Lindsay Guare

All Publications

• Multi-ancestry sequencing analysis in 293,141 participants identifies predisposition DNA repair genes associated with HCC risk. JHEP reports : innovation in hepatology Garofalo, A. M., Chotiprasidhi, P., Johnson, J. P., Sato-Espinoza, K., Ma, J., Miller, H., O'Brien, D., Guare, L., Cardone, K. M., Palmiero, N., Rodriguez, Z., Kaplan, D. E., Lynch, J. A., Tsao, P. S., Rader, D. J., Roberts, L. R., Debes, J. D., Odeghe, E., Lesi, F., Oyeleke, G., Mattos, Â. Z., Arrese, M., Carrera, E., Prieto, J., Boonstra, A., Diaz-Ferrer, J., Okeke, E. N., Wang, J., Hou, L., Agyei-Nkansah, A., Afihene, M. Y., Awuku, Y. A., Nyanga, A., Chang, K. M., Antwi, S. O., Vujković, M., Verma, A., Wangensteen, K. J. 2026: 102019

  Abstract

  Genetic testing for Lynch and BRCA1/2-associated hereditary cancer syndromes is recommended in colon or pancreatic cancer patients, but their association with hepatocellular carcinoma (HCC) risk is unknown. We evaluated associations between rare germline variants in DNA-repair genes and HCC risk across ancestrally diverse cohorts.We analyzed whole exome (WES) and whole genome sequencing (WGS) data from 2,594 HCC cases and 290,547 cancer-free controls from diverse biobanks and cohorts: Penn Medicine BioBank, All of Us, Mayo Clinic, ESCALON, and the Million Veteran Program. Participants were classified into six population groups. We focused on six DNA-repair genes previously implicated in HCC: BRCA2, BRIP1, MSH6, PMS2, CHEK2, and FANCA. Gene-level burden analyses of rare predicted loss-of-function (pLoF) and damaging missense variants were performed in European and African populations, and across all six ancestry groups.In the European population, MSH6, a Lynch syndrome-associated gene, had the strongest association with HCC, with a 2.75-fold increased HCC risk at 1% minor-allele frequency (MAF) (OR= 2.75 [1.50, 5.04], P=0.001, FDR q=0.02), while PMS2, showed a nominally significant association at the same MAF threshold (OR=1.90 [1.08, 3.34], P=0.03, FDR q=0.09). Combined analysis across all populations strengthened the MSH6 finding (OR=2.53 [1.43, 4.49], P=0.001, FDR q=0.01) at a MAF of 0.1%. A significant BRCA2 association was also observed in the combined analysis at a MAF of 0.1% (OR=2.26 [1.39, 3.67], P=0.001, FDR q=0.01).Rare variants in MSH6 and BRCA2 are significantly associated with increased HCC risk, revealing a previously unconfirmed role for DNA repair genes in HCC susceptibility across ancestrally diverse populations. These findings may inform genetic risk stratification and surveillance strategies.Rare variants in MSH6 and BRCA2 genes are associated with 2.3 to 2.8-fold higher HCC risk. These findings may warrant further evaluation of liver cancer risk in individuals with MSH6-associated Lynch syndrome or BRCA2-associated hereditary cancer syndromes.

  View details for DOI 10.1016/j.jhepr.2026.102019

  View details for PubMedID 42667987

• Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science (New York, N.Y.) Verma, A., Huffman, J. E., Rodriguez, A., Conery, M., Liu, M., Ho, Y. L., Kim, Y., Heise, D. A., Guare, L., Panickan, V. A., Garcon, H., Linares, F., Costa, L., Goethert, I., Tipton, R., Honerlaw, J., Davies, L., Whitbourne, S., Cohen, J., Posner, D. C., Sangar, R., Murray, M., Wang, X., Dochtermann, D. R., Devineni, P., Shi, Y., Nandi, T. N., Assimes, T. L., Brunette, C. A., Carroll, R. J., Clifford, R., Duvall, S., Gelernter, J., Hung, A., Iyengar, S. K., Joseph, J., Kember, R., Kranzler, H., Kripke, C. M., Levey, D., Luoh, S. W., Merritt, V. C., Overstreet, C., Deak, J. D., Grant, S. F., Polimanti, R., Roussos, P., Shakt, G., Sun, Y. V., Tsao, N., Venkatesh, S., Voloudakis, G., Justice, A., Begoli, E., Ramoni, R., Tourassi, G., Pyarajan, S., Tsao, P., O'Donnell, C. J., Muralidhar, S., Moser, J., Casas, J. P., Bick, A. G., Zhou, W., Cai, T., Voight, B. F., Cho, K., Gaziano, J. M., Madduri, R. K., Damrauer, S., Liao, K. P. 2024; 385 (6706): eadj1182

  Abstract

  One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

  View details for DOI 10.1126/science.adj1182

  View details for PubMedID 39024449

• Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature Suzuki, K., Hatzikotoulas, K., Southam, L., Taylor, H. J., Yin, X., Lorenz, K. M., Mandla, R., Huerta-Chagoya, A., Melloni, G. E., Kanoni, S., Rayner, N. W., Bocher, O., Arruda, A. L., Sonehara, K., Namba, S., Lee, S. S., Preuss, M. H., Petty, L. E., Schroeder, P., Vanderwerff, B., Kals, M., Bragg, F., Lin, K., Guo, X., Zhang, W., Yao, J., Kim, Y. J., Graff, M., Takeuchi, F., Nano, J., Lamri, A., Nakatochi, M., Moon, S., Scott, R. A., Cook, J. P., Lee, J. J., Pan, I., Taliun, D., Parra, E. J., Chai, J. F., Bielak, L. F., Tabara, Y., Hai, Y., Thorleifsson, G., Grarup, N., Sofer, T., Wuttke, M., Sarnowski, C., Gieger, C., Nousome, D., Trompet, S., Kwak, S. H., Long, J., Sun, M., Tong, L., Chen, W. M., Nongmaithem, S. S., Noordam, R., Lim, V. J., Tam, C. H., Joo, Y. Y., Chen, C. H., Raffield, L. M., Prins, B. P., Nicolas, A., Yanek, L. R., Chen, G., Brody, J. A., Kabagambe, E., An, P., Xiang, A. H., Choi, H. S., Cade, B. E., Tan, J., Broadaway, K. A., Williamson, A., Kamali, Z., Cui, J., Thangam, M., Adair, L. S., Adeyemo, A., Aguilar-Salinas, C. A., Ahluwalia, T. S., Anand, S. S., Bertoni, A., Bork-Jensen, J., Brandslund, I., Buchanan, T. A., Burant, C. F., Butterworth, A. S., Canouil, M., Chan, J. C., Chang, L. C., Chee, M. L., Chen, J., Chen, S. H., Chen, Y. T., Chen, Z., Chuang, L. M., Cushman, M., Danesh, J., Das, S. K., de Silva, H. J., Dedoussis, G., Dimitrov, L., Doumatey, A. P., Du, S., Duan, Q., Eckardt, K. U., Emery, L. S., Evans, D. S., Evans, M. K., Fischer, K., Floyd, J. S., Ford, I., Franco, O. H., Frayling, T. M., Freedman, B. I., Genter, P., Gerstein, H. C., Giedraitis, V., González-Villalpando, C., González-Villalpando, M. E., Gordon-Larsen, P., Gross, M., Guare, L. A., Hackinger, S., Hakaste, L., Han, S., Hattersley, A. T., Herder, C., Horikoshi, M., Howard, A. G., Hsueh, W., Huang, M., Huang, W., Hung, Y. J., Hwang, M. Y., Hwu, C. M., Ichihara, S., Ikram, M. A., Ingelsson, M., Islam, M. T., Isono, M., Jang, H. M., Jasmine, F., Jiang, G., Jonas, J. B., Jørgensen, T., Kamanu, F. K., Kandeel, F. R., Kasturiratne, A., Katsuya, T., Kaur, V., Kawaguchi, T., Keaton, J. M., Kho, A. N., Khor, C. C., Kibriya, M. G., Kim, D. H., Kronenberg, F., Kuusisto, J., Läll, K., Lange, L. A., Lee, K. M., Lee, M. S., Lee, N. R., Leong, A., Li, L., Li, Y., Li-Gao, R., Ligthart, S., Lindgren, C. M., Linneberg, A., Liu, C. T., Liu, J., Locke, A. E., Louie, T., Luan, J., Luk, A. O., Luo, X., Lv, J., Lynch, J. A., Lyssenko, V., Maeda, S., Mamakou, V., Mansuri, S. R., Matsuda, K., Meitinger, T., Melander, O., Metspalu, A., Mo, H., Morris, A. D., Moura, F. A., Nadler, J. L., Nalls, M. A., Nayak, U., Ntalla, I., Okada, Y., Orozco, L., Patel, S. R., Patil, S., Pei, P., Pereira, M. A., Peters, A., Pirie, F. J., Polikowsky, H. G., Porneala, B., Prasad, G., Rasmussen-Torvik, L. J., Reiner, A. P., Roden, M., Rohde, R., Roll, K., Sabanayagam, C., Sandow, K., Sankareswaran, A., Sattar, N., Schönherr, S., Shahriar, M., Shen, B., Shi, J., Shin, D. M., Shojima, N., Smith, J. A., So, W. Y., Stančáková, A., Steinthorsdottir, V., Stilp, A. M., Strauch, K., Taylor, K. D., Thorand, B., Thorsteinsdottir, U., Tomlinson, B., Tran, T. C., Tsai, F. J., Tuomilehto, J., Tusie-Luna, T., Udler, M. S., Valladares-Salgado, A., van Dam, R. M., van Klinken, J. B., Varma, R., Wacher-Rodarte, N., Wheeler, E., Wickremasinghe, A. R., van Dijk, K. W., Witte, D. R., Yajnik, C. S., Yamamoto, K., Yamamoto, K., Yoon, K., Yu, C., Yuan, J. M., Yusuf, S., Zawistowski, M., Zhang, L., Zheng, W., Raffel, L. J., Igase, M., Ipp, E., Redline, S., Cho, Y. S., Lind, L., Province, M. A., Fornage, M., Hanis, C. L., Ingelsson, E., Zonderman, A. B., Psaty, B. M., Wang, Y. X., Rotimi, C. N., Becker, D. M., Matsuda, F., Liu, Y., Yokota, M., Kardia, S. L., Peyser, P. A., Pankow, J. S., Engert, J. C., Bonnefond, A., Froguel, P., Wilson, J. G., Sheu, W. H., Wu, J. Y., Hayes, M. G., Ma, R. C., Wong, T. Y., Mook-Kanamori, D. O., Tuomi, T., Chandak, G. R., Collins, F. S., Bharadwaj, D., Paré, G., Sale, M. M., Ahsan, H., Motala, A. A., Shu, X. O., Park, K. S., Jukema, J. W., Cruz, M., Chen, Y. I., Rich, S. S., McKean-Cowdin, R., Grallert, H., Cheng, C. Y., Ghanbari, M., Tai, E. S., Dupuis, J., Kato, N., Laakso, M., Köttgen, A., Koh, W. P., Bowden, D. W., Palmer, C. N., Kooner, J. S., Kooperberg, C., Liu, S., North, K. E., Saleheen, D., Hansen, T., Pedersen, O., Wareham, N. J., Lee, J., Kim, B. J., Millwood, I. Y., Walters, R. G., Stefansson, K., Ahlqvist, E., Goodarzi, M. O., Mohlke, K. L., Langenberg, C., Haiman, C. A., Loos, R. J., Florez, J. C., Rader, D. J., Ritchie, M. D., Zöllner, S., Mägi, R., Marston, N. A., Ruff, C. T., van Heel, D. A., Finer, S., Denny, J. C., Yamauchi, T., Kadowaki, T., Chambers, J. C., Ng, M. C., Sim, X., Below, J. E., Tsao, P. S., Chang, K. M., McCarthy, M. I., Meigs, J. B., Mahajan, A., Spracklen, C. N., Mercader, J. M., Boehnke, M., Rotter, J. I., Vujkovic, M., Voight, B. F., Morris, A. P., Zeggini, E. 2024

  Abstract

  Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

  View details for DOI 10.1038/s41586-024-07019-6

  View details for PubMedID 38374256

  View details for PubMedCentralID 6518376

• Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease. Cell genomics Zhou, W., Kanai, M., Wu, K. H., Rasheed, H., Tsuo, K., Hirbo, J. B., Wang, Y., Bhattacharya, A., Zhao, H., Namba, S., Surakka, I., Wolford, B. N., Lo Faro, V., Lopera-Maya, E. A., Läll, K., Favé, M. J., Partanen, J. J., Chapman, S. B., Karjalainen, J., Kurki, M., Maasha, M., Brumpton, B. M., Chavan, S., Chen, T. T., Daya, M., Ding, Y., Feng, Y. A., Guare, L. A., Gignoux, C. R., Graham, S. E., Hornsby, W. E., Ingold, N., Ismail, S. I., Johnson, R., Laisk, T., Lin, K., Lv, J., Millwood, I. Y., Moreno-Grau, S., Nam, K., Palta, P., Pandit, A., Preuss, M. H., Saad, C., Setia-Verma, S., Thorsteinsdottir, U., Uzunovic, J., Verma, A., Zawistowski, M., Zhong, X., Afifi, N., Al-Dabhani, K. M., Al Thani, A., Bradford, Y., Campbell, A., Crooks, K., de Bock, G. H., Damrauer, S. M., Douville, N. J., Finer, S., Fritsche, L. G., Fthenou, E., Gonzalez-Arroyo, G., Griffiths, C. J., Guo, Y., Hunt, K. A., Ioannidis, A., Jansonius, N. M., Konuma, T., Lee, M. T., Lopez-Pineda, A., Matsuda, Y., Marioni, R. E., Moatamed, B., Nava-Aguilar, M. A., Numakura, K., Patil, S., Rafaels, N., Richmond, A., Rojas-Muñoz, A., Shortt, J. A., Straub, P., Tao, R., Vanderwerff, B., Vernekar, M., Veturi, Y., Barnes, K. C., Boezen, M., Chen, Z., Chen, C. Y., Cho, J., Smith, G. D., Finucane, H. K., Franke, L., Gamazon, E. R., Ganna, A., Gaunt, T. R., Ge, T., Huang, H., Huffman, J., Katsanis, N., Koskela, J. T., Lajonchere, C., Law, M. H., Li, L., Lindgren, C. M., Loos, R. J., MacGregor, S., Matsuda, K., Olsen, C. M., Porteous, D. J., Shavit, J. A., Snieder, H., Takano, T., Trembath, R. C., Vonk, J. M., Whiteman, D. C., Wicks, S. J., Wijmenga, C., Wright, J., Zheng, J., Zhou, X., Awadalla, P., Boehnke, M., Bustamante, C. D., Cox, N. J., Fatumo, S., Geschwind, D. H., Hayward, C., Hveem, K., Kenny, E. E., Lee, S., Lin, Y. F., Mbarek, H., Mägi, R., Martin, H. C., Medland, S. E., Okada, Y., Palotie, A. V., Pasaniuc, B., Rader, D. J., Ritchie, M. D., Sanna, S., Smoller, J. W., Stefansson, K., van Heel, D. A., Walters, R. G., Zöllner, S., Martin, A. R., Willer, C. J., Daly, M. J., Neale, B. M. 2022; 2 (10): 100192

  Abstract

  Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

  View details for DOI 10.1016/j.xgen.2022.100192

  View details for PubMedID 36777996

  View details for PubMedCentralID PMC9903716