Implementing meta-analysis from genome-wide association studies for pork quality traits1

Abstract

Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a meta-analysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining -scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [] and calpastatin []), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit []). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial []) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [] and ferritin, light polypeptide []). Thus, implementation of MA-GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.

Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

“…Meta-analysis of genome-wide association studies results can increase the power to detect association signals by increasing sample size. The use of this approach grew substantially in the genomics field in the last decade as the scientific community recognized the value of collaborating to combine genetic resources [ 6 , 8 – 10 ]. The output of the inverse variance based meta-analysis strategy is dependent on the standard errors of each SNP in each of the study because the weight assigned to each variant is being calculated as the inverse of the squared standard error.…”

“…The MA is widely used in human genetics, where access to the original datasets is usually limited due to privacy protection policies. In the last years, the meta-analysis has been employed for pig association studies in an effort to maximize the use of available genomic information from commercial or experimental pig populations [ 8 – 10 ].…”

Joint QTL mapping and gene expression analysis identify positional candidate genes influencing pork quality traits

“…For instance, when we compared our a*, b* and pH 24 data with those described in six additional GWAS studies4629303132 we only found one positional coincidence between the SSC10 (70.6 Mb) genomic region associated with LD a* in the Lipgen population (Table 3) and the SSC10 (72.8 Mb) region identified by Ma et al 4. as associated with the same trait in the semimembranosus muscle of White Duroc × Erhualian F 2 pigs.…”

“…These shared regions were: (a*) SSC4 (80–85 Mb)630, SSC6 (17–22 Mb)430, SSC7 (31–32 Mb)431, SSC12 (58–63 Mb)3031, SSC15 (133–136 Mb)303132; (b*) SSC15 (129–133 Mb)3032; and (pH 24 ), SSC3 (15–19 Mb)3031, SSC15 (133–136 Mb)2932. This latter region on SSC15 (133–136 Mb) appeared to be associated with a*, b*, pH 24 , shear force and cook loss in many independent studies29303132 but not in ours. Interestingly, this SSC15 region contains the protein kinase AMP-activated non-catalytic subunit gamma 3 ( PRKAG3 ) gene, whose polymorphism has causal effects on muscle glycogen depletion, a parameter that can have a strong influence on meat quality traits33.…”

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

“…Combining data sets for large-scale joint GWAS has been used as an effective method to increase GWAS power in human disease association studies (Major Depressive Disorder Working Group, 2013;Sung et al, 2014) and to some extent in livestock studies (Veerkamp et al, 2012). An alternative approach for the discovery of QTL for common human diseases (Begum et al, 2012) and livestock phenotypes (Bernal Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits Rubio et al, 2015;Bouwman et al, 2018) has been the meta-analysis of individual GWAS. Different methods of meta-analysis have been proposed, depending on the sources of information used and assumptions regarding SNP effects in the different populations (Whitlock, 2005;Han and Eskin, 2011).…”