Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip
1 Departamento de Mejora Genética Animal, INIA, Ctra. De la Coruña km. 7, Madrid, 28040, Spain
2 Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, UAB, 08193, Bellaterra, Spain. Present address: Consorci CSIC-IRTA-UAB (Centre de Recerca en Agrigenòmica), Edifici CRAG, Campus UAB, Bellaterra, Spain
3 Centre for Research in Agricultural Genomics (CRAG), Consortium CSIC-IRTA-UAB-UB. Edifici CRAG, Campus Universitat Autonoma Barcelona, 08193, Bellaterra, Spain
4 Genètica i Millora Animal, IRTA Lleida, 25198, Lleida, Spain
BMC Genetics 2012, 13:41 doi:10.1186/1471-2156-13-41Published: 20 May 2012
The traditional strategy to map QTL is to use linkage analysis employing a limited number of markers. These analyses report wide QTL confidence intervals, making very difficult to identify the gene and polymorphisms underlying the QTL effects. The arrival of genome-wide panels of SNPs makes available thousands of markers increasing the information content and therefore the likelihood of detecting and fine mapping QTL regions. The aims of the current study are to confirm previous QTL regions for growth and body composition traits in different generations of an Iberian x Landrace intercross (IBMAP) and especially identify new ones with narrow confidence intervals by employing the PorcineSNP60 BeadChip in linkage analyses.
Three generations (F3, Backcross 1 and Backcross 2) of the IBMAP and their related animals were genotyped with PorcineSNP60 BeadChip. A total of 8,417 SNPs equidistantly distributed across autosomes were selected after filtering by quality, position and frequency to perform the QTL scan. The joint and separate analyses of the different IBMAP generations allowed confirming QTL regions previously identified in chromosomes 4 and 6 as well as new ones mainly for backfat thickness in chromosomes 4, 5, 11, 14 and 17 and shoulder weight in chromosomes 1, 2, 9 and 13; and many other to the chromosome-wide signification level. In addition, most of the detected QTLs displayed narrow confidence intervals, making easier the selection of positional candidate genes.
The use of higher density of markers has allowed to confirm results obtained in previous QTL scans carried out with microsatellites. Moreover several new QTL regions have been now identified in regions probably not covered by markers in previous scans, most of these QTLs displayed narrow confidence intervals. Finally, prominent putative biological and positional candidate genes underlying those QTL effects are listed based on recent porcine genome annotation.