Genome-Wide Linkage and Positional Association Analyses Identify Associations of Novel AFF3 and NTM Genes with Triglycerides: The GenSalt Study

Changwei Li; Lydia A.L. Bazzano; Dabeeru C. Rao; James E. Hixson; Jiang He; Dongfeng Gu; Charles C. Gu; Lawrence C. Shimmin; Cashell E. Jaquish; Karen Schwander; De-Pei Liu; Jianfeng Huang; Fanghong Lu; Jie Cao; Shen Chong; Xiangfeng Lu; Tanika N. Kelly

doi:10.1016/j.jgg.2015.02.003

Volume 42 Issue 3

Mar. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2015 > 42(3): 107-117

PDF( 1500 KB)

Genome-Wide Linkage and Positional Association Analyses Identify Associations of Novel AFF3 and NTM Genes with Triglycerides: The GenSalt Study

doi: 10.1016/j.jgg.2015.02.003

a
Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA
b
Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA
c
Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
d
Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX 77030, USA
e
State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
f
Division of Prevention and Population Sciences, National Heart, Lung, Blood Institute, Bethesda, MD 20892-7936, USA
g
National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
h
Institute of Basic Medicine, Shandong Academy of Medical Sciences, Ji'nan 250062, China
i
Department of Epidemiology and Biostatistics, Nanjing Medical University School of Public Health, Nanjing 210029, China

More Information

Corresponding author: E-mail address: tkelly@tulane.edu (Tanika N. Kelly)
Received Date: 2014-11-06
Accepted Date: 2015-02-10
Rev Recd Date: 2015-02-06

Available Online: 2015-02-17

Publish Date: 2015-03-20

Abstract

Abstract

We conducted a genome-wide linkage scan and positional association study to identify genes and variants influencing blood lipid levels among participants of the Genetic Epidemiology Network of Salt-Sensitivity (GenSalt) study. The GenSalt study was conducted among 1906 participants from 633 Han Chinese families. Lipids were measured from overnight fasting blood samples using standard methods. Multipoint quantitative trait genome-wide linkage scans were performed on the high-density lipoprotein, low-density lipoprotein, and log-transformed triglyceride phenotypes. Using dense panels of single nucleotide polymorphisms (SNPs), single-marker and gene-based association analyses were conducted to follow-up on promising linkage signals. Additive associations between each SNP and lipid phenotypes were tested using mixed linear regression models. Gene-based analyses were performed by combining P-values from single-marker analyses within each gene using the truncated product method (TPM). Significant associations were assessed for replication among 777 Asian participants of the Multi-ethnic Study of Atherosclerosis (MESA). Bonferroni correction was used to adjust for multiple testing. In the GenSalt study, suggestive linkage signals were identified at 2p11.2‒2q12.1 [maximum multipoint LOD score (MML) = 2.18 at 2q11.2] and 11q24.3‒11q25 (MML = 2.29 at 11q25) for the log-transformed triglyceride phenotype. Follow-up analyses of these two regions revealed gene-based associations of charged multivesicular body protein 3 (CHMP3), ring finger protein 103 (RNF103), AF4/FMR2 family, member 3 (AFF3), and neurotrimin (NTM) with triglycerides (P = 4 × 10⁻⁴, 1.00 × 10⁻⁵, 2.00 × 10⁻⁵, and 1.00 × 10⁻⁷, respectively). Both the AFF3 and NTM triglyceride associations were replicated among MESA study participants (P = 1.00 × 10⁻⁷ and 8.00 × 10⁻⁵, respectively). Furthermore, NTM explained the linkage signal on chromosome 11. In conclusion, we identified novel genes associated with lipid phenotypes in linkage regions on chromosomes 2 and 11.
- Lipids,
- Linkage analysis,
- Positional association analysis,
- Gene-based analysis

FullText(HTML)

References(54)

References

[1]	Abecasis, G.R., Cherny, S.S., Cookson, W.O. et al. GRR: graphical representation of relationship errors Bioinformatics, 17 (2001),pp. 742-743
[2]	Allain, C.C., Poon, L.S., Chan, C.S. et al. Enzymatic determination of total serum cholesterol Clin. Chem., 20 (1974),pp. 470-475
[3]	Almasy, L., Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees Am. J. Hum. Genet., 62 (1998),pp. 1198-1211
[4]	Aulchenko, Y.S., Ripatti, S., Lindqvist, I. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts Nat. Genet., 41 (2009),pp. 47-55
[5]	Barrett, J.C., Clayton, D.G., Concannon, P. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes Nat. Genet., 41 (2009),pp. 703-707
[6]	Barton, A., Eyre, S., Ke, X. et al. Identification of AF4/FMR2 family, member 3 (AFF3) as a novel rheumatoid arthritis susceptibility locus and confirmation of two further pan-autoimmune susceptibility genes Hum. Mol. Genet., 18 (2009),pp. 2518-2522
[7]	Bosse, Y., Chagnon, Y.C., Despres, J.P. et al. Genome-wide linkage scan reveals multiple susceptibility loci influencing lipid and lipoprotein levels in the Quebec Family Study J. Lipid Res., 45 (2004),pp. 419-426
[8]	Danai, L.V., Guilherme, A., Guntur, K.V. et al. Map4k4 suppresses Srebp-1 and adipocyte lipogenesis independent of JNK signaling J. Lipid Res., 54 (2013),pp. 2697-2707
[9]	Di Angelantonio, E., Sarwar, N., Perry, P. et al. Major lipids, apolipoproteins, and risk of vascular disease JAMA, 302 (2009),pp. 1993-2000
[10]	Flicek, P., Amode, M.R., Barrell, D. et al. Ensembl 2014 Nucleic Acids Res., 42 (2014),pp. D749-D755
[11]	Friedewald, W.T., Levy, R.I., Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge Clin. Chem., 18 (1972),pp. 499-502
[12]	Friedlander, Y., Austin, M.A., Newman, B. et al. Heritability of longitudinal changes in coronary-heart-disease risk factors in women twins Am. J. Hum. Genet., 60 (1997),pp. 1502-1512
[13]	Group, G.C.R. GenSalt: rationale, design, methods and baseline characteristics of study participants J. Hum. Hypertens., 21 (2007),pp. 639-646
[14]	Guy, J., Ogden, L., Wadwa, R.P. et al. Lipid and lipoprotein profiles in youth with and without type 1 diabetes: the SEARCH for Diabetes in Youth case-control study Diabetes Care, 32 (2009),pp. 416-420
[15]	He, J., Gu, D., Wu, X. et al. Major causes of death among men and women in China N. Engl. J. Med., 353 (2005),pp. 1124-1134
[16]	Hinds, D., Risch, N.
[17]	Hokanson, J.E., Austin, M.A. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: a meta-analysis of population-based prospective studies J. Cardiovasc. Risk, 3 (1996),pp. 213-219
[18]	Huxley, R.R., Barzi, F., Lam, T.H. et al. Isolated low levels of high-density lipoprotein cholesterol are associated with an increased risk of coronary heart disease: an individual participant data meta-analysis of 23 studies in the Asia-Pacific region Circulation, 124 (2011),pp. 2056-2064
[19]	Kathiresan, S., Melander, O., Guiducci, C. et al.
[20]	Kathiresan, S., Willer, C.J., Peloso, G.M. et al. Common variants at 30 loci contribute to polygenic dyslipidemia Nat. Genet., 41 (2009),pp. 56-65
[21]	Kim, Y.J., Go, M.J., Hu, C. et al. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits Nat. Genet., 43 (2011),pp. 990-995
[22]	Kooner, J.S., Chambers, J.C., Aguilar-Salinas, C.A. et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides Nat. Genet., 40 (2008),pp. 149-151
[23]	LaRosa, J.C., He, J., Vupputuri, S.
[24]	Li, C., Yang, X., He, J. et al. PLoS One, 9 (2014),p. e98432
[25]	Liu, J., Rosner, M.H. Lipid abnormalities associated with end-stage renal disease Semin. Dial., 19 (2006),pp. 32-40
[26]	Lozano, R., Naghavi, M., Foreman, K. et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010 Lancet, 380 (2013),pp. 2095-2128
[27]	Luo, B.F., Du, L., Li, J.X. et al. Heritability of metabolic syndrome traits among healthy younger adults: a population based study in China J. Med. Genet., 47 (2010),pp. 415-420
[28]	Luukkonen, T.M., Pöyhönen, M., Palotie, A. et al. A balanced translocation truncates Neurotrimin in a family with intracranial and thoracic aortic aneurysm J. Med. Genet., 49 (2012),pp. 621-629
[29]	Ma, L., Clark, A.G., Keinan, A. Gene-based testing of interactions in association studies of quantitative traits PLoS Genet., 9 (2013),p. e1003321
[30]	Mailman, M.D., Feolo, M., Jin, Y. et al. The NCBI dbGaP database of genotypes and phenotypes Nat. Genet., 39 (2007),pp. 1181-1186
[31]	Malhotra, A., Wolford, J.K. Analysis of quantitative lipid traits in the genetics of NIDDM (GENNID) study Diabetes, 54 (2005),pp. 3007-3014
[32]	Moschovi, M., Trimis, G., Apostolakou, F. et al. Serum lipid alterations in acute lymphoblastic leukemia of childhood J. Pediatr. Hematol. Oncol., 26 (2004),pp. 289-293
[33]	Murray, C.J.L., Vos, T., Lozano, R. et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 Lancet, 380 (2013),pp. 2197-2223
[34]	Nishida, N., Koike, A., Tajima, A. et al. Evaluating the performance of Affymetrix SNP Array 6.0 platform with 400 Japanese individuals BMC Genomics, 9 (2008),p. 431
[35]	O'Connell, J.R., Weeks, D.E. PedCheck: a program for identification of genotype incompatibilities in linkage analysis Am. J. Hum. Genet., 63 (1998),pp. 259-266
[36]	Plant, D., Flynn, E., Mbarek, H. et al. Investigation of potential non-HLA rheumatoid arthritis susceptibility loci in a European cohort increases the evidence for nine markers Ann. Rheum. Dis., 69 (2010),pp. 1548-1553
[37]	Pratt, S.C., Daly, M.J., Kruglyak, L. Exact multipoint quantitative-trait linkage analysis in pedigrees by variance components Am. J. Hum. Genet., 66 (2000),pp. 1153-1157
[38]	Puri, V., Virbasius, J.V., Guilherme, A. et al. RNAi screens reveal novel metabolic regulators: RIP140, MAP4k4 and the lipid droplet associated fat specific protein (FSP) 27 Acta Physiol. (Oxf), 192 (2008),pp. 103-115
[39]	Ramirez, C.M., Dávalos, A., Goedeke, L. et al. MicroRNA-758 regulates cholesterol efflux through posttranscriptional repression of ATP-binding cassette transporter A1 Arterioscler. Thromb. Vasc. Biol., 31 (2011),pp. 2707-2714
[40]	Sandholm, N., Salem, R.M., McKnight, A.J. et al. New susceptibility loci associated with kidney disease in type 1 diabetes PLoS Genet., 8 (2012),p. e1002921
[41]	Saxena, R., Voight, B.F., Lyssenko, V. et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels Science, 316 (2007),pp. 1331-1336
[42]	Sheng, X., Yang, J. Truncated product methods for panel unit root tests Oxf. Bull. Econ. Stat., 75 (2013),pp. 624-636
[43]	Steiner, G., Urowitz, M.B. Lipid profiles in patients with rheumatoid arthritis: mechanisms and the impact of treatment Semin. Arthritis Rheum, 38 (2009),pp. 372-381
[44]	Tan, A., Sun, J., Xia, N. et al. A genome-wide association and gene-environment interaction study for serum triglycerides levels in a healthy Chinese male population Hum. Mol. Genet., 21 (2012),pp. 1658-1664
[45]	Teslovich, T.M., Musunuru, K., Smith, A.V. et al. Biological, clinical and population relevance of 95 loci for blood lipids Nature, 466 (2010),pp. 707-713
[46]	von Bergh, A.R., Beverloo, H.B., Rombout, P. et al. Genes Chromosomes Cancer, 35 (2002),pp. 92-96
[47]	Wallace, C., Newhouse, S.J., Braund, P. et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia Am. J. Hum. Genet., 82 (2008),pp. 139-149
[48]	Wallace, C., Rotival, M., Cooper, J.D. et al. Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes Hum. Mol. Genet., 21 (2012),pp. 2815-2824
[49]	Waterworth, D.M., Ricketts, S.L., Song, K. et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease Arterioscler. Thromb. Vasc. Biol., 30 (2010),pp. 2264-2276
[50]	Willer, C.J., Sanna, S., Jackson, A.U. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease Nat. Genet., 40 (2008),pp. 161-169
[51]	Yang, J., Zhu, Y., Cole, S.A. et al. A gene-family analysis of 61 genetic variants in the nicotinic acetylcholine receptor genes for insulin resistance and type 2 diabetes in American Indians Diabetes, 61 (2012),pp. 1888-1894
[52]	Yang, J., Zhu, Y., Lee, E.T. et al. Joint associations of 61 genetic variants in the nicotinic acetylcholine receptor genes with subclinical atherosclerosis in American Indians: a gene-family analysis Circ. Cardiovasc. Genet., 6 (2013),pp. 89-96
[53]	Zhang, S., Liu, X., Necheles, J. et al. Genetic and environmental influences on serum lipid tracking: a population-based, longitudinal Chinese twin study Pediatr. Res., 68 (2010),pp. 316-322
[54]	Zhu, Y., Yang, J., Yeh, F. et al. Joint association of nicotinic acetylcholine receptor variants with abdominal obesity in American Indians: the Strong Heart Family Study PLoS One, 9 (2014),p. e102220