Multiple-trait Model Research Articles

Genetic analysis of semen production traits of Japanese Black and Holstein bulls: genome-wide marker-based estimation of genetic parameters and environmental effect trends.

The semen production traits of bulls from 2 major cattle breeds in Japan, Holstein and Japanese Black, were analyzed comprehensively using genome-wide markers. Weaker genetic correlations were observed between the 2 age groups (1 to 3 yr old and 4 to 6 yr old) regarding semen volume and sperm motility compared with those observed for sperm number and motility after freeze-thawing. The preselection of collected semen for freezing had a limited effect. Given the increasing importance of bull proofs at a young age because of genomic selection and the results from preliminary studies, we used a multiple-trait model that included motility after freeze-thawing with records collected at young ages. Based on variations in contemporary group effects, accounting for both seasonal and management factors, Holstein bulls may be more sensitive than Japanese Black bulls to seasonal environmental variations; however, the seasonal variations of contemporary group effects were smaller than those of overall contemporary group effects. The improvement of motilities, recorded immediately after collection and freeze-thawing, was observed in recent years; thus, good management and better freeze-thawing protocol may alleviate seasonal phenotypic differences. The detrimental effects of inbreeding were observed in all traits of both breeds; accordingly, the selection of candidate bulls with high inbreeding coefficients should be avoided per general recommendations. Semen production traits have never been considered for bull selection. However, negative genetic trends were observed. The magnitudes of the estimated h were comparable to those of other economically important traits. A single-step genomic BLUP will provide more accurate predictions of breeding values compared with BLUP; thus, marker genotype information is useful for estimating the genetic merits of bulls for semen production traits. The selection of these traits would improve sperm viability, a component related to breeding success, and alleviate negative genetic trends.

Joint genetic analysis using variant sets reveals polygenic gene-context interactions.

Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.

Genetic trends for fertility, udder health and protein yield in Swedish red cattle estimated with different models.

The aim of this study was to estimate and compare genetic trends in Swedish Red cattle using a full multiple-trait (MT) model and trait-group-wise models for female fertility, udder health and protein yield. Field data for maiden heifers from 1989 and cows with a first and second lactation between 1990 and 2007 were included. (Co)variance components were estimated prior to prediction of breeding values. The estimated genetic trends were clearly favourable for protein yield and udder conformation, and in most cases neutral to favourable for clinical mastitis and calving to first insemination. In maiden heifers, the trends were neutral for number of inseminations per service period. Unfavourable genetic trends were estimated for number of inseminations in the first two lactations, but the trends seemed less unfavourable from evaluations within trait groups compared with when using the full MT model. Excluding maiden heifer data affected genetic trends less than using trait-group-wise analyses instead of a full MT model. Unfavourable genetic trends in functional traits may be missed unless the traits are evaluated in a MT model including traits under strong selection.

Repeatability vs. multiple-trait models to evaluate shell dynamic stiffness for layer chickens.

Shell quality is one of the most important traits for improvement in layer chickens. Proper consideration of repeated records can increase the accuracy of estimated breeding values and thus genetic improvement of shell quality. The objective of this study was to compare different models for genetic evaluation of the collected data. For this study, 81,646 dynamic stiffness records on 21,321 brown egg layers and 93,748 records on 24,678 white egg layers from 4 generations were analyzed. Across generations, data were collected at 2 to 4 ages (at approximately 26, 42, 65, and 86 wk), with repeated records at each age. Seven models were compared, including 5 repeatability models with increasing complexity, a random regression model, and a multitrait model. The models were compared using Akaike Information Criteria with significance testing of nested models with a Log Likelihood Ratio test. Estimates of heritability were 0.31-0.36 for the brown line and 0.23-0.26 for the white line, but repeatability was higher for the model with age-specific permanent environment effects (0.59 for both lines) than for the model with an overall permanent environmental effects (0.47 for the brown and 0.41 for the white line). The model that allowed for permanent environmental effect within age and heterogeneous residual variance between ages resulted in improved fit compared to the traditional model that fits single permanent environment and residual effects, but was inferior in fit and predictive ability to the full multiple-trait model. The random regression model had better fit to the data than repeatability models but slightly worse than the multiple-trait model. For traits with repeated records at different ages, repeatability within and across ages as well as genetic correlations should be considered while choosing the number of records collected per individual as well as the model for genetic evaluation.

Repeatability vs. multiple-trait models to evaluate shell dynamic stiffness for layer chickens

Repeatability vs. multiple-trait models to evaluate shell dynamic stiffness for layer chickens

Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle.

Meat quality is one of the most important traits determining carcass price in the Japanese beef market. Optimized breeding goals and management practices for the improvement of meat quality traits requires knowledge regarding any potential functional relationships between them. In this context, the objective of this research was to infer phenotypic causal networks involving beef marbling score (BMS), beef color score (BCL), firmness of beef (FIR), texture of beef (TEX), beef fat color score (BFS), and the ratio of MUFA to SFA (MUS) from 11,855 Japanese Black cattle. The inductive causation (IC) algorithm was implemented to search for causal links among these traits and was conditionally applied to their joint distribution on genetic effects. This information was obtained from the posterior distribution of the residual (co)variance matrix of a standard Bayesian multiple trait model (MTM). Apart from BFS, the IC algorithm implemented with 95% highest posterior density (HPD) intervals detected only undirected links among the traits. However, as a result of the application of 80% HPD intervals, more links were recovered and the undirected links were changed into directed ones, except between FIR and TEX. Therefore, 2 competing causal networks resulting from the IC algorithm, with either the arrow FIR → TEX or the arrow FIR ← TEX, were fitted using a structural equation model () to infer causal structure coefficients between the selected traits. Results indicated similar genetic and residual variances as well as genetic correlation estimates from both structural equation models. The genetic variances in BMS, FIR, and TEX from the structural equation models were smaller than those obtained from the MTM. In contrast, the variances in BCL, BFS, and MUS, which were not conditioned on any of the other traits in the causal structures, had no significant differences between the structural equation model and MTM. The structural coefficient for the path from MUS (BCL) to BMS showed that a 1-unit improvement in MUS (BCL) resulted in an increase of 0.85 or 1.45 (an decrease of 0.52 or 0.54) in BMS in the causal structures. The analysis revealed some interesting functional relationships, direct genetic effects, and the magnitude of the causal effects between these traits, for example, indicating that BMS would be affected by interventions on MUS and BCL. In addition, if interventions existed in this scenario, a breeding strategy based only on the MTM would lead to a mistaken selection for BMS.

Sexual dimorphism in livestock species selected for economically important traits.

Most breeding companies evaluate economically important traits in males and females as a single trait, assuming genetic correlation of 1 between phenotypes measured in both sexes. This assumption may not be true because genes may be differently expressed in males and females. We estimated genetic correlations between males and females for growth and efficiency traits in broiler chickens, growth traits in American Angus beef cattle, and birth weight and preweaning mortality in purebred pigs; therefore, each trait was treated differently in males and females. Variance components were estimated in single- and multiple-trait models, jointly or separated into both sexes. Furthermore, we calculated traditional and genomic evaluations, and we correlated EBV or genomic EBV (GEBV) from joint and separate evaluations for males and females. For broiler chickens, genetic correlations ranged from 0.86 to 0.94. For Angus cattle, genetic correlations ranged from 0.86 to 0.98 for early growth traits and were less, ranging from 0.68 to 0.84, for postweaning gain. In pigs, genetic correlations ranged from 0.98 to 0.99 for birth weight and from 0.71 to 0.73 for preweaning mortality. For some models in all 3 animal species, the joint and separate analyses had different heritabilities. Despite differences in heritability, the correlations within the sex-specific trait EBV and between the sex-specific and the joint trait EBV were very strong, regardless of the model or inclusion of genomic information. Males and females differed for traits measured late in the animal's life; however, strong traditional EBV correlations and also GEBV correlations indicate that considering the traits equal in males and females may have no negative impact on selection.

An alternative approach to modeling genetic merit of feed efficiency in dairy cattle

Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A widely accepted yet occasionally contested measure of FE in dairy cattle is residual feed intake (RFI). The use of RFI is limiting for several reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI, and potential differences in genetic versus nongenetic relationships between dry matter intake (DMI) and FE component traits. Hence, analyses focusing on DMI as the response are often preferred. We propose an alternative multiple-trait (MT) modeling strategy that exploits the Cholesky decomposition to provide a potentially more robust measure of FE. We demonstrate that our proposed FE measure is identical to RFI provided that genetic and nongenetic relationships between DMI and component traits of FE are identical. We assessed both approaches (MT and RFI) by simulation as well as by application to 26,383 weekly records from 50 to 200 d in milk on 2,470 cows from a dairy FE consortium study involving 7 institutions. Although the proposed MT model fared better than the RFI model when simulated genetic and nongenetic associations between DMI and FE component traits were substantially different from each other, no meaningful differences were found in predictive performance between the 2 models when applied to the consortium data.

Multiple interval QTL mapping and searching for PSTOL1 homologs associated with root morphology, biomass accumulation and phosphorus content in maize seedlings under low-P.

BackgroundModifications in root morphology are important strategies to maximize soil exploitation under phosphorus starvation in plants. Here, we used two multiple interval models to map QTLs related to root traits, biomass accumulation and P content in a maize RIL population cultivated in nutrient solution. In addition, we searched for putative maize homologs to PSTOL1, a gene responsible to enhance early root growth, P uptake and grain yield in rice and sorghum.ResultsBased on path analysis, root surface area was the root morphology component that most strongly contributed to total dry weight and to P content in maize seedling under low-P availability. Multiple interval mapping models for single (MIM) and multiple traits (MT-MIM) were combined and revealed 13 genomic regions significantly associated with the target traits in a complementary way. The phenotypic variances explained by all QTLs and their epistatic interactions using MT-MIM (23.4 to 35.5 %) were higher than in previous studies, and presented superior statistical power. Some of these QTLs were coincident with QTLs for root morphology traits and grain yield previously mapped, whereas others harbored ZmPSTOL candidate genes, which shared more than 55 % of amino acid sequence identity and a conserved serine/threonine kinase domain with OsPSTOL1. Additionally, four ZmPSTOL candidate genes co-localized with QTLs for root morphology, biomass accumulation and/or P content were preferentially expressed in roots of the parental lines that contributed the alleles enhancing the respective phenotypes.ConclusionsQTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy. Some QTLs, mainly the ones that were also associated with yield performance in other studies, can be good targets for marker-assisted selection to improve P-use efficiency in maize. Based on the co-localization with QTLs, the protein domain conservation and the coincidence of gene expression, we selected novel maize genes as putative homologs to PSTOL1 that will require further validation studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0561-y) contains supplementary material, which is available to authorized users.

Genetic relationships between claw health traits of dairy cows in different parities, lactation stages, and herds with different claw disorder frequencies

Claw disorders affect cow welfare and profitability of farms. Not only claw disorders but also the need for trimming, known as trimming status, has been shown to be heritable. Limited knowledge is available on whether claw health traits (claw disorders and trimming status) are genetically the same trait in different parities, lactation stages, or in herds with low or high frequency of claw disorders. The aim of the current study was to estimate frequencies, heritabilities, and genetic correlations of claw health traits measured in different parities (first vs. later parities), in different lactation stages (early vs. late lactation), and in herds with different frequencies of claw disorders (low vs. high frequency). Analyses revealed that heritabilities measured in different parities, lactation stages, or herds with different trait frequencies are similar for most claw health traits. Also, genetic correlations (rg) for most claw health traits were not found to be different from unity for traits in different parities, lactation stages, or herds with different trait frequencies. Sole hemorrhage and infectious lesions were genetically different traits in first or later parities (rg=0.29±0.31 and 0.66±0.15, respectively). White line separation and infectious lesions were genetically different in early and late lactation (rg=0.53±0.20 and 0.69±0.13, respectively), and sole ulcer was genetically different in herds with low or high frequency of sole ulcer (rg=0.75±0.14). In our analysis, we did not find convincing evidence to supports the use of multiple trait models for the analysis of claw health traits treating them as different traits in different parities, lactation stages, and herds with different claw disorder frequency.

Genetic analysis of the cumulative pseudo-survival rate during lactation of Holstein cattle in Japan by using random regression models

Longevity is a crucial economic trait in the dairy farming industry. In this study, our objective was to develop a random regression model for genetic evaluation of survival. For the analysis, we used test-day records obtained for the first 5 lactations of 380,252 cows from 1,296 herds in Japan between 2001 and 2010; this data set was randomly divided into 7 subsets. The cumulative pseudo-survival rate (PSR) was determined according to whether a cow was alive (1) or absent (0) in her herd on the test day within each lactation group. Each lactation number was treated as an independent trait in a random regression multiple-trait model (MTM) or as a repeated measure in a random regression single-trait repeatability model (STRM). A proportional hazard model (PHM) was also developed as a piecewise-hazards model. The average (± standard deviation) heritability estimates of the PSR at 365 d in milk (DIM) among the 7 data sets in the first (LG1), second (LG2), and third to fifth lactations (LG3) of the MTM were 0.042±0.007, 0.070±0.012, and 0.084±0.007, respectively. The heritability estimate of the STRM was 0.038±0.004. The genetic correlations of PSR between distinct DIM within or between lactation groups were high when the interval between DIM was short. These results indicated that whereas the genetic factors contributing to the PSR between closely associated DIM would be similar even for different lactation numbers, the genetic factors contributing to PSR would differ between distinct lactation periods. The average (± standard deviation) effective heritability estimate based on the relative risk of the PHM among the 7 data sets was 0.068±0.009. The estimated breeding values (EBV) in LG1, LG2, LG3, the STRM, and the PHM were unbiased estimates of the genetic trend. The absolute values of the Spearman’s rank correlation coefficients between the EBV of the relative risk of the PHM and the EBV of PSR at 365 DIM for LG1, LG2, LG3, and the STRM were 0.75, 0.87, 0.91, and 0.93, respectively. These results indicated that the EBV of PSR could predict the genetic contribution to survival. The EBV based on the PSR of the STRM was highly correlated with that of the MTM (0.83–0.96). Furthermore, the calculation load of the STRM was lighter than that of the MTM because the rank of the matrix of the STRM was smaller than that of the MTM. These results indicated that the STRM is an appropriate model for estimating survivability by using random regression models.

Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model.

Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.

Modelos para avaliação genética da produção de leite em múltiplas lactações

O objetivo deste trabalho foi avaliar componentes de covariância e valores genéticos para produção de leite acumulada em até 305 dias, a partir dos dados das três primeiras lactações de vacas Gir. Foram analisados dados de 14.659 lactações, de 9.079 vacas, por meio dos modelos de repetibilidade, multicaracterístico (Mult) e de regressão aleatória com variância residual homogênea (MRAHo) ou heterogênea (MRAHe). A produção de leite foi considerada como característica distinta em cada lactação, no modelo Mult. Polinômios lineares foram utilizados nos modelos de regressão aleatória para ajuste das trajetórias médias e dos efeitos genético aditivo e de ambiente permanente individuais, de acordo com a ordem de parto. As médias a posteriori da herdabilidade foram semelhantes entre os diferentes modelos e lactações, e variaram entre 0,24 e 0,29. Os modelos Mult e MRAHe ajustaram-se melhor aos dados, uma vez que observou-se heterogeneidade de variâncias genéticas e residuais entre lactações. As correlações genéticas da produção acumulada de leite em até 305 dias nas três primeiras lactações foram próximas de 1,0; portanto, a seleção de reprodutores já pode ser feita a partir dos resultados da primeira lactação. Modelos de regressão aleatória com variâncias genéticas e residuais heterogêneas permitem modelar adequadamente as covariâncias das produções de leite acumuladas em múltiplas lactações e obter valores genéticos para seleção de reprodutores com base nos dados já da primeira lactação.

Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.

Genomic selection for the improvement of antibody response to Newcastle disease and avian influenza virus in chickens.

Newcastle disease (ND) and avian influenza (AI) are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV) and antibody response to Avian Influenza virus (Ab-AIV) in chickens. The data were collected from a crossbred population. Breeding values for Ab-NDV and Ab-AIV were estimated using a pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP). Single-trait and multiple-trait analyses were implemented. According to the analysis using the pedigree-based model, the heritability for Ab-NDV estimated from the single-trait and multiple-trait models was 0.478 and 0.487, respectively. The heritability for Ab-AIV estimated from the two models was 0.301 and 0.291, respectively. The estimated genetic correlation between the two traits was 0.438. A four-fold cross-validation was used to assess the accuracy of the estimated breeding values (EBV) in the two validation scenarios. In the family sample scenario each half-sib family is randomly allocated to one of four subsets and in the random sample scenario the individuals are randomly divided into four subsets. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a slightly higher accuracy of genomic prediction for both traits. These results indicate that genomic selection for antibody response to ND and AI in chickens is promising.

Estimation of genetic parameters of fertility traits in Chinese Holstein cattle

Guo, G., Guo, X., Wang, Y., Zhang, X., Zhang, S., Li, X., Liu, L., Shi, W., Usman, T., Wang, X., Du, L. and Zhang, Q. 2014. Estimation of genetic parameters of fertility traits in Chinese Holstein cattle. Can. J. Anim. Sci. 94: 281-285. The objective of this study was to estimate genetic parameters for fertility traits in Chinese Holstein heifers and cows. Data of 20169 animals with 42106 records over a period of 10 yr (2001-2010) were collected from Sanyuan Lvhe Dairy Cattle Center in Beijing, China. Traits included age at first service (AFS), number of services (NS), days from calving to first service (CTFS), days open (DO), and calving interval (CI). Genetic parameters were estimated with multiple-trait animal model using the DMU software. Heritability estimates for AFS, NS, CTFS, DO and CI were 0.100±0.012, 0.040±0.017, 0.034±0.011, 0.053±0.019 and 0.056±0.014, respectively. Genetic correlations between traits observed ranged from -0.13 to 0.99. Genetic correlations between AFS with NS, CTFS, DO and C...

Single Step, a general approach for genomic selection

Genomic evaluation methods assume that the reference population is genotyped and phenotyped. This is most often false and the generation of pseudo-phenotypes is uncertain and inaccurate. However, markers obey transmission rules and therefore the covariances of marker genotypes across individuals can be modelled using pedigree relationships. Based on this, an extension of the genomic relationship matrix can be constructed in which genomic relationships are propagated to all individuals, resulting in a combined relationship matrix, which can be used in a BLUP procedure called the Single Step Genomic BLUP. This procedure provides so far the most comprehensive option for genomic evaluation. Several extensions, options and details are described: compatibility of genomic and pedigree relationships, Bayesian regressions, multiple trait models, computational aspects, etc. Many details scattered through a series of papers are put together into this paper.

Random regression test-day model for clinical mastitis: Genetic parameters, model comparison, and correlations with indicator traits

The objective was to study genetic (co)variance components for binary clinical mastitis (CM), test-day protein yield, and udder health indicator traits [test-day somatic cell score (SCS) and type traits of the udder composite] in the course of lactation with random regression models (RRM). The study used a data set from selected 15 large-scale contract herds including 26,651 Holstein cows. Test-day production and CM data were recorded from 2007 to 2012 and comprised parities 1 to 3. A longitudinal CM data structure was generated by assigning CM records to adjacent official test dates. Bivariate threshold-linear RRM were applied to estimate genetic (co)variance components between longitudinal binary CM (0=healthy; 1=diseased) and longitudinal Gaussian distributed protein yield and SCS test-day data. Heritabilities for liability to CM (heritability ~0.15 from 0 to 305 d after calving) were slightly higher than for SCS for corresponding days in milk (DIM) in the course of lactation. Daily genetic correlations between CM and SCS were moderate to high (genetic correlation ~0.70), but substantially decreased at the very end of lactation. Genetic correlations between CM at different test days were close to 1 for adjacent test days, but were close to zero for test days far apart. Daily genetic correlations between CM and protein yield were low to moderate. For identical DIM (e.g., DIM 20, 160, and 300), genetic correlations were −0.03, 0.11, and 0.18, respectively, and disproved pronounced genetic antagonisms between udder health and productivity. Correlations between estimated breeding values (EBV) for CM from the RRM and official EBV for linear type traits of the udder composite, including EBV from 74 influential sires (sires with >60 daughters), were −0.31 for front teat placement, −0.01 for rear teat placement, −0.31 for fore udder attachment, −0.32 for udder depth, and −0.08 for teat length. Estimated breeding values for CM from the RRM were compared with EBV from a multiple-trait model and with EBV from a repeatability model. For test days covering an identical time span and on a lactation level, correlations between EBV from RRM, multiple-trait model, and repeatability model were close to 1. Most relevant results suggest the routine application of threshold RRM to binary CM to (1) allow selection of genetically superior sires for distinct stages of lactation and (2) achieve higher selection response in CM compared with selection strategies based on indicator type traits or based on the indicator-trait SCS.

Comparison of single-trait and multiple-trait genomic prediction models

BackgroundIn this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.ResultsThe results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.ConclusionsThese results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.

Genetic parameters for litter size in Black Slavonian pigs

The objective of this study was to estimate genetic parameters for litter size of Black Slavonian pigs using the repeatability, multiple trait, and random regression models, and to consider the possibility to increase litter size in Black Slavonian pigs by selection. A total of 4733 litter records from the first to the sixth parity from sows that farrowed between January 1998 and December 2010 were included in the analysis. Individual record consisted of the following variables: breeding organisation (eight regions), parity (1-6), service boar, and farrowing season (month-year interaction). Estimation of all the covariance components with three different models was based on the residual maximum likelihood method. Estimate of additive genetic variance and heritability for number of piglets born alive with repeatability model was 0.23 and 0.10, respectively. Estimates of additive genetic variance with multiple trait and random regression model were in a wider range from 0.05 to 0.65 across parities, and heritabilities were estimated in the range between 0.03 and 0.26. Estimates of phenotypic and additive genetic correlations were much smoother with random regression model in comparison with multiple trait model. Due to unexpected changes of variances along trajectory obtained with multiple trait and random regression model, the best option for genetic evaluation of litter size for now could be the use of repeatability model. With increasing number of data with proper data structure alternative modelling of litter size of Black Slavonian pig using multiple trait and random regression model could be taken into consideration.