Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 533470
Title Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency
Author(s) Lu, Y.; Vandehaar, M.J.; Spurlock, D.M.; Weigel, K.A.; Armentano, L.E.; Connor, E.E.; Coffey, M.; Veerkamp, R.F.; Haas, Y. de; Staples, C.R.; Wang, Z.; Hanigan, M.D.; Tempelman, R.J.
Source Journal of Dairy Science 101 (2018)4. - ISSN 0022-0302 - p. 3140 - 3154.
DOI http://dx.doi.org/10.3168/jds.2017-13364
Department(s) LR - Animal Breeding & Genomics
WIAS
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Feed efficiency - Genome-wide association - Multiple trait
Abstract Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.
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