Test for positional candidate genes for body composition on pig chromosome 6

Óvilo, Cristina; Oliver, Angels; Noguera, J. L.; Clop, Alex; Barragán, C.; Varona, L.; Rodrı́guez, Carmen; Toro, Miguel Á; Sánchez, Armand; Pérez‐Enciso, Miguel; Silió, L.

doi:10.1186/1297-9686-34-4-465

Cited by 76 publications

(10 citation statements)

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“…Our results for backfat thickness traits were in agreement with other studies (Malek et al, 2001b; Óvilo et al, 2002b; Varona et al, 2002) that identified QTL for fatness traits in this region of SSC6. This region includes the leptin receptor ( LEPR ) gene which is considered as a potential candidate gene for fatness (Ernst et al, 1997; Óvilo et al, 2005; Mohrmann et al, 2006), and studies to identify a causal mutation in LEPR have been conducted (Mackowski et al, 2005; Muñoz et al, 2009).…”

Section: Discussionsupporting

confidence: 93%

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

Choi¹,

Steibel²,

Bates³

et al. 2011

Front. Gene.

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A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F2 animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F2 animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL.

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Section: Discussionsupporting

confidence: 93%

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

Choi¹,

Steibel²,

Bates³

et al. 2011

Front. Gene.

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“…In fact, a highly significant association of a polymorphism located in exon 14, LEPR c. 1987 C > T, with growth and fatness has been previously found in several generations of the IBMAP population [11,12]. These effects have been also confirmed in other porcine populations [58-61].…”

Section: Discussionmentioning

confidence: 70%

“…The LEPR gene is the most powerful candidate underlying the QTL for fatness and conformation traits mapped in SSC6 in the F3 + BC2 generation. In fact, a highly significant association of a polymorphism located in exon 14, LEPR c. 1987 C > T, with growth and fatness has been previously found in several generations of the IBMAP population [ 11 , 12 ]. These effects have been also confirmed in other porcine populations [ 58 - 61 ].…”

Section: Discussionmentioning

confidence: 99%

Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip

Fernández¹,

Pérez-Montarelo²,

Barragán³

et al. 2012

BMC Genet

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BackgroundThe 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.ResultsThree 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.ConclusionsThe 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.

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“…For instance, a genome wide LD scan can be carried out by including also a polygenic effect. Alternatively, a combined linkage/LD analyses can be implemented [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Qxpak.5: Old mixed model solutions for new genomics problems

Pérez‐Enciso

Misztal

2011

BMC Bioinformatics

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BackgroundMixed models have a long and fruitful history in statistics. They are pertinent to genomics problems because they are highly versatile, accommodating a wide variety of situations within the same theoretical and algorithmic framework.ResultsQxpak is a package for versatile statistical genomics, specifically designed for sophisticated quantitative trait loci and association analyses. Multiple loci, multiple trait, infinitesimal genetic effects, imprinting, epistasis or sex linked loci can be fitted. The new version (v. 5) allows us, among other new features, to include either relationship matrices obtained with molecular information or user defined matrices that can be read from an input file. This feature can be used for genome selection or - more importantly - to correct for population structure in association studies. In crosses, two parental lines, not necessarily inbred, can be accommodated.ConclusionsThis software aims at simplifying statistical genetic analyses implementing a coherent and unified approach by mixed models. It provides a tool that can be used in a wide variety of situations with ample genetic and statistical modeling flexibility. The software, a complete manual and examples are available at http://www.icrea.cat/Web/OtherSectionViewer.aspx?key=485&titol=Software:Qxpak.

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Test for positional candidate genes for body composition on pig chromosome 6

Cited by 76 publications

References 20 publications

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

Genome-wide linkage analysis of QTL for growth and body composition employing the PorcineSNP60 BeadChip

Qxpak.5: Old mixed model solutions for new genomics problems

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