Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep

Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.

Genomic dissection of maternal, additive and non-additive genetic effects for growth and carcass traits in Nile tilapia

Background

The availability of both pedigree and genomic sources of information for animal breeding and genetics has created new challenges in understanding how they can be best used and interpreted. This study estimated genetic variance components based on genomic information and compared these to the variance components estimated from pedigree alone in a population generated to estimate non-additive genetic variance. Furthermore, the study examined the impact of the assumptions of Hardy–Weinberg equilibrium (HWE) on estimates of genetic variance components. For the first time, the magnitude of inbreeding depression for important commercial traits in Nile tilapia was estimated by using genomic data.

Results

The study estimated the non-additive genetic variance in a Nile tilapia population of full-sib families and, when present, it was almost entirely represented by additive-by-additive epistatic variance, although in pedigree studies this non-additive variance is commonly assumed to arise from dominance. For body depth (BD) and body weight at harvest (BWH), the proportion of additive-by-additive epistatic to phenotypic variance was estimated to be 0.15 and 0.17 using genomic data (P < 0.05). In addition, with genomic data, the maternal variance (P < 0.05) for BD, BWH, body length (BL) and fillet weight (FW) explained approximately 10% of the phenotypic variances, which was comparable to pedigree-based estimates. The study also showed the detrimental effects of inbreeding on commercial traits of tilapia, which was estimated to reduce trait values by 1.1, 0.9, 0.4 and 0.3% per 1% increase in the individual homozygosity for FW, BWH, BD and BL, respectively. The presence of inbreeding depression but lack of dominance variance was consistent with an infinitesimal dominance model for the traits.

Conclusions

The benefit of including non-additive genetic effects for genetic evaluations in tilapia breeding schemes is not evident from these findings, but the observed inbreeding depression points to a role for reciprocal recurrent selection. Commercially, this conclusion will depend on the scheme’s operational costs and resources. The creation of maternal lines in Tilapia breeding schemes may be a possibility if the variation associated with maternal effects is heritable.

Genome-wide association studies, meta-analyses and derived gene network for meat quality and carcass traits in pigs

A large number of quantitative trait loci (QTL) for meat quality and carcass traits has been reported in pigs over the past 20 years. However, few QTL have been validated and the biological meaning of the genes associated to these QTL has been underexploited. In this context, a meta-analysis was performed to compare the significant markers with meta-QTL previously reported in literature. Genome association studies were performed for 12 traits, from which 144 SNPs were found out to be significant (P < 0.05). They were validated in the meta-analysis and used to build the Association Weight Matrix, a matrix framework employed to investigate co-association of pairwise SNP across phenotypes enabling to derive a gene network. A total of 45 genes were selected from the Association Weight Matrix analysis, from which 25 significant transcription factors were identified and used to construct the networks associated to meat quality and carcass traits. These networks allowed the identification of key transcription factors, such as SOX5 and NKX2–5, gene–gene interactions (e.g. ATP5A1, JPH1, DPT and NEDD4) and pathways related to the regulation of adipose tissue metabolism and skeletal muscle development. Validated SNPs and knowledge of key genes driving these important industry traits might assist future strategies in pig breeding.

Genome-Wide SNP Linkage Mapping and QTL Analysis for Fiber Quality and Yield Traits in the Upland Cotton Recombinant Inbred Lines Population

It is of significance to discover genes related to fiber quality and yield traits and tightly linked markers for marker-assisted selection (MAS) in cotton breeding. In this study, 188 F8 recombinant inbred lines (RILs), derived from a intraspecific cross between HS46 and MARCABUCAG8US-1-88 were genotyped by the cotton 63K single nucleotide polymorphism (SNP) assay. Field trials were conducted in Sanya, Hainan Province, during the 2014-2015 cropping seasons under standard conditions. Results revealed significant differences (P < 0.05) among RILs, environments and replications for fiber quality and yield traits. Broad-sense heritabilities of all traits including fiber length, fiber uniformity, micronaire, fiber elongation, fiber strength, boll weight, and lint percentage ranged from 0.26 to 0.66. A 1784.28 cM (centimorgans) linkage map, harboring 2618 polymorphic SNP markers, was constructed, which had 0.68 cM per marker density. Seventy-one quantitative trait locus (QTLs) for fiber quality and yield traits were detected on 21 chromosomes, explaining 4.70∼32.28% phenotypic variance, in which 16 were identified as stable QTLs across two environments. Meanwhile, 12 certain regions were investigated to be involved in the control of one (hotspot) or more (cluster) traits, mainly focused on Chr05, Chr09, Chr10, Chr14, Chr19, and Chr20. Nineteen pairs of epistatic QTLs (e-QTLs) were identified, of which two pairs involved in two additive QTLs. These additive QTLs, e-QTLs, and QTL clusters were tightly linked to SNP markers, which may serve as target regions for map-based cloning, gene discovery, and MAS in cotton breeding.

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy.

A genetical genomics approach reveals new candidates and confirms known candidate genes for drip loss in a porcine resource population

In this study lean meat water-holding capacity (WHC) of a Duroc × Pietrain (DuPi) resource population with corresponding genotypes and transcriptomes was investigated using genetical genomics. WHC was characterized by drip loss measured in M. longissimus dorsi. The 60K Illumina SNP chips identified genotypes of 169 F2 DuPi animals. Whole-genome transcriptomes of muscle samples were available for 132 F2 animals using the Affymetrix 24K GeneChip® Porcine Genome Array. Performing genome-wide association studies of transcriptional profiles, which are correlated with phenotypes, allows elucidation of cis- and trans-regulation. Expression levels of 1,228 genes were significantly correlated with drip loss and were further analyzed for enrichment of functional annotation groups as defined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. A hypergeometric gene set enrichment test was performed and revealed glycolysis/glyconeogenesis, pentose phosphate pathway, and pyruvate metabolism as the most promising pathways. For 267 selected transcripts, expression quantitative trait loci (eQTL) analysis was performed and revealed a total of 1,541 significant associations. Because of positional accordance of the gene underlying transcript and the eQTL location, it was possible to identify eight eQTL that can be assumed to be cis-regulated. Comparing the results of gene set enrichment and the eQTL detection tests, molecular networks and potential candidate genes, which seemed to play key roles in the expression of WHC, were detected. The α-1-microglobulin/bikunin precursor (AMBP) gene was assumed to be cis-regulated and was part of the glycolysis pathway. This approach supports the identification of trait-associated SNPs and the further biological understanding of complex traits.

Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers

Non-additive genetic variation is usually ignored when genome-wide markers are used to study the genetic architecture and genomic prediction of complex traits in human, wild life, model organisms or farm animals. However, non-additive genetic effects may have an important contribution to total genetic variation of complex traits. This study presented a genomic BLUP model including additive and non-additive genetic effects, in which additive and non-additive genetic relation matrices were constructed from information of genome-wide dense single nucleotide polymorphism (SNP) markers. In addition, this study for the first time proposed a method to construct dominance relationship matrix using SNP markers and demonstrated it in detail. The proposed model was implemented to investigate the amounts of additive genetic, dominance and epistatic variations, and assessed the accuracy and unbiasedness of genomic predictions for daily gain in pigs. In the analysis of daily gain, four linear models were used: 1) a simple additive genetic model (MA), 2) a model including both additive and additive by additive epistatic genetic effects (MAE), 3) a model including both additive and dominance genetic effects (MAD), and 4) a full model including all three genetic components (MAED). Estimates of narrow-sense heritability were 0.397, 0.373, 0.379 and 0.357 for models MA, MAE, MAD and MAED, respectively. Estimated dominance variance and additive by additive epistatic variance accounted for 5.6% and 9.5% of the total phenotypic variance, respectively. Based on model MAED, the estimate of broad-sense heritability was 0.506. Reliabilities of genomic predicted breeding values for the animals without performance records were 28.5%, 28.8%, 29.2% and 29.5% for models MA, MAE, MAD and MAED, respectively. In addition, models including non-additive genetic effects improved unbiasedness of genomic predictions.

Genetic components and major QTL confer resistance to bean pyralid (Lamprosema indicata Fabricius) under multiple environments in four RIL populations of soybean

Bean pyralid (BP; Lamprosema indicata Fabricius) is one of the major leaf-feeding insects that affect soybean crops in central and southern China. Four recombinant inbred line populations (KY, WT, XG and SX) were tested during 2004-2006 in Nanjing, China, to identify quantitative trait loci (QTL) for resistance to BP on the basis of data for rolled leaflet percentage under field infestation conditions. The mapping was performed using QTL Network V2.0 and checked with Windows QTL Cartographer V2.5 and IciMapping V2.2. The results showed that 81-92 % of the phenotypic variation was accounted for by additive QTL (27-43 %), epistatic QTL pairs (5-13 %), and collective unmapped minor QTL (38-58 %). In total, 17 QTL were detected on 11 linkage groups, of which two had additive effects, six had both additive and epistatic effects, and nine had only epistatic effects. Eight epistatic QTL pairs were observed, of which three pairs involved two QTL with additive effects, one involved one QTL with additive effect, and four involved no QTL with additive effects. Different genetic structures for BP resistance were found among the populations. Eight QTL (five additive and three epistatic pairs) were detected in KY, ten QTL (four additive and five epistatic pairs) were detected in WT, and only one additive QTL was detected in both the XG and the SX populations. BP12-1 and BP1-1 are major QTL, with the former accounting for 15, 31, and 50 % of the total genetic variation (including epistasis) in KY, WT, and XG, respectively, and the latter accounting for 13 and 32 % of the total genetic variation in KY and SX, respectively. The additive × year and epistasis × year interaction effects were negligible, indicating that the QTL were stable over the years. Because 41-68 % of the total genetic variation could not be accounted for by these QTL, the use of both identified QTL and unmapped minor QTL in breeding for BP resistance should be considered.

A two-step approach to map quantitative trait loci for meat quality in connected porcine F(2) crosses considering main and epistatic effects

The aim of this study was to map QTL for meat quality traits in three connected porcine F(2) crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two-step procedure. In the first step, the QTL were mapped using a multi-QTL multi-allele model that was tailored to analyse multiple connected F(2) crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.

Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis

Background

A quantitative and a binary trait for the 14^th QTLMAS 2010 workshop were simulated under a model which combined additive inheritance, epistasis and imprinting. This paper aimed to compare results submitted by the participants of the workshop.

Methods

The results were compared according to three criteria: the success rate (ratio of mapped QTL to the total number of simulated QTL), and the error rate (ratio of false positives to the number of reported positions), and mean distance between a true mapped QTL and the nearest submitted position.

Results

Seven groups submitted results for the quantitative trait and five for the binary trait. Among the 37 simulated QTL 17 remained undetected. Success rate ranged from 0.05 to 0.43, error rate was between 0.00 and 0.92, and the mean distance ranged from 0.26 to 0.77 Mb.

Conclusions

Our comparison shows that differences among methods used by the participants increases with the complexity of genetic architecture. It was particularly visible for the quantitative trait which was determined partly by non-additive QTL. Furthermore, an imprinted QTL with a large effect may remain undetected if the applied model tests only for Mendelian genes.

QTLMAS 2010: simulated dataset

Background

Objective was to simulate the data for the QTLMAS 2010 Workshop under a model that includes major additive, epistatic and parent-of-origin effects.

Results

Data were simulated for 3226 individuals in 5 generations. Genomic data for 5 chromosomes were simulated using coalescent model. In total, the data included 10,031 SNPs, 30 additive QTLs, 2 interacting QTL pairs, and 3 imprinted loci. The density was 20 SNPs/1Mb, whereas mean linkage disequilibrium between adjacent SNPs was 0.1. One quantitative and one binary trait were simulated with heritability of 0.39-0.52 and additive correlation of 0.59. The data can be used as a benchmark for comparison of QTL mapping methods and models for genomic breeding value estimation under complex genetic architecture.