1 Department of Epidemiology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina
2 Department of Internal Medicine, Wake Forest University, Winston Salem, North Carolina
3 Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
4 Division of Epidemiology, University of Minnesota, Minneapolis, Minnesota
5 Department of Preventive Medicine, University of Alabama Birmingham, Birmingham, Alabama
6 Section of Neurogenetics, Boston University, Boston, Massachusetts
7 Cardiovascular Genetics Division, University of Utah, Salt Lake City, Utah
8 Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas
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ABSTRACT |
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Type 2 diabetes is a common disease with susceptibility determined by multiple genetic and environmental factors. Several rare mutations have been identified that influence diabetes susceptibility, yet only one common susceptibility gene has been identified, the CAPN10 gene (1), and it is associated with diabetes in some study populations but not others (2). Genome-wide scans for linkage have been conducted in several populations, localizing a number of common chromosomal regions possibly harboring susceptibility genes on chromosomes 1q21-24 (3), 2q37 (1), 12q24 (4), and 20q (3). Such variability in linkage findings reported between populations underscores the probable genetic heterogeneity of type 2 diabetes. Thus, we conducted a genome scan of liability to diabetes in an attempt to localize new quantitative trait loci (QTLs) influencing diabetes susceptibility and/or to provide new evidence in support of previously identified QTLs.
This study examined 3,153 participants in the Hypertension Genetic Epidemiology Network (HyperGEN) of the Family Blood Pressure Program. HyperGEN methods, participant information, and exclusion criteria are detailed in the online appendix (available at http://care.diabetesjournals.org). The distribution of covariates by diabetes status for the combined sample is shown in Table 1. The prevalence of type 2 diabetes was 18% (n = 567), with a higher prevalence of female (64%) compared with male (36%) subjects. In African-American participants, 21% were classified as diabetic compared with 15% of the Caucasian sample. Overall, 385 of 437 HyperGEN families had at least 1 affected participant, with 98 families having 2 diabetic family members, 11 families having 3 diabetic members, 3 families having 4 diabetic members, and 1 family with 5 diabetic members.
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A secondary significant genome-wide LOD score of 3.1 was detected on chromosome eight at map location 65 cM (8q11.23, nearest marker GATA8G10) for the combined sample after PBF adjustment (Fig. 1). This LOD score was only slightly attenuated (LOD = 2.5) in the absence of PBF adjustment. The robust P value that corresponds to our observed LOD score is 0.003, indicating that our nominal P value may have slightly overstated the evidence of linkage. This finding also supports three previous genome-wide scans. Elbein et al. (9) detected linkage to type 2 diabetes in 379 members of 19 multiplex Caucasian families using variance component methods (LOD = 1.4). Additionally, Mitchell et al. (10) and Chagnon et al. (11) both detected linkage to BMI measures in 10 large Mexican-American families (LOD = 3.2) and 364 Caucasian sibpairs (LOD = 2.0), employing variance component and affected sibpair linkage methods, respectively.
Evidence for linkage was also detected on chromosome 17 map location 53 cM (17q11.2, nearest marker GGAA9D03) for the combined (LOD = 3.0) and the Caucasian samples (LOD = 2.7). This signal appears to attenuate after PBF adjustment, which may indicate shared genetic effects between obesity and diabetes. The robust P value that corresponds to our observed LOD score is 0.003, indicating that our nominal P value may have slightly overstated the evidence of linkage. Likewise, this finding supports numerous genome-wide scans related to obesity measures, such as plasma leptin and adiponectin levels and BMI. Kissebah et al. (12), Wu et al. (13), and Comuzzie et al. (14) detected linkage to plasma leptin (LOD = 5.0), BMI (LOD = 2.5), and adiponectin (LOD = 1.7), respectively, using variance component linkage methods. In addition to the QTL on chromosomes 22, 8, and 17, we found several other regions demonstrating suggestive evidence for linkage on chromosomes 1, 2, 5, 14, and 19. Of these locations, all have been detected (based on various criteria) in other genome screens of diabetes-related phenotypes with the exception of the signal at 14q21.1.
Several candidate genes underlie the 1-cM LOD unit drop support interval (9 cM, 8.3 megabases) corresponding to the linkage signal observed on 22q12.1, including the galanin receptor 3 (GALR3), leukemia inhibitory factor (LIF), and the apolipoprotein L1-L4 (APOL1-4) cluster. Neuroanatomical distribution of GALR3 mRNA suggests a mediation of galanin on food intake, fluid homeostasis, and cardiovascular function (15). LIF is a member of the interlukin 6 cytokine family and is a candidate for leptin circumvention and weight loss (16). APOL1-4 represents a cluster of tandem gene duplications expressed in various human organs, including vascular tissue and endothelial cells. A lipid-related candidate gene may be relevant to diabetes susceptibility, as insulin resistance is associated with a lipid profile typified by increased triglyceride and LDL cholesterol levels and a predominance of small LDL particles. Moreover, North et al. (17) demonstrated common genetic effects on diabetes status and lipid values in American-Indian families participating in the Strong Heart Family Study.
Candidate genes within the 1-cM LOD unit drop support interval (24 cM, 33.3 megabases) surrounding the 8q11.23 QTL include the adrenergic ß-3 receptor (ADRB3), corticotropin releasing hormone (CRH), and fibroblast growth factor receptor 1 (FGFR1). ADRB3 is expressed in adipose tissue where it mediates lipolysis. Furthermore, Xiang et al. (18) detected an association between the ADRB3 Trp64Arg mutation and BMI (P = 0.019) and waist circumference (P = 0.045) in type 2 diabetic Chinese subjects. CRH secretion by the paraventricular nucleus of the hypothalamus is one of the first steps in the mammalian stress response. Hart et al. (19) perturbed FGFR1 dominant-negative signaling in the mouse pancreas and observed a phenotype typical of type 2 diabetes. They demonstrated that mice with attenuated FGFR1 signaling developed diabetes with age, exhibited decreased numbers of ß-cells, and had increased proinsulin content in ß-cells.
Genetic heterogeneity of common complex traits is a long-standing problem for linkage studies. When genetic heterogeneity is present, there is reduced power for detection of QTLs. Genetic heterogeneity may be reduced by stratifying possible etiological subgroups, incorporating gene-environment and gene-gene interactions, and accounting for environmental influences of disease. Our study design may have reduced the problem of genetic heterogeneity, as we conducted a genome scan for diabetes in a largely hypertensive population.
This study may have been limited by our inability to adjust for potential disease confounders. Moreover, we lacked information on the age of onset of diabetes status for 99 participants who we classified as diabetic, which may have introduced type 1 diabetic participants into our sample. The population we examined was selected for hypertension, a phenotype correlated with diabetes; however, we have not used an ascertainment correction in these analyses. This is not likely a strong ascertainment effect because enriching our sample for hypertension-related susceptibility alleles would not be expected to strongly influence the distribution of diabetes QTLs. Neglecting a modest ascertainment effect would only result in some loss of information and a reduced power for our analysis, making the results presented here conservative, without any increase in the type I error rate (20). Nonetheless, these loci were detected in a largely hypertensive sample and may not be applicable to the general population.
We assumed that pleiotropic effects between type 2 diabetes and obesity QTLs were possible; yet we know that obesity is an important confounder, such that only by accounting for the variance due to obesity effects would we be able to identify type 2 diabetes QTLs. Indeed, the QTL-specific genetic signals varied substantially on chromosomes 22, 8, and 17, after adjustment for PBF. For example, the LOD score on chromosome 22q decreased from 3.4 to 2.0 in the combined-races sample after adjustment for PBF. Similarly, the LOD score on chromosome 17 decreased from 3.0 to 0.60 in the combined-races sample after adjustment for PBF, indicating the possibility of shared genetic effects between PBF and diabetes susceptibility. In contrast, upon adjustment for PBF, the QTL on chromosome 8q increased from 2.5 to 3.1 in the combined- races sample, suggesting that adjustment for PBF slightly improved our ability to detect this particular QTL. Further research is needed to test if there is a joint action of genes on diabetes status and PBF and whether modeling such an effect would improve our ability to localize diabetes susceptibility genes.
In conclusion, we found highly significant linkage results for diabetes susceptibility QTLs on chromosomes 22, 8, and 17. Specifically, the signal on 22q12.1 overlaps positive findings for diabetes status, abdominal subcutaneous fat, and plasma glucose and harbors several candidate genes, including GALR3, LIF, and the APOL1-L4 cluster. The 8q11.23 signal supports previous findings for diabetes status and BMI and points to several candidate genes: ADRB3, CRH, and FGFR1. Additionally, all QTLs that demonstrated suggestive evidence of linkage with type 2 diabetes support earlier studies, with the exception of the QTL at 14q12.1. Ultimately, these findings suggest that multiple genes may regulate susceptibility to diabetes and that further research in this population may help to identify additional candidate genes that interact with the environment to influence diabetes susceptibility.
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RESEARCH DESIGN AND METHODS |
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We conducted a genome scan of liability to diabetes using the variance component approach as implemented in SOLAR (Sequential Oligogenic Linkage Analysis Routines) (25). This approach is applicable to dichotomous traits under the assumption that an individual is classified as affected if an underlying genetically influenced liability, which is presumed to have a multivariate normal distribution, exceeds a certain threshold (online appendix).
We computed exact conditional probabilities using the Lander-Green algorithm in MERLIN (24). Allele frequencies from the random sample were calculated separately in African-American and Caucasian groups. The identical by descent probabilities computed by MERLIN were then combined into a single set of multipoint identical by descent files in the SOLAR format using the program Mer2sol (available at http://taxa.epi.umn.edu/mer2sol/), developed by Michael Miller at the University of Minnesota (online appendix).
We estimated the heritability of type 2 diabetes status using a minimum adjustment strategy with the covariates race/study center, age, age2, and sex. Although glycemic control deteriorates with diabetes progression, and some individuals become less obese (26) whereas others become more obese, models were also estimated with PBF. The heritability of type 2 diabetes status was also estimated with PBF, as previous research has demonstrated that obesity and body fat distribution are the strongest risk factors for the development of diabetes (27). Two participants with biologically implausible negative PBF values and 381 individuals with missing PBF measurements were excluded from these analyses. No additional covariate adjustments were made because we investigated prevalent diabetes status and did not know the true covariate values at diabetes onset.
As nonnormal trait distributions substantially increase type I error in variance component models, all continuously distributed covariates were examined for extreme observations outside 4 SDs (28). No covariates had observations that fell outside 4 SDs.
To verify our major linkage findings, we calculated the empirical distribution of the LOD scores under the assumption of multivariate normality, using 10,000 replicates and simulation methods incorporated into SOLAR (5). We used the empirical distribution of the simulated LOD scores to assign percentiles to each replicate and calculated an expected test statistic on the basis of the percentile. SOLAR produces a correction constant by regressing the expected LOD scores on the observed simulated LOD scores, which we used to determine the robust P value (36).
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ACKNOWLEDGMENTS |
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In addition, we thank the HyperGEN study participants. We also acknowledge the HyperGEN participating institutions and principal staff: Network Center/University of Utah Field Center: Steven C. Hunt, Roger R. Williams (deceased), Hilary Coon, Paul N. Hopkins, Janet Hood, Lily Wu, Jan Skuppin; University of Alabama at Birmingham Field Center: Albert Oberman, Cora E. Lewis, Michael T. Weaver, Phillip Johnson, Susan Walker, Christie Oden; Boston University/Framingham Field Center: R. Curtis Ellison, Richard H. Myers, Yuqing Zhang, Luc Djoussé, Jemma B. Wilk, Greta Lee Splansky; University of Minnesota Field Center: Donna Arnett, Aaron R. Folsom, Mike Miller, Jim Pankow, Gregory Feitl, Barb Lux; University of North Carolina Field Center: Gerardo Heiss, Barry I. Freedman, Kari North, Kathryn Rose, Amy Haire; Data Coordinating Center, Washington University: D.C. Rao, Michael A. Province, Ingrid B. Borecki, Avril Adelman, Derek Morgan, Karen Schwander, David Lehner, Aldi Kraja, Stephen Mandel; Central Biochemistry Lab, University of Minnesota: John H. Eckfeldt, Ronald C. McGlennen, Michael Y. Tsai, Catherine Leiendecker-Foster, Greg Rynders, Jean Bucksa; Molecular Genetics Laboratory, University of Utah: Mark Leppert, Steven C. Hunt, Jean-Marc Lalouel, Robert Weiss; National Heart, Lung, & Blood Institute: Susan E. Old, Millicent Higgins (retired), Cashell Jaquish, Martha Lundberg, Mariana Gerschenson.
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FOOTNOTES |
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Address correspondence and reprint requests to Kari E. North, PhD, Department of Epidemiology, CB no. 8050, Suite 306, Bank of America, University of North Carolina Chapel Hill, Chapel Hill, NC 27514. E-mail: kari_north{at}unc.edu
Received for publication June 16, 2004 and accepted in revised form August 23, 2004
HyperGEN, Hypertension Genetic Epidemiology Network; LOD, logarithm of odds; MERLIN, multipoint engine for rapid likelihood inference; PBF, percent body fat; QTL, quantitative trait locus
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REFERENCES |
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