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5 results on '"Jaquish C"'

1. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium

Author

Kraja, A. T., Vaidya, D., Pankow, J. S., Goodarzi, M. O., Assimes, T. L., Kullo, I. J., Sovio, U., Mathias, R. A., Sun, Y. V., Franceschini, N., Absher, D., Li, G., Zhang, Q., Feitosa, M. F., Glazer, N. L., Haritunians, T., Hartikainen, A.-L., Knowles, J. W., North, K. E., Iribarren, C., Kral, B., Yanek, L., O'Reilly, P. F., McCarthy, M. I., Jaquish, C., Couper, D. J., Chakravarti, A., Psaty, B. M., Becker, L. C., Province, M. A., Boerwinkle, E., Quertermous, T., Palotie, L., Jarvelin, M.-R., Becker, D. M., Kardia, S. L. R., Rotter, J. I., Chen, Y.-D. I., Borecki, I. B., and Medical Research Council (MRC)

Subjects
Adult, Male, Genotype, BLOOD-PRESSURE, HEART-DISEASE, Cardiovascular, Polymorphism, Single Nucleotide, Medical and Health Sciences, Endocrinology & Metabolism, Meta-Analysis as Topic, Clinical Research, Genetics, Humans, 2.1 Biological and endogenous factors, Genetic Predisposition to Disease, Obesity, Polymorphism, Aetiology, 11 Medical and Health Sciences, Metabolic and endocrine, Aged, Nutrition, Metabolic Syndrome, INSULIN-RESISTANCE, HDL CHOLESTEROL, Science & Technology, Human Genome, Diabetes, Single Nucleotide, Middle Aged, APOLIPOPROTEIN A5, FASTING PLASMA-GLUCOSE, Phenotype, DENSITY-LIPOPROTEIN CHOLESTEROL, CORONARY-ARTERY-DISEASE, Female, TYPE-2 DIABETES RISK, Life Sciences & Biomedicine, MEXICAN-AMERICANS, Genome-Wide Association Study
Abstract

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.

Published
2011

3. The Framingham Heart Study, on its way to becoming the gold standard for Cardiovascular Genetic Epidemiology?

Author

Jaquish Cashell E

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract The Framingham Heart Study, founded in 1948 to examine the epidemiology of cardiovascular disease in a small town outside of Boston, has become the worldwide standard for cardiovascular epidemiology. It is among the longest running, most comprehensively characterized multi-generational studies in the world. Such seminal findings as the effects of smoking and high cholesterol on heart disease came from the Framingham Heart Study. At the time of publication these were novel cardiovascular disease (CVD) risk factors, now they are the basis of treatment and prevention in the US. Is the Framingham study now on it's way to becoming the gold standard for genetic epidemiology of CVD? Will the novel genetic findings of today become the health care standards of tomorrow? The accompanying articles summarizing the results of genome-wide association studies (GWAS) give the reader a first glimpse into the possibilities.

Published
2007
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4. Sex and age specific effects of chromosomal regions linked to body mass index in the Framingham Study

Author

Fox Caroline S, Heard-Costa Nancy L, Atwood Larry D, Jaquish Cashell E, and Cupples L Adrienne

Subjects
Genetics, QH426-470
Abstract

Abstract Background Previously, we reported significant linkage of body mass index (BMI) to chromosomes 6 and 11 across six examinations, covering 28 years, of the Framingham Heart Study. These results were on all individuals available at each exam, thus the sample size varied from exam to exam. To remove any effect of sample size variation we have now constructed six subsets; for each exam individuals were only included if they were measured at every exam, i.e. for each exam, included individuals comprise the intersection of the original six exams. This strategy preferentially removed older individuals who died before reaching the sixth exam, thus the intersection datasets are smaller (n = 1114) and significantly younger than the full datasets. We performed variance components linkage analysis on these intersection datasets and on their sex-specific subsets. Results Results from the sex-specific genome scans revealed 11 regions in which a sex-specific maximum lodscore was at least 2.0 for at least one dataset. Randomization tests indicated that all 11 regions had significant (p < 0.05) differences in sex-specific maximum lodscores for at least three datasets. The strongest sex-specific linkage was for men on chromosome 16 with maximum lodscores 2.70, 3.00, 3.42, 3.61, 2.56 and 1.93 for datasets 1–6 respectively. Results from the full genome scans revealed that linked regions on chromosomes 6 and 11 remained significantly and consistently linked in the intersection datasets. Surprisingly, the maximum lodscore on chromosome 10 for dataset 1 increased from 0.97 in the older original dataset to 4.23 in the younger smaller intersection dataset. This difference in maximum lodscores was highly significant (p < 0.0001), implying that the effect of this chromosome may vary with age. Age effects may also exist for the linked regions on chromosomes 6 and 11. Conclusion Sex specific effects of chromosomal regions on BMI are common in the Framingham study. Some evidence also exists for age-specific effects of chromosomal regions.

Published
2006
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5. A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Author

Adolfo Correa, Kotaro Ogawa, Yukinori Okada, Paul J. McLaren, Philip E. Stuart, Kenichi Yamamoto, Peter K. Gregersen, Saori Sakaue, David W Haas, Tõnu Esko, Michael H. Cho, Albert V. Smith, Wanson Choi, Sebastian Schönherr, Yii-Der Ida Chen, James T. Elder, Soumya Raychaudhuri, Maria Gutierrez-Arcelus, Lukas Forer, Kent D. Taylor, Yang Luo, Xiuqing Guo, Jerome I. Rotter, Stephen S. Rich, Nicholette D. Palmer, Mary Carrington, Masahiro Kanai, Christian Fuchsberger, Buhm Han, Andres Metspalu, Xinyi Li, Sekar Kathiresan, James G. Wilson, Jacques Fellay, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, 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, X., Arnett, D.K., Ashley-Koch, A., Aslibekyan, S., Assimes, T., Auer, P., 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., Benjamin, E., Benos, T., Bezerra, M., Bielak, L., Bis, J., Blackwell, T., Blangero, J., Boerwinkle, E., Bowden, D.W., Bowler, R., Brody, J., Broeckel, U., Broome, J., Brown, D., Bunting, K., Burchard, E., Bustamante, C., Buth, E., Cade, B., Cardwell, J., Carey, V., Carrier, J., Carty, C., Casaburi, R., Romero, JPC, Casella, J., Castaldi, P., Chaffin, M., Chang, C., Chang, Y.C., Chasman, D., Chavan, S., Chen, B.J., Chen, W.M., Choi, S.H., Chuang, L.M., Chung, M., Chung, R.H., Clish, C., Comhair, S., Conomos, M., Cornell, E., Crandall, C., Crapo, J., Cupples, L.A., Curran, J., Curtis, J., Custer, B., Damcott, C., Darbar, D., David, S., Davis, C., Daya, M., de Andrade, M., Fuentes, L.L., de Vries, P., DeBaun, M., Deka, R., DeMeo, D., Devine, S., Dinh, H., Doddapaneni, H., Duan, Q., Dugan-Perez, S., Duggirala, R., Durda, J.P., Dutcher, S.K., Eaton, C., Ekunwe, L., Boueiz, A.E., Ellinor, P., Emery, L., Erzurum, S., Farber, C., Farek, J., Fingerlin, T., Flickinger, M., Fornage, M., Franceschini, N., 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.W., Goring, H., Graw, S., Gray, K.J., Grine, D., Gross, C., Gu, C.C., Guan, Y., Gupta, N., Haas, D.M., Haessler, J., Hall, M., Han, Y., Hanly, P., Harris, D., Hawley, N.L., He, J., Heavner, B., 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., Hung, Y.J., Huston, H., Hwu, C.M., Irvin, M.R., Jackson, R., Jain, D., Jaquish, C., Johnsen, J., Johnson, A., Johnson, C., Johnston, R., Jones, K., Kang, H.M., Kaplan, R., Kardia, S., Kelly, S., Kenny, E., Kessler, M., Khan, A., Khan, Z., Kim, W., Kimoff, J., Kinney, G., Konkle, B., Kooperberg, C., Kramer, H., Lange, C., Lange, E., Lange, L., Laurie, C., LeBoff, M., Lee, J., Lee, S., Lee, W.J., LeFaive, J., Levine, D., Dan Levy, X., Lewis, J., Li, X., Li, Y., Lin, H., Lin, X., Liu, S., Liu, Y., Loos, RJF, Lubitz, S., Lunetta, K., Luo, J., Magalang, U., Mahaney, M., Make, B., Manichaikul, A., Manning, A., Manson, J., Martin, L., Marton, M., Mathai, S., Mathias, R., May, S., McArdle, P., McDonald, M.L., McFarland, S., McGarvey, S., McGoldrick, D., McHugh, C., McNeil, B., Mei, H., Meigs, J., Menon, V., Mestroni, L., Metcalf, G., Meyers, D.A., Mignot, E., Mikulla, J., Min, N., Minear, M., Minster, R.L., Mitchell, B.D., Moll, M., Momin, Z., Montasser, M.E., Montgomery, C., Muzny, D., Mychaleckyj, J.C., Nadkarni, G., Naik, R., Naseri, T., Natarajan, P., Nekhai, S., Nelson, S.C., Neltner, B., Nessner, C., Nickerson, D., Nkechinyere, O., North, K., O'Connell, J., O'Connor, T., Ochs-Balcom, H., Okwuonu, G., Pack, A., Paik, D.T., Pankow, J., Papanicolaou, G., Parker, C., Peloso, G., Peralta, J.M., Perez, M., Perry, J., Peters, U., Peyser, P., Phillips, L.S., Pleiness, J., Pollin, T., Post, W., Becker, J.P., Boorgula, M.P., Preuss, M., Psaty, B., Qasba, P., Qiao, D., Qin, Z., Rafaels, N., Raffield, L., Rajendran, M., Ramachandran, V.S., Rao, D.C., Rasmussen-Torvik, L., Ratan, A., Redline, S., Reed, R., Reeves, C., Regan, E., Reiner, A., Reupena, M.S., Rice, K., Robillard, R., Robine, N., Dan Roden, X., 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., Sériès, F., Sheehan, V., Sherman, S.L., Shetty, A., Sheu, W.H., Shoemaker, M.B., Silver, B., Silverman, E., Skomro, R., 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., Taliun, D., Tang, H., Taub, M., Taylor, M., Taylor, S., Telen, M., Thornton, T.A., Threlkeld, M., Tinker, L., Tirschwell, D., Tishkoff, S., Tiwari, H., 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.C., Wessel, J., Willer, C., Williams, K., Williams, L.K., Wilson, C., Wilson, J., Winterkorn, L., Wong, Q., Wu, J., Xu, H., Yanek, L., Yang, I., Yu, K., Zekavat, S.M., Zhang, Y., Zhao, S.X., Zhao, W., Zhu, X., Zody, M., Zoellner, S., and Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed)

Subjects
haplotypes, Population genetics, Alleles, Amino Acids/genetics, Gene Frequency/genetics, Genetic Variation, Genetics, Population, HIV Infections/genetics, HIV-1/genetics, HLA Antigens/genetics, Haplotypes/genetics, Host-Pathogen Interactions/genetics, Humans, Linkage Disequilibrium/genetics, Physical Chromosome Mapping, Reference Standards, Selection, Genetic, Viral Load, HIV Infections, Immunogenetics, Human leukocyte antigen, Major histocompatibility complex, Linkage Disequilibrium, Article, Gene Frequency, HLA Antigens, Genetics, mhc, Amino Acids, Allele, biology, Haplotype, association, genetic-basis, micropolymorphism, polygenic risk scores, Evolutionary biology, Host-Pathogen Interactions, alleles, loci, HIV-1, biology.protein, amino-acid, identification, Viral load, Imputation (genetics)
Abstract

A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response., Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.

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