Feasibility Of Genomic Selection Research Articles

Erratum to: Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle.

Erratum to: Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle.

Genomic selection for heterobothriosis resistance concurrent with body size in the tiger pufferfish, Takifugu rubripes

Parasite resistance traits in aquaculture species often have moderate heritability, indicating the potential for genetic improvements by selective breeding. However, parasite resistance is often synonymous with an undesirable negative correlation with body size. In this study, we first tested the feasibility of genomic selection (GS) on resistance to heterobothriosis, caused by the monogenean parasite Heterobothrium okamotoi, which leads to huge economic losses in aquaculture of the tiger pufferfish Takifugu rubripes. Then, using a simulation study, we tested the possibility of simultaneous improvement of parasite resistance, assessed by parasite counts on host fish (HC), and standard length (SL). Each trait showed moderate heritability (square-root transformed HC: h2 = 0.308 ± 0.123, S.E.; SL: h2 = 0.405 ± 0.131). The predictive abilities of genomic prediction among 12 models, including genomic Best Linear Unbiased Predictor (GBLUP), Bayesian regressions, and machine learning procedures, were also moderate for both transformed HC (0.248‒0.344) and SL (0.340‒0.481). These results confirmed the feasibility of GS for this trait. Although an undesirable genetic correlation was suggested between transformed HC and SL (rg = 0.228), the simulation study suggested the desired gains index can help achieve simultaneous genetic improvements in both traits.

A rapid genomic selection method combining Haseman-Elston (HE) model and algorithm for proven and young (APY)

To increase the predictive accuracy of genomic selection, large-scale phenotypic and genomic data is needed, which results in enormous consumption of computational resources, hampering the feasibility of genomic selection in return. In order to address computational limitation, a rapid genomic selection method HEAPY|A is developed in this study, combining Haseman-Elston (HE) model and algorithm for proven and young (APY). The proposed approach utilizes (i) HE regression to estimate the heritability and then (ii) APY to solve the inverse of the large matrix in best linear prediction (BLP); both HE and APY can reduce the computational cost compared with conventional techniques. When the size of a core population is half of that of a large training population, GBLUP|A, HEAPY|A, and HEBLP|A have similar performance in simulation studies (the core population can be further reduced if the training population size is further increased). When the size of a core population is half of that of a small training population, the predictive accuracy of HEAPY|A is a little lower than that of GBLUP|A and HEBLP|A in simulation study and empirical data—an Arabidopsis thaliana F2 population. HEAPY|A helps in predicting large genomic selection dataset with comparable accuracy without significant expense of time seen in traditional genomic selection algorithm.

Evaluation on the genomic selection in Litopenaeus vannamei for the resistance against Vibrio parahaemolyticus

Vibrio parahaemolyticus (V. parahaemolyticus) carrying the toxic plasmid of pirA and pirB has been identified as the causative agent of acute hepatopancreatic necrosis disease (AHPND), which has caused serious economic loss to the aquaculture industry of penaeid shrimp Litopenaeus vannamei (L. vannamei) in recent years. To effectively control the outbreak of this disease, breeding of Vibrio resistant broodstocks of L. vannamei was regarded as an important approach. Due to the advantages in selection accuracy and efficiency, genomic selection (GS) was expected to be a feasible alternative to accelerate the genetic improvement of disease resistance traits. In the present study, the heritability of shrimp resistance to V. parahaemolyticus was estimated and the feasibility of GS was evaluated in L. vannamei based on the real and simulation data. The heritability of shrimp resistance against V. parahaemolyticus was around 0.15–0.24, which indicated that the genetic improvement can be achieved by selective breeding. Subsequent analysis for GS based on real data showed that the genomic best linear unbiased prediction (GBLUP) can result in more accurate prediction than the traditional pedigree-based best linear unbiased prediction (PBLUP), with a 6.8% increase in the prediction accuracy for the survival time, and a 3.5% increase for binary survival. Similarly, for the simulated data, a relative increase (3.0% and 5.0%) in the prediction accuracy was obtained for survival time and binary survival when comparing the PBLUP to GBLUP. Overall results suggest that GS could be an alternative approach to improve the genetic gains in L. vannamei for the resistance to V. parahaemolyticus.

Genomic selection in the German Landrace population of the Bavarian herdbook.

The aim of our study was to compare different validation methods with respect to their impact on validation results and to evaluate the feasibility of genomic selection in the German Landrace population of the Bavarian herdbook. For this purpose, a sample of 337 boars and 1,676 sows was genotyped with the Illumina PorcineSNP60 BeadChip. Conventional BLUP breeding values for fertility, growth, carcass, and quality traits were deregressed and used as phenotypes in genomic BLUP. The resulting genomic breeding values were also blended with information from the full conventional breeding value estimation to include information from nongenotyped parents. Subsequent validation used forward prediction, realized reliabilities, and theoretical reliabilities. The results indicate that the validation methods showed a relatively large effect on in the displayed reliability levels in our study: forward prediction reliabilities were found to be much lower than the conventional parent-average reliabilities whereas corresponding realized and theoretical reliabilities were found substantially greater. Theoretical reliabilities appear to be the most consistent validation approach tested in our study, because they avoid the use of proxy variables. Generally, our results suggest a substantial potential for a genomic selection implementation for the Bavarian herdbook by using both sows and boars. Theoretical genomic reliabilities of direct genomic values of selection candidates were, on average, 31 to 36% greater than the conventional parent average reliabilities. However, the inclusion of residual information from conventional breeding values had only a marginal effect on reliabilities.

Designing dairy cattle breeding schemes under genomic selection: a review of international research

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

Animal breeding in India - a time for reflection, and action

Animal breeding in India - a time for reflection, and action