Interval and composite interval mapping of somatic cell score, yield, and components of milk in dairy cattle
Interval and composite interval mapping of somatic cell score, yield, and components of milk in dairy cattle
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DOI：
10.3168/jds.s0022-0302(02)74395-6
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发表时间：
2002-11-01
影响因子：
3.5
通讯作者：
Lewin, HA
中科院分区：
文献类型：
--
作者：
Rodriguez-Zas, SL;Southey, BR;Lewin, HA
本研究采用单标记、区间作图和复合区间作图方法，对控制乳、脂、蛋白质含量的数量性状基因座（QTL）进行了检测。蛋白质产量和体细胞评分（SCS）。采用孙女设计将分子遗传信息联合收割机。预测传递能力（PTA）和估计的子代产量。来自八个奶牛DNA库荷斯坦家庭的DNA偏差（DYD）。模型，包括和排除权重占不确定性的响应变量进行了评估，在每个性状，家庭和表型（DYD和PTA）组合。基因型信息由沿着29条普通牛常染色体分布的174个微卫星标记组成。每个常染色体的平均信息标记数为3，每个家庭和标记的信息儿子的数量在21和173之间变化。最小二乘单标志物分析的家族内结果用于期望最大似然IM。和CIM与QTL制图。不同的CIM模型规格，提供互补控制的背景QTL以外的区间，正在研究中，进行了评估。使用排列技术，计算在1000个样品上烘烤的全基因组阈值检验统计值。从DYD和PTA分析的结果是高度一致的性状和家庭。解释或忽略DYD（方差）和PTA（可靠性倒数）不确定性的模型估计值的微小差异可能与儿子之间DYD和PTA的精度提高和一致有关。CIM模型得到数据的最佳支持，有10个标记控制背景效应。在常染色体（BTA）3上，一个位于32 cM的影响蛋白质产量的QTL位于家系5，一个位于74 cM的影响脂肪产量的QTL位于家系8。在BTA 21上检测到两个与SCS相关的图位，一个在家族1中的33 cM处，另一个在家族中的84 cM处。三.蛋白质产量的QTL在BTA 6，家庭6，检测到26和36 cM之间，和牛奶产量的QTL检测在BTA 7在116 cM。家庭三。IM和CIM方法在BTA 14上的3cM处检测到影响家系4中脂肪产量yi的QTL。BTA 29上的两个位点与家系7的产奶量（0cM）和产脂量（14cM）的显著变异相关。这些结果表明，在标记区间内存在一个对多个性状具有多效性效应的QTL或多个QTL。本研究的结果可用于后续的精细定位工作，并应用于奶牛生产的标记辅助选择。和健康特征。
Single-marker, interval-mapping (IM) and composite interval mapping (CIM) were I used to detect quantitai tive trait loci (QTL) controlling milk, fat and. protein yields, and somatic cell score (SCS). A granddaughter design was used to combine molecular genetic informa-. tion with predicted transmitting abilities (PTA) and estimated daughter yield. deviations (DYD) from eight Dairy Bull DNA Repository Holstein families. Models that included and excluded weights accounting for the uncertainty of the response variable were evaluated in each trait, family and phenotype (DYD and PTA) combination. The genotypic information consisted of 174 microsatellite markers along 29 Bos taurus autosomes. The average number of informative markers per autosome was three and the number of informative sons per family and marker varied between 21 and 173. Within-family results from the least squares single-marker analyses were used in expectation-maximization likelihood IM. and CIM implemented with QTL Cartographer. Different CIM model specifications, offering complementary control on the background QTL outside the interval, under study, were evaluated. Permutation techniques were used,to calculate the genome-wide threshold test statistic values baked on 1000 samples. Results from the DYD and PTA analyses were highly consistent across traits and families. The minor differences in the estimates from the models that accounted for or ignored the uncertainty of the DYD (vari7 ance) and PTA (inverse of reliability) may be associated to the elevated and consistent precision of the DYD and PTA among sons. The CIM model best supported by the data had 10 markers controlling for background effects. On autosome (BTA) three, a QTL at 32 cM influencing protein yield was located in family five and a QTL at 74 cM for fat yield was located in family eight. Two map positions associated with SCS were detected on BTA 21, one at 33 cM in family one and the other at 84 cM in family. three. A QTL for protein yield was detected between 26 and 36 cM on BTA six, family six, and a QTL for milk yield was detected at 116 cM on BTA seven in. family three. The IM and CIM approaches detected a QTL at 3 cM on BTA 14 influencing fat yield yi in family four. Two map positions on BTA 29 were associated ciated with significant Variation of milk (0 cM) and fat yield (14 cM in family seven These results suggest the presence of one QTL with pleiotropic effects on multiple traits or multiple QTL within the marker interval. Findings from this study could be used in subsequent fine-mapping work and applied to marker-assisted selection of dairy production. and health traits.