The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

1. Vazquez G., et al., Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol Rev, 2007. 29: p. 115–28. 17494056

2. Pischon T., et al., General and abdominal adiposity and risk of death in Europe. N Engl J Med, 2008. 359(20): p. 2105–20. doi: 10.1056/NEJMoa0801891 19005195

3. Mokdad A.H., et al., Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA, 2003. 289(1): p. 76–9. 12503980

4. Must A., et al., The disease burden associated with overweight and obesity. JAMA, 1999. 282(16): p. 1523–9. 10546691

5. Yusuf S., et al., Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet, 2005. 366(9497): p. 1640–9. 16271645

6. Canoy D., et al., Body fat distribution and risk of coronary heart disease in men and women in the European Prospective Investigation Into Cancer and Nutrition in Norfolk cohort: a population-based prospective study. Circulation, 2007. 116(25): p. 2933–43. 18071080

7. De Mello J.J., et al., Gender Differences In The Evaluation Of Adult Body Composition. Medicine and Science in Sports and Exercise, 2005. 37: p. S299–S299.

8. Kirchengast S., Gender Differences in Body Composition from Childhood to Old Age: An Evolutionary Point of View. Journal of Life Sciences, 2010. 2(1): p. 1–10.

9. Legato M.J., Beyond women's health the new discipline of gender-specific medicine. Med Clin North Am, 2003. 87(5): p. 917–37, vii. 14621324

10. Randall J.C., et al., Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits. Plos Genetics, 2013. 9(6): p. e1003500. doi: 10.1371/journal.pgen.1003500 23754948

11. Walter A.A., et al., Sarcopenia Indices: Age- And Gender-related Differences In Body Composition, Strength, And Muscle Quality. Medicine and Science in Sports and Exercise, 2012. 44: p. 98–98.

12. Wells J.C., Sexual dimorphism of body composition. Best Pract Res Clin Endocrinol Metab, 2007. 21(3): p. 415–30. 17875489

13. Loomba-Albrecht L.A. and Styne D.M., Effect of puberty on body composition. Curr Opin Endocrinol Diabetes Obes, 2009. 16(1): p. 10–5. 19115520

14. Rogol A.D., Roemmich J.N., and Clark P.A., Growth at puberty. J Adolesc Health, 2002. 31(6 Suppl): p. 192–200. 12470915

15. Rosenbaum M. and Leibel R.L., Clinical review 107: Role of gonadal steroids in the sexual dimorphisms in body composition and circulating concentrations of leptin. J Clin Endocrinol Metab, 1999. 84(6): p. 1784–9. 10372664

16. Kuk J.L., et al., Age-related changes in total and regional fat distribution. Ageing Res Rev, 2009. 8(4): p. 339–48. doi: 10.1016/j.arr.2009.06.001 19576300

17. Mott J.W., et al., Relation between body fat and age in 4 ethnic groups. American Journal of Clinical Nutrition, 1999. 69(5): p. 1007–1013. 10232643

18. Shungin D., et al., New genetic loci link adipose and insulin biology to body fat distribution. Nature, 2015. 518(7538): p. 187–96. doi: 10.1038/nature14132 25673412

19. Locke A.E., et al., Genetic studies of body mass index yield new insights for obesity biology. Nature, 2015. 518(7538): p. 197–206. doi: 10.1038/nature14177 25673413

20. Abdulnour J., et al., The effect of the menopausal transition on body composition and cardiometabolic risk factors: a Montreal-Ottawa New Emerging Team group study. Menopause, 2012. 19(7): p. 760–7. doi: 10.1097/gme.0b013e318240f6f3 22395454

21. Douchi T., et al., Precedence of bone loss over changes in body composition and body fat distribution within a few years after menopause. Maturitas, 2003. 46(2): p. 133–138. 14559384

22. Morita Y., et al., Precedence of the shift of body-fat distribution over the change in body composition after menopause. Journal of Obstetrics and Gynaecology Research, 2006. 32(5): p. 513–516. 16984520

23. Bromberger J.T., et al., Prospective study of the determinants of age at menopause. Am J Epidemiol, 1997. 145(2): p. 124–33. 9006309

24. Gold E.B., et al., Factors associated with age at natural menopause in a multiethnic sample of midlife women. Am J Epidemiol, 2001. 153(9): p. 865–74. 11323317

25. Gold E.B., et al., Factors Related to Age at Natural Menopause: Longitudinal Analyses From SWAN. Am J Epidemiol, 2013. 178(1): p. 70–83. doi: 10.1093/aje/kws421 23788671

26. Kooperberg C. and Leblanc M., Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet Epidemiol, 2008. 32(3): p. 255–63. doi: 10.1002/gepi.20300 18200600

27. Aschard H., et al., Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Hum Hered, 2010. 70(4): p. 292–300. doi: 10.1159/000323318 21293137

28. Speliotes E.K., et al., Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet, 2010. 42(11): p. 937–48. doi: 10.1038/ng.686 20935630

29. Heid I.M., et al., Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nature Genetics, 2010. 42(11): p. 949–60. doi: 10.1038/ng.685 20935629

30. Newton-Cheh C., et al., Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet, 2009. 41(6): p. 666–76. doi: 10.1038/ng.361 19430483

31. Teslovich T.M., et al., Biological, clinical and population relevance of 95 loci for blood lipids. Nature, 2010. 466(7307): p. 707–13. doi: 10.1038/nature09270 20686565

32. Morris A.P., et al., Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet, 2012. 44(9): p. 981–90. doi: 10.1038/ng.2383 22885922

33. Scott R.A., et al., Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet, 2012. 44(9): p. 991–1005. doi: 10.1038/ng.2385 22885924

34. Simino J., et al., Gene-Age Interactions in Blood Pressure Regulation: A Large-Scale Investigation with the CHARGE, Global BPgen, and ICBP Consortia. Am J Hum Genet, 2014. 95(1): p. 24–38. doi: 10.1016/j.ajhg.2014.05.010 24954895

35. Hindroff, L.A., et al., A Catalog of Published Genome-Wide Association Studies Available at www.genome.gov/gwastudies. 2010.

36. Horikoshi M., et al., New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat Genet, 2013. 45(1): p. 76–82. doi: 10.1038/ng.2477 23202124

37. Bradfield J.P., et al., A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet, 2012. 44(5): p. 526–31. doi: 10.1038/ng.2247 22484627

38. Graff M., et al., The influence of obesity-related single nucleotide polymorphisms on BMI across the life course: the PAGE study. Diabetes, 2013. 62(5): p. 1763–7. doi: 10.2337/db12-0863 23300277

39. Leitsalu L., et al., Cohort Profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int J Epidemiol, 2014.

40. Fehrmann R.S., et al., Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet, 2011. 7(8): p. e1002197. doi: 10.1371/journal.pgen.1002197 21829388

41. Luong A., et al., Molecular characterization of human acetyl-CoA synthetase, an enzyme regulated by sterol regulatory element-binding proteins. J Biol Chem, 2000. 275(34): p. 26458–66. 10843999

42. Wright F.A., et al., Heritability and genomics of gene expression in peripheral blood. Nat Genet, 2014. 46(5): p. 430–7. doi: 10.1038/ng.2951 24728292

43. Jansen R., et al., Sex differences in the human peripheral blood transcriptome. BMC Genomics, 2014. 15: p. 33. doi: 10.1186/1471-2164-15-33 24438232

44. Grove K.L., et al., A microarray analysis of sexual dimorphism of adipose tissues in high-fat-diet-induced obese mice. Int J Obes (Lond), 2010. 34(6): p. 989–1000.

45. Baumgartner B.G., et al., Identification of a novel modulator of thyroid hormone receptor-mediated action. PLoS One, 2007. 2(11): p. e1183. 18030323

46. Sala D., et al., Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes. J Clin Invest, 2014. 124(5): p. 1914–27. doi: 10.1172/JCI72327 24713655

47. Pers T.H., et al., Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun, 2015. 6: p. 5890. doi: 10.1038/ncomms6890 25597830

48. Veilleux A., et al., Glucocorticoid-induced androgen inactivation by aldo-keto reductase 1C2 promotes adipogenesis in human preadipocytes. Am J Physiol Endocrinol Metab, 2012. 302(8): p. E941–9. doi: 10.1152/ajpendo.00069.2011 22275760

49. Yoon M., The role of PPARalpha in lipid metabolism and obesity: focusing on the effects of estrogen on PPARalpha actions. Pharmacol Res, 2009. 60(3): p. 151–9. doi: 10.1016/j.phrs.2009.02.004 19646654

50. Yang J., et al., Common SNPs explain a large proportion of the heritability for human height. Nat Genet, 2010. 42(7): p. 565–9. doi: 10.1038/ng.608 20562875

51. Hardy R., et al., Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet, 2010. 19(3): p. 545–52. doi: 10.1093/hmg/ddp504 19880856

52. Hertel J.K., et al., FTO, type 2 diabetes, and weight gain throughout adult life: a meta-analysis of 41,504 subjects from the Scandinavian HUNT, MDC, and MPP studies. Diabetes, 2011. 60(5): p. 1637–44. doi: 10.2337/db10-1340 21398525

53. den Hoed M., et al., Genetic susceptibility to obesity and related traits in childhood and adolescence: influence of loci identified by genome-wide association studies. Diabetes, 2010. 59(11): p. 2980–8. doi: 10.2337/db10-0370 20724581

54. Graff M., et al., Estimation of genetic effects on BMI during adolescence in an ethnically diverse cohort: The National Longitudinal Study of Adolescent Health. Nutr Diabetes, 2012. 2: p. e47. doi: 10.1038/nutd.2012.20 23168566

55. Murphy R.A., et al., Candidate Gene Association Study of BMI-Related Loci, Weight, and Adiposity in Old Age. J Gerontol A Biol Sci Med Sci, 2013. 68(6): p. 661–6. doi: 10.1093/gerona/gls227 23160366

56. Elks C.E., et al., Adult obesity susceptibility variants are associated with greater childhood weight gain and a faster tempo of growth: the 1946 British Birth Cohort Study. Am J Clin Nutr, 2012. 95(5): p. 1150–6. doi: 10.3945/ajcn.111.027870 22456663

57. Sovio U., et al., Association between common variation at the FTO locus and changes in body mass index from infancy to late childhood: the complex nature of genetic association through growth and development. PLoS Genet, 2011. 7(2): p. e1001307. doi: 10.1371/journal.pgen.1001307 21379325

58. Graff M., et al., Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum Mol Genet, 2013. 22(17): p. 3597–607. doi: 10.1093/hmg/ddt205 23669352

59. Do R., et al., Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet, 2013. 45(11): p. 1345–52. doi: 10.1038/ng.2795 24097064

60. Fromm-Dornieden C., et al., Extrinsic and intrinsic regulation of DOR/TP53INP2 expression in mice: effects of dietary fat content, tissue type and sex in adipose and muscle tissues. Nutr Metab (Lond), 2012. 9(1): p. 86.

61. McCarthy M.I., Genomics, type 2 diabetes, and obesity. N Engl J Med, 2010. 363(24): p. 2339–50. doi: 10.1056/NEJMra0906948 21142536

62. Travers M.E. and McCarthy M.I., Type 2 diabetes and obesity: genomics and the clinic. Hum Genet, 2011. 130(1): p. 41–58. doi: 10.1007/s00439-011-1023-8 21647602

63. Li Y., et al., Genotype Imputation. Annual Review of Genomics and Human Genetics, 2009. 10(1): p. 387–406.

64. Marchini J., et al., A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet, 2007. 39(7): p. 906–913. 17572673

65. Guan Y. and Stephens M., Practical Issues in Imputation-Based Association Mapping. PLoS Genet, 2008. 4(12): p. e1000279. doi: 10.1371/journal.pgen.1000279 19057666

66. Voight B.F., et al., The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet, 2012. 8(8): p. e1002793. doi: 10.1371/journal.pgen.1002793 22876189

67. Li Y., et al., MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genetic Epidemiology, 2010. 34(8): p. 816–34. doi: 10.1002/gepi.20533 21058334

68. Aulchenko Y.S., Struchalin M.V., and van Duijn C.M., ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics, 2010. 11: p. 134. doi: 10.1186/1471-2105-11-134 20233392

69. Aulchenko Y.S., et al., GenABEL: an R library for genome-wide association analysis. Bioinformatics, 2007. 23(10): p. 1294–6. 17384015

70. Abecasis G.R. and Wigginton J.E., Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers. Am J Hum Genet, 2005. 77(5): p. 754–67. 16252236

71. Purcell S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 2007. 81: p. 559–575. 17701901

72. Kutalik Z., et al., Methods for testing association between uncertain genotypes and quantitative traits. Biostatistics, 2011. 12(1): p. 1–17. doi: 10.1093/biostatistics/kxq039 20543033

73. Winkler T.W., et al., Quality control and conduct of genome-wide association meta-analyses. Nat Protoc, 2014. 9(5): p. 1192–212. doi: 10.1038/nprot.2014.071 24762786

74. Devlin B. and Roeder K., Genomic control for association studies. Biometrics, 1999. 55(4): p. 997–1004. 11315092

75. Willer C.J., Li Y., and Abecasis G.R., METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 2010. 26(17): p. 2190–1. doi: 10.1093/bioinformatics/btq340 20616382

76. Benjamini Y. and Hochberg Y., Controlling the False Discovery Rate—a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological, 1995. 57(1): p. 289–300.

77. Rowland M.L., Self-reported weight and height. Am J Clin Nutr, 1990. 52(6): p. 1125–33. 2239790

78. Elgar F.J., et al., Validity of self-reported height and weight and predictors of bias in adolescents. J Adolesc Health, 2005. 37(5): p. 371–5. 16227121

79. Keith S.W., et al., Use of self-reported height and weight biases the body mass index-mortality association. Int J Obes (Lond), 2011. 35(3): p. 401–8.

80. Purcell S., et al., PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007. 81(3): p. 559–75. 17701901

81. Genomes Project, C., et al., A map of human genome variation from population-scale sequencing. Nature, 2010. 467(7319): p. 1061–73. doi: 10.1038/nature09534 20981092

82. Hindorff, L.A., et al., A Catalog of Published Genome-Wide Association Studies. 2010.

83. Lango Allen, H., et al., Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature, 2010. 467(7317): p. 832–8. doi: 10.1038/nature09410 20881960

84. Kamatani Y., et al., Genome-wide association study of hematological and biochemical traits in a Japanese population. Nature Genetics, 2010. 42(3): p. 210–5. doi: 10.1038/ng.531 20139978

85. Franke A., et al., Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nature Genetics, 2010. 42(12): p. 1118–25. doi: 10.1038/ng.717 21102463

86. Sawcer S., et al., Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature, 2011. 476(7359): p. 214–9. doi: 10.1038/nature10251 21833088

87. Wang K.S., Liu X.F., and Aragam N., A genome-wide meta-analysis identifies novel loci associated with schizophrenia and bipolar disorder. Schizophr Res, 2010. 124(1–3): p. 192–9. doi: 10.1016/j.schres.2010.09.002 20889312

88. Cirulli E.T., et al., Common genetic variation and performance on standardized cognitive tests. Eur J Hum Genet, 2010. 18(7): p. 815–20. doi: 10.1038/ejhg.2010.2 20125193

89. Estrada K., et al., Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nature Genetics, 2012. 44(5): p. 491–501. doi: 10.1038/ng.2249 22504420

90. Gieger C., et al., New gene functions in megakaryopoiesis and platelet formation. Nature, 2011. 480(7376): p. 201–8. doi: 10.1038/nature10659 22139419

91. Need A.C., et al., A genome-wide study of common SNPs and CNVs in cognitive performance in the CANTAB. Hum Mol Genet, 2009. 18(23): p. 4650–61. doi: 10.1093/hmg/ddp413 19734545

92. Calvano S.E., et al., A network-based analysis of systemic inflammation in humans. Nature, 2005. 437(7061): p. 1032–7. 16136080

93. Yang J., et al., GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet, 2011. 88(1): p. 76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468

94. Kutalik Z., et al., Novel method to estimate the phenotypic variation explained by genome-wide association studies reveals large fraction of the missing heritability. Genet Epidemiol, 2011. 35(5): p. 341–9. doi: 10.1002/gepi.20582 21465548

95. Heid I.M., et al., Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet, 2010. 42(11): p. 949–960. doi: 10.1038/ng.685 20935629

96. Johnson A.D., et al., SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics, 2008. 24(24): p. 2938–9. doi: 10.1093/bioinformatics/btn564 18974171

97. Kumar P., Henikoff S., and Ng P.C., Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc, 2009. 4(7): p. 1073–81. doi: 10.1038/nprot.2009.86 19561590

98. Wang K., Li M., and Hakonarson H., ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res, 2010. 38(16): p. e164. doi: 10.1093/nar/gkq603 20601685

99. Boyle A.P., et al., Annotation of functional variation in personal genomes using RegulomeDB. Genome Res, 2012. 22(9): p. 1790–7. doi: 10.1101/gr.137323.112 22955989