Mapping QTLs on BTA6 affecting milk production traits in a Chinese Holstein population

• Published in: Chinese science bulletin Vol. 50; no. 16; pp. 1737 - 1742
• Main Author: CHEN, Huiyong
• Format: Journal Article
• Language: English
• Published: Beijing Springer Nature B.V 2005; Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100094, China%College of Animal Science and Technology, China Agricultural University, Beijing 100094, China
• Subjects: Chromosome 6; Confidence intervals; Gene mapping; Microsatellites; Milk production; Proteins; Quantitative trait loci; Regression analysis; Regression models; Statistical analysis; 乳制品; 变异结构; 荷兰乳牛; 食品安全; milk production trait; variance component approach; linear regression approach; Chinese Holstein cattle; daughter design
• ISSN: 1001-6538; 2095-9273; 1861-9541; 2095-9281
• DOI: 10.1360/982005-725

A Chinese Holstein population with daughter design was analyzed using 14 microsatellites covering a map distance of 55.7 cM on chromosome 6 to fine map QTL for five milk production traits. 26 paternal half-sib families with 2356 daughters were involved. Two different approaches, linear regression approach and variance component approach, were employed, with a one-QTL model and two-QTL model fitted. With a one-QTL model, the linear regression approach revealed a QTL near BMS470 with effects on milk yield, fat yield, protein yield, and fat percentage, and another QTL near BMS2460 for protein percentage. The variance component approach confirmed the results of linear regression approach for the three yield traits, with the exception that the QTL for fat yield was mapped to a different position near BMS1242. The 95% confidence intervals resulted from linear regression, obtained by bootstrapping, were generally large, ranging from 31 to 53 cM, whereas the variance component approach revealed very small confidence intervals, calculated by LOD drop-off method, for the three yield traits, only 4-5 cM. With a two-QTL model, both approaches pro- vided strong evidence for the existence of two QTLs for the three yield traits. Along with the QTLs identified in one-QTL model analyses, the linear regression approach revealed a second QTL near BP7 with effects on all the three yield traits, whereas the variance component approach located the second QTL near ILSS035, BMS470, and BP7 for the three traits, respectively.