Reproductive traits, such as number of teats, uniformity and litter size, are essential for animal breeding programs due to the importance for the production chain, since they influence the maternal ability of sow and can affect the number of weaned piglets. Our objective was to identify candidate genes associated with reproductive traits in pigs, using GWAS data, from a systematic review combined with sequencing data, to build networks of biological processes and Gene-TFs (transcription factors networks from the identified genes in order to highlight the most candidate genes for litter size, uniformity and number of teats. In the systematic review only peer-reviewed articles were used, with descriptors related to the evaluated traits, and selected based on eligibility criteria. Fourteen papers were selected and classified into groups for functional analysis of gene networks with 2077 candidate genes identified. After combining with the list of genes presenting known structural variants in the 5'UTR and/or coding region, 306 genes remained to be used to build the networks of biological processes and TFs gene networks, highlighting processes associated with litter size (e.g., ionotropic glutamate receptor signaling pathway and blastocyte growth) and teats number (e.g., growth hormone receptor, regulation of the BMP - Bone Morphogenetic Proteins signaling pathway and blood vessel proliferation). Three most candidate genes for litter size trait (GRID2and PALB2) and six most candidate genes (GHR, IFT80, FSTL3, SKOR1, SMURF1 and AKT3) for teats number were prioritized. TF associated with candidate genes were also identified for litter size (PALB2 and GRID2) and teat number (RIN, LTBP2 and COL6A6). Thus, it is suggested that the genes and TFs presented in this study may play an important role in the traits studied, being important for genetic studies and animal breeding. The highlighted genes may bring new considerations to the current knowledge of the genetic architecture of these traits, since the markers associated with these genes can be assigned higher weights in genomic selection and validated in specific populations.