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A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.
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A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. Nature computational science Li, X., Chen, H., Selvaraj, M. S., Van Buren, E., Zhou, H., Wang, Y., Sun, R., McCaw, Z. R., Yu, Z., Jiang, M., DiCorpo, D., Gaynor, S. M., Dey, R., Arnett, D. K., Benjamin, E. J., Bis, J. C., Blangero, J., Boerwinkle, E., Bowden, D. W., Brody, J. A., Cade, B. E., Carson, A. P., Carlson, J. C., Chami, N., Chen, Y. I., Curran, J. E., de Vries, P. S., Fornage, M., Franceschini, N., Freedman, B. I., Gu, C., Heard-Costa, N. L., He, J., Hou, L., Hung, Y., Irvin, M. R., Kaplan, R. C., Kardia, S. L., Kelly, T. N., Konigsberg, I., Kooperberg, C., Kral, B. G., Li, C., Li, Y., Lin, H., Liu, C., Loos, R. J., Mahaney, M. C., Martin, L. W., Mathias, R. A., Mitchell, B. D., Montasser, M. E., Morrison, A. C., Naseri, T., North, K. E., Palmer, N. D., Peyser, P. A., Psaty, B. M., Redline, S., Reiner, A. P., Rich, S. S., Sitlani, C. M., Smith, J. A., Taylor, K. D., Tiwari, H. K., Vasan, R. S., Viali, S., Wang, Z., Wessel, J., Yanek, L. R., Yu, B., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Dupuis, J., Meigs, J. B., Auer, P. L., Raffield, L. M., Manning, A. K., Rice, K. M., Rotter, J. I., Peloso, G. M., Natarajan, P., Li, Z., Liu, Z., Lin, X., Abe, N., Abecasis, G., Aguet, F., Albert, C., Almasy, L., Alonso, A., Ament, S., Anderson, P., Anugu, P., Applebaum-Bowden, D., Ardlie, K., Dan Arking, A., Ashley-Koch, A., Aslibekyan, S., Assimes, T., Avramopoulos, D., Ayas, N., Balasubramanian, A., Barnard, J., Barnes, K., Barr, R. G., Barron-Casella, E., Barwick, L., Beaty, T., Beck, G., Becker, D., Becker, L., Beer, R., Beitelshees, A., Benos, T., Bezerra, M., Bielak, L., Blackwell, T., Blue, N., Bowler, R., Broeckel, U., Broome, J., Brown, D., Bunting, K., Burchard, E., Bustamante, C., Buth, E., Cardwell, J., Carey, V., Carrier, J., Carty, C., Casaburi, R., Romero, J. P., Casella, J., Castaldi, P., Chaffin, M., Chang, C., Chang, Y., Chasman, D., Chavan, S., Chen, B., Chen, W., Cho, M., Choi, S. H., Chuang, L., Chung, M., Chung, R., Clish, C., Comhair, S., Conomos, M., Cornell, E., Correa, A., Crandall, C., Crapo, J., Cupples, L. A., Curtis, J., Custer, B., Damcott, C., Darbar, D., David, S., Davis, C., Daya, M., DeBaun, M., DeMeo, D., Deka, R., Devine, S., Dinh, H., Doddapaneni, H., Duan, Q., Dugan-Perez, S., Duggirala, R., Durda, J. P., Dutcher, S. K., Eaton, C., Ekunwe, L., El Boueiz, A., Ellinor, P. T., Emery, L., Erzurum, S., Farber, C., Farek, J., Fingerlin, T., Flickinger, M., Frazar, C., Fu, M., Fullerton, S. M., Fulton, L., Gabriel, S., Gan, W., Gao, S., Gao, Y., Gass, M., Geiger, H., Gelb, B., Geraci, M., Germer, S., Gerszten, R., Ghosh, A., Gibbs, R., Gignoux, C., Gladwin, M., Glahn, D., Gogarten, S., Gong, D., Goring, H., Graw, S., Gray, K. J., Grine, D., Gross, C., Guan, Y., Guo, X., Gupta, N., Haessler, J., Hall, M., Han, Y., Hanly, P., Harris, D., Hawley, N. L., Ben Heavner, H., Heckbert, S., Hernandez, R., Herrington, D., Hersh, C., Hidalgo, B., Hixson, J., Hobbs, B., Hokanson, J., Hong, E., Hoth, K., Hsiung, C. A., Hu, J., Huston, H., Hwu, C. M., Jackson, R., Jain, D., Jaquish, C., Johnsen, J., Johnson, A., Johnson, C., Johnston, R., Jones, K., Kang, H. M., Kelly, S., Kenny, E., Kessler, M., Khan, A., Khan, Z., Kim, W., Kimoff, J., Kinney, G., Konkle, B., Kramer, H., Lange, C., Lange, E., Lange, L., Laurie, C., Laurie, C., LeBoff, M., LeFaive, J., Lee, J., Lee, S., Lee, W., Levine, D., Levy, D., Lewis, J., Li, X., Lin, H., Liu, S., Liu, Y., Liu, Y., Lubitz, S. A., Lunetta, K. L., Luo, J., Magalang, U., Make, B., Manichaikul, A., Manson, J., Marton, M., Mathai, S., May, S., McArdle, P., McDonald, M., McFarland, S., McGarvey, S., McGoldrick, D., McHugh, C., McNeil, B., Mei, H., Menon, V., Mestroni, L., Metcalf, G., Meyers, D. A., Mignot, E., Mikulla, J., Min, N., Minear, M., Minster, R. L., Moll, M., Momin, Z., Montgomery, C., Muzny, D., Mychaleckyj, J. C., Nadkarni, G., Naik, R., Nekhai, S., Nelson, S. C., Neltner, B., Nessner, C., Nickerson, D., Nkechinyere, O., O'Connell, J., O'Connor, T., Ochs-Balcom, H., Okwuonu, G., Pack, A., Paik, D. T., Pankow, J., Papanicolaou, G., Parker, C., Peralta, J. M., Perez, M., Perry, J., Peters, U., Phillips, L. S., Pleiness, J., Pollin, T., Post, W., Becker, J. P., Boorgula, M. P., Preuss, M., Qasba, P., Qiao, D., Qin, Z., Rafaels, N., Rajendran, M., Rao, D. C., Rasmussen-Torvik, L., Ratan, A., Reed, R., Reeves, C., Regan, E., Sefuiva Reupena, M., Robillard, R., Robine, N., Dan Roden, R., Roselli, C., Ruczinski, I., Runnels, A., Russell, P., Ruuska, S., Ryan, K., Sabino, E. C., Saleheen, D., Salimi, S., Salvi, S., Salzberg, S., Sandow, K., Sankaran, V. G., Santibanez, J., Schwander, K., Schwartz, D., Sciurba, F., Seidman, C., Seidman, J., Sheehan, V., Sherman, S. L., Shetty, A., Shetty, A., Sheu, W. H., Shoemaker, M. B., Silver, B., Silverman, E., Skomro, R., Smith, A. V., Smith, J., Smith, N., Smith, T., Smoller, S., Snively, B., Snyder, M., Sofer, T., Sotoodehnia, N., Stilp, A. M., Storm, G., Streeten, E., Su, J. L., Sung, Y. J., Sylvia, J., Szpiro, A., Series, F., Taliun, D., Tang, H., Taub, M., Taylor, M., Taylor, S., Telen, M., Thornton, T. A., Threlkeld, M., Tinker, L., Tirschwell, D., Tishkoff, S., Tong, C., Tracy, R., Tsai, M., Vaidya, D., Van Den Berg, D., VandeHaar, P., Vrieze, S., Walker, T., Wallace, R., Walts, A., Wang, F. F., Wang, H., Wang, J., Watson, K., Watt, J., Weeks, D. E., Weinstock, J., Weir, B., Weiss, S. T., Weng, L., Willer, C., Williams, K., Williams, L. K., Williams, S., Wilson, C., Wilson, J., Winterkorn, L., Wong, Q., Wu, B., Wu, J., Xu, H., Yang, I., Yu, K., Zekavat, S. M., Zhang, Y., Zhao, S. X., Zhao, W., Zhu, X., Ziv, E., Zody, M., Zoellner, S., de Andrade, M., de Las Fuentes, L. 2025Abstract
Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.
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