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Wheat breeding programs generate a large amount of variation which cannot be completely explored because of limited phenotyping throughput. Genomic prediction (GP) has been proposed as a new tool which provides breeding values estimations without the need of phenotyping all the material produced but only a subset of it named training population (TP). However, genotyping of all the accessions under analysis is needed and, therefore, optimizing TP dimension and genotyping strategy is pivotal to implement GP in commercial breeding schemes. Here, we explored the optimum TP size and we integrated pedigree records and genome wide association studies (GWAS) results to optimize the genotyping strategy. A total of 988 advanced wheat breeding lines were genotyped with the Illumina 15K SNPs wheat chip and phenotyped across several years and locations for yield, lodging, and starch content. Cross-validation using the largest possible TP size and all the SNPs available after editing (~11k), yielded predictive abilities (rGP) ranging between 0.5–0.6. In order to explore the Training population size, rGP were computed using progressively smaller TP. These exercises showed that TP of around 700 lines were enough to yield the highest observed rGP. Moreover, rGP were calculated by randomly reducing the SNPs number. This showed that around 1K markers were enough to reach the highest observed rGP. GWAS was used to identify markers associated with the traits analyzed. A GWAS-based selection of SNPs resulted in increased rGP when compared with random selection and few hundreds SNPs were sufficient to obtain the highest observed rGP. For each of these scenarios, advantages of adding the pedigree information were shown. Our results indicate that moderate TP sizes were enough to yield high rGP and that pedigree information and GWAS results can be used to greatly optimize the genotyping strategy. [ABSTRACT FROM AUTHOR]

Background: Litter size and piglet mortality are important traits in pig production. The study aimed to identify quantitative trait loci (QTL) for litter size and mortality traits, including total number of piglets born (TNB), litter size at day 5 (LS5) and mortality rate before day 5 (MORT) in Danish Landrace and Yorkshire pigs by genome-wide association studies (GWAS). Methods: The phenotypic records and genotypes were available in 5,977 Landrace pigs and 6,000 Yorkshire pigs born from 1998 to 2014. A linear mixed model (LM) with a single SNP regression and a Bayesian mixture model (BM) including effects of all SNPs simultaneously were used for GWAS to detect significant QTL association. The response variable used in the GWAS was corrected phenotypic value which was obtained by adjusting original observations for non-genetic effects. For BM, the QTL region was determined by using a novel post-Gibbs analysis based on the posterior mixture probability. Results: The detected association patterns from LM and BM models were generally similar. However, BM gave more distinct detection signals than LM. The clearer peaks from BM indicated that the BM model has an advantage in respect of identifying and distinguishing regions of putative QTL. Using BM and QTL region analysis, for the three traits and two breeds a total of 15 QTL regions were identified on SSC1, 2, 3, 6, 7, 9, 13 and 14. Among these QTL regions, 6 regions located on SSC2, 3, 6, 7 and 13 were associated with more than one trait. Conclusion: This study detected QTL regions associated with litter size and piglet mortality traits in Danish pigs using a novel approach of post-Gibbs analysis based on posterior mixture probability. All of the detected QTL regions overlapped with regions previously reported for reproduction traits. The regions commonly detected in different traits and breeds could be resources for multi-trait and across-bred selection. The proposed novel QTL region analysis method would be a good alternative to detect and define QTL regions. [ABSTRACT FROM AUTHOR]

Until now, genomic prediction (GP) in plant breeding has only used information from individuals that have been genotyped. Information from nongenotyped relatives of genotyped individuals can also be used. Single-step GP combines marker and pedigree information into a single relationship matrix to perform GP. The objective of this study was to evaluate single-step GP in a wheat breeding program. We compared the performance of pedigree-based, marker-based, and single-step models (ABLUP, GBLUP, and HBLUP, respectively). Data consisted of 1176 genotyped (via genotyping-by-sequencing) and 11,131 nongenotyped wheat lines replicated in five management environments at the CIMMYT experiment station in Obregon, Mexico. Analyses involved three scenarios: (i) all lines had pedigree information but only some were genotyped, with phenotypes from one or two environments in the 2011-2012 season, (ii) all lines had genotype and pedigree information and phenotypes from four or five environments in the 2012-2013 season, and (iii) the combination of Scenarios 1 and 2. Prediction accuracies were calculated by fivefold cross validation on plant height, maturity, heading date, and grain yield. The single-step HBLUP outperformed GBLUP and pedigreebased ABLUP in all cases. We conclude that the single-step procedure combining pedigree and genomic marker data should be favored where appropriate data is available for GP in wheat breeding programs. [ABSTRACT FROM AUTHOR]

Keymessage: By using the genotyping-by-sequencing method, it is feasible to characterize genomic relationships directly at the level of family pools and to estimate genomic heritabilities from phenotypes scored on family-pools in outbreeding species. Abstract: Genotyping-by-sequencing (GBS) has recently become a promising approach for characterizing plant genetic diversity on a genome-wide scale. We use GBS to extend the concept of heritability beyond individuals by genotyping family-pool samples by GBS and computing genomic relationship matrices (GRMs) and genomic heritabilities directly at the level of family-pools from pool-frequencies obtained by sequencing. The concept is of interest for species where breeding and phenotyping is not done at the individual level but operates uniquely at the level of (multi-parent) families. As an example we demonstrate the approach using a set of 990 two-parent F2 families of perennial ryegrass ( Lolium Perenne). The families were phenotyped as a family-unit in field plots for heading date and crown rust resistance. A total of 728 K single nucleotide polymorphism (SNP) variants were available and were divided in groups of different sequencing depths. GRMs based on GBS data showed diagonal values biased upwards at low sequencing depth, while off-diagonals were little affected by the sequencing depth. Using variants with high sequencing depth, genomic heritability for crown rust resistance was 0.33, and for heading date 0.22, and these genomic heritabilities were biased downwards when using variants with lower sequencing depth. Broad sense heritabilities were 0.61 and 0.66, respectively. Underestimation of genomic heritability at lower sequencing depth was confirmed with simulated data. We conclude that it is feasible to use GBS to describe relationships between family-pools and to estimate genomic heritability directly at the level of F2 family-pool samples, but estimates are biased at low sequencing depth. [ABSTRACT FROM AUTHOR]

Objectives: Pharmacogenetic studies of tumour necrosis factor inhibitors (TNFi) response in patients with rheumatoid arthritis (RA) have largely relied on the changes in complex disease scores, such as disease activity score 28 (DAS28), as a measure of treatment response. It is expected that genetic architecture of such complex score is heterogeneous and not very suitable for pharmacogenetic studies. We aimed to select the most optimal phenotype for TNFi response using heritability estimates.Methods: Using two linear mixed-modelling approaches (Bayz and GCTA), we estimated heritability, together with genomic and environmental correlations for the TNFi drug-response phenotype ΔDAS28 and its separate components: Δ swollen joint count (SJC), Δ tender joint count (TJC), Δ erythrocyte sedimentation rate (ESR) and Δ visual-analogue scale of general health (VAS-GH). For this, we used genome-wide single nucleotide polymorphism (SNP) data from 878 TNFi-treated Dutch patients with RA. Furthermore, a multivariate genome-wide association study (GWAS) approach was implemented, analysing separate DAS28 components simultaneously.Results: The highest heritability estimates were found for ΔSJC (h(2)gbayz=0.76 and h(2)gGCTA=0.87) and ΔTJC (h(2)gbayz=0.62 and h(2)gGCTA=0.82); lower heritability was found for ΔDAS28 (h(2)gbayz=0.59 and h(2)gGCTA=0.71) while estimates for ΔESR and ΔVASGH were near or equal to zero. The highest genomic correlations were observed for ΔSJC and ΔTJC (0.49), and the highest environmental correlation was seen between ΔTJC and ΔVASGH (0.62). The multivariate GWAS did not generate excess of low p values as compared with a univariate analysis of ΔDAS28.Conclusions: Our results indicate that multiple SNPs together explain a substantial portion of the variation in change in joint counts in TNFi-treated patients with RA. In conclusion, of the outcomes studied, the joint counts are most suitable for TNFi pharmacogenetics in RA. [ABSTRACT FROM AUTHOR]

Background: Reduced total brain volume is a consistent finding in children with Attention Deficit/Hyperactivity Disorder (ADHD). In order to get a better understanding of the neurobiology of ADHD, we take the first step in studying the dimensionality of current self-reported adult ADHD symptoms, by looking at its relation with total brain volume. Methodology/Principal Findings: In a sample of 652 highly educated adults, the association between total brain volume, assessed with magnetic resonance imaging, and current number of self-reported ADHD symptoms was studied. The results showed an association between these self-reported ADHD symptoms and total brain volume. Post-hoc analysis revealed that the symptom domain of inattention had the strongest association with total brain volume. In addition, the threshold for impairment coincides with the threshold for brain volume reduction. Conclusions/Significance: This finding improves our understanding of the biological substrates of self-reported ADHD symptoms, and suggests total brain volume as a target intermediate phenotype for future gene-finding in ADHD. [ABSTRACT FROM AUTHOR]

Background: Genomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families. Results: Genomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models. Conclusions: Genomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model. [ABSTRACT FROM AUTHOR]

Background: We analyzed simulated data from the 14th QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. Results: For the quantitative trait we mapped 8 out of 30 additive QTL, 1 out of 3 imprinted QTL and both epistatic pairs of QTL successfully. For the binary trait we mapped 11 out of 22 additive QTL successfully. Four out of 22 pleiotropic QTL were detected as such. Conclusions: The Bayesian variable selection method showed to be a successful method for genome-wide association. This method was reasonably fast using dense marker maps. [ABSTRACT FROM AUTHOR]

Breeding values for animals with marker data are estimated using a genomic selection approach where data is analyzed using Bayesian multi-marker association models. Fourteen model scenarios with varying haplotype lengths, hyper parameter and prior distributions were compared to find the scenario expected to give the most correct genomic estimated breeding values for animals with marker information only. Five-fold cross validation was performed to assess the ability of models to estimate breeding values for animals in generation 3. In each of the five subsets, 20% of phenotypic records in generation 3 were left out. Correlations between breeding values estimated on full data and on subsets for the "leave-out" animals varied between 0.77-0.99. Regression coefficients of breeding values from full data on breeding values from subsets ranged from 0.78- 1.01. Single-SNP marker models didn't perform well. Correlations were 0.77-0.89 and predicted breeding values were biased. In addition the models seemed to over fit the genomic part of the variation. Highest correlations and most unbiased results were obtained when SNP markers were joined into haplotypes. Especially the scenarios with 5-SNP haplotypes gave promising results (distance between adjacent SNPs is 0.1 cM evenly over the genome). All correlations were 0.99 and regression coefficients were 0.99-1.01. Models with 5-SNP markers seemed robust to hyper parameter and prior changes. Haplotypes up to 40 SNPs also gave good results. However, longer haplotypes are expected to have less predictive ability over several generations and therefore the 5-SNP haplotypes are expected to give the best predictions for generations 4-6. [ABSTRACT FROM AUTHOR]

Genomic prediction allow breeders to predict breeding values (gEBVs) of unphenotyped new lines based on genotype and phenotype information from a training population (TP). Most relevant studies were conducted on the basis of single trait analysis. However, plant breeders usually record several phenotypes including yield, grain quality, and disease resistance. Benefitting from the genetic correlation of these traits, the multivariate analysis is likely to improve the predictive ability in comparison with univariate analysis. The objectives of this study were to (1) estimate the heritability of economically important traits in wheat and barley, (2) compare the prediction accuracy of several traits using multiple (MTGS) and single (STGS) trait genomic selection approach using pedigree (A), genomic relationship matrix (G), and both matrices (G + A), (3) apply F5 and F6 dataset as multiple training populations to predict the future yield performance in wheat and barley breeding lines. 1317 barley and 1325 wheat breeding lines were genotyped with the Illumina 9K (barley) and 15K (wheat) SNP chip, and phenotyped across multiple years and locations. In total, data from about 15K (barley) and 13K (wheat) individual plots were included in the analysis. After quality control steps, 4K and 11K SNPs for barley and wheat were retained for analysis. The models were cross-validated by a leave-one-set out strategy, where set is defined as the yearly sets of crosses. Results showed that the MTGS increased the predictive ability for yield by 7% in barley and 57% in wheat compared with STGS alone. The analyses combining genomic and pedigree information increased the prediction accuracy by 4% (barley) and 3% (wheat) when compared with using the genomic relationship matrix only. Using MTGS the predictive ability was improved by 38% for powdery mildew resistance and 2% for ramularia resistance in barley, and 10% for protein content improvement when modelling protein content and Zeleny in wheat analysis. In conclusion, MTGS can considerably increase the prediction accuracy for yield compared to STGS in wheat and barley breeding. This is especially the case when phenotypes are included from both F5 and F6 generations. [ABSTRACT FROM AUTHOR]

Traditionally, exploration of genetic variance in humans, plants, and livestock species has been limited mostly to the use of additive effects estimated using pedigree data. However, with the development of dense panels of single-nucleotide polymorphisms (SNPs), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs, we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance by using a SNP regression method. The method was validated in simulated data and applied to three traits (number of teats, backfat, and lifetime daily gain) in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1-3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been described previously for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases. [ABSTRACT FROM AUTHOR]

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes' regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions. [ABSTRACT FROM AUTHOR]