|Title||After genome-wide association studies : Gene networks elucidating candidate genes divergences for number of teats across two pig populations|
|Author(s)||Verardo, L.L.; Lopes, M.S.; Wijga, S.; Madsen, O.; Silva, F.F.; Groenen, M.A.M.; Knol, E.F.; Lopes, P.S.; Guimarães, S.E.F.|
|Source||Journal of Animal Science 94 (2016)4. - ISSN 0021-8812 - p. 1446 - 1458.|
Animal Breeding and Genomics
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Complex trait - Genetic diversity - Genomewide association study|
Number of teats (NT) is an important trait affecting both piglet’s welfare and the production level of pig farms. Biologically, embryonic mammary gland development requires the coordination of many signaling pathways necessary for the proper development of teats. Several QTL for NT have been identified; however, further analysis is still lacking. Therefore, gene networks derived from genomewide association study (GWAS) results can be used to examine shared pathways and functions of putative candidate genes. Besides, such analyses may also be helpful to understand the genetic diversity between populations for the same trait or traits. In this study, we identified significant SNP for Landrace-based (line C) and Large White–based (line D) dam lines. Besides, gene– transcription factor (TF) networks were constructed aiming to obtain the most likely candidate genes for NT in each line followed by a comparative analysis between both lines to access similarities or dissimilarities at the marker and gene level. We identified 24 and 19 significant SNP (Bayes factor ≥ 100) for lines C and D, respectively. Only 1 significant SNP overlapped both lines. Network analysis illustrated gene interactions consistent with known mammal’s breast biology and captured known TF. We observed different sets of putative candidate genes for NT in each line evaluated that may have common effects on the phenotype. Based on these results, we demonstrated the importance of post-GWAS analyses increasing the biological understanding of relevant genes for a complex trait. Moreover, we believe that this genomic diversity across lines should be taken into account, considering breed-specific reference populations for genomic selection.