👤 Gerald S Berenson

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articles
Hao Mei, Wei Chen, Fan Jiang +6 more · 2012 · PloS one · PLOS · added 2026-04-24
Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for Show more
Genome-wide association studies (GWAS) have identified multiple common variants associated with body mass index (BMI). In this study, we tested 23 genotyped GWAS-significant SNPs (p-value<5*10-8) for longitudinal associations with BMI during childhood (3-17 years) and adulthood (18-45 years) for 658 subjects. We also proposed a heuristic forward search for the best joint effect model to explain the longitudinal BMI variation. After using false discovery rate (FDR) to adjust for multiple tests, childhood and adulthood BMI were found to be significantly associated with six SNPs each (q-value<0.05), with one SNP associated with both BMI measurements: KCTD15 rs29941 (q-value<7.6*10-4). These 12 SNPs are located at or near genes either expressed in the brain (BDNF, KCTD15, TMEM18, MTCH2, and FTO) or implicated in cell apoptosis and proliferation (FAIM2, MAP2K5, and TFAP2B). The longitudinal effects of FAIM2 rs7138803 on childhood BMI and MAP2K5 rs2241423 on adulthood BMI decreased as age increased (q-value<0.05). The FTO candidate SNPs, rs6499640 at the 5 '-end and rs1121980 and rs8050136 downstream, were associated with childhood and adulthood BMI, respectively, and the risk effects of rs6499640 and rs1121980 increased as birth weight decreased. The best joint effect model for childhood and adulthood BMI contained 14 and 15 SNPs each, with 11 in common, and the percentage of explained variance increased from 0.17% and 9.0*10(-6)% to 2.22% and 2.71%, respectively. In summary, this study evidenced the presence of long-term major effects of genes on obesity development, implicated in pathways related to neural development and cell metabolism, and different sets of genes associated with childhood and adulthood BMI, respectively. The gene effects can vary with age and be modified by prenatal development. The best joint effect model indicated that multiple variants with effects that are weak or absent alone can nevertheless jointly exert a large longitudinal effect on BMI. Show less
📄 PDF DOI: 10.1371/journal.pone.0031470
MAP2K5
Cathy E Elks, John R B Perry, Patrick Sulem +172 more · 2010 · Nature genetics · Nature · added 2026-04-24
Cathy E Elks, John R B Perry, Patrick Sulem, Daniel I Chasman, Nora Franceschini, Chunyan He, Kathryn L Lunetta, Jenny A Visser, Enda M Byrne, Diana L Cousminer, Daniel F Gudbjartsson, Tõnu Esko, Bjarke Feenstra, Jouke-Jan Hottenga, Daniel L Koller, Zoltán Kutalik, Peng Lin, Massimo Mangino, Mara Marongiu, Patrick F McArdle, Albert V Smith, Lisette Stolk, Sophie H van Wingerden, Jing Hua Zhao, Eva Albrecht, Tanguy Corre, Erik Ingelsson, Caroline Hayward, Patrik K E Magnusson, Erin N Smith, Shelia Ulivi, Nicole M Warrington, Lina Zgaga, Helen Alavere, Najaf Amin, Thor Aspelund, Stefania Bandinelli, Inês Barroso, Gerald S Berenson, Sven Bergmann, Hannah Blackburn, Eric Boerwinkle, Julie E Buring, Fabio Busonero, Harry Campbell, Stephen J Chanock, Wei Chen, Marilyn C Cornelis, David Couper, Andrea D Coviello, Pio d'Adamo, Ulf de Faire, Eco J C de Geus, Panos Deloukas, Angela Döring, George Davey Smith, Douglas F Easton, Gudny Eiriksdottir, Valur Emilsson, Johan Eriksson, Luigi Ferrucci, Aaron R Folsom, Tatiana Foroud, Melissa Garcia, Paolo Gasparini, Frank Geller, Christian Gieger, GIANT Consortium, Vilmundur Gudnason, Per Hall, Susan E Hankinson, Liana Ferreli, Andrew C Heath, Dena G Hernandez, Albert Hofman, Frank B Hu, Thomas Illig, Marjo-Riitta Järvelin, Andrew D Johnson, David Karasik, Kay-Tee Khaw, Douglas P Kiel, Tuomas O Kilpeläinen, Ivana Kolcic, Peter Kraft, Lenore J Launer, Joop S E Laven, Shengxu Li, Jianjun Liu, Daniel Levy, Nicholas G Martin, Wendy L McArdle, Mads Melbye, Vincent Mooser, Jeffrey C Murray, Sarah S Murray, Michael A Nalls, Pau Navarro, Mari Nelis, Andrew R Ness, Kate Northstone, Ben A Oostra, Munro Peacock, Lyle J Palmer, Aarno Palotie, Guillaume Paré, Alex N Parker, Nancy L Pedersen, Leena Peltonen, Craig E Pennell, Paul Pharoah, Ozren Polasek, Andrew S Plump, Anneli Pouta, Eleonora Porcu, Thorunn Rafnar, John P Rice, Susan M Ring, Fernando Rivadeneira, Igor Rudan, Cinzia Sala, Veikko Salomaa, Serena Sanna, David Schlessinger, Nicholas J Schork, Angelo Scuteri, Ayellet V Segrè, Alan R Shuldiner, Nicole Soranzo, Ulla Sovio, Sathanur R Srinivasan, David P Strachan, Mar-Liis Tammesoo, Emmi Tikkanen, Daniela Toniolo, Kim Tsui, Laufey Tryggvadottir, Jonathon Tyrer, Manuela Uda, Rob M Van Dam, Joyce B J van Meurs, Peter Vollenweider, Gerard Waeber, Nicholas J Wareham, Dawn M Waterworth, Michael N Weedon, H Erich Wichmann, Gonneke Willemsen, James F Wilson, Alan F Wright, Lauren Young, Guangju Zhai, Wei Vivian Zhuang, Laura J Bierut, Dorret I Boomsma, Heather A Boyd, Laura Crisponi, Ellen W Demerath, Cornelia M Van Duijn, Michael J Econs, Tamara B Harris, David J Hunter, Ruth J F Loos, Andres Metspalu, Grant W Montgomery, Paul M Ridker, Tim D Spector, Elizabeth A Streeten, Kari Stefansson, Unnur Thorsteinsdottir, André G Uitterlinden, Elisabeth Widen, Joanne M Murabito, Ken K Ong, Anna Murray Show less
To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the Show more
To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10⁻⁶⁰) and 9q31.2 (P = 2.2 × 10⁻³³), we identified 30 new menarche loci (all P < 5 × 10⁻⁸) and found suggestive evidence for a further 10 loci (P < 1.9 × 10⁻⁶). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing. Show less
no PDF DOI: 10.1038/ng.714
SEC16B
D Michael Hallman, Sathanur R Srinivasan, Wei Chen +2 more · 2006 · Metabolism: clinical and experimental · Elsevier · added 2026-04-24
Polymorphisms in the APOC3 and APOA5 genes, from the APOA1/APOC3/APOA4/APOA5 gene cluster on chromosome 11q23, have been associated with interindividual variation in plasma triglycerides. APOA5 polymo Show more
Polymorphisms in the APOC3 and APOA5 genes, from the APOA1/APOC3/APOA4/APOA5 gene cluster on chromosome 11q23, have been associated with interindividual variation in plasma triglycerides. APOA5 polymorphisms implicated include 2 in the promoter region (-1131 T/C and -3 A/G) and 1 in exon 2 (+56 C/G). APOC3 polymorphisms implicated include 1 (SstI) in the 3' untranslated region and 1 (-2854 G/T) in the APOC3-APOA4 intergenic region. We analyzed the associations of haplotypes and multilocus genotypes of these polymorphisms on longitudinal serum triglyceride profiles in 360 African American and 823 white subjects from the Bogalusa Heart Study. Subjects were examined from 2 to 8 times (mean +/- SD, 5.4 +/- 1.3) between 1973 and 1996, at ages ranging from 4 to 38 years, with 1978 observations in African Americans and 4465 in whites. Serum triglycerides were significantly higher among whites across all ages. Allele frequencies differed significantly between African Americans and whites at all but the APOA5 +56 C/G locus. Linkage disequilibrium among the loci was higher in whites and haplotype diversity lower: 6 haplotypes had estimated frequencies of more than 1% in African Americans, 5 in whites. Individually, all polymorphisms except APOC3 -2854 G/T showed significant associations with triglyceride levels in the full sample. However, genotype models including all 5 loci showed significant triglyceride associations for only 3 (APOC3 SstI, APOA5 -1131 T/C, and APOA5 +56 C/G); significant interactions among them indicated their effects were not independent. Neither APOC3 -2854 G/T nor APOA5 -3 A/G had significant effects when the other 3 loci were in the models. The EM algorithm was used to estimate haplotype frequencies and assign haplotype probabilities to individuals, which is conditional on their genotypes; individuals' haplotype probability vectors were then used as predictors in multilevel mixed models of longitudinal triglyceride profiles. Of haplotypes comprising, in order, APOC3 SstI and -2854 G/T and APOA5 -1131 T/C, -3 A/G, and +56 C/G, 3 were significantly associated with higher triglycerides, even after adjusting for multiple tests: GGTAG (P = .002), GTTAG (P < .0001), and CGCGC (P = .0002). Each GGTAG haplotype carried would be expected to raise triglyceride levels (relative to those of GTTAC homozygotes) by approximately 19 mg/dL, each GTTAG haplotype by approximately 15 mg/dL, and each CGCGC haplotype by approximately 7 mg/dL. Haplotypes comprising the 3 loci implicated by genotype analyses (SstI, -1131 T/C, and +56 C/G) were also tested: haplotypes C_C_C and G_T_G significantly raised triglycerides, even after adjustment for multiple comparisons (P < .002 for both), with each copy of C_C_C expected to raise triglycerides by approximately 7 mg/dL and each copy of G_T_G by approximately 15 mg/dL. Overall, our findings support those of others in associating specific polymorphisms and haplotypes in the APOA1/C3/A4/A5 gene cluster with higher serum triglyceride levels. However, the degree to which polymorphisms in the APOC3 and APOA5 genes may be independently associated with triglyceride levels remains to be determined. Show less
no PDF DOI: 10.1016/j.metabol.2006.07.018
APOA4