Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies
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Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.
KeywordsLifestyle Genetics Genealogy Biobank Complex disease Scandinavia
We are grateful to the study participants whose data have contributed to the GLACIER and VIKING Studies through the studies that comprise the Northern Sweden Health and Disease Study. We are also thankful for the work and expertise of the many investigators and support staff who collected and managed data and biomaterials within those studies. The VIKING and GLACIER Studies have been primarily funded by the Swedish Research Council, Novo Nordisk Foundation, Swedish Heart Lung Foundation, Albert Påhlsson Foundation and the Swedish Diabetes Association (all grants to PWF).
Compliance with Ethics Guidelines
Conflict of Interest
Azra Kurbasic declares that she has no conflict of interest.
Alaitz Poveda has received research support through a grant from the Basque Government.
Yan Chen declares that he has no conflict of interest.
Åsa Ågren declares that she has no conflict of interest.
Elisabeth Engberg declares that she has no conflict of interest.
Frank B. Hu declares that he has no conflict of interest.
Ingegerd Johansson declares that she has no conflict of interest.
Ines Barroso, along with her spouse, owns stock in GlaxoSmithKline and Incyte Corporation.
Anders Brändström declares that he has no conflict of interest.
Göran Hallmans declares that he has no conflict of interest.
Frida Renström declares that she has no conflict of interest.
Paul W. Franks declares that he has no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of outstanding importance
- 1.•Langenberg C Sharp SJ, Franks PW, Scott RA, Deloukas P, Forouhi NG et al. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med. 2014;11(5). This paper describes the largest study of gene-lifestyle interactions in incident type 2 diabetes to date. The study found that genetic effects on diabetes are greater at a younger age and in leaner participants. No evidence of interaction with Mediterranean diet or physical activity were observed.Google Scholar
- 3.Pearson ER, Starkey BJ, Powell RJ, Gribble FM, Clark PM, Hattersley AT. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet. 2003;362(9392):1275–81.Google Scholar
- 5.Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39.Google Scholar
- 6.•Hatzikotoulas K, Gilly A, Zeggini E. Using population isolates in genetic association studies. Brief Funct Genomics. 2014. This paper describes statistical approaches to genetic analysis in heavily admixed populations.Google Scholar
- 8.Mannucci E, Dicembrini I, Lauria A, Pozzilli P. Is glucose control important for prevention of cardiovascular disease in diabetes? Diabetes Care. 2013;36 Suppl 2:S259–63.Google Scholar
- 9.Santana S, Recuero M, Bullido MJ, Valdivieso F, Aldudo J. Herpes simplex virus type I induces the accumulation of intracellular beta-amyloid in autophagic compartments and the inhibition of the non-amyloidogenic pathway in human neuroblastoma cells. Neurobiol Aging. 2012;33(2):430.e19. 33.Google Scholar
- 10.Ball MJ, Lukiw WJ, Kammerman EM, Hill JM. Intracerebral propagation of Alzheimer's disease: strengthening evidence of a herpes simplex virus etiology. Alzheimers Dement. 2013;9(2):169–75.Google Scholar
- 11.Siscovick DS, Schwartz SM, Corey L, Grayston JT, Ashley R, Wang SP et al. Chlamydia pneumoniae, herpes simplex virus type 1, and cytomegalovirus and incident myocardial infarction and coronary heart disease death in older adults : the Cardiovascular Health Study. Circulation. 2000;102(19):2335–40.Google Scholar
- 12.Caselli RJ, Dueck AC, Osborne D, Sabbagh MN, Connor DJ, Ahern GL et al. Longitudinal modeling of age-related memory decline and the APOE epsilon4 effect. N Engl J Med. 2009;361(3):255–63.Google Scholar
- 13.Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83.Google Scholar
- 14.Moore LE, Brennan P, Karami S, Menashe I, Berndt SI, Dong LM et al. Apolipoprotein E/C1 locus variants modify renal cell carcinoma risk. Cancer Res. 2009;69(20):8001–8.Google Scholar
- 18.Nilson F, Moniruzzaman S, Andersson R. A comparison of hip fracture incidence rates among elderly in Sweden by latitude and sunlight exposure. Scand J Public Health, 2013.Google Scholar
- 20.Wactawski-Wende J, Kotchen JM, Anderson GL, Assaf AR, Brunner RL, O'Sullivan MJ et al. Calcium plus vitamin D supplementation and the risk of colorectal cancer. N Engl J Med. 2006;354(7):684–96.Google Scholar
- 22.Strohmaier S, Edlinger M, Manjer J, Stocks T, Bjorge T, Borena W et al. Total serum cholesterol and cancer incidence in the Metabolic syndrome and Cancer Project (Me-Can). PLoS One. 2013;8(1):e54242.Google Scholar
- 23.Ulmer H, Bjorge T, Concin H, Lukanova A, Manjer J, Hallmans G et al. Metabolic risk factors and cervical cancer in the metabolic syndrome and cancer project (Me-Can). Gynecol Oncol. 2012;125(2):330–5.Google Scholar
- 24.Haggstrom C, Rapp K, Stocks T, Manjer J, Bjorge T, Ulmer H et al. Metabolic factors associated with risk of renal cell carcinoma. PLoS One. 2013;8(2):e57475.Google Scholar
- 25.Lindkvist B, Almquist M, Bjorge T, Stocks T, Borena W, Johansen D et al. Prospective cohort study of metabolic risk factors and gastric adenocarcinoma risk in the Metabolic Syndrome and Cancer Project (Me-Can). Cancer Causes Control. 2013;24(1):107–16.Google Scholar
- 26.•Haggstrom C, Stocks T, Ulmert D, Bjorge T, Ulmer H, Hallmans G et al. Prospective study on metabolic factors and risk of prostate cancer. Cancer. 2012;118(24):6199–206. This study describes observational analyses linking features of the metabolic syndrome and prostate cancer in European adults. The study includes a cohort from the Northern Sweden Biobank, where the GLACIER and VIKING Studies are set.Google Scholar
- 27.Nagel G, Stocks T, Spath D, Hjartaker A, Lindkvist B, Hallmans G et al. Metabolic factors and blood cancers among 578,000 adults in the metabolic syndrome and cancer project (Me-Can). Ann Hematol. 2012;91(10):1519–31.Google Scholar
- 28.Stocks T, Van Hemelrijck M, Manjer J, Bjorge T, Ulmer H, Hallmans G et al. Blood pressure and risk of cancer incidence and mortality in the Metabolic Syndrome and Cancer Project. Hypertension. 2012;59(4):802–10.Google Scholar
- 30.Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38(3):320–3.Google Scholar
- 31.Seshagiri S, Stawiski EW, Durinck S, Modrusan Z, Storm EE, Conboy CB et al. Recurrent R-spondin fusions in colon cancer. Nature. 2012;488(7413):660–4.Google Scholar
- 32.•Helgason A, Palsson S, Thorleifsson G, Grant SF, Emilsson V, Gunnarsdottir S et al. Refining the impact of TCF7L2 gene variants on type 2 diabetes and adaptive evolution. Nat Genet. 2007;39(2):218–25. This paper provides an eloquent example of how genetic association signals can be refined using pedigree-based data in a population isolate.Google Scholar
- 33.Koontz JI, Soreng AL, Nucci M, Kuo FC, Pauwels P, van Den Berghe H et al. Frequent fusion of the JAZF1 and JJAZ1 genes in endometrial stromal tumors. Proc Natl Acad Sci U S A. 2001;98(11):6348–53.Google Scholar
- 34.Stevens VL, Ahn J, Sun J, Jacobs EJ, Moore SC, Patel AV et al. HNF1B and JAZF1 genes, diabetes, and prostate cancer risk. Prostate. 2010;70(6):601–7.Google Scholar
- 35.Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3(7):e115.Google Scholar
- 36.Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353–61. 361e1-2.Google Scholar
- 37.Jacobs KB, Yeager M, Zhou W, Wacholder S, Wang Z, Rodriguez-Santiago B et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat Genet. 2012;44(6):651–8.Google Scholar
- 38.Bonnefond A, Skrobek B, Lobbens S, Eury E, Thuillier D, Cauchi S et al. Association between large detectable clonal mosaicism and type 2 diabetes with vascular complications. Nat Genet. 2013;45(9):1040–3.Google Scholar
- 39.••Hallmans G, Agren A, Johansson G, Johansson A, Stegmayr B, Jansson JH et al. Cardiovascular disease and diabetes in the Northern Sweden Health and Disease Study Cohort - evaluation of risk factors and their interactions. Scand J Public Health Suppl. 2003;61:18–24. This paper describes the Northern Sweden Health and Disease Study, the collection of cohorts making up the Northern Sweden Biobank, within which the VIKING and GLACIER Studies are set.Google Scholar
- 40.Johansson G, Wikman A, Ahren AM, Hallmans G, Johansson I. Underreporting of energy intake in repeated 24-hour recalls related to gender, age, weight status, day of interview, educational level, reported food intake, smoking habits and area of living. Public Health Nutr. 2001;4(4):919–27.Google Scholar
- 41.••Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R. Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr. 2002;5(3):487–96. This paper describes some of the dietary methods and available data in the GLACIER and VIKING Studies.Google Scholar
- 42.Sullivan M, Karlsson J, Bengtsson C, Furunes B, Lapidus L, Lissner L. "The Goteborg Quality of Life Instrument"–a psychometric evaluation of assessments of symptoms and well-being among women in a general population. Scand J Prim Health Care. 1993;11(4):267–75.Google Scholar
- 43.Karasek R, Theorell T. Healthy work : stress, productivity, and the reconstruction of working life. New York: Basic Books; 1990.Google Scholar
- 44.Theorell T, Perski A, Akerstedt T, Sigala F, Ahlberg-Hulten G, Svensson J et al. Changes in job strain in relation to changes in physiological state. A longitudinal study. Scand J Work Environ Health. 1988;14(3):189–96.Google Scholar
- 45.Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG, World Health Organization. Dept. of Mental Health and Substance Dependence. AUDIT : the Alcohol Use Disorders Identification Test : guidelines for use in primary health care. 2nd ed. Geneva: World Health Organization; 2001. 38 p.Google Scholar
- 47.Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8(8):e1002793.Google Scholar
- 48.Einarsdottir E, Egerbladh I, Beckman L, Holmberg D, Escher SA. The genetic population structure of northern Sweden and its implications for mapping genetic diseases. Hereditas. 2007;144(5):171–80.Google Scholar
- 49.Vikström P, Edvinsson S, Brändström A. Longitudinal databases –sources for analyzing the life course: characteristics, difficulties and possibilities. Hist Comput. 2004;14:1–2.Google Scholar
- 50.Mandemakers, K, Dillon L. Best Practices with Large Databases on Historical Populations. Historical Methods, 2004. 37(1).Google Scholar
- 51.Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J et al. Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies. Diabetes Care. 2010;33(12):2684–91.Google Scholar
- 52.Kanoni S, Nettleton JA, Hivert MF, Ye Z, van Rooij FJ, Shungin D et al. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes. 2011;60(9):2407–16.Google Scholar
- 53.Nettleton JA, Hivert MF, Lemaitre RN, McKeown NM, Mozaffarian D, Tanaka T et al. Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts. Am J Epidemiol. 2013;177(2):103–15.Google Scholar
- 54.Hruby A, Ngwa JS, Renstrom F, Wojczynski MK, Ganna A, Hallmans G et al. Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies. J Nutr. 2013;143(3):345–53.Google Scholar
- 55.Scott RA, Chu AY, Grarup N, Manning AK, Hivert MF, Shungin D et al. No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels. Diabetes. 2012;61(5):1291–6.Google Scholar
- 56.Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44(6):659–69.Google Scholar
- 57.Kilpelainen TO, Qi L, Brage S, Sharp SJ, Sonestedt E, Demerath E et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 2011;8(11):e1001116.Google Scholar
- 58.Varga TV, Hallmans G, Hu FB, Renstrom F, Franks PW. Smoking status, snus use, and variation at the CHRNA5-CHRNA3-CHRNB4 locus in relation to obesity: the GLACIER study. Am J Epidemiol. 2013;178(1):31–7.Google Scholar
- 59.Freathy RM, Kazeem GR, Morris RW, Johnson PC, Paternoster L, Ebrahim S et al. Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index. Int J Epidemiol. 2011;40(6):1617–28.Google Scholar
- 60.Ahmad S, Rukh G, Varga TV, Ali A, Kurbasic A, Shungin D et al. Gene x physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry. PLoS Genet. 2013;9(7):e1003607.Google Scholar
- 61.Li S, Zhao JH, Luan J, Ekelund U, Luben RN, Khaw KT et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med, 2010. 7(8).Google Scholar
- 62.Tanaka T, Ngwa JS, van Rooij FJA, Zillikens MC, Wojczynski MK, Frazier-Wood AC et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am J Clin Nutr. 2013;97(6):1395–402.Google Scholar
- 63.Qi Q, Kilpelainen TO, Downer MK, Tanaka T, Smith CE, Sluijs I et al. FTO genetic variants, dietary intake and body mass index: insights from 177 330 individuals. Hum Mol Genet, 2014.Google Scholar
- 64.Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45(11):1345–52.Google Scholar
- 65.Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012;380(9841):572–80.Google Scholar
- 66.Consortium CAD, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45(1):25–33.Google Scholar
- 67.••Varga TV, Sonestedt E, Shungin D, Koivula RW, Hallmans G, Escher SA et al. Genetic determinants of long-term changes in blood lipid concentrations: 10-year follow-up of the GLACIER study. PLoS Genet. 2014;10(6):e1004388. This paper provides a detailed description of the GLACIER Study and reports data on the association of gene variants at 157 loci and long-term deteriorations in blood lipid concentrations in people from Northern Sweden.Google Scholar
- 68.Franks PW, Rolandsson O, Debenham SL, Fawcett KA, Payne F, Dina C et al. Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations. Diabetologia. 2008;51(3):458–63.Google Scholar
- 69.Sandhu MS, Weedon MN, Fawcett KA, Wasson J, Debenham SL, Daly A et al. Common variants in WFS1 confer risk of type 2 diabetes. Nat Genet. 2007;39(8):951–3.Google Scholar
- 70.Fawcett KA, Wheeler E, Morris AP, Ricketts SL, Hallmans G, Rolandsson O et al. Detailed investigation of the role of common and low-frequency WFS1 variants in type 2 diabetes risk. Diabetes. 2010;59(3):741–6.Google Scholar
- 71.Fontaine-Bisson B, Renstrom F, Rolandsson O, Magic, Payne F, Hallmans G et al. Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia. 2010;53(10):2155–62.Google Scholar
- 72.Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16.Google Scholar
- 73.••Renstrom F, Shungin D, Johansson I, Investigators M, Florez JC, Hallmans G et al. Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study. Diabetes. 2011;60(1):345–54. This paper provides a detailed description of the GLACIER Study and reports data on the association of gene variants at 16 loci and long-term deteriorations in blood glucose concentrations in people from Northern Sweden.Google Scholar
- 74.Albrechtsen A, Grarup N, Li Y, Sparso T, Tian G, Cao H et al. Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia. 2013;56(2):298–310.Google Scholar
- 75.Renstrom F, Payne F, Nordstrom A, Brito EC, Rolandsson O, Hallmans G et al. Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet. 2009;18(8):1489–96.Google Scholar
- 76.Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–90.Google Scholar
- 77.Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, Qi L et al. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. PLoS Genet. 2009;5(6):e1000508.Google Scholar
- 78.Berndt SI, Skibola CF, Joseph V, Camp NJ, Nieters A, Wang Z et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013;45(5):501–12.Google Scholar
- 79.Randall JC, Winkler TW, Kutalik Z, Berndt SI, Jackson AU, Monda KL et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 2013;9(6):e1003500.Google Scholar
- 80.Kong A, Masson G, Frigge ML, Gylfason A, Zusmanovich P, Thorleifsson G et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat Genet. 2008;40(9):1068–75.Google Scholar
- 81.Styrkarsdottir U, Thorleifsson G, Sulem P, Gudbjartsson DF, Sigurdsson A, Jonasdottir A et al. Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature. 2013;497(7450):517–20.Google Scholar
- 82.Palotie A, Widen E, Ripatti S. From genetic discovery to future personalized health research. N Biotechnol, 2012.Google Scholar
- 83.Mott R, Yuan W, Kaisaki P, Gan X, Cleak J, Edwards A et al. The architecture of parent-of-origin effects in mice. Cell. 2014;156(1–2):332–42.Google Scholar
- 84.Hoggart CJ, Venturini G, Mangino M, Gomez F, Ascari G, Zhao JH et al. Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index. PLoS Genet. 2014;10(7):e1004508.Google Scholar
- 85.Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG et al. The sequence of the human genome. Science. 2001;291(5507):1304–51.Google Scholar
- 86.Lander ES, Consortium IHGS, Linton LM, Birren B, Nusbaum C, Zody MC et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921.Google Scholar
- 87.Bennett ST, Barnes C, Cox A, Davies L, Brown C. Toward the 1,000 dollars human genome. Pharmacogenomics. 2005;6(4):373–82.Google Scholar
- 88.Mardis ER. Anticipating the 1,000 dollar genome. Genome Biol. 2006;7(7):112.Google Scholar