with multiple haplotypes/alleles. A relatively novel procedure for restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS) involving two methods was applied, including multiple environments and four single environments. In total, 75, 77, 66, 66, and 60 significant SNPLDBs associated with the upper half mean length (UHML), fibre strength (FS), fibre micronaire (FM), fibre uniformity index (FUI), and fibre elongation (FE), respectively, were identified via the multiple-environment RTM-GWAS method. Based on these results, the haplotype/allele effects of the significant SNPLDB loci were estimated, and QTL-allele matrices were established to provide the abbreviated genetic composition of the population. Most importantly, nine stable SNPLDB loci were simultaneously detected in multiple environments and two or more single test environments with large –lg(P) values and percentages of explained phenotypic variation. Furthermore, favourable haplotypes/alleles of the nine stable SNPLDB loci were identified, and 79 potential candidate genes related to UHML and FS traits were predicted from RNA-seq data. Favourable haplotypes/alleles, potential candidate genes and QTL-allele matrices can offer important insights into the genetic basis of fibre quality traits in upland cotton and may facilitate the breeding of cotton varieties with excellent fibre quality properties.

Loci harboring genes with important role in drought and related abiotic stress responses in flax revealed by multiple GWAS models

QTNs associated with drought tolerance traits and indices were identified in a flax mini-core collection through multiple GWAS models and phenotyping at multiple locations under irrigated and non-irrigated field conditions. Drought is a critical phenomenon challenging today's agricultural sector. Crop varieties adapted to moisture deficit are becoming vital. Flax can be greatly affected by limiting moisture conditions, especially during the early development and reproductive stages. Here, a mini-core collection comprising genotypes from more than 20 major growing countries was evaluated for 11 drought-related traits in irrigated and non-irrigated fields for 3years. Heritability of the traits ranged from 44.7 to 86%. Six of the 11 traits showed significant phenotypic difference between irrigated and non-irrigated conditions. A genome-wide association study (GWAS) was performed for these six traits and their corresponding stress indices based on 106 genotypes and 12,316 single nucleotide polymorphisms (SNPs) using six multi-locus and one single-locus models. The SNPs were then assigned to 8050 linkage disequilibrium (LD) blocks to which a restricted two-stage multi-locus multi-allele GWAS was applied. A total of 144 quantitative trait nucleotides (QTNs) and 13 LD blocks were associated with at least one trait or stress index. Of these, 16 explained more than 15% of the genetic variance. Most large-effect QTN loci harbored gene(s) previously predicted to play role(s) in the associated traits. Genes mediating responses to abiotic stresses resided at loci associated with stress indices. Flax genes Lus10009480 and Lus10030150 that are predicted to encode WAX INDUCER1 and STRESS-ASSOCIATED PROTEIN (SAP), respectively, are among the important candidates detected. Accessions with multiple favorable alleles outperformed others for grain yield, thousand seed weight and fiber/biomass in non-irrigated conditions, suggesting their potential usefulness in breeding and genomic selection.

Detection of QTL and QTN and candidate genes for oil content in soybean using a combination of four-way-RIL and germplasm populations

Soybean is a source of edible oil for humans and provides a third of the vegetable oil consumed worldwide. Increasing seed oil content in seeds is thus a key objective in soybean breeding. In the present study, a four-way recombinant inbred line (FW-RIL) population comprising 144 lines, planted in 10 environments, and a germplasm panel of 455 accessions, planted in two environments, were used to collect oil-content phenotypes. First, 59 quantitative trait loci (QTL) were detected in the FW-RIL population by inclusive complete interval mapping on a linkage map consisting of 2232 single-nucleotide polymorphism (SNP) markers. Also in the FW-RILs, 44 quantitative trait nucleotides (QTNs) were detected by association analysis using 109,676 SNP markers and five methods of multi-locus genome-wide association study. Second, 77 QTN were detected by association analysis in the germplasm panel using 63,306 markers. Comparison of the QTL and QTN suggested four QTN controlling oil content. Pathway analysis was performed on genes in attenuation regions of these four QTN, and two candidate genes involved in the synthesis or metabolism of soybean oil were identified. These findings provide useful information about the genetics of oil content and may contribute to its genetic improvement by marker-assisted selection.

Multi-locus genome-wide association studies reveal novel genomic regions associated with vegetative stage salt tolerance in bread wheat (Triticum aestivum L.)

Soil salinity is one of the typical abiotic stresses affecting sustainability of wheat production worldwide. In the present study, we performed a 35 K SNP genotyping assay on association panel of 135 diverse wheat genotypes evaluated for vegetative stage tolerance in hydroponics. Association analyses using five multi-locus GWAS models revealed 42 reliable QTNs for 10 salt tolerance associated traits. Among these 42 reliable QTNs, 9, 17 and 16 QTNs were associated with physiological, biomass and shoot ionic traits respectively. Novel major QTNs were identified for chlorophyll content, shoot fresh weight, seedling total biomass, Na+ and K+ concentration and Na+/K+ ratio in shoots. Further, 10 major QTNs showed significant effect on the corresponding salt tolerance traits. Gene ontology analysis of the associated genomic regions identified 58 candidate genes. The information generated in this study will be of potential value for improvement of salt tolerance of wheat cultivars using marker assisted selection.

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS.

Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.

MrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network.

In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.

Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies.

Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean (Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization.

Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

Genome-wide association analyses identify known and novel loci for teat number in Duroc pigs using single-locus and multi-locus models

BackgroundMore teats are necessary for sows to nurse larger litters to provide immunity and nutrient for piglets prior to weaning. Previous studies have reported the strong effect of an insertion mutation in the Vertebrae Development Associated (VRTN) gene on Sus scrofa chromosome 7 (SSC7) that increased the number of thoracic vertebrae and teat number in pigs. We used genome-wide association studies (GWAS) to map genetic markers and genes associated with teat number in two Duroc pig populations with different genetic backgrounds. A single marker method and several multi-locus methods were utilized. A meta-analysis that combined the effects and P-values of 34,681 single nucleotide polymorphisms (SNPs) that were common in the results of single marker GWAS of American and Canadian Duroc pigs was conducted. We also performed association tests between the VRTN insertion and teat number in the same populations.ResultsA total of 97 SNPs were found to be associated with teat number. Among these, six, eight and seven SNPs were consistently detected with two, three and four multi-locus methods, respectively. Seven SNPs were concordantly identified between single marker and multi-locus methods. Moreover, 26 SNPs were newly found by multi-locus methods to be associated with teat number. Notably, we detected one consistent quantitative trait locus (QTL) on SSC7 for teat number using single-locus and meta-analysis of GWAS and the top SNP (rs692640845) explained 8.68% phenotypic variance of teat number in the Canadian Duroc pigs. The associations between the VRTN insertion and teat number in two Duroc pig populations were substantially weaker. Further analysis revealed that the effect of VRTN on teat number may be mediated by its LD with the true causal mutation.ConclusionsOur study suggested that VRTN insertion may not be a strong or the only candidate causal mutation for the QTL on SSC7 for teat number in the analyzed Duroc pig populations. The combination of single-locus and multi-locus GWAS detected additional SNPs that were absent using only one model. The identified SNPs will be useful for the genetic improvement of teat number in pigs by assigning higher weights to associated SNPs in genomic selection.