Reproductive technologies and genomic selection in dairy cattle

2014 ◽  
Vol 26 (1) ◽  
pp. 12 ◽  
Author(s):  
C. Ponsart ◽  
D. Le Bourhis ◽  
H. Knijn ◽  
S. Fritz ◽  
C. Guyader-Joly ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Reproductive Technologies ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Preimplantation Stage ◽  
Genomic Tools ◽  
Dna Production ◽  
New Strategies ◽  
Selection Of

Genomic tools are now available for most livestock species and are used routinely for genomic selection (GS) in cattle. One of the most important developments resulting from the introduction of genomic testing for dairy cattle is the application of reasonably priced low-density single nucleotide polymorphism technology in the selection of females. In this context, combining genome testing and reproductive biotechnologies in young heifers enables new strategies to generate replacement and elite females in a given period of time. Moreover, multiple markers have been detected in biopsies of preimplantation stage embryos, thus paving the way to develop new strategies based on preimplantation diagnosis and the genetic screening of embryos. Based on recent advances in GS, the present review focuses on new possibilities inherent in reproductive technologies used for commercial purposes and in genetic schemes, possible side effects and beneficial impacts on reproductive efficiency. A particular focus is on the different steps allowing embryo genotyping, including embryo micromanipulation, DNA production and quality assessment.

scholarly journals Genomic selection in livestock populations

2010 ◽  
Vol 92 (5-6) ◽  
pp. 413-421 ◽  
Author(s):  
MICHAEL E. GODDARD ◽  
BEN J. HAYES ◽  
THEO H. E. MEUWISSEN
Keyword(s):  
Genomic Selection ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Factors Affecting ◽  
Snp Data ◽  
Genomic Tools ◽  
Breeding Programmes ◽  
Estimated Breeding Values ◽  
Term Response

SummaryMost traits of economic importance in livestock are either quantitative or complex. Despite considerable efforts, there has been only limited success in identifying the polymorphisms that cause variation in these traits. Nevertheless, selection based on estimated breeding values (BVs), calculated from data on phenotypic performance and pedigree has been very successful. Genomic tools, such as single nucleotide polymorphism (SNP) chips, have led to a new method of selection called ‘genomic selection’ in which dense SNP genotypes covering the genome are used to predict the BV. In this review we consider the statistical methodology for estimating BVs from SNP data, factors affecting the accuracy, the long-term response to genomic selection and the design of breeding programmes including the management of inbreeding.

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

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

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.

Within-family selection at an otherwise unselected locus in dairy cattle

Genome ◽  
1993 ◽  
Vol 36 (3) ◽  
pp. 433-439 ◽  
Author(s):  
L. Gomez-Raya ◽  
J. P. Gibson
Keyword(s):  
Dairy Cattle ◽  
Random Mating ◽  
Molecular Genetic ◽  
Large Family ◽  
Reproductive Technologies ◽  
Mating Strategy ◽  
Embryo Selection ◽  
Family Selection ◽  
Favourable Allele ◽  
Selection Of

Identification of allelic variants with economic importance is feasible via molecular genetic techniques. This information can be used to increase the frequency of favourable alleles in dairy cattle. The effect of selection on the genotype within families in the early stages of life is examined. Three different strategies are considered: (1) random mating of bull sires with bull dams and with cows, with embryo selection of young bulls and all cows; (2) random mating of bull sires with bull dams, with embryo selection of young bulls only; (3) minimizing or avoiding matings between homozygotes for the unfavourable allele, with embryo selection of young bulls. Selection strategies assume the use of reproductive technologies such as embryo transfer to produce large family sizes for within-family selection to be practiced. All the three strategies increase the frequency of the favourable allele rapidly. Strategy 1 gives the fastest increase in the frequency of the favourable allele. The increase in the frequency of the favourable allele is slower under random mating (strategy 2) than under a negative assortative mating (strategy 3). This is a novel example of increased selection response with negative assortative mating.Key words: selection, breeding strategies, dairy cattle.

scholarly journals HAHap: a read-based haplotyping method using hierarchical assembly

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5852
Author(s):  
Yu-Yu Lin ◽  
Ping Chun Wu ◽  
Pei-Lung Chen ◽  
Yen-Jen Oyang ◽  
Chien-Yu Chen
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Error Rates ◽  
Lower Number ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Hierarchical Assembly ◽  
Sequencing Technologies ◽  
Error Corrections ◽  
Selection Of

Background The need for read-based phasing arises with advances in sequencing technologies. The minimum error correction (MEC) approach is the primary trend to resolve haplotypes by reducing conflicts in a single nucleotide polymorphism-fragment matrix. However, it is frequently observed that the solution with the optimal MEC might not be the real haplotypes, due to the fact that MEC methods consider all positions together and sometimes the conflicts in noisy regions might mislead the selection of corrections. To tackle this problem, we present a hierarchical assembly-based method designed to progressively resolve local conflicts. Results This study presents HAHap, a new phasing algorithm based on hierarchical assembly. HAHap leverages high-confident variant pairs to build haplotypes progressively. The phasing results by HAHap on both real and simulated data, compared to other MEC-based methods, revealed better phasing error rates for constructing haplotypes using short reads from whole-genome sequencing. We compared the number of error corrections (ECs) on real data with other methods, and it reveals the ability of HAHap to predict haplotypes with a lower number of ECs. We also used simulated data to investigate the behavior of HAHap under different sequencing conditions, highlighting the applicability of HAHap in certain situations.

Sign in / Sign up

Export Citation Format

Share Document