Genomic prediction based on data from three layer lines: a comparison between linear methods

Background

The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction.

Methods

Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we evaluated the following genomic prediction models: genome-enabled BLUP (GBLUP), ridge regression BLUP (RRBLUP), principal component analysis followed by ridge regression (RRPCA), BayesC and Bayesian stochastic search variable selection. Prediction accuracy was measured as the correlation between predicted breeding values and observed phenotypes divided by the square root of the heritability. The data used concerned laying hens with phenotypes for number of eggs in the first production period and known genotypes. The hens were from two closely-related brown layer lines (B1 and B2), and a third distantly-related white layer line (W1). Lines had 1004 to 1023 training animals and 238 to 240 validation animals. Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms.

Results

Genomic prediction models yielded 0.13 to 0.16 higher accuracies than pedigree-based BLUP. When excluding the line itself from the training dataset, genomic predictions were generally inaccurate. Use of multiple lines marginally improved prediction accuracy for B2 but did not affect or slightly decreased prediction accuracy for B1 and W1. Differences between models were generally small except for RRPCA which gave considerably higher accuracies for B2. Correlations between genomic predictions from different methods were higher than 0.96 for W1 and higher than 0.88 for B1 and B2. The greater differences between methods for B1 and B2 were probably due to the lower accuracy of predictions for B1 (~0.45) and B2 (~0.40) compared to W1 (~0.76).

Conclusions

Multi-line genomic prediction did not affect or slightly improved prediction accuracy for closely-related lines. For distantly-related lines, multi-line genomic prediction yielded similar or slightly lower accuracies than single-line genomic prediction. Bayesian variable selection and GBLUP generally gave similar accuracies. Overall, RRPCA yielded the greatest accuracies for two lines, suggesting that using PCA helps to alleviate the “n ≪ p” problem in genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0057-5) contains supplementary material, which is available to authorized users.     

Identity-by-descent genomic selection using selective and sparse genotyping for binary traits

Background

Genomic selection (GS) allows estimation of the breeding value of individuals, even for non-phenotyped animals. The aim of the study was to examine the potential of identity-by-descent genomic selection (IBD-GS) in genomic selection for a binary, sib-evaluated trait, using different strategies of selective genotyping. This low-cost GS approach is based on linkage analysis of sparse genome-wide marker loci.

Findings

Lowly to highly heritable (h² = 0.15, 0.30 or 0.60) binary traits with varying incidences (10 to 90%) were simulated for an aquaculture-like population. Genotyping was restricted to the 30% best families according to phenotype, using three genotyping strategies for training sibs. IBD-GS increased genetic gain compared to classical pedigree-based selection; the differences were largest at incidences of 10 to 50% of the desired category (i.e. a relative increase in genetic gain greater than 20%). Furthermore, the relative advantage of IBD-GS increased as the heritability of the trait increased. Differences were small between genotyping strategies, and most of the improvement was achieved by restricting genotyping to sibs with the least common binary phenotype. Genetic gains of IBD-GS relative to pedigree-based models were highest at low to moderate (10 to 50%) incidences of the category selected for, but decreased substantially at higher incidences (80 to 90%).

Conclusions

The IBD-GS approach, combined with sparse and selective genotyping, is well suited for genetic evaluation of binary traits. Genetic gain increased considerably compared with classical pedigree-based selection. Most of the improvement was achieved by selective genotyping of the sibs with the least common (minor) binary category phenotype. Furthermore, IBD-GS had greater advantage over classical pedigree-based models at low to moderate incidences of the category selected for.     

Genotyping by sequencing for genomic prediction in a soybean breeding population

Background

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations.

Results

Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller.

Conclusions

Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.     

Comparison of joint versus purebred genomic evaluation in the French multi-breed dairy goat population

Background

All progeny-tested bucks from the two main French dairy goat breeds (Alpine and Saanen) were genotyped with the Illumina goat SNP50 BeadChip. The reference population consisted of 677 bucks and 148 selection candidates. With the two-step approach based on genomic best linear unbiased prediction (GBLUP), prediction accuracy of candidates did not outperform that of the parental average. We investigated a GBLUP method based on a single-step approach, with or without blending of the two breeds in the reference population.

Methods

Three models were used: (1) a multi-breed model, in which Alpine and Saanen breeds were considered as a single breed; (2) a within-breed model, with separate genomic evaluation per breed; and (3) a multiple-trait model, in which a trait in the Alpine was assumed to be correlated to the same trait in the Saanen breed, using three levels of between-breed genetic correlations (ρ): ρ = 0, ρ = 0.99, or estimated ρ. Quality of genomic predictions was assessed on progeny-tested bucks, by cross-validation of the Pearson correlation coefficients for validation accuracy and the regression coefficients of daughter yield deviations (DYD) on genomic breeding values (GEBV). Model-based estimates of average accuracy were calculated on the 148 candidates.

Results

The genetic correlations between Alpine and Saanen breeds were highest for udder type traits, ranging from 0.45 to 0.76. Pearson correlations with the single-step approach were higher than previously reported with a two-step approach. Correlations between GEBV and DYD were similar for the three models (within-breed, multi-breed and multiple traits). Regression coefficients of DYD on GEBV were greater with the within-breed model and multiple-trait model with ρ = 0.99 than with the other models. The single-step approach improved prediction accuracy of candidates from 22 to 37% for both breeds compared to the two-step method.

Conclusions

Using a single-step approach with GBLUP, prediction accuracy of candidates was greater than that based on parent average of official evaluations and accuracies obtained with a two-step approach. Except for regression coefficients of DYD on GEBV, there were no significant differences between the three models.     

Interferon regulatory factor 5 genetic variants are associated with cardiovascular disease in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a complex polygenic inflammatory disease associated with accelerated atherosclerosis and increased cardiovascular (CV) disease risk. Interferon regulatory factor 5 (IRF5) is a regulator of type I interferon induction. Recently, researchers have described an association between multiple single-nucleotide polymorphisms of the IRF5 gene and some rheumatic disorders. In this study, we aimed to evaluate whether three different haplotype blocks within the IRF5 locus which have been shown to alter the protein function are involved in the risk of CV events occurring in Spanish RA patients.

Methods

Three IRF5 polymorphisms (rs2004640, rs2070197 and rs10954213) representative of each haplotype group were genotyped by performing TaqMan assays using a 7900HT Fast Real-Time PCR System with tissue from a total of 2,137 Spanish patients diagnosed with RA. Among them, 390 (18.2%) had experienced CV events. The relationship of IRF5 genotypes and haplotypes to CV events was tested using Cox regression.

Results

Male sex, age at RA diagnosis and most traditional risk factors (hypertension, dyslipidemia and smoking habit) were associated with increased risk for CV events in the RA population. Interestingly, a protective effect of both IRF5 rs2004640 GG and IRF5 rs10954213 GG genotypes against the risk for CV events after adjusting the results for sex, age at RA diagnosis and traditional CV disease risk factors was observed (hazard ratio (HR) = 0.6, 95% confidence interval (CI) = 0.38 to 0.92, P = 0.02; and HR = 0.58, 95% CI = 0.36 to 0.95, P = 0.03, respectively). Moreover, we detected a protective effect of the GTG haplotype against the risk for CV events after adjusting the results for potential confounding factors (HR = 0.72, 95% CI = 0.56 to 0.93, P = 0.012).

Conclusions

Our results reveal that IRF5 gene variants are associated with risk of CV events in patients with RA.     

Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle

Background

While several studies have examined the accuracy of direct genomic breeding values (DGV) within and across purebred cattle populations, the accuracy of DGV in crossbred or multi-breed cattle populations has been less well examined. Interest in the use of genomic tools for both selection and management has increased within the hybrid seedstock and commercial cattle sectors and research is needed to determine their efficacy. We predicted DGV for six traits using training populations of various sizes and alternative Bayesian models for a population of 3240 crossbred animals. Our objective was to compare alternate models with different assumptions regarding the distributions of single nucleotide polymorphism (SNP) effects to determine the optimal model for enhancing feasibility of multi-breed DGV prediction for the commercial beef industry.

Results

Realized accuracies ranged from 0.40 to 0.78. Randomly assigning 60 to 70% of animals to training (n ≈ 2000 records) yielded DGV accuracies with the smallest coefficients of variation. Mixture models (BayesB95, BayesCπ) and models that allow SNP effects to be sampled from distributions with unequal variances (BayesA, BayesB95) were advantageous for traits that appear or are known to be influenced by large-effect genes. For other traits, models differed little in prediction accuracy (~0.3 to 0.6%), suggesting that they are mainly controlled by small-effect loci.

Conclusions

The proportion (60 to 70%) of data allocated to training that optimized DGV accuracy and minimized the coefficient of variation of accuracy was similar to large dairy populations. Larger effects were estimated for some SNPs using BayesA and BayesB95 models because they allow unequal SNP variances. This substantially increased DGV accuracy for Warner-Bratzler Shear Force, for which large-effect quantitative trait loci (QTL) are known, while no loss in accuracy was observed for traits that appear to follow the infinitesimal model. Large decreases in accuracy (up to 0.07) occurred when SNPs that presumably tag large-effect QTL were over-regressed towards the mean in BayesC0 analyses. The DGV accuracies achieved here indicate that genomic selection has predictive utility in the commercial beef industry and that using models that reflect the genomic architecture of the trait can have predictive advantages in multi-breed populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0106-8) contains supplementary material, which is available to authorized users.     

A comparison of principal component regression and genomic REML for genomic prediction across populations

Background

Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.

Methods

The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.

Results

In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.

Conclusions

On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users.     

Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing

Background

Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost.

Results

Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada. Four imputation algorithms were compared (mean value (MI), singular value decomposition (SVD), expectation maximization (EM), and a newly derived, family-based k-nearest neighbor (kNN-Fam)). Trees were phenotyped for several yield and wood attributes. Single- and multi-site GS prediction models were developed using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) and the Generalized Ridge Regression (GRR) to test different assumption about trait architecture. Finally, using PCA, multi-trait GS prediction models were developed. The EM and kNN-Fam imputation methods were superior for 30 and 60% missing data, respectively. The RR-BLUP GS prediction model produced better accuracies than the GRR indicating that the genetic architecture for these traits is complex. GS prediction accuracies for multi-site were high and better than those of single-sites while multi-site predictability produced the lowest accuracies reflecting type-b genetic correlations and deemed unreliable. The incorporation of genomic information in quantitative genetics analyses produced more realistic heritability estimates as half-sib pedigree tended to inflate the additive genetic variance and subsequently both heritability and gain estimates. Principle component scores as representatives of multi-trait GS prediction models produced surprising results where negatively correlated traits could be concurrently selected for using PCA2 and PCA3.

Conclusions

The application of GS to open-pollinated family testing, the simplest form of tree improvement evaluation methods, was proven to be effective. Prediction accuracies obtained for all traits greatly support the integration of GS in tree breeding. While the within-site GS prediction accuracies were high, the results clearly indicate that single-site GS models ability to predict other sites are unreliable supporting the utilization of multi-site approach. Principle component scores provided an opportunity for the concurrent selection of traits with different phenotypic optima.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1597-y) contains supplementary material, which is available to authorized users.     

Peripheral arterial tonometry cannot detect patients at low risk of coronary artery disease

Background

Endothelial dysfunction precedes coronary artery disease (CAD) and can be measured by peripheral arterial tonometry (PAT). We examined the applicability of PAT to detect a low risk of CAD in a chest pain clinic.

Methods

In 93 patients, PAT was performed resulting in reactive hyperaemia (RHI) and augmentation (AIx) indices. Patients were risk classified according to HeartScore, Diamond and Forrester pretest probability (DF), exercise testing (X-ECG), and computed tomography calcium scoring (CCS) and angiography (CTA). Correlations, risk group differences and prediction of revascularisation within 1 year were calculated.

Results

RHI correlated with HeartScore (r = − 0.21, p = 0.05), AIx with DF (r = 0.26, p = 0.01). However, both were not significantly different between normal and ischaemic X-ECG groups. In addition RHI and AIx were similar between low risk as compared with intermediate-to-high risk, based on risk algorithms (RHI: 1.98 (0.67) vs 1.94 (0.78); AIx: 0.0 (21) vs 5.0 (25); p = NS), or CCS and CTA (RHI: 1.99 (0.58) vs 1.89 (0.82); AIx: − 2.0 (24) vs 4.0 (25); p = NS). Finally, RHI and AIx failed to predict revascularisation (RHI: OR 1.42, CI 0.65–3.1; AIx: OR 1.02, CI 0.98–1.05).

Conclusions

PAT cannot detect a low risk of CAD, possibly because RHI and AIx versus X-ECG, CCS and CTA represent independent processes.     

Imputation of genotypes in Danish purebred and two-way crossbred pigs using low-density panels

Background

Genotype imputation is commonly used as an initial step in genomic selection since the accuracy of genomic selection does not decline if accurately imputed genotypes are used instead of actual genotypes but for a lower cost. Performance of imputation has rarely been investigated in crossbred animals and, in particular, in pigs. The extent and pattern of linkage disequilibrium differ in crossbred versus purebred animals, which may impact the performance of imputation. In this study, first we compared different scenarios of imputation from 5 K to 8 K single nucleotide polymorphisms (SNPs) in genotyped Danish Landrace and Yorkshire and crossbred Landrace-Yorkshire datasets and, second, we compared imputation from 8 K to 60 K SNPs in genotyped purebred and simulated crossbred datasets. All imputations were done using software Beagle version 3.3.2. Then, we investigated the reasons that could explain the differences observed.

Results

Genotype imputation performs as well in crossbred animals as in purebred animals when both parental breeds are included in the reference population. When the size of the reference population is very large, it is not necessary to use a reference population that combines the two breeds to impute the genotypes of purebred animals because a within-breed reference population can provide a very high level of imputation accuracy (correct rate ≥ 0.99, correlation ≥ 0.95). However, to ensure that similar imputation accuracies are obtained for crossbred animals, a reference population that combines both parental purebred animals is required. Imputation accuracies are higher when a larger proportion of haplotypes are shared between the reference population and the validation (imputed) populations.

Conclusions

The results from both real data and pedigree-based simulated data demonstrate that genotype imputation from low-density panels to medium-density panels is highly accurate in both purebred and crossbred pigs. In crossbred pigs, combining the parental purebred animals in the reference population is necessary to obtain high imputation accuracy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0134-4) contains supplementary material, which is available to authorized users.     

Bone microarchitecture in ankylosing spondylitis and the association with bone mineral density,fractures, and syndesmophytes

Introduction

Osteoporosis of the axial skeleton is a known complication of ankylosing spondylitis (AS), but bone loss affecting the peripheral skeleton is less studied. This study on volumetric bone mineral density (vBMD) and bone microarchitecture in AS was conducted to compare peripheral vBMD in AS patients with that in healthy controls, to study vBMD in axial compared with peripheral bone, and to explore the relation between vertebral fractures, spinal osteoproliferation, and peripheral bone microarchitecture and density.

Methods

High-resolution peripheral quantitative computed tomography (HRpQCT) of ultradistal radius and tibia and QCT and dual-energy x-ray absorptiometry (DXA) of lumbar spine were performed in 69 male AS patients (NY criteria). Spinal radiographs were assessed for vertebral fractures and syndesmophyte formation (mSASSS). The HRpQCT measurements were compared with the measurements of healthy controls.

Results

The AS patients had lower cortical vBMD in radius (P = 0.004) and lower trabecular vBMD in tibia (P = 0.033), than did the controls. Strong correlations were found between trabecular vBMD in lumbar spine, radius (r_S = 0.762; P < 0.001), and tibia (r_S = 0.712; P < 0.001).When compared with age-matched AS controls, patients with vertebral fractures had lower lumbar cortical vBMD (-22%; P = 0.019), lower cortical cross-sectional area in radius (-28.3%; P = 0.001) and tibia (-24.0%; P = 0.013), and thinner cortical bone in radius (-28.3%; P = 0.001) and tibia (-26.9%; P = 0.016).mSASSS correlated negatively with trabecular vBMD in lumbar spine (r_S = -0.620; P < 0.001), radius (r_S = -0.400; p = 0.001) and tibia (r_S = -0.475; p < 0.001) and also with trabecular thickness in radius (r_S = -0.528; P < 0.001) and tibia (r_S = -0.488; P < 0.001).Adjusted for age, syndesmophytes were significantly associated with decreasing trabecular vBMD, but increasing cortical vBMD in lumbar spine, but not with increasing cortical thickness or density in peripheral bone. Estimated lumbar vBMD by DXA correlated with trabecular vBMD measured by QCT (r_S = 0.636; P < 0.001).

Conclusions

Lumbar osteoporosis, syndesmophytes, and vertebral fractures were associated with both lower vBMD and deteriorated microarchitecture in peripheral bone. The results indicate that trabecular bone loss is general, whereas osteoproliferation is local in AS.     

Degree of Contracture Related to Residual Muscle Shoulder Strength in Children with Obstetric Brachial Plexus Lesions

Background and Objectives

 Little is known about the relation between residual muscle strength and joint contracture formation in neuromuscular disorders. This study aimed to investigate the relation between residual muscle strength and shoulder joint contractures in children with sequelae of obstetric brachial plexus lesion (OBPL). In OBPL a shoulder joint contracture is a frequent finding. We hypothesize that residual internal and external rotator strength and their balance are related to the extent of shoulder joint contracture.

Methods

 Clinical assessment was performed in 34 children (mean 10.0 years) with unilateral OBPL and Narakas classes I–III. External and internal rotation strengths were measured with the shoulder in neutral position using a handheld dynamometer. Strength on the affected side was given as percentage of the normal side. Contracture was assessed by passive internal and external rotations in degrees (in 0° abduction). Mallet classification was used for active shoulder function.

Results

 External and internal rotation strengths on the affected side were approximately 50% of the normal side and on average both equally affected: 56% (SD 18%) respectively 51% (SD 27%); r = 0.600, p = 0.000. Residual strengths were not related to passive internal or external rotation (p > 0.200). Internal rotation strength (r =  − 0.425, p <0.05) was related to Narakas class. Mallet score was related to external and internal rotation strengths (r = 0.451 and r = 0.515, respectively; p < 0.01).

Conclusion

 The intuitive notion that imbalances in residual muscle strength influence contracture formation cannot be confirmed in this study. Our results are of interest for the understanding of contracture formation in OBPL.     

Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals

Background

Genotyping accounts for a substantial part of the cost of genomic selection (GS). Using both dense and sparse SNP chips, together with imputation of missing genotypes, can reduce these costs. The aim of this study was to identify the set of candidates that are most important for dense genotyping, when they are used to impute the genotypes of sparsely genotyped animals. In a real pig pedigree, the 2500 most recently born pigs of the last generation, i.e. the target animals, were used for sparse genotyping. Their missing genotypes were imputed using either Beagle or LDMIP from T densely genotyped candidates chosen from the whole pedigree. A new optimization method was derived to identify the best animals for dense genotyping, which minimized the conditional genetic variance of the target animals, using either the pedigree-based relationship matrix (MCA), or a genotypic relationship matrix based on sparse marker genotypes (MCG). These, and five other methods for selecting the T animals were compared, using T = 100 or 200 animals, SNP genotypes were obtained assuming Ne =100 or 200, and MAF thresholds set to D = 0.01, 0.05 or 0.10. The performances of the methods were compared using the following criteria: call rate of true genotypes, accuracy of genotype prediction, and accuracy of genomic evaluations using the imputed genotypes.

Results

For all criteria, MCA and MCG performed better than other selection methods, significantly so for all methods other than selection of sires with the largest numbers of offspring. Methods that choose animals that have the closest average relationship or contribution to the target population gave the lowest accuracy of imputation, in some cases worse than random selection, and should be avoided in practice.

Conclusion

Minimization of the conditional variance of the genotypes in target animals provided an effective optimization procedure for prioritizing animals for genotyping or sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/1297-9686-46-46) contains supplementary material, which is available to authorized users.     

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

Background

When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r_g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r_g between BW and its uniformity were influenced by a scale effect.

Results

Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r_g = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r_g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r_g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.

Conclusions

Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r_g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r_g.     

The ZC3HC1 rs11556924 polymorphism is associated with increased carotid intima-media thickness in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a complex polygenic disease associated with chronic inflammation, accelerated atherosclerosis and increased cardiovascular (CV) mortality. A recent meta-analysis has described the ZC3HC1 rs11556924 polymorphism as one of the most important signals associated with coronary artery disease (CAD) in non-rheumatic Caucasian individuals. In this study we evaluated the potential association of this gene polymorphism with subclinical atherosclerosis assessed by the evaluation of carotid intima-media thickness (cIMT) in RA patients.

Methods

This study included 502 RA patients from Northern Spain. The ZC3HC1 rs11556924 polymorphism was genotyped with TaqMan single-nucleotide polymorphism (SNP) genotyping assays (C__31283062_10) in a 7900HT real-time polymerase chain reaction (PCR) system. cIMT was also assessed in these patients by carotid ultrasonography (US) technology.

Results

RA patients carrying the TT genotype had significantly higher cIMT values than those homozygous for the CC genotype (mean ± standard deviation (SD): 0.76 ± 0.18 mm and mean ± SD: 0.71 ± 0.16 mm respectively; P = 0.03) even after adjusting the results for sex, age at the time of US study, follow-up time and traditional CV risk factors (P = 0.04) evidencing that the effect conferred by ZC3HC1 rs11556924 polymorphism is independent of the traditional CV risk factors.

Conclusion

Our results indicate that ZC3HC1 rs11556924 polymorphism is associated with subclinical atherosclerosis in RA.     

CMR-derived TAPSE measurement: a semi-quantitative method of right ventricular function assessment in patients with hypertrophic cardiomyopathy

Aim

To compare cardiovascular magnetic resonance (CMR)-derived right ventricular fractional shortening (RVFS), tricuspid annular plane systolic excursion with a reference point within the right ventricular apex (TAPSE_in) and with one outside the ventricle (TAPSE_out) with the standard volumetric approach in patients with hypertrophic cardiomyopathy (HCM).

Methods and results

105 patients with HCM and 20 healthy subjects underwent CMR. In patients with HCM, TAPSE_in (r = 0.31, p = 0.001) and RVFS (r = 0.35, p = 0.0002) revealed a significant but weak correlation with right ventricular ejection fraction (RVEF), whereas TAPSE_out (r = 0.57, p < 0.0001) showed a moderate correlation with RVEF. The ability to predict RVEF < 45 % in HCM patients was best for TAPSE_out. In patients with hypertrophic obstructive cardiomyopathy (HOCM), RVEF showed a significant but weak correlation with TAPSE_out (r = 0.36, p = 0.02) and no correlation with TAPSE_in (r = 0.05, p = 0.07) and RVFS (r = 0.02, p = 0.2). In patients with hypertrophic non-obstructive cardiomyopathy (HNCM), there was a moderate correlation between RVEF and TAPSE_out (r = 0.57, p < 0.0001) and a weak correlation with TAPSE_in (r = 0.39, p = 0.001) and RVFS (r = 0.38, p = 0.002). In the 20 healthy controls, there was a strong correlation between RVEF and all semi-quantitative measurements.

Conclusion

CMR-derived TAPSE_in is not suitable to determine right ventricular function in HCM patients. TAPSE_out showed a good correlation with RVEF in HNCM patients but only a weak correlation in HOCM patients. TAPSE_out might be used for screening but the detection of subtle changes in RV function requires the 3D volumetric approach.     

Genome-wide QTL analysis of meat quality-related traits in a large F2 intercross between Landrace and Korean native pigs

Background

We conducted a genome-wide linkage analysis to identify quantitative trait loci (QTL) that influence meat quality-related traits in a large F₂ intercross between Landrace and Korean native pigs. Thirteen meat quality-related traits of the m. longissimus lumborum et thoracis were measured in more than 830 F₂ progeny. All these animals were genotyped with 173 microsatellite markers located throughout the pig genome, and the GridQTL program based on the least squares regression model was used to perform the QTL analysis.

Results

We identified 23 genome-wide significant QTL in eight chromosome regions (SSC1, 2, 6, 7, 9, 12, 13, and 16) (SSC for Sus Scrofa) and detected 51 suggestive QTL in the 17 chromosome regions. QTL that affect 10 meat quality traits were detected on SSC12 and were highly significant at the genome-wide level. In particular, the QTL with the largest effect affected crude fat percentage and explained 22.5% of the phenotypic variance (F-ratio = 278.0 under the additive model, nominal P = 5.5 × 10⁻⁵⁵). Interestingly, the QTL on SSC12 that influenced meat quality traits showed an obvious trend for co-localization.

Conclusions

Our results confirm several previously reported QTL. In addition, we identified novel QTL for meat quality traits, which together with the associated positional candidate genes improve the knowledge on the genetic structure that underlies genetic variation for meat quality traits in pigs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0080-6) contains supplementary material, which is available to authorized users.     

Cardiovascular disease is increased prior to onset of rheumatoid arthritis but not osteoarthritis: the population-based Nord-Tr?ndelag health study (HUNT)

Introduction

Patients with rheumatoid arthritis (RA) have increased risk of cardiovascular (CV) events. We sought to test the hypothesis that due to increased inflammation, CV disease and risk factors are associated with increased risk of future RA development.

Methods

The population-based Nord-Trøndelag health surveys (HUNT) were conducted among the entire adult population of Nord-Trøndelag, Norway. All inhabitants 20 years or older were invited, and information was collected through comprehensive questionnaires, a clinical examination, and blood samples. In a cohort design, data from HUNT2 (1995–1997, baseline) and HUNT3 (2006–2008, follow-up) were obtained to study participants with RA (n = 786) or osteoarthritis (n = 3,586) at HUNT3 alone, in comparison with individuals without RA or osteoarthritis at both times (n = 33,567).

Results

Female gender, age, smoking, body mass index, and history of previous CV disease were associated with self-reported incident RA (previous CV disease: odds ratio 1.52 (95% confidence interval 1.11-2.07). The findings regarding previous CV disease were confirmed in sensitivity analyses excluding participants with psoriasis (odds ratio (OR) 1.70 (1.23-2.36)) or restricting the analysis to cases with a hospital diagnosis of RA (OR 1.90 (1.10-3.27)) or carriers of the shared epitope (OR 1.76 (1.13-2.74)). History of previous CV disease was not associated with increased risk of osteoarthritis (OR 1.04 (0.86-1.27)).

Conclusion

A history of previous CV disease was associated with increased risk of incident RA but not osteoarthritis.     

Genome-wide interaction of genotype by erythrocyte n-3 fatty acids contributes to phenotypic variance of diabetes-related traits

Background

Little is known about the interplay between n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level. The present study aimed to examine variance contributions of genotype by environment (GxE) interactions for different erythrocyte n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level in a non-Hispanic white population living in the U.S.A. (n = 820). A tool for Genome-wide Complex Trait Analysis (GCTA) was used to estimate the genome-wide GxE variance contribution of four diabetes-related traits: HOMA-Insulin Resistance (HOMA-IR), fasting plasma insulin, glucose and adiponectin. A GxE genome-wide association study (GWAS) was conducted to further elucidate the GCTA results. Replication was conducted in the participants of the Boston Puerto Rican Health Study (BPRHS) without diabetes (n = 716).

Results

In GOLDN, docosapentaenoic acid (DPA) contributed the most significant GxE variance to the total phenotypic variance of both HOMA-IR (26.5%, P-nominal = 0.034) and fasting insulin (24.3%, P-nominal = 0.042). The ratio of arachidonic acid to eicosapentaenoic acid + docosahexaenoic acid contributed the most significant GxE variance to the total variance of fasting glucose (27.0%, P-nominal = 0.023). GxE variance of the arachidonic acid/eicosapentaenoic acid ratio showed a marginally significant contribution to the adiponectin variance (16.0%, P-nominal = 0.058). None of the GCTA results were significant after Bonferroni correction (P < 0.001). For each trait, the GxE GWAS identified a far larger number of significant single-nucleotide polymorphisms (P-interaction ≤ 10E-5) for the significant E factor (significant GxE variance contributor) than a control E factor (non-significant GxE variance contributor). In the BPRHS, DPA contributed a marginally significant GxE variance to the phenotypic variance of HOMA-IR (12.9%, P-nominal = 0.068) and fasting insulin (18.0%, P-nominal = 0.033).

Conclusion

Erythrocyte n-3 fatty acids contributed a significant GxE variance to diabetes-related traits at the genome-wide level.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-781) contains supplementary material, which is available to authorized users.