Genome-wide association studies for production, respiratory disease, and immune-related traits in Landrace pigs

Identification of a quantitative trait locus (QTL) related to a chronic respiratory disease such as Mycoplasmal pneumonia of swine (MPS) and immune-related traits is important for the genetic improvement of disease resistance in pigs. The objective of this study was to detect a novel QTL for a total of 22 production, respiratory disease, and immune-related traits in Landrace pigs. A total of 874 Landrace purebred pigs, which were selected based on MPS resistance, were genotyped using the Illumina PorcineSNP60 BeadChip. We performed single nucleotide polymorphism (SNP)-based and haplotype-based genome-wide association studies (GWAS) to detect a novel QTL and to evaluate the possibility of a pleiotropic QTL for these traits. SNP-based GWAS detected a total of six significant regions in backfat thickness, ratio of granular leucocytes to lymphatic cells, plasma concentration of cortisol at different ages, and complement alternative pathway activity in serum. The significant region detected by haplotype-based GWAS was overlapped across the region detected by SNP-based GWAS. Most of these detected QTL regions were novel regions with some candidate genes located in them. With regard to a pleiotropic QTL among traits, only three of these detected QTL regions overlapped among traits, and many detected regions independently affected the traits.

Fine Mapping of a Major Pleiotropic QTL Associated with Sesamin and Sesamolin Variation in Sesame (Sesamum indicum L.)

Deciphering the genetic basis of quantitative agronomic traits is a prerequisite for their improvement. Herein, we identified loci governing the main sesame lignans, sesamin and sesamolin variation in a recombinant inbred lines (RILs, F8) population under two environments. The content of the two lignans in the seeds was investigated by HPLC. The sesamin and sesamolin contents ranged from 0.33 to 7.52 mg/g and 0.36 to 2.70 mg/g, respectively. In total, we revealed 26 QTLs on a linkage map comprising 424 SSR markers, including 16 and 10 loci associated with sesamin and sesamolin variation, respectively. Among them, qSmin_11.1 and qSmol_11.1 detected in both the two environments explained 67.69% and 46.05% of the phenotypic variation of sesamin and sesamolin, respectively. Notably, qSmin11-1 and qSmol11-1 were located in the same interval of 127-127.21cM on LG11 between markers ZMM1776 and ZM918 and acted as a pleiotropic locus. Furthermore, two potential candidate genes (SIN_1005755 and SIN_1005756) at the same locus were identified based on comparative transcriptome analysis. Our results suggest the existence of a single gene of large effect that controls expression, both of sesamin and sesamolin, and provide genetic information for further investigation of the regulation of lignan biosynthesis in sesame.

Identification of Genetic Loci and Candidate Genes Related to Grain Zinc and Iron Concentration Using a Zinc-Enriched Wheat ‘Zinc-Shakti’

The development of nutritionally enhanced wheat (Triticum aestivum L.) with higher levels of grain iron (Fe) and zinc (Zn) offers a sustainable solution to micronutrient deficiency among resource-poor wheat consumers. One hundred and ninety recombinant inbred lines (RILs) from ‘Kachu’ × ‘Zinc-Shakti’ cross were phenotyped for grain Fe and Zn concentrations and phenological and agronomically important traits at Ciudad Obregon, Mexico in the 2017–2018, 2018–2019, and 2019–2020 growing seasons and Diversity Arrays Technology (DArT) molecular marker data were used to determine genomic regions controlling grain micronutrients and agronomic traits. We identified seven new pleiotropic quantitative trait loci (QTL) for grain Zn and Fe on chromosomes 1B, 1D, 2B, 6A, and 7D. The stable pleiotropic QTL identified have expanded the diversity of QTL that could be used in breeding for wheat biofortification. Nine RILs with the best combination of pleiotropic QTL for Zn and Fe have been identified to be used in future crossing programs and to be screened in elite yield trials before releasing as biofortified varieties. In silico analysis revealed several candidate genes underlying QTL, including those belonging to the families of the transporters and kinases known to transport small peptides and minerals (thus assisting mineral uptake) and catalyzing phosphorylation processes, respectively.

A statistical framework for QTL hotspot detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γn,α profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γn,α profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

Dynamic QTL analysis for developmental behavior of cell wall components and forage digestibility in maize (Zea mays L.)

Background Cell wall architecture plays a key role in stalk strength and forage digestibility. Lignin, cellulose and hemicellulose are the three main components of the plant cell wall and can impact stalk quality by affecting cell wall structure and strength. To explore cell wall development during secondary cell wall lignification in maize stalks, conventional and conditional genetic mappings was used to identify the dynamic quantitative trait locus (QTL) for cell wall components and digestibility traits in five growth stages after silking. Results Acid detergent lignin (ADL), cellulose (CEL), Acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVDMD) of stalk were evaluated in a maize recombinant inbred line (RIL) population. The cell wall components gradually increased in the 10–40 days after silking (DAS), reached a maximum at 30–40 DAS, and then steadily decreased. IVDMD decreased over the initial 40 DAS and then increased slightly. Seventy-two QTL were identified for five traits and each accounted for 3.48–24.04% of the phenotypic resistance variation. Twenty-six conditional QTL were detected using conditional QTL mapping. 22 out of 24 conditional QTL were found for stages III|II and V|IV. Six QTL hotspots were found localized in bins 1.08, 2.04, 2.07, 7.03, 8.05, and 9.03 in the maize genome. Conclusion The unconditional pleiotropic QTL in bins 1.08 and 8.05 were also associated with stalk strength. Furthermore, several pleiotropic QTL for cell wall and digestibility were found not associated with stalk strength. A simultaneous improvement in forage digestibility and lodging resistance can be achieved by pyramiding multiple effective QTL identified in the present study.

Major QTLs for seedling traits in barley using a DArT-based linkage map

Seed vigor is considered as the most critical stage for barley production, and cultivar with high early seedling vigour (ESV) allow plants to form a canopy more quickly. In this study, the QTLs of seedling vigour related-traits were investigated using 185 RILs derived from Xena and H94061120 using DArT approach. In total, 46 significant QTLs for ESV related-traits were detected. The total map length was 1075.1 cM with an average adjacent-marker distance of 3.28 cM. Fourteen QTLs for BY were found on all chromosomes, two of them co-located with QTLs on 1H for GY. The related-traits; LL1, LL2, LA1 and LDW1 had high heritability (>60%). Meanwhile, a significant correlation was observed between GY and BY, which provide the clear image of these traits in the selection process. Our results demonstrate that a pleiotropic QTL related to SLA2, BY, and GY was linked to to the DArT markers bPb-9280 and bPb-9108 on 1H, which could be used to significantly improve the seed vigor by marker-assisted selection and possible future map-based cloning of the gene of intrest.

Genome-wide association and genotype by environment interactions for growth traits in U.S. Gelbvieh cattle

Background Single nucleotide polymorphism (SNP) arrays have facilitated discovery of genetic markers associated with complex traits in domestic cattle; thereby enabling modern breeding and selection programs. Genome-wide association analyses (GWAA) for growth traits were conducted on 10,837 geographically diverse U.S. Gelbvieh cattle using a union set of 856,527 imputed SNPs. Birth weight (BW), weaning weight (WW), and yearling weight (YW) were analyzed using GEMMA and EMMAX (via imputed genotypes). Genotype-by-environment (GxE) interactions were also investigated. Results GEMMA and EMMAX produced moderate marker-based heritability estimates that were similar for BW (0.36–0.37, SE = 0.02–0.06), WW (0.27–0.29, SE = 0.01), and YW (0.39–0.41, SE = 0.01–0.02). GWAA using 856K imputed SNPs (GEMMA; EMMAX) revealed common positional candidate genes underlying pleiotropic QTL for Gelbvieh growth traits on BTA6, BTA7, BTA14, and BTA20. The estimated proportion of phenotypic variance explained (PVE) by the lead SNP defining these QTL (EMMAX) was larger and most similar for BW and YW, and smaller for WW. Collectively, GWAAs (GEMMA; EMMAX) produced a highly concordant set of BW, WW, and YW QTL that met a nominal significance level (P ≤ 1e-05), with prioritization of common positional candidate genes; including genes previously associated with stature, feed efficiency, and growth traits (i.e., PLAG1, NCAPG, LCORL, ARRDC3, STC2). Genotype-by-environment QTL were not consistent among traits at the nominal significance threshold (P ≤ 1e-05); although some shared QTL were apparent at less stringent significance thresholds (i.e., P ≤ 2e-05). Conclusions Pleiotropic QTL for growth traits were detected on BTA6, BTA7, BTA14, and BTA20 for U.S. Gelbvieh beef cattle. Seven QTL detected for Gelbvieh growth traits were also recently detected for feed efficiency and growth traits in U.S. Angus, SimAngus, and Hereford cattle. Marker-based heritability estimates and the detection of pleiotropic QTL segregating in multiple breeds support the implementation of multiple-breed genomic selection.