{Gene-gene Interaction Analyses for Atrial Fibrillation - Lin, H.; Mueller-Nurasyid, M.; Smith, A. V.; Arking, D. E.; Barnard, J.; Bartz, T. M.; Lunetta, K. L.; Lohman, K.; Kleber, M. E.; Lubitz, S. A.; Geelhoed, B.; Trompet, S.; et al. (Sci Rep, 2016)
{Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237 AF-free referents collected from 15 studies for discovery. We examined putative interactions between genome-wide SNPs and 17 known AF-related SNPs. The top interactions were then tested for association in an independent cohort for replication, which included more than 2,363 AF cases and 114,746 AF-free referents. One interaction, between rs7164883 at the HCN4 locus and rs4980345 at the SLC28A1 locus, was found to be significantly associated with AF in the discovery cohorts (interaction OR = 1.44, 95% CI: 1.27-1.65

{Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale - Li, X.; Li, Z.; Zhou, H.; Gaynor, S. M.; Liu, Y.; Chen, H.; Sun, R.; Dey, R.; Arnett, D. K.; Aslibekyan, S.; Ballantyne, C. M.; Bielak, L. F.; et al. (Nat Genet, 2020)
Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

{The power of genetic diversity in genome-wide association studies of lipids - Graham, S. E.; Clarke, S. L.; Wu, K. H.; Kanoni, S.; Zajac, G. J. M.; Ramdas, S.; Surakka, I.; Ntalla, I.; Vedantam, S.; Winkler, T. W.; Locke, A. E.; Marouli, E.; et al. (Nature, 2021)
application of polygenic scores in clinical practice.

{Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study - Portilla-Fernandez, E.; Klarin, D.; Hwang, S. J.; Biggs, M. L.; Bis, J. C.; Weiss, S.; Rospleszcz, S.; Natarajan, P.; Hoffmann, U.; Rogers, I. S.; Truong, Q. A.; lker, U.; et al. (Hum Mol Genet, 2022)
0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases.

{Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis - Kanoni, S.; Graham, S. E.; Wang, Y.; Surakka, I.; Ramdas, S.; Zhu, X.; Clarke, S. L.; Bhatti, K. F.; Vedantam, S.; Winkler, T. W.; Locke, A. E.; Marouli, E.; et al. (Genome Biol, 2022)
Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.\ 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.\ Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.