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Physiol. Genomics 34: 42-53, 2008. First published April 8, 2008; doi:10.1152/physiolgenomics.00267.2007
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Received 15 November 2007; accepted in final form 1 April 2008.
Physiological Genomics 34:42-53 (2008)
1094-8341/08 $8.00 © 2008 American Physiological Society

A meta-analysis of QTL for diabetes-related traits in rodents

Christian Schmidt 1,*, Nina P. Gonzaludo 1,*, Sarah Strunk 1, Stefan Dahm 2, Johannes Schuchhardt 3, Frank Kleinjung 3, Stefan Wuschke 1, Hans-Georg Joost 1 and Hadi Al-Hasani 1

1 Department of Pharmacology, German Institute for Human Nutrition Potsdam-Rehbrücke, Nuthetal
2 Department of Epidemiology, German Institute for Human Nutrition Potsdam-Rehbrücke, Nuthetal
3 Microdiscovery GmbH, Berlin, Germany


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Crossbreeding studies in rodents have identified numerous quantitative trait loci (QTL) that are linked to diabetes-related component traits. To identify genetic consensus regions implicated in insulin action and glucose homeostasis, we have performed a meta-analysis of genomewide linkage scans for diabetes-related traits. From a total of 43 published genomewide scans we assembled a nonredundant collection of 153 QTL for glucose levels, insulin levels, and glucose tolerance. Collectively, these studies include data from 48 different parental strains and >11,000 individual animals. The results of the studies were analyzed by the truncated product method (TPM). The analysis revealed significant evidence for linkage of glucose levels, insulin levels, and glucose tolerance to 27 different segments of the mouse genome. The most prominent consensus regions [localized to chromosomes 2, 4, 7, 9, 11, 13, and 19; logarithm of odds (LOD) scores 10.5–17.4] cover ~11% of the mouse genome and collectively contain the peak markers for 47 QTL. Approximately half of these genomic segments also show significant linkage to body weight and adiposity, indicating the presence of multiple obesity-dependent and -independent consensus regions for diabetes-related traits. At least 84 human genetic markers from genomewide scans and >80 candidate genes from human and rodent studies map into the mouse consensus regions for diabetes-related traits, indicating a substantial overlap between the species. Our results provide guidance for the identification of novel candidate genes and demonstrate the presence of numerous distinct consensus QTL regions with highly significant LOD scores that control glucose homeostasis. An interactive physical map of the QTL is available online at http://www.diabesitygenes.org.

diabesity; genomewide scan; hidden Markov model; quantitative trait loci; truncated product method


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
THE HERITABILITY of type 2 diabetes mellitus is well established. In humans, twin and adoption studies have clearly demonstrated an important role of genetic factors for the development of diabetes in response to a particular environment (50, 68). However, identification of relevant susceptibility genes has been difficult, mainly because the diabetes risk has been attributed to the interaction of multiple variant genes with the environment, where each of these genes only makes a small contribution to overall heritability (20). In addition to studies in humans, many genomewide scans for diabetes genes have been performed in mouse models (9). In these studies, F2 intercross or backcross populations of inbred parental mouse lines are analyzed for molecular markers across the genome to search for diabetes susceptibility loci (quantitative trait loci, QTL). As a result, numerous QTL for diabetes and diabetes-related phenotypes have been reported, but since the critical chromosomal segments of QTL usually comprise tens to hundreds of genes, identification and subsequent positional cloning of candidates has been mostly unsuccessful (9). Several QTL from independent crossbreeding experiments have been mapped to the same regions on a certain chromosome, but it is unclear whether these QTL are unique, or whether neighboring QTL represent different alleles at the same loci or reflect the involvement of independent genes. To identify genetic consensus regions implicated in insulin action and glucose homeostasis, we have performed a meta-analysis of genomewide linkage scans for diabetes-related traits. Our results indicate the presence of numerous consensus regions for diabetes-related traits and a substantial overlap between human and mouse studies.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
General search strategy, inclusion criteria, and selection of QTL.
We used key words including "QTL," "glucose," "insulin," "diabetes," and "diabesity" to conduct a literature search of NCBI's PubMed database. In addition, we performed a manual search of reference lists from original research papers and review articles, and we queried public databases including the Mouse Genome Informatics (MGI; www.informatics.jax.org) database and the Rat Genome Database (RGD; http://rgd.mcw.edu) for information on diabetes-related QTL. Studies that met all of the following criteria were included in our analysis: 1) studies of mice or rats; 2) crossbreeding experiments that involved genomewide scans; 3) studies providing measurements for phenotypic traits that directly assess levels of circulating glucose and insulin and glucose tolerance, respectively. In restricting the studies to genomewide scans, we ensured that each chromosomal region from each scan might contribute equally to the combined evidence for linkage with the respective trait across the studies. In a second step, the QTL from the studies were categorized into three component traits, circulating glucose and insulin levels and glucose tolerance. Finally, we excluded redundant QTL, i.e., QTL for multiple diabetes-related component traits from the same study that map to the same position.

Physical mapping and visualization of QTL.
Physical mapping of markers for QTL was performed by utilizing NCBI's Entrez-UniSTS database on the basis of NCBI Mus musculus Build 36, released February 2006, and the Rat Genome Sequencing Consortium (RGSC) assembly v3.4, released July 2006. For markers where physical positions were not available, we used the average position of at least three neighboring (<1 cM) markers based on map information provided by the MGI database or RGD. For mapping of the syntenic regions in the genomes of human, mouse, and rat, we used NCBI's HomoloGene database (Build 50.1; July 2006). The rat QTL were mapped to the mouse genome when at least 90% of the genes in the corresponding syntenic regions were orthologs. For visualization of QTL, we developed a web-based tool (http://www.diabesitygenes.org).

Meta-analysis and statistics.
The meta-analysis was performed as described previously (83). Briefly, the mouse genome (chromosomes 1-19 and X) was subdivided into 100 bins of approximately equal size (~25 Mbp; see Supplemental Table S1 for genomic coordinates), and the QTL were assigned to the corresponding bins according to the location of the peak markers.1 This bin size results from the median number of markers (120) used per study and ensures that on average there is at least one marker genotyped in each bin by the studies and the smaller chromosomes are made up of at least two bins. Nevertheless, because there are studies with <100 markers, some intervals are not covered by markers in every study. For the analysis we included all reported QTL that are linked with glucose levels, insulin levels, glucose tolerance, or all three traits combined. When a single study provided two or more QTL for the same trait that mapped to the same bin, we included these as one QTL with their highest logarithm of odds (LOD) score. We combined the reported LOD scores of the different studies by using the truncated product method (TPM) (86). Zaykin et al. (86) presented TPM, a generalization of Fisher's method, for combining P values where only P values below a certain threshold ({tau}) are considered. This addresses the fact that most studies only report statistically significant results, i.e., QTL with LOD scores exceeding a certain value (14, 41). Zaykin and coworkers derive the exact distribution of the product of these P values under the null hypothesis by conditioning on the number of P values less than {tau} and suggest a Monte Carlo method to compute the critical value when a large number of P values has to be combined. LOD scores were transformed to {chi}2-values of the log-likelihood ratio statistic by multiplying them with the factor 2 ln(10) (61). The corresponding P values were used as input for TPM; unreported P values were substituted with P = 0.5. In TPM we set the truncation point {tau} = 0.05 as suggested (86), so that the substituted P values did not affect the result of the TPM method. Calculations were performed with a C++ program provided by Dr. D. V. Zaykin. We regarded LOD > 4.3 as threshold of significance for linkage as proposed by Lander and Kruglyak (41).

Hidden Markov model-based prediction of haplotype regions.
Single nucleotide polymorphisms (SNPs) were obtained from the Mouse Phenome Database (http://www.jax.org/phenome/snp.html). For each pair of sequences contiguous haplotype regions were inferred from the SNP data with a three-state hidden Markov model (18, 54, 78). System states correspond to the possible situations encountered in comparing two sequences: "identical," "different," "missing data." On the basis of physical arguments conditional transition and emission probabilities were reduced to a set of four parameters optimized on select regions of sequence pairs. Viterbi algorithm was employed to extract predicted haplotype regions from the optimized model.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Selection of studies and crossbred populations.
In our survey of the literature (PubMed-indexed journals) we found >50 papers reporting QTL for diabetes-related traits in rodent crossbreeding experiments (not shown). Of these, 43 unique genomewide scans (30 for mouse, 13 for rat; published 1996–2006) for genetic markers that show linkage with glucose levels, insulin levels, and/or glucose tolerance were included in our meta-analysis (Table 1). In the majority of studies, fasting levels of glucose and insulin were measured as quantitative traits, in addition to glucose tolerance measured by intraperitoneal glucose tolerance test (IPGTT) (Table 1). To meta-analyze the data, we assembled a list of QTL categorized into three different component traits: glucose levels (61 QTL; mean LOD 3.7), insulin levels (50 QTL; mean LOD 3.8), and glucose tolerance (59 QTL; mean LOD 4.7), thus a total of 170 QTL for diabetes-related traits (134 QTL from mouse studies, 36 QTL from rat studies; mapping to 153 unique genetic loci; Table 2). In summary, 31 different parental mouse strains and 17 different parental rat strains (derived from 25 and 14 major strains, respectively) representing a wide range of phenotypic variation, including inbred, wild-derived inbred, and outbred rodent lines were used in these studies. All together, the studies contributed more than 8,700 offspring mice and 2,500 rats from F2 intercrosses and backcrosses (mean: 255 animals/study). Most studies (24) analyzed male animals; the mean age for trait determination was 20 mo (Table 1). The majority of the studies used microsatellite marker mapping for genotyping of the crossbred populations, resulting in an average resolution of 176 markers per study (~1 marker per 10 cM).


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  		Table 1. Summary of studies included in meta-analysis of genomewide scans for diabetes-related traits in mice

 

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  		Table 2. QTL for glucose levels, insulin levels, and glucose tolerance (IPGTT, OGTT) included in meta-analysis

 
Physical mapping of QTL to mouse genome.
We next generated a physical map of the QTL for diabetes-related traits. The peak markers for the 134 mouse QTL were mapped to the mouse chromosomes with NCBI's UniSTS database (see METHODS). In addition, we used NCBI's Rat-Mouse synteny database to map an additional 36 rat QTL to the syntenic regions of the mouse genome (see METHODS). Figure 1 illustrates the distribution of the 170 QTL for diabetes-related traits within the mouse genome. About half of the QTL (81) map to only six chromosomes (chromosomes 1, 2, 4, 6, 11, and 14), reaching a density of ~1 QTL per 10 Mbp. Conversely, only 18 QTL (~10%) map to chromosomes 3, 5, 10, and X, resulting in a density of ~0.3 QTL per 10 Mbp. Since the density of QTL varies >10-fold for the different chromosomes (highest for chromosome 19; 1.5 QTL/10 Mb), there is no clear relationship between the size of the chromosome and the number of QTL localized. To facilitate visualization of the QTL, we have developed a web-based tool and online repository of all QTL listed in this study (http://www.diabesitygenes.org).


Figure 1
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  		Fig. 1. Physical map of rodent quantitative trait loci (QTL) for diabetes-related traits. A total of 153 QTL from 43 published genomewide scans for blood glucose levels (G), circulating insulin levels (I), and glucose tolerance (GT) were localized to the mouse genome as described in METHODS. QTL from rat studies that have been mapped to syntenic regions in the mouse genome are in gray. An interactive physical map of the QTL is available online at http://www.diabesitygenes.org.

 
Meta-analysis of QTL for glucose and insulin levels and glucose tolerance.
In analogy to our recent meta-analysis of obesity-related traits (83, 86), we used TPM to combine the reported LOD scores of the different studies. The mouse genome was subdivided into 100 bins of about equal size (~25 Mbp; see Supplemental Table S1 for genomic coordinates). The LOD score values for the QTL were then converted into P values and allocated to one of 100 corresponding bins depending on the map position as described in METHODS. As illustrated in Fig. 2, the result of our meta-analysis provides evidence of linkage (P < 1 x 10–5, corresponding to LOD > 4.3) of diabetes-related traits to several chromosomal segments of the mouse genome. For glucose levels, insulin levels, and glucose tolerance, 11, 9, and 14 bins, a total of 27 individual bins, showed significant linkage with maximum LOD values of 8.0, 10.8, and 15.2, respectively. Three bins (10, 50, 94) showed significant linkage to two traits; for two bins (24, 61) TPM yielded LOD values >4.3 for all three traits.


Figure 2
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  		Fig. 2. Meta-analysis of QTL for diabetes-related traits. The mouse genome was subdivided into 100 bins of ~25-Mbp size, and the individual QTL were assigned to these bins according to their respective physical positions. P values for each bin were calculated either separately for the traits (A) or for the nonredundant combined traits (B) with the truncated product method (TPM). Bars represent the number of QTL per bin for each individual trait, glucose levels (black), insulin levels (red), glucose tolerance (green), and the nonredundant combined trait (white). Horizontal blue lines indicate the threshold for logarithm of odds (LOD) > 4.3; blue circles indicate bins with significant linkage to at least 2 traits.

 
Since the levels of glucose and insulin and glucose tolerance are interrelated, we also calculated TPM for all three traits combined. Several studies reported overlapping QTL for multiple traits. We prevented redundancy in the analysis by excluding the QTL with the lower LOD score so that only one QTL from a given study per bin would be allowed to contribute to the calculations. As a result, 29 chromosomal segments, i.e., almost a third of the mouse genome, showed significant linkage of the combined traits glucose levels, insulin levels, and glucose tolerance, reaching a maximal LOD score of 17.4 (Fig. 2). However, combining the traits had only a minor effect on the number of genomic segments with significant linkage. The P values for three segments (bins 2, 7, and 75) were above the significance threshold (P > 1 x 10–5; LOD > 4.3) for the combined traits but not for a single trait. Conversely, two segments (bins 9 and 74) had P values above the significance threshold for a single trait but not for the combined traits. The most prominent consensus regions with linkage of the combined traits (chromosomes 2, 4, 7, 9, 11, 13, and 19; bins 10, 13, 24, 25, 42, 50, 61-63, 72, and 94; P = 2.72 x 10–10–1.12 x 10–17; LOD scores 10.5–17.4) cover only a small fraction of the bins (~11%) and collectively contain the peak markers for 47 QTL (31%) for glucose levels, insulin levels, and glucose tolerance, corresponding to a threefold enrichment. Conversely, for 32 of 100 bins, no QTL were found to map within the designated region.

In mice, the sensitivity for diet-induced obesity as well as the susceptibility for beta cell failure is strain specific. Moreover, QTL for a diabetes-related trait may imply different modes of action (43, 81). To account for the heterogeneity within the different animal models, we segregated the studies into subgroups based on the mouse strains and experimental study conditions and performed separate TPM for each group. Mouse strains were grouped according to literature data (9, 43). The LOD profile for the combined diabetes-related traits was only marginally affected when the six studies that utilized a high-fat diet were excluded (Refs. 2, 46, 52, 59, 65, 73; Table 1, Supplemental Fig. S1b). We also performed TPM with studies in which only diabetes-resistant (DR) strains (C57BL/6J, 129/J, A/J, C3H, SM; 26 QTL from 7 studies; Refs. 2–4, 32, 33, 38, 75) were utilized and studies that involved diabetes-susceptible (DS) mouse strains (C57BLKS/J, DBA2, NZO, NSY, KK, BTBR; 39 QTL from 10 studies; Refs. 44, 52, 55, 62, 67, 70, 71, 74, 77, 84; Table 1). As illustrated in Supplemental Figs. S2 and S3, 7 of the 10 most prominent consensus regions (bins 10, 24, 25, 50, 61, 62, 94) showed significant or suggestive evidence for linkage with the combined diabetes-related traits in studies that involved DS strains. Conversely, bins 9, 13, 24, 50, 57, 59, 72, and 74 showed significant or suggestive evidence for linkage in studies with DR strains (Supplemental Fig. S3). Interestingly, three bins (24, 50, 59) showed linkage with diabetes-related traits in all subsets of studies.

Correlation with obesity-related traits.
Obesity may cause insulin resistance and is a strong risk factor for diabetes (31). In fact, the strong association of obesity and diabetes has led to the coined term "diabesity," which suggests a causal pathophysiological link between the phenomena (60). A total of 34 QTL for diabetes-related traits were also found to be linked to body weight and/or body fat (Table 2). However, since only a minority of the studies (17) provided information concerning body weight and/or body composition of the crossbred populations, this number is likely to be underestimated (2, 23, 27, 38, 39, 44, 45, 47, 55, 65, 67, 69, 73–75, 77, 79). In a previous study (83) we performed a meta-analysis for obesity-related traits in mice. As a result, we found multiple genetic loci that are significantly associated with body weight and adiposity in mice (LOD scores 14.8–21.8). To investigate the relationship of consensus regions for obesity and diabetes-related traits, we compared the derived LOD scores from both meta-analyses. Figure 3A shows the genomewide LOD score distribution for both obesity-related and diabetes-related traits. As shown in Fig. 3B, a total of 27 chromosomal segments show significant linkage (LOD > 4.3) for at least one diabetes-related trait, i.e., glucose and insulin levels or glucose tolerance. Of these, 15 segments (bins 10, 11, 13, 24-26, 31, 42, 50, 58, 61, 62, 72, 80, 82) were found to be significantly linked with body weight and body fat in our previous meta-analysis (83) (Supplemental Table S2). Conversely, 11 segments (bins 9, 19, 34, 45, 57, 59, 63, 77, 78, 83, 94) show linkage with diabetes- but not obesity-related traits. Thus the 15 bins with LOD > 4.3 for both obesity-related (body weight/fat) and diabetes-related (glucose/insulin levels, glucose tolerance) traits might reflect consensus regions for diabesity, whereas the other 11 bins likely indicate consensus regions for diabetes-related traits that are less dependent on body weight and adiposity.


Figure 3
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  		Fig. 3. Genomewide comparison of consensus segments from meta-analyses for obesity and diabetes-related traits in rodents. A: P values for genomewide linkage of obesity-related (body weight/fat; red dashed line) and diabetes-related (glucose/insulin/glucose tolerance; solid black line) traits. B: for each genomic segment, the P values for levels of glucose (GLC, black triangles) and insulin (INS, red diamonds) and glucose tolerance (GT, green squares) are plotted against the P value for obesity-related traits (body weight, body fat) of the corresponding segment derived from a meta-analysis of QTL for obesity in mice (83). The blue line indicates the threshold for LOD > 4.3. Numbers indicate bins with significant linkage to the respective trait.

 
Syntenic regions on human chromosomes and candidate genes for diabetes-related traits.
We have mapped the most prominent consensus regions for diabetes-related traits from seven mouse chromosomes (chromosomes 2, 4, 7, 9, 11, 13, and 19; bins 10, 13, 24, 25, 42, 50, 61-63, 72, and 94; P = 2.72 x 10–10–1.12 x 10–17; LOD scores 10.5–17.4) to syntenic fragments on eight human chromosomes (chromosomes 1, 2, 5, 9, 10, 11, 17, 20: 1p31-36; 2q23-24, 31; 5q11-13; 5q31, 33, 35; 9p21-23; 10q11, 23-26; 11p15; 11q13-14, 23-25; 17p11-13; 17q11-12, 21-25; 20p11-13, 20q11). In these segments, we found 164 genetic loci from human studies and candidate genes from rodent models that have been implicated in diabetes-related traits, including glucose and insulin levels, insulin sensitivity, and diabetes risk (Supplemental Table S3). A total of 84 human genetic markers, mostly from microsatellite marker-based genomewide scans, are included in the list, representing the result of 48 published association studies. These studies include data from the Africa America Diabetes Mellitus (AADM) study, the Framingham Study, the FUSION (Finland-United States Investigation of NIDDM Genetics) Study, the HERITAGE family study, the Hypertension Genetic Epidemiology Network (HyperGEN) Study, the Insulin Resistance Atherosclerosis Study (IRAS), the Diabetes UK Warren 2 Repository, and others (Supplemental Table S3). Moreover, 80 candidate genes for diabetes-related traits from human association studies and rodent models were found to map to these segments.

Recently, a total of nine candidate genes for type 2 diabetes have been identified and replicated in humans through multiple genomewide association (GWA) studies of common variants by using high-density SNP mapping approaches: CDKAL1, CDKN2A/CDKN2B, FTO, HHEX, IGF2BP2, KCNJ11, PPARG, SLC30A8, and TCF7L2 (21, 25, 57, 58, 64, 66, 87). More than half of these genes in mice (cdkn2a/cdkn2b, hhex, igf2bp2, slc30a8, tcf7l2; bins 24, 80, 82, and 94) map to chromosomal segments with LOD > 4.3 for diabetes-related traits, indicating a moderate enrichment of these genes in the consensus regions.

Interval-specific haplotype analysis of consensus regions.
About 97% of the genetic variation between inbred mouse strains is ancestral (22). Thus the regions of identity by descent (IBD) define chromosomal segments that are very unlikely to contain the causal genetic polymorphism underlying a QTL (82). To identify candidate genes within the QTL, we performed interval-specific haplotype analysis of several prominent consensus regions for diabetes-related traits. For definition of haplotype blocks, we derived a hidden Markov model to calculate the coordinates of IBD regions in the two parental strains for each QTL based on publicly available dense (SNP) maps (see METHODS). As a result, regions within a QTL interval where the two parental mice share alleles derived from the same ancestral source are unlikely to contain a relevant, i.e., causal polymorphism and were excluded as candidate regions. Accordingly, candidate regions are defined as segments where the parental animals contain alleles from different ancestral sources.

We used high-density SNP data from public databases to investigate IBD and non-IBD regions for four mouse QTL (t2dm3, D2Mit48, D4Mit15, Gluhos3) that are localized to prominent consensus segments (bins 10 and 13, chromosome 2; bin 24, chromosome 4; and bin 50, chromosome 9). t2dm3 (fasting insulin, LOD 5.2; glucose, LOD 2.7) was identified in an intercross population of C57BL/6J x BTBR (67); D2Mit48 (IPGTT, LOD 8.3), D4Mit15 (IPGTT, LOD 5.4), and Gluhos3 (insulin, LOD 6.7) were found in two intercrosses of C57BL/6J x C3H/He (32, 75). A total of 16 QTL for diabetes-related traits from rodent studies and 32 genetic markers from human studies (chromosomes 1, 2, 9, 11 and 20) map to these segments (Supplemental Table S3). Figure 4 illustrates the results of our haplotype analysis. Approximately 1,100 NCBI RefSeq entries, referring to known and predicted genes, are localized to these four segments, bins 10, 13, 24, and 50, and when IBD regions within the respective QTL are excluded the number of candidates in these segments is reduced by approximately half (not shown). For nearly 200 protein coding genes in the non-IBD regions of the two respective parental strains we found potentially relevant SNPs that either result in nonsynonymous amino acid changes or might affect regulatory genomic elements (Supplemental Table S4). The list includes several genes that have been implicated in pancreatic beta cell function, insulin action, or diabetes, such as PDK1, FOXA2, NKX2-2, ADFP, and APOA1/APOC3, and >100 predicted genes of unknown function (7, 26, 28, 42, 49, 53, 85). For 82 genes we found 129 coding SNPs that result in nonsynonymous amino acid changes (Supplemental Table S4). Some of these genes have been annotated to play roles in energy and lipid metabolism and obesity, such as ASIP, ATRN, and APOA1/APOC3; other genes have been implicated in signaling and/or membrane trafficking, such as 1700065A05Rik, Arhgef12, BAZ2B, CPNE1, Dnajc6, Fastkd1, and VPS16. Thus genes in the non-IBD/QTL peak regions with functionally relevant SNPs might represent plausible candidate genes for diabetes-related traits in future analyses.


Figure 4
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  		Fig. 4. Interval-specific haplotype analysis of mouse QTL in consensus regions. Coordinates (Mm, mouse positions; Hs, human syntenic regions) for identity by descent (IBD)/non-IBD regions were calculated for four QTL localized to consensus regions: T2dm3, C57BL/6J x BTBR (A); D2Mit48 (B), D4Mit15 (C), and Gluhos3, C57BL/6J x C3H/He (D) (32, 67, 75). Polymorphic single nucleotide polymorphism (SNP) count refers to the number of polymorphic SNPs in a 25-kb window between the 2 respective parental strains. The colored bar represents nonpolymorphic (IBD, blue) and polymorphic (non-IBD, yellow) regions and regions with insufficient data (green) derived from the hidden Markov model with a total of 376,160 informative SNPs (see METHODS). Diamonds indicate the position of rodent QTL and markers from human studies; triangles represent known candidate genes for diabetes-related traits (Supplemental Table S3). B6, C57BL/6; PTPRA, protein tyrosine phosphatase, receptor type, A; ASIP, agouti signaling protein; FOXA2, forkhead box A2; APO-A1/C3, apolipoproteins A-I, C3.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
We used QTL data from 43 genomewide scans to identify consensus regions that are linked to diabetes-related traits in rodents (Table 1). The parental mouse strains used in these crossbreeding studies include many commonly used inbred strains (e.g., C57BL/6, C3H, DBA/2, 129) that harbor diabetogenic alleles but do not develop spontaneous diabetes, as well as mouse strains with pronounced polygenic obesity such as KK, NZO, or TSOD mice that show very high susceptibility to diabetes. Likewise, the rat studies utilized obese models [e.g., Otsuka Long-Evans Tokushima Fatty (OLETF) rats] as well as nonobese models for diabetes [e.g., Goto Kakisaki (GK) rats] that both show hyperglycemia, hyperinsulinemia, and glucose intolerance and spontaneously develop diabetes. Thus there is considerable variation concerning glycemic control, insulin secretion and insulin sensitivity, and susceptibility to obesity and diabetes among the different animal models used in the studies.

The majority of the studies measured fasting levels of glucose and insulin and performed IPGTT to assess diabetes-related quantitative traits (Table 1). For our meta-analysis we included all reported QTL that are linked with glucose levels, insulin levels, glucose tolerance, or all three traits combined. In fact, for at least 17 QTL evidence for linkage of two traits was found within the same study (Fig. 1), and in many cases QTL for single diabetes-related traits showed substantial overlap in their position and confidence intervals, indicating that the observed proximity of QTL for multiple traits might be the result of pleiotropy and/or linkage of distinct genes (30). Several QTL have been replicated in a separate genomewide scan involving the same parental animals (C57BL/6J x 129S6/SvEvTac, D14Mit55/D14Mit52; C57BL/6J x C3H/He Gluhos1, D13Mit148/D13Mit262; C57BL/KsJ x DBA/2, D9Mit93/D9Mit209; and KK/Ay x C57BL/6J, Giq1/D8Mit191; Refs. 2, 3, 32, 70, 71, 74, 75, 84), indicating a relatively high reproducibility of the QTL, considering the different phenotyping of the animals (e.g., sex, age, diet) even within two replicate experiments (83).

Since the majority of linkage studies report only markers from genome scans with statistically significant or suggestive evidence, we used TPM for calculating P values for chromosomal segments of the mouse genome (86). TPM represents a generalization of Fisher's method for combining P values where only P values below a certain threshold are considered (see METHODS). The result of our meta-analysis provides evidence of linkage (LOD > 4.3) of glucose and insulin levels and glucose tolerance to 27 different chromosomal segments on 15 chromosomes where five segments (bins 10, 24, 50, 61, and 94) showed significant linkage to at least two traits (Fig. 2). Combining all traits (i.e., calculating TPM for all of the 153 QTL in the nonredundant set) had only a minor effect and increased the number of genomic segments with significant linkage to 29.

Several studies have shown that in mice insulin resistance and diabetes prevalence strongly and positively correlate with adiposity (9). In fact, dissection of genetic loci responsible for obesity and diabetes has allowed further genetic and functional analyses of the underlying pathophysiological mechanisms (51, 56). Interestingly, more than half of the consensus regions for diabetes-related traits overlap with consensus regions for obesity from our previous meta-analysis (83), perhaps defining segments linked to diabesity (Fig. 3). In fact, 15 of 52 QTL (29%) that map into these 15 segments are also significantly linked with body weight or body fat (Table 2). On the other hand, a total of 31 QTL map into the 11 bins that lack linkage with obesity; only 6 QTL of those (19%) were reported to show linkage to body weight or body fat, indicating an enrichment of QTL for body weight/fat in the consensus regions defined by our previous meta-analysis for obesity QTL. One might speculate that these diabesity genes in fact constitute susceptibility genes that require a certain degree of adiposity to exert their diabetogenic effect.

In mice, the sensitivity for diet-induced obesity as well as the susceptibility for beta cell failure is strain specific (43, 81). This is exemplified by studies of loss-of-function mutations in both the leptin gene (ob) and the gene for the leptin receptor (db) (10, 29). In C57BL/6 mice, the db/db allele results in moderately elevated glucose levels, glucose intolerance, and hyperinsulinemia. However, in C57BL/Ks mice the db/db mutation also causes glucose intolerance but eventually leads to beta cell destruction and overt diabetes (29). We thus segregated the studies into subgroups according to parental strains and dietary fat and performed separate TPM for each group. Exclusion of the studies in which the animals were fed a high-fat diet had only a minor effect on the LOD profiles, perhaps because this treatment was rather underrepresented in the studies (Table 1, Supplemental Fig. S1). A total of 10 studies utilized DS mouse strains (C57BLKS/J, DBA2, NZO, NSY, KK, BTBR) in crossbreeding experiments. A meta-analysis with the 39 QTL from these studies yielded a LOD profile that was in part comparable to the profile obtained with all 153 QTL from the 43 studies. In fact, 7 of the 10 most prominent consensus regions (bins 10, 24, 25, 50, 61, 62, 94; Supplemental Fig. S2) showed significant or suggestive evidence for linkage with diabetes-related traits, implicating many of the consensus regions identified in this study as biased toward a genetic background that is susceptible for diabetes. Relatively little overlap in the consensus regions was observed with DR strains (C57BL/6J, 129/J, A/J, C3H, SM; Supplemental Fig. S2). However, because of the rather low number of QTL in the DR and DS subgroups (17–25% of the total QTL), the data basis may not be sufficient to unambiguously identify subgroup-related consensus regions. Nevertheless, bins 24, 50, and 59 on chromosomes 4, 9, and 11 showed linkage with diabetes-related traits in all subsets, indicating that these loci might represent more common risk factors.

The most prominent consensus regions with linkage to the combined traits on chromosomes 2, 4, 7, 9, 11, 13, and 19 (LOD scores 10.5–17.4) contain a total of 47 QTL, indicating a significant enrichment of QTL in the consensus regions (Fig. 2). In the corresponding human syntenic fragments, we found almost a hundred genetic markers derived from 48 published association studies for diabetes-related traits in humans, implicating a substantial overlap between the studies (Supplemental Table S3). More than half of the nine diabetes candidate genes identified in recent human genomewide scans (CDKN2A/CDKN2B, HHEX, IGF2BP2, SLC30A8, and TCF7L2) map to segments with significant linkage to diabetes-related traits, even though no specific gene variants from different mouse strains have been reported so far (21, 25, 57, 58, 64, 66, 87). Since the contribution of most of the known risk alleles to the development of type 2 diabetes is rather small, it is likely that additional genetic factors will be discovered in future human GWAs. Thus further studies are required to finally assess the significance of the overlap of candidate loci between animal models and human subjects.

The consensus segments identified in the present study are likely to contain novel candidate genes involved in insulin action and glycemic control. Haplotype analysis based on high-density SNP maps of multiple inbred mouse strains has been used in several cases to narrow down QTL intervals and to identify candidate genes within the QTL (13). We have therefore applied high-density SNP maps to narrow down QTL intervals by interval-specific haplotype analysis. By using a hidden Markov model to identify IBD regions of QTL (t2dm3, D2Mit48, D4Mit15, Gluhos3) derived from three parental mouse strains (C57BL/6J, C3H/He, BTBR), we generated a list of candidate genes implicated in insulin action and the pathogenesis of type 2 diabetes (Fig. 4, Supplemental Table S4). The list includes several known genes implicated in pancreatic beta cell function, insulin action, or diabetes (e.g., PDK1, FOXA2, NKX2-2, ADFP, and APOA1/APOC3) but also predicted genes of unknown function that may be analyzed in future studies (7, 26, 28, 42, 49, 53, 85). At least 82 genes, many of them not annotated, have nonsynonymous amino acid changes in the coding sequence of the polypeptides, implicating a potential relevance of some of these proteins in the respective diabetes-related trait (Supplemental Table S4). The increasing availability of high-density SNP maps for the common rodent models used in studies of metabolic diseases will eventually allow more sophisticated genetic analyses of haplotype data to identify novel candidate genes for diabetes.

Significant progress has already been made recently toward identifying novel human candidate genes for type 2 diabetes in several large-scale GWA surveys (21, 25, 57, 58, 64, 66, 87). Genetically engineered mice, however, will continue to be critical tools for investigating the pathophysiology underlying the genes predisposing to diabetes. Furthermore, the analysis of rodent models including inbred, congenic, and consomic lines (15, 16, 19, 63) combined with bioinformatics methods (17) might provide novel susceptibility genes for diabetes that have not been detected by recent GWAs. Our study may provide guidance for future systematic investigation of candidate genes within the consensus QTL regions. Therefore, our interactive physical map might be a useful online tool for the visualization and data mining of rodent QTL for diabetes-related traits (http://www.diabesitygenes.org).


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was in part supported by the German Bundesministerium für Bildung und Forschung (PhysioSim, 0313325) and the European Union (EUGENE2; LSHM-CT-2004-512013).


   ACKNOWLEDGMENTS
 
We thank Dagmar Kollhof and Regine Heidmann for excellent literature service and Dr. David Adler for providing the idiograms of the mouse chromosomes.


   FOOTNOTES
 
Address for reprint requests and other correspondence: H. Al-Hasani, German Institute for Human Nutrition, Potsdam-Rehbrücke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany (e-mail: al-hasani{at}dife.de).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

* C. Schmidt and N. P. Gonzaludo contributed equally to this work. Back

1 The online version of this article contains supplemental material. Back


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 RESULTS
 DISCUSSION
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