This project involves the analysis of somatic variants, in particular clonal hematopoiesis driver mutations (CHDMs), in whole-genome sequencing (WGS) data from 117 individuals in 22 BMI-discordant families (N = 117). In each family, there is at least one sibling-pair with 10 units difference in BMI; one sibling with normal weight and one with obesity. We have previously shown that, in individuals with obesity, prevalence of clonal hematopoiesis is higher and that obesity-related metabolic dysfunction promote clone growth (Andersson-Assarsson, EBioMed 2023, doi: 10.1016/j.ebiom.2023.104621). The main aim in this project is to evaluate prevalence and frequency of somatic variants, in particular clonal hematopoiesis driver mutations (CHDMs), in a cohort of BMI-discordant families. We will correlate prevalence and frequency to BMI and other metabolic parameters as well as obesity-related disease. We will also take advantage of the family structure in our data and evaluate if there is a heritable sensitivity to development of such mutations. The mutations occur in somatic cells and are hence not inherited, but previous data in the literature indicate that heritable variants influence the risk of developing CHDMs. Somatic genetic variants in the WGS data will be analyzed using somatic variant calling tools. For detection of single-nucleotide variants (SNVs) and short insertions and deletions, GATK Mutect2 algorithm will be used. To control for common germline variants, gnomAD allele frequencies. Custom scripts (python, R, and shell) will be used to filter out false positive somatic variants and determine the presence of clonal hematopoiesis. To identify somatic structural variants, we will utilize SvABA. These analyses will require other bioinformatic tools such as bwa-mem and samtools. Further processing structural variant calls will be done by custom scripts (shell, python, and R).