Literature DB >> 19448026

Distribution and location of genetic effects for dairy traits.

J B Cole1, P M VanRaden, J R O'Connell, C P Van Tassell, T S Sonstegard, R D Schnabel, J F Taylor, G R Wiggans.   

Abstract

Genetic effects for many dairy traits and for total economic merit are evenly distributed across all chromosomes. A high-density scan using 38,416 single nucleotide polymorphism markers for 5,285 bulls confirmed 2 previously known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared with the individual models. Differences between models were small (1 to 2%) for traits with no major genes and larger for heavy tails with traits having known quantitative trait loci (QTL; 6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual single nucleotide polymorphisms were not tracking causative mutations with the exception of diacylglycerol O-acyltransferase 1. Additive genetic merits were constructed for each chromosome, and the distribution of BTA14-specific estimated breeding value (EBV) showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Such chromosomal EBV also may be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appear to be related to calf size or birth weight and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19448026     DOI: 10.3168/jds.2008-1762

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  83 in total

Review 1.  Gene polymorphisms: the keys for marker assisted selection and unraveling core regulatory pathways for mastitis resistance.

Authors:  Gina M Pighetti; A A Elliott
Journal:  J Mammary Gland Biol Neoplasia       Date:  2011-10-14       Impact factor: 2.673

2.  A genetical genomics approach reveals new candidates and confirms known candidate genes for drip loss in a porcine resource population.

Authors:  Hanna Heidt; Mehmet Ulas Cinar; Muhammad Jasim Uddin; Christian Looft; Heinz Jüngst; Dawit Tesfaye; Astrid Becker; Andreas Zimmer; Siriluck Ponsuksili; Klaus Wimmers; Ernst Tholen; Karl Schellander; Christine Große-Brinkhaus
Journal:  Mamm Genome       Date:  2013-09-12       Impact factor: 2.957

Review 3.  Biological underpinnings of breastfeeding challenges: the role of genetics, diet, and environment on lactation physiology.

Authors:  Sooyeon Lee; Shannon L Kelleher
Journal:  Am J Physiol Endocrinol Metab       Date:  2016-06-28       Impact factor: 4.310

4.  Establishing gene Amelogenin as sex-specific marker in yak by genomic approach.

Authors:  P P Das; G Krishnan; J Doley; D Bhattacharya; S M Deb; P Chakravarty; P J Das
Journal:  J Genet       Date:  2019-03       Impact factor: 1.166

5.  Molecular techniques revealed highly diverse microbial communities in natural marine biofilms on polystyrene dishes for invertebrate larval settlement.

Authors:  On On Lee; Hong Chun Chung; Jiangke Yang; Yong Wang; Swagatika Dash; Hao Wang; Pei-Yuan Qian
Journal:  Microb Ecol       Date:  2014-01-09       Impact factor: 4.552

6.  The relation between the genetic architecture of quantitative traits and long-term genetic response.

Authors:  Rostam Abdollahi-Arpanahi; Abbas Pakdel; Ardeshir Nejati-Javaremi; Mohammad Moradi Shahrbabak; Farhad Ghafouri-Kesbi
Journal:  J Appl Genet       Date:  2014-03-27       Impact factor: 3.240

7.  Genome-wide association study identifies two major loci affecting calving ease and growth-related traits in cattle.

Authors:  Hubert Pausch; Krzysztof Flisikowski; Simone Jung; Reiner Emmerling; Christian Edel; Kay-Uwe Götz; Ruedi Fries
Journal:  Genetics       Date:  2010-11-08       Impact factor: 4.562

8.  Dominance effects estimation of TLR4 and CACNA2D1 genes for health and production traits using logistic regression.

Authors:  Masoumeh Bagheri; Azadeh Zahmatkesh
Journal:  J Genet       Date:  2017-12       Impact factor: 1.166

9.  Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits.

Authors:  Ben J Hayes; Jennie Pryce; Amanda J Chamberlain; Phil J Bowman; Mike E Goddard
Journal:  PLoS Genet       Date:  2010-09-23       Impact factor: 5.917

10.  A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.

Authors:  Gerhard Moser; Bruce Tier; Ron E Crump; Mehar S Khatkar; Herman W Raadsma
Journal:  Genet Sel Evol       Date:  2009-12-31       Impact factor: 4.297

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.