2004•Journal of Animal Breeding and GeneticsRequires access

Investigations in the character of QTL affecting negatively correlated milk traits

Gertraude Freyer, Peter Borgen Sørensen, C. Kühn, Rosemarie Weikard

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Abstract

Summary The primary aim of this study was to investigate the quantitative trait loci (QTL) on BTA6 that affect negatively correlated milk traits, using bivariate covariance component analysis of milk yield and fat (or protein) content, protein yield and fat content, and fat yield and protein content. A set of five different genetic models was adapted to differentiate trait‐specific QTL in close linkage from pleiotropy. Using a grand‐daughter design consisting of five half‐sib families from the German Holstein population and 298 sons genotyped for 16 microsatellite markers on BTA6, we found significant trait‐specific QTL for fat content and protein yield, 24 cM apart. Markers BM1329 and FBN12 bracketed the QTL for fat content, and the region between TGLA37 and FBN13 most likely harbours a QTL for protein yield. The analysis based on the close linkage model fully confirmed this result. Despite the pure QTL findings confirming results from the literature, distinguishing pleiotropic and closely linked QTL for competitive traits is a new aspect. Our multivariate analysis results did not suggest a pleiotropic QTL for the investigated negatively correlated traits. The QTL‐based trait correlations were discussed as an important aspect of modelling that needs to be considered in the future.

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Summary The primary aim of this study was to investigate the quantitative trait loci (QTL) on BTA6 that affect negatively correlated milk traits, using bivariate covariance component analysis of milk yield and fat (or protein) content, protein yield and fat content, and fat yield and protein content. A set of five different genetic models was adapted to differentiate trait‐specific QTL in close linkage from pleiotropy. Using a grand‐daughter design consisting of five half‐sib families from the German Holstein population and 298 sons genotyped for 16 microsatellite markers on BTA6, we found significant trait‐specific QTL for fat content and protein yield, 24 cM apart. Markers BM1329 and FBN12 bracketed the QTL for fat content, and the region between TGLA37 and FBN13 most likely harbours a QTL for protein yield. The analysis based on the close linkage model fully confirmed this result. Despite the pure QTL findings confirming results from the literature, distinguishing pleiotropic and closely linked QTL for competitive traits is a new aspect. Our multivariate analysis results did not suggest a pleiotropic QTL for the investigated negatively correlated traits. The QTL‐based trait correlations were discussed as an important aspect of modelling that needs to be considered in the future.

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Available abstract

Summary The primary aim of this study was to investigate the quantitative trait loci (QTL) on BTA6 that affect negatively correlated milk traits, using bivariate covariance component analysis of milk yield and fat (or protein) content, protein yield and fat content, and fat yield and protein content. A set of five different genetic models was adapted to differentiate trait‐specific QTL in close linkage from pleiotropy. Using a grand‐daughter design consisting of five half‐sib families from the German Holstein population and 298 sons genotyped for 16 microsatellite markers on BTA6, we found significant trait‐specific QTL for fat content and protein yield, 24 cM apart. Markers BM1329 and FBN12 bracketed the QTL for fat content, and the region between TGLA37 and FBN13 most likely harbours a QTL for protein yield. The analysis based on the close linkage model fully confirmed this result. Despite the pure QTL findings confirming results from the literature, distinguishing pleiotropic and closely linked QTL for competitive traits is a new aspect. Our multivariate analysis results did not suggest a pleiotropic QTL for the investigated negatively correlated traits. The QTL‐based trait correlations were discussed as an important aspect of modelling that needs to be considered in the future.

Key concepts: Quantitative trait locus, Pleiotropy, Trait, Biology, Family-based QTL mapping, Population, Genetics, Linkage (software)

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