Siddhartha Kar

This study identifies seven novel genetic loci associated with high-grade serous ovarian cancer risk using a large-scale GWAS and develops polygenic scores for improved risk prediction.

This study performs cis- and trans-eQTL TWAS on breast and ovarian cancer data to identify 101 and 8 risk-associated genes, respectively, with several driven by trans-eQTLs.

This study investigates the relationship between genetically predicted leukocyte telomere length and lung cancer risk, finding a causal association and novel susceptibility loci.

This study identifies eight novel lung cancer susceptibility loci and shows that germline variants related to smoking and DNA repair influence somatic mutation burden in lung tumors.

chromMAGMA identifies candidate risk genes for epithelial ovarian cancer by mapping risk variants to noncoding regulatory elements, revealing new genes and pathways involved in the disease.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper identifies RCCD1 as a susceptibility gene for breast and ovarian cancer by fine-mapping a risk locus and showing its expression and interaction patterns are associated with cancer risk.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.

This study uses Mendelian randomisation to examine the causal effects of BMI, body fat composition, and height on cancer risk, finding limited evidence for BMI's role but associations for FMI and height.

This study fine-maps 150 breast cancer risk regions to identify 178 high confidence target genes using genomic and functional annotations.

The paper identifies 14 genes, including NTN4, associated with breast cancer risk variants through allelic expression imbalance analysis.

The paper identifies genetic determinants of mosaic Y chromosome loss in blood and links it to genomic instability and non-haematological cancer risk.

This study identifies 21 novel candidate susceptibility genes for high grade serous ovarian cancer using transcriptome-wide association analysis across multiple tissue types.