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

Rubio, Y. L. Bernal; Duarte, José Luis Gualdrón; Bates, R. O.; Ernst, Catherine W.; Nonneman, Dan; Rohrer, G. A.; King, D. A.; Shackelford, S. D.; Wheeler, T. L.; Cantet, Rodolfo Juan Carlos; Steibel, Juan P.

doi:10.2527/jas.2015-9502

Cited by 17 publications

(14 citation statements)

References 73 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…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.…”

Section: Resultsmentioning

confidence: 66%

“…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.…”

Section: Resultsmentioning

confidence: 99%

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

González-Prendes

Quintanilla

Cánovas

et al. 2017

Sci Rep

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Meat quality traits have an increasing importance in the pig industry because of their strong impact on consumer acceptance. Herewith, we have combined phenotypic and microarray expression data to map loci with potential effects on five meat quality traits recorded in the longissimus dorsi (LD) and gluteus medius (GM) muscles of 350 Duroc pigs, i.e. pH at 24 hours post-mortem (pH24), electric conductivity (CE) and muscle redness (a*), lightness (L*) and yellowness (b*). We have found significant genome-wide associations for CE of LD on SSC4 (~104 Mb), SSC5 (~15 Mb) and SSC13 (~137 Mb), while several additional regions were significantly associated with meat quality traits at the chromosome-wide level. There was a low positional concordance between the associations found for LD and GM traits, a feature that reflects the existence of differences in the genetic determinism of meat quality phenotypes in these two muscles. The performance of an eQTL search for SNPs mapping to the regions associated with meat quality traits demonstrated that the GM a* SSC3 and pH24 SSC17 QTL display positional concordance with cis-eQTL regulating the expression of several genes with a potential role on muscle metabolism.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Gebreyesus

Buitenhuis

Poulsen

et al. 2019

Journal of Dairy Science

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In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the populationspecific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multipopulation data sets to undertake joint GWAS.

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

Cited by 17 publications

References 73 publications

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

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

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

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

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