TAILIEUCHUNG - Báo cáo sinh học: "The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học thế giới đề tài: The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP | Genet. Sel. Evol. 36 2004 29-48 29 INRA EDP Sciences 2004 DOI gse 2003049 Original article The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP Liviu R. ToTiRa Rohan L. Fernando b Jack . Dekkers b Soledad A. Fernandezc Bernt GuLDBRANDTSENd a Department of Animal Science Iowa State University Ames IA 50011 USA b Lawrence H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50011 USA c Department of Statistics The Ohio State University Columbus OH 43210 USA d Danish Institute of Animal Science Foulum Denmark Received 19 September 2002 accepted 13 May 2003 Abstract - Under additive inheritance the Henderson mixed model equations HMME provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction MABLUP given pedigree relationships trait and marker data. For large pedigrees with many missing markers however it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method Method A completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method Method B makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2 4 6 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci MQTL was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using

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