The genetics of boar taint

Boar taint is an offensive odour or taste that can be evident in cooked pork from non-castrated adult male pigs. It has been likened to the smell of urine, faeces and sweat. The ability to detect the taint is itself determined by genetic variation in humans, but as approximately 70% of the human population is able to detect it, there is an economic imperative for producers to control it.   

The main causes of boar taint are the molecules androstenone and skatole, which accumulate in the fat of mature non-castrated male pigs. 

The current most effective solution is to castrate young males shortly after birth. However, non-castrates are leaner with 10-30% better feed conversion efficiency and superior meat quality (Rowe et al. 2014). Furthermore, animal welfare concerns have led to a call for a Europe-wide agreement within the industry to cease castration by 2018 (http://ec.europa.eu/food/animal/welfare/farm/initiatives_en.htm)

Selective breeding to reduce androstenone and skatole levels is highly desirable, but it is only recently that these complex traits have started to be understood. Recent studies have found quantitative trait loci (QTL) associated with effects on skatole or androstenone levels, but with QTL mapped to almost every chromosome (Rowe et al. and refs therein). The genetic variation also differs between breeds - for example Duroc pigs tend to have higher levels of androstenone but Landrace has higher amounts of skatole.

Studies which use single SNP analysis with genome wide association (GWAS) are very successful at identifying new QTL but are often unable to account for  a large proportion of the genetic variation in complex traits. There is now a new method to capture this missing information, called regional heritability mapping (Uemoto et al 2013).  This method captures variance caused by QTL with low minor allelic frequency (MAF) as well as multiple independent QTL in a region. 

Rowe et al. have used both methods to study a population of 6,000 Landrace boars. They found significant associations for skatole on chromosome 14 and for androstenone on chromosome 5.  The main contributions of different SNPs to phenotypic variance are shown in the table below.  They then divided the pig genome into the 18 autosomes and jointly estimated the contribution of each to androstenone. Using this regional heritability method they find that for androstenone there is a further variation explained by chromosomes 2 and 3 that is not detected in the GWAS, and conversely that the SNP on chromosome 17 does not appear to contribute to the autosomal heritability. The total heritability summed over all autosomes was 0.29 for androstenone.

There is a bias inherent in the study design leading to an underestimate of the autosomal heritability for skatole (i.e. as only boars with very high measures of skatole and very low measures were selected for genotyping).

Table 2 in Rowe et al. 2014:

Descriptive statistics for most significant SNP effects

Chr

SNP

Pos (bp)§

P value

SNP effect

Proportion

phenotypic

 variance

Sig-fullϮ

Skatole

14

SIRI0000194

153,477,507

1.40E-09**

   −0.26

0.05

1.66E-10

8

ASGA0039716

125,083,628

0.00029

0.04

0.001

0.0018

 

5

ASGA0025182

28,884,161

0.00052

0.12

0.02

0.00011

3

ALGA0020313

103,881,028

0.00082

0.17

0.01

0.0006

 

6

MARC0040638

4,515,061

0.00144

−0.13

0.01

0.00031

Androstenone

5

H3GA0016037

20,902,965

6.82E-07**

0.26

0.04

5.17E-07

5

ASGA0025097

24,354,867

3.51E-06*

0.28

0.03

2.03E-06

17

ASGA0095898

50,429,537

1.08E-05*

−0.52

0.02

0.0001

13

ALGA0073594

203,892,414

2.38E-05*

−0.17

0.02

3.63E-05

8

ASGA0093454

80,694,489

0.0002

−0.22

0.02

0.00024

*exceeds 5% genome-wide false discovery rate **exceeds genome-wide significance threshold estimated from 100,000 permutations Ϯsignificance when tested in linear mixed model using ASReml software. § SNP position in base pairs in the Sscrofa10.2 genome assembly.

 

References

Rowe, S.J. et al., 2014, Analysis of the genetics of boar taint reveals both single SNPs and regional effects, BMC Genomics 2014 15:424, doi:10.1186/1471-2164-15-424  

Uemoto, Y. et al., The power of regional heritability analysis for rare and common variant detection: simulations and application to eye biometrical traits, Frontiers in Genetics, 2013, 4, 232, doi:10.3389/fgene.2013.00232      

 

 

Date: 27/07/2014
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