Literature DB >> 9777515

Strategies to utilize marker-quantitative trait loci associations.

C S Haley1, P M Visscher.   

Abstract

Marker-assisted selection holds promise because genetic markers provide completely heritable traits than can be measured at any age in either sex and that are potentially correlated with traits of economic value. Theoretical and simulation studies show that the advantage of using marker-assisted selection can be substantial, particularly when marker information is used, because normal selection is less effective, for example, for sex-limited or carcass traits. Assessment of the available information and its most effective use is difficult, but approaches such as crossvalidation may help in this respect. Marker systems are now becoming available that allow the high density of markers required for close associations between marker loci and trait loci. Emerging technologies could allow large numbers of polymorphic sites to be identified, practically guaranteeing that markers will be available that are in complete association with any trait locus. Identifying which polymorphism out of many that is associated with any trait will remain problematic, but multiple-locus disequilibrium measures may allow performance to be associated with unique marker haplotypes. This type of approach, combined with cheap and high density markers, could allow a move from selection based on a combination of "infinitesimal" effects plus individual loci to effective total genomic selection. In such a unified model, each region of the genome would be given its appropriate weight in a breeding program. However, the collection of good quality trait information will remain central to the use of these technologies for the foreseeable future.

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Year:  1998        PMID: 9777515     DOI: 10.3168/jds.s0022-0302(98)70157-2

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


  15 in total

1.  Prediction of total genetic value using genome-wide dense marker maps.

Authors:  T H Meuwissen; B J Hayes; M E Goddard
Journal:  Genetics       Date:  2001-04       Impact factor: 4.562

2.  Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.

Authors:  Adriana García-Ruiz; John B Cole; Paul M VanRaden; George R Wiggans; Felipe J Ruiz-López; Curtis P Van Tassell
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-27       Impact factor: 11.205

Review 3.  Missing heritability of complex diseases: case solved?

Authors:  Emmanuelle Génin
Journal:  Hum Genet       Date:  2019-06-04       Impact factor: 4.132

4.  Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

Authors:  John M Hickey; Tinashe Chiurugwi; Ian Mackay; Wayne Powell
Journal:  Nat Genet       Date:  2017-08-30       Impact factor: 38.330

Review 5.  Prediction of malting quality traits in barley based on genome-wide marker data to assess the potential of genomic selection.

Authors:  Malthe Schmidt; Sonja Kollers; Anja Maasberg-Prelle; Jörg Großer; Burkhard Schinkel; Alexandra Tomerius; Andreas Graner; Viktor Korzun
Journal:  Theor Appl Genet       Date:  2015-12-09       Impact factor: 5.699

Review 6.  Whole-genome regression and prediction methods applied to plant and animal breeding.

Authors:  Gustavo de Los Campos; John M Hickey; Ricardo Pong-Wong; Hans D Daetwyler; Mario P L Calus
Journal:  Genetics       Date:  2012-06-28       Impact factor: 4.562

7.  Estimating genomic breeding values and detecting QTL using univariate and bivariate models.

Authors:  Mario Pl Calus; Han A Mulder; Roel F Veerkamp
Journal:  BMC Proc       Date:  2011-05-27

8.  Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs.

Authors:  Janez Jenko; Gregor Gorjanc; Matthew A Cleveland; Rajeev K Varshney; C Bruce A Whitelaw; John A Woolliams; John M Hickey
Journal:  Genet Sel Evol       Date:  2015-07-02       Impact factor: 4.297

9.  Discovery of novel genetic networks associated with 19 economically important traits in beef cattle.

Authors:  Zhihua Jiang; Jennifer J Michal; Jie Chen; Tyler F Daniels; Tanja Kunej; Matthew D Garcia; Charles T Gaskins; Jan R Busboom; Leeson J Alexander; Raymond W Wright; Michael D Macneil
Journal:  Int J Biol Sci       Date:  2009-07-29       Impact factor: 6.580

10.  A consideration of resistance and tolerance for ruminant nematode infections.

Authors:  Stephen C Bishop
Journal:  Front Genet       Date:  2012-12-14       Impact factor: 4.599

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