Asselbergs, FW; Guo, Y; van Iperen, EP; Sivapalaratnam, S; Tragante, V; Lanktree, MB; Lange, LA; Almoguera, B; Appelman, YE; Barnard, J; Baumert, J; Beitelshees, AL; Bhangale, TR; Chen, YD; Gaunt, TR; Gong, Y; Hopewell, JC; Johnson, T; Kleber, ME; Langaee, TY; Li, M; Li, YR; Liu, K; McDonough, CW; Meijs, MF; Middelberg, RP; Musunuru, K; Nelson, CP; O'Connell, JR; Padmanabhan, S; Pankow, JS; Pankratz, N; Rafelt, S; Rajagopalan, R; Romaine, SP; Schork, NJ; Shaffer, J; Shen, H; Smith, EN; Tischfield, SE; van der Most, PJ; van Vliet-Ostaptchouk, JV; Verweij, N; Volcik, KA; Zhang, L; Bailey, KR; Bailey, KM; Bauer, F; Boer, JM; Braund, PS; Burt, A; Burton, PR; Buxbaum, SG; Chen, W; Cooper-Dehoff, RM; Cupples, LA; deJong, JS; Delles, C; Duggan, D; Fornage, M; Furlong, CE; Glazer, N; Gums, JG; Hastie, C; Holmes, MV; Illig, T; Kirkland, SA; Kivimaki, M; Klein, R; Klein, BE; Kooperberg, C; Kottke-Marchant, K; Kumari, M; LaCroix, AZ; Mallela, L; Murugesan, G; Ordovas, J; Ouwehand, WH; Post, WS; Saxena, R; Scharnagl, H; Schreiner, PJ; Shah, T; Shields, DC; Shimbo, D; Srinivasan, SR; Stolk, RP; Swerdlow, DI; Taylor, HA; Topol, EJ; Toskala, E; van Pelt, JL; van Setten, J; Yusuf, S; Whittaker, JC; Zwinderman, AH; Anand, SS; Balmforth, AJ; Berenson, GS; Bezzina, CR; Boehm, BO; Boerwinkle, E; Casas, JP; Caulfield, MJ; Clarke, R; Connell, JM; Cruickshanks, KJ; Davidson, KW; Day, IN; de Bakker, PI; Doevendans, PA; Dominiczak, AF; Hall, AS; Hartman, CA; Hengstenberg, C; Hillege, HL; Hofker, MH; Humphries, SE; Jarvik, GP; Johnson, JA; Kaess, BM; Kathiresan, S; Koenig, W; Lawlor, DA; März, W; Melander, O; Mitchell, BD; Montgomery, GW; Munroe, PB; Murray, SS; Newhouse, SJ; Onland-Moret, NC; Poulter, N; Psaty, B; Redline, S; Rich, SS; Rotter, JI; Schunkert, H; Sever, P; Shuldiner, AR; Silverstein, RL; Stanton, A; Thorand, B; Trip, MD; Tsai, MY; van der Harst, P; van der Schoot, E; van der Schouw, YT; Verschuren, WM; Watkins, H; Wilde, AA; Wolffenbuttel, BH; Whitfield, JB; Hovingh, GK; Ballantyne, CM; Wijmenga, C; Reilly, MP; Martin, NG; Wilson, JG; Rader, DJ; Samani, NJ; Reiner, AP; Hegele, RA; Kastelein, JJ; Hingorani, AD; Talmud, PJ; Hakonarson, H; Elbers, CC; Keating, BJ; Drenos, F     «

Asselbergs, FW; Guo, Y; van Iperen, EP; Sivapalaratnam, S; Tragante, V; Lanktree, MB; Lange, LA; Almoguera, B; Appelman, YE; Barnard, J; Baumert, J; Beitelshees, AL; Bhangale, TR; Chen, YD; Gaunt, TR; Gong, Y; Hopewell, JC; Johnson, T; Kleber, ME; Langaee, TY; Li, M; Li, YR; Liu, K; McDonough, CW; Meijs, MF; Middelberg, RP; Musunuru, K; Nelson, CP; O'Connell, JR; Padmanabhan, S; Pankow, JS; Pankratz, N; Rafelt, S; Rajagopalan, R; Romaine, SP; Schork, NJ; Shaffer, J; Shen, H; Smith, EN; Tischfiel...     »

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ~50,000 SNP genotyping array (the ITMAT-Broad-CARe array) covering ~2,000 candidate genes. SNP-lipid associations were replicated either in a cohort comprising an additional 24,736 samples or within the Global Lipid Genetic Consortium. We identified four, six, ten, and four unreported SNPs in established lipid genes for HDL-C, LDL-C, TC, and TGs, respectively. We also identified several lipid-related SNPs in previously unreported genes: DGAT2, HCAR2, GPIHBP1, PPARG, and FTO for HDL-C; SOCS3, APOH, SPTY2D1, BRCA2, and VLDLR for LDL-C; SOCS3, UGT1A1, BRCA2, UBE3B, FCGR2A, CHUK, and INSIG2 for TC; and SERPINF2, C4B, GCK, GATA4, INSR, and LPAL2 for TGs. The proportion of explained phenotypic variance in the subset of studies providing individual-level data was 9.9% for HDL-C, 9.5% for LDL-C, 10.3% for TC, and 8.0% for TGs. This large meta-analysis of lipid phenotypes with the use of a dense gene-centric approach identified multiple SNPs not previously described in established lipid genes and several previously unknown loci. The explained phenotypic variance from this approach was comparable to that from a meta-analysis of GWAS data, suggesting that a focused genotyping approach can further increase the understanding of heritability of plasma lipids.     «

Genome-wide association studies (GWASs) have identified many SNPs underlying variations in plasma-lipid levels. We explore whether additional loci associated with plasma-lipid phenotypes, such as high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglycerides (TGs), can be identified by a dense gene-centric approach. Our meta-analysis of 32 studies in 66,240 individuals of European ancestry was based on the custom ~50,000 SNP g...     »