Multivariate mixed inheritance models for QTL detection on porcine chromosome 6

Szyda, Joanna; Grindflek, Eli; Liu, Zengting; Lien, Sigbjørn

doi:10.1017/s0016672302006043

Cited by 22 publications

(19 citation statements)

References 31 publications

(34 reference statements)

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“…In the same study, QTLs from a sensory panel for subjective pork flavour and boar flavour in lean meat were described that are also located nearby the area in our study. Finally, Szyda et al (2003)[38] identified a QTL for smell intensity in a Duroc X Norwegian Landrace cross covering the area of interest. Considering the low resolution of these QTL studies, it is not possible to conclude whether they might be caused by the same genes segregating as in our study.…”

Section: Discussionmentioning

confidence: 99%

A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6

Duijvesteijn¹,

Knol²,

Merks³

et al. 2010

BMC Genet

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BackgroundIn many countries, male piglets are castrated shortly after birth because a proportion of un-castrated male pigs produce meat with an unpleasant flavour and odour. Main compounds of boar taint are androstenone and skatole. The aim of this high-density genome-wide association study was to identify single nucleotide polymorphisms (SNPs) associated with androstenone levels in a commercial sire line of pigs. The identification of major genetic effects causing boar taint would accelerate the reduction of boar taint through breeding to finally eliminate the need for castration.ResultsThe Illumina Porcine 60K+SNP Beadchip was genotyped on 987 pigs divergent for androstenone concentration from a commercial Duroc-based sire line. The association analysis with 47,897 SNPs revealed that androstenone levels in fat tissue were significantly affected by 37 SNPs on pig chromosomes SSC1 and SSC6. Among them, the 5 most significant SNPs explained together 13.7% of the genetic variance in androstenone. On SSC6, a larger region of 10 Mb was shown to be associated with androstenone covering several candidate genes potentially involved in the synthesis and metabolism of androgens. Besides known candidate genes, such as cytochrome P450 A19 (CYP2A19), sulfotransferases SULT2A1, and SULT2B1, also new members of the cytochrome P450 CYP2 gene subfamilies and of the hydroxysteroid-dehydrogenases (HSD17B14) were found. In addition, the gene encoding the ß-chain of the luteinizing hormone (LHB) which induces steroid synthesis in the Leydig cells of the testis at onset of puberty maps to this area on SSC6. Interestingly, the gene encoding the α-chain of LH is also located in one of the highly significant areas on SSC1.ConclusionsThis study reveals several areas of the genome at high resolution responsible for variation of androstenone levels in intact boars. Major genetic factors on SSC1 and SSC6 showing moderate to large effects on androstenone concentration were identified in this commercial breeding line of pigs. Known and new candidate genes cluster especially on SSC6. For one of the most significant SNP variants, the difference in the proportion of animals surpassing the threshold of consumer acceptance between the two homozygous genotypes was as much as 15.6%.

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Section: Discussionmentioning

confidence: 99%

A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6

Duijvesteijn¹,

Knol²,

Merks³

et al. 2010

BMC Genet

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“…Most of the multi-trait QTL mapping approaches model the genetic (co)variances by an unstructured model (Jiang and Zeng 1995;Korol et al 1998;Knott and Haley 2000;Hackett et al 2001;Lund et al 2003), which in practice limits the number of traits that can be handled. It is symptomatic that the applications of multi-trait QTL mapping under the use of unstructured (co)variance matrices never included more than just a few traits, i.e., mostly 2-5 (Calinski et al 2000;Hackett et al 2001;Szyda et al 2003;Mercadé et al 2005;Olsen et al 2005). With few traits and/or environments, the unstructured model is a possible option, although not necessarily the optimal one.…”

Section: Discussionmentioning

confidence: 99%

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

et al. 2007

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Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance-covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.

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“…Fatness is one of the most crucial variables determining meat quality. Moreover, QTL related to fatness and meat quality traits, such as intramuscular fat content (IMF; de Koning et al, 1999de Koning et al, , 2000Gerbens et al, 1999Gerbens et al, , 2000Ovilo et al, 2000;Grindñek et al, 2001;Uleberg et al, 2005) and backfat thickness (BFT; Malek et al, 2001;Ovilo et al, 2002a;Szyda et al, 2003;Soma et al, 2011) have been identified on SSG6. Most QTL results have been integrated into Animal QTLdb (littp://www.animalgenome.org/QTLdb) by trait ontology and individual chromosomes (Hu et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Single nucleotide polymorphism association study for backfat and intramuscular fat content in the region between SW2098 and SW1881 on pig chromosome 61

Lee

Byun

Kang

et al. 2012

Journal of Animal Science

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This study was carried out to identify SNP associated with fatness traits on pig chromosome 6. In total, 11,067 putative genomic variations were detected in 125 complete bacterial artificial chromosome sequences corresponding to the region between SW2098 and SW1881, which harbors multiple QTL affecting intramuscular fat content (IMF) and backfat thickness (BFT). Among 173 putative SNP validated by MassArray, 120 SNP were used in an association study on 541 offspring produced by a cross of Korean native pig and Landrace breeds. The significance level of each SNP was determined using single marker regression analysis. Further, significant threshold values were determined using a false discovery rate. Nine out of 120 SNP showed significant effects on BFT or IMF or both. Of the 9 significant SNP, 4 were significantly associated with IMF, 7 were significantly related to BFT, and 2 SNP (Kps8172 and Kps6413) showed significant effects on both traits. Moreover, multiple regression analysis considering all significant SNP was used to correct spurious false positives due to linkage disequilibrium. Consequently, only 1 SNP (Kps6413) was significant for IMF, whereas 4 SNP including Kps6413 showed significant effects on BFT. The significant SNP had generally additive effects and on average explained 1.72% of the genetic variation for IMF and 3.92% for BFT, respectively. These markers can potentially be applied in pig breeding programs for improving IMF and BFT traits after validation in other populations.

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Multivariate mixed inheritance models for QTL detection on porcine chromosome 6

Cited by 22 publications

References 31 publications

A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6

A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Single nucleotide polymorphism association study for backfat and intramuscular fat content in the region between SW2098 and SW1881 on pig chromosome 61

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