Cardiovascular diseases are the leading cause of death for women. Traditional risk factors (inactivity, hypertension, hypercholesterolemia, diabetes) increase the risk of CVD in both women and men. However, several female-specific risk factors, mainly related to reproductive function, have also been associated with CVD, including polycystic ovary syndrome, pregnancy complications and menopause. The mechanisms underlying the associations between female-specific risk factors and cardiometabolic disease are unknown. Elucidating the genetic architecture of an inheritable trait may help to provide insight in the role of the trait in causing disease. Lead variants from genome-wide association studies (GWAS) are often not the causal variant itself. So the top hits from any GWAS need to be annotated, interpreted, and placed within a biological context. Bioinformatics approaches and functional assays are essential for pinpointing the causal variant or the affected gene, and determining the mechanism by which the variant(s) exerts its effects. In this research, you will perform functional annotation of the current 56 GWAS variants for age at natural menopause to check whether associated SNPs cause changes in the amino acid sequence, disrupt predicted transcription factor binding sites, or have already been implicated in other diseases. Various bioinformatics annotation tools, such as ingenuity pathway analyses, gene set enrichment analyses, and online annotation tools will be used.