Linkage disequilibrium and selection response in two-stage marker-assisted selection of dairy cattle over several generations

2005 ◽  
Vol 122 (2) ◽  
pp. 110-116 ◽  
Author(s):  
N.F. Schulman ◽  
M.R. Dentine
1987 ◽  
Vol 44 (1) ◽  
pp. 29-38 ◽  
Author(s):  
M. E. Goddard

ABSTRACTIn the breeding of dairy cattle the selection of bulls to breed young bulls for progeny testing is a crucial process. This paper compares several policies for making this selection based on the criteria-selection response, inbreeding depression, loss of genetic variance and variability of response. A number called the ‘effective number of new bulls to breed bulls selected each year’ (NBBe) is defined which is closely related to the last three of these criteria. Past studies of the design of dairy cattle breeding programmes have assumed that selection is within a group of bulls progeny tested in the same year (policy I). However, modern sire evaluation methods allow comparison of sires tested in different years. To evaluate the effect of selecting bulls to breed bulls from all available bulls (policy II) a computer simulation program was used. Policy II results in an increase in the response to selection but a substantial decrease in NBBe. When compared at the same NBBe, policy II results in a smaller selection response than policy I. A policy which allows the best bulls to be used for more than 1 year but which limits the maximum number of years for which they can be used, results in the best compromise. If bulls are to be used for several years there is little advantage to be gained from making more matings within each year to more high-rated bulls or to older, more reliably evaluated bulls.


2000 ◽  
Vol 75 (2) ◽  
pp. 249-252 ◽  
Author(s):  
JOHN C. WHITTAKER ◽  
ROBIN THOMPSON ◽  
MIKE C. DENHAM

In crosses between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.


2019 ◽  
Author(s):  
Zhen Wang ◽  
Yun Pan ◽  
Libang He ◽  
Hong Chen ◽  
Chuanying Pan ◽  
...  

Abstract Background: Multiple morphological abnormalities of the sperm flagella (MMAF) makes an assignable contribution to male infertility, including QRICH2 , CFAP43 , CFAP44 , CFAP69 , CCDC39 , AKAP4 and DNAH1 gene. This work studied 28 putative indel mutations of MMAF related genes including QRICH2 , CFAP69 , CFAP43 , CCDC39 and DNAH1 gene and their correlation with the first-born litter sizes of 769 Shaanbei white cashmere (SBWC) goats. Results: Electrophoresis and DNA sequencing analysis showed the 11-bp indel within QRICH2 ( QRICH2 -P4), the three indel variations in CFAP69 ( CFAP69 -P4, CFAP69 -P6 and CFAP69 -P7) and the 27-bp indel of DNAH1 ( DNAH1 -P1) were found to be polymorphic. The 27-bp indel variation within DNAH1 was not in consistent with HWE and the other four indel of QRICH2 and CFAP69 were in consistent with HWE. The linkage disequilibrium (LD) analysis showed the 8-bp indel ( CFAP69 -P4) and the 6-bp indel ( CFAP69- P6) within CFAP69 were in complete LD with each other (D'=0.99, r 2 =1.00). The 27-bp indel mutation within DNAH1 was strongly significantly associated with first-born litter sizes of SBWC goats ( P <0.01) and the average litter size of II genotype was significantly greater than ID and DD genotypes ( P = 0.003). In single-lamb and multi-lamb of goat groups, the genotype distributions of the 27-bp indel was significantly different ( P = 0.002). While the 11-bp indel variation of QRICH2 and three indel mutations (P4, P6 and P7) of CFAP69 identified were not ( P >0.05). Conclusions: These findings suggest the 27-bp indels in the goat DNAH1 can be used as an effective molecular marker for marker-assisted selection of goats reproduction breeding in the future.


1999 ◽  
Vol 116 (2) ◽  
pp. 99-110 ◽  
Author(s):  
By N. F. Schulman ◽  
M. J. De Vries ◽  
M. R. Dentine

2016 ◽  
Vol 11 (3) ◽  
pp. 217
Author(s):  
Estu Nugroho ◽  
Budi Setyono ◽  
Mochammad Su’eb ◽  
Tri Heru Prihadi

Program pemuliaan ikan mas varietas Punten dilakukan dengan seleksi individu terhadap karakter bobot ikan. Pembentukan populasi dasar untuk kegiatan seleksi dilakukan dengan memijahkan secara massal induk ikan mas yang terdiri atas 20 induk betina dan 21 induk jantan yang dikoleksi dari daerah Punten, Kepanjen (delapan betina dan enam jantan), Kediri (tujuh betina dan 12 jantan), Sragen (27 betina dan 10 jantan), dan Blitar (15 betina dan 11 jantan). Larva umur 10 hari dipelihara selama empat bulan. Selanjutnya dilakukan penjarangan sebesar 50% dan benih dipelihara selama 14 bulan untuk dilakukan seleksi dengan panduan hasil sampling 250 ekor individu setiap populasi. Seleksi terhadap calon induk dilakukan saat umur 18 bulan pada populasi jantan dan betina secara terpisah dengan memilih berdasarkan 10% bobot ikan yang terbaik. Calon induk yang terseleksi kemudian dipelihara hingga matang gonad, kemudian dipilih sebanyak 150 pasang dan dipijahkan secara massal. Didapatkan respons positif dari hasil seleksi berdasarkan bobot ikan, yaitu 49,89 g atau 3,66% (populasi ikan jantan) dan 168,47 g atau 11,43% (populasi ikan betina). Nilai heritabilitas untuk bobot ikan adalah 0,238 (jantan) dan 0,505 (betina).Punten carp breeding programs were carried out by individual selection for body weight trait. The base population for selection activities were conducted by mass breeding of parent consisted of 20 female and 21 male collected from area Punten, eight female and six male (Kepanjen), seven female and 12 male (Kediri), 27 female and 10 male (Sragen), 15 female and 11 male (Blitar). Larvae 10 days old reared for four moths. Then after spacing out 50% of total harvest, the offspring reared for 14 months for selection activity based on the sampling of 250 individual each population. Selection of broodstock candidates performed since 18 months age on male and female populations separately by selecting based on 10% of fish with best body weight. Candidates selected broodstocks were then maintained until mature. In oder to produce the next generation 150 pairs were sets and held for mass spawning. The results revealed that selection response were positive, 49.89 g (3.66%) for male and 168.47 (11.43%) for female. Heritability for body weight is 0.238 (male) and 0.505 (female).


2016 ◽  
Vol 52 ◽  
pp. 6-12 ◽  
Author(s):  
M. V. Gladiy ◽  
G. S. Kovalenko ◽  
S. V. Priyma ◽  
G. A. Holyosa ◽  
A. V. Tuchyk ◽  
...  

The main goal of dairy breeds selection should be improving breeding and productive qualities of animals under modern conditions. The majority of farms, using native breeds to produce milk, has created optimal conditions for keeping and feeding, selection and matching, growing of replacements etc. Further improvement of created native dairy breeds for economically useful traits occurs at total use of purebred Holstein bulls (semen) of foreign selection. In order to realistically assess milk productivity (milk yield, fat content in milk and fat yield) of Ukrainian Black-and-White and Red-and-White Dairy cows should be conducted a comparative analysis of Holstein cows under the same conditions of feeding and keeping. It was established that Ukrainian Red-and-White Dairy cows were characterized by the highest milk yields for 305 days of all lactations, taken into account, the among three investigated breeds. Their milk yield during the first lactation was 5933 kg of milk, during the second – 6393 kg, the third – 6391 kg and during higher lactation – 6650 kg. Ukrainian Black-and-White Dairy cows were second by milk yield (except for the second lactation), during the first lactation – 5932 kg of milk, the third – 6462 kg and higher – 6541 kg, and Holstein cows were third, during the first lactation – 5794 kg of milk, the second – 6381 kg, the third – 6335 kg and higher – 6469 kg. The fat content was almost the same and varied within 3.49-3.58% in milk of Ukrainian Red-and-White Dairy cattle, 3.50-3.60% in milk of Ukrainian Black-and-White Dairy cattle and 3.50-3.56% in Holsteins’ milk. The difference between the breeds was within 0.01-0.04%. All the investigated breeds had predominance in fat yield for three lactations over standards of these breeds: Ukrainian Red-and-White Dairy cows from 75.1 to 93.4 kg, Ukrainian Black-and-White Dairy cows – 75.1-89.0 kg respectively and Holstein cows – 41.9-60.2 kg. It was found different level of positive correlation between milk yield and fat yield in all the cases and high correlation (r = 0.604-0.921, P < 0.001) in five cases (41.7%) Negative correlation coefficients indicate that selection of animals to higher milk yield in the herd will decrease the second trait – fat content in milk. Positive and highly significant correlation between milk yield and fat yield indicates that selection of cows in the herd to higher milk yields will increase fat yield. It was revealed that bulls were among the factors impacted the milk productivity (milk yield, fat content, fat yield) of three investigated breeds. So, the force (η²x) of father’s impact on milk yield was15.4-47.9%, fat content – 22.0-43.4% and fat yield – 14.9-47.7% taking into account a lactation and a breed. The force of lines impact (η²x) was second; it was on milk yield 6.1-24.5%, fat content – 4.1-17.1 and fat yield – 5.8-23.5%. The force of breeds impact (η²x) was last; it was on milk yield 0.3-2.9%, fat content – 0.2-0.3% and fat yield – 0.6-2.7%. So, the comparative studies of milk productivity of Ukrainian Red-and-White and Black-and-White Dairy cattle with Holsteins indicate that under similar conditions of feeding and keeping, these native breeds can compete with Holstein cattle. The milk yield for 305 days of higher lactation was 6650 kg of milk in Ukrainian Red-and-White Dairy cows, 6541 kg in Ukrainian Black-and-White Dairy cows and 6469 kg in Holsteins. It was found the inverse correlation r = -0.025-0.316 between milk yield and fat content in milk in most cases. Selection and matching of animals in the herd should be carried out simultaneously on these traits. It was found positive repeatability of milk yields between the first and second, the third and higher lactations (rs = 0.036-0.741), indicating the reliability of forecasting increase in milk productivity during the next lactations in all herd. Bulls have the greatest impact (η²x) on milk productivity among the factors taken into account: milk yield – 15.4-47.9%, fat content in milk – 22.0-43.4% and fat yield – 14.9-47.7%.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicole Pretini ◽  
Leonardo S. Vanzetti ◽  
Ignacio I. Terrile ◽  
Guillermo Donaire ◽  
Fernanda G. González

Abstract Background In breeding programs, the selection of cultivars with the highest yield potential consisted in the selection of the yield per se, which resulted in cultivars with higher grains per spike (GN) and occasionally increased grain weight (GW) (main numerical components of the yield). In this study, quantitative trait loci (QTL) for GW, GN and spike fertility traits related to GN determination were mapped using two doubled haploid (DH) populations (Baguette Premium 11 × BioINTA 2002 and Baguette 19 × BioINTA 2002). Results In total 305 QTL were identified for 14 traits, out of which 12 QTL were identified in more than three environments and explained more than 10% of the phenotypic variation in at least one environment. Eight hotspot regions were detected on chromosomes 1A, 2B, 3A, 5A, 5B, 7A and 7B in which at least two major and stable QTL sheared confidence intervals. QTL on two of these regions (R5A.1 and R5A.2) have previously been described, but the other six regions are novel. Conclusions Based on the pleiotropic analysis within a robust physiological model we conclude that two hotspot genomic regions (R5A.1 and R5A.2) together with the QGW.perg-6B are of high relevance to be used in marker assisted selection in order to improve the spike yield potential. All the QTL identified for the spike related traits are the first step to search for their candidate genes, which will allow their better manipulation in the future.


Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.


Genetics ◽  
1996 ◽  
Vol 144 (4) ◽  
pp. 1961-1974 ◽  
Author(s):  
Ming Wei ◽  
Armando Caballero ◽  
William G Hill

Formulae were derived to predict genetic response under various selection schemes assuming an infinitesimal model. Account was taken of genetic drift, gametic (linkage) disequilibrium (Bulmer effect), inbreeding depression, common environmental variance, and both initial segregating variance within families (σAW02) and mutational (σM2) variance. The cumulative response to selection until generation t(CRt) can be approximated asCRt≈R0[t−β(1−σAW∞2σAW02)t24Ne]−Dt2Ne,where Ne is the effective population size, σAW∞2=NeσM2 is the genetic variance within families at the steady state (or one-half the genic variance, which is unaffected by selection), and D is the inbreeding depression per unit of inbreeding. R  0 is the selection response at generation 0 assuming preselection so that the linkage disequilibrium effect has stabilized. β is the derivative of the logarithm of the asymptotic response with respect to the logarithm of the within-family genetic variance, i.e., their relative rate of change. R  0 is the major determinant of the short term selection response, but σM2, Ne and β are also important for the long term. A selection method of high accuracy using family information gives a small Ne and will lead to a larger response in the short term and a smaller response in the long term, utilizing mutation less efficiently.


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