¹Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Department of Crop Genetics and Breeding, Nanjing Agricultural University, Nanjing, China

Correspondence: Dr Y-M Zhang, College of Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, PR China. E-mail: soyzhang@njau.edu.cn

Received 11 February 2009; Revised 18 April 2009; Accepted 30 April 2009; Published online 3 June 2009.

Abstract

The in silico mapping (ISM) technique and its extension represent major advances for novel gene discovery in germplasm resources of inbred lines. However, the techniques suffer from a relatively high false-positive rate (FPR) and they do not consider the effect of linkage disequilibrium (LD) markers around the identified quantitative trait locus (QTL). In addition, it has not yet been established whether it is optimal to use absolute trait differences as the response variable. To address these problems, this article presents the multiple loci ISM (MLISM) approach, which uses all markers on the entire genome, along with a penalized maximum likelihood. The method proposed here was verified by a series of simulation experiments with a maize pedigree population of inbred lines of known ancestry. Results from the simulated studies show that the best response variable is the trait product. The MLISM FPR is substantially decreased and the proportion of the number of false QTL to the number of LD markers around the identified QTL is adequately reduced. The MLISM method, with the trait product as the response variable, is an improvement on the existing methods for novel QTL mapping in germplasm resources of inbred lines.