Human Molecular Genetics, 2001, Vol. 10, No. 24 2751-2765
© 2001 Oxford University Press
A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on chromosome 16p13 and replicates linkage with the metabolic syndrome on 3q27
1Institute of Biology of Lille, CNRS UPRES A 8090, Lille, France, 2SSR Centre for Medical Studies and Research, Moka, Mauritius, 3Endocrinology Service, Saint Louis Hospital, Paris, France, 4Cardiac Trust Fund, Pamplemousses, Mauritius, 5Cardiac Unit, Victoria Hospital, Mauritius, 6SSR National Hospital, Pamplemousses, Mauritius, 7Barts and The London Genome Centre, Queen Mary and Westfield College, London, UK and 8Janssen Research Foundation, Beerse, Belgium
Received July 5, 2001; Revised and Accepted September 11, 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIAL AND METHODSREFERENCES |
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Prevalence of coronary heart disease (CHD), of type 2 diabetes (T2DM) and of the metabolic syndrome are in Mauritius amongst the highest in the world. As T2DM and CHD are closely associated and have both a polygenic basis, we conducted a 10 cM genome scan with 403 microsatellite markers in 99 independent families of North-Eastern Indian origin including 535 individuals. Families were ascertained through a proband with CHD before 52 years of age and additional sibs with myocardial infarction (MI) or T2DM. Model-free two-point and multipoint linkage analysis were performed using the Mapmarker-Sibs (MLS) and maximum-likelihood-binomial (MLB) programs for autosomal markers and the Aspex program for chromosome X markers. In a second step, additional markers were studied to increase the genetic map density in three regions on chromosomes 3, 8 and 16 where initial indication for linkage was found. Our data show suggestive linkage with CHD on chromosome 16p13-pter with the MLS statistics at 8.69 cM (LOD = 3.06, P = 0.00017) which partially overlaps with a high pressure (HBP) peak. At the same locus, a nominal indication for linkage with T2DM was found in 35 large T2DM Pondicherian families also having Indian origin. With respect to region 8q23, we found suggestive linkage with T2DM (LOD = 2.55, P = 0.00058) as well as with HBP. On 3q27, we replicated previous indication for linkage found in Caucasians (for the metabolic syndrome and for diabetes) according to the categorized trait for CHD and MI with the MLB statistics (LOD = 2.13, P = 0.0009). The genome scan also revealed nominal evidence of linkage with CHD on 10q23 (LOD = 2.06, P = 0.00188). Interestingly, we detected in the same region overlapping linkages with three QTLs: age of onset of CHD (LOD = 2.03), HDL cholesterol (LOD = 1.48) and LDL/HDL ratio (LOD = 1.34). Ordered-subset analysis based on family body mass index ranking replicated finding on 2q37 for T2DM (at Calpain 10 locus). These results show the first evidence for susceptibility loci that predispose to CHD, T2DM and HBP in the context of the metabolic syndrome.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIAL AND METHODSREFERENCES |
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Type 2 diabetes (T2DM), insulin resistance and obesity, and their complications are amongst the main causes of death in the older strata of the human population worldwide (1). Atherosclerosis is a chronic process that is caused, or at least accelerated, in part by diabetes and hyperlipidaemia, as well as high blood pressure (HBP), cigarette smoking and heavy drinking. It involves hyperplasia of arterial smooth muscle cell, the development of fatty streaks, atheroma formation, plaque rupture, and ultimately thrombus formation and vessel occlusion (2). Coronary heart disease (CHD) combines symptoms from angina pectoris to sudden death. Angina pectoris is considered a stable form of CHD where chest pain is caused by an increase in oxygen demand which cannot be met because of coronary stenosis (myocardial ischaemia). Stable angina is associated with smooth fibrous coronary artery plaques, whereas unstable angina, acute myocardial infarction (MI) and sudden cardiac death are almost invariably associated with irregular or ruptured plaques. Risks of major thrombotic and thromboembolic complications of atherosclerosis are more related to the stability of atheromatous plaques than to the extent of disease, plaques with a high lipid content and a thin fibrous cap or which are rich in macrophages are prone to disruption because of their softness (3).
Even though mortality due to CHD is declining in westernized countries (4), it is expected to be the world wide leading cause of death in 20 years once the developing countries have approached the same life style (5). Importantly, the Minnesota Heart Study has shown an increasing prevalence of diabetes mellitus in patients with MI, which is remarkable against a background of general decreasing prevalence and mortality due to CHD in all human groups (6). T2DM, insulin resistance and obesity are rapidly increasing in developing countries. In this regard, Asian populations are already known for their strong susceptibility for T2DM, specially when migrating in a ‘westernised’ environment like the Indian population in London (7,8) or the Japanese immigrants in America (9,10).
It is now accepted that death from CHD is to a great extent determined by genetic factors: a positive family history of CHD is considered to be a major risk factor for those events, especially for those with an early age of onset. Compared to dizygotic twins, monozygotic twins had a doubled risk of death from CHD when one of them had already died before 55 years of age (11). Considerable efforts have been undertaken during more than 10 years to evaluate the genetic background of MI through the analysis of candidate genes, like angiotension-I converting enzyme (ACE) and apolipoprotein E (APOE). Cambien et al. (12) initially found an association between an insertion/deletion (ID) polymorphism of the ACE gene and MI, but further studies showed controversial results (13–15). A protective effect of Apo E2 isoform for CHD and the deleterious effect of E4 were also reported (16). Other genes were also suggested to be implicated in the development of CHD: genes encoding proteins of the lipid metabolism like apoliprotein B (17), CETP (18) or lipoprotein lipase (19); genes encoding coagulation or fibrinolysis factors like ß-fibrinogen (20), PAI-1 (21) and factor VII (22). However, apart from rare monogenic syndromes, like LDL receptor deficiency and autosomal dominant dysbetalipoproteinemia due to Apo E mutations (23), most of the key genetic risk factors involved in CHD remain unknown.
The genetics of CHD was also undertaken by familial linkage analysis of polygenic syndromes related to lipid abnormalities or HBP, which are risk factors for CHD. The atherogenic lipoprotein phenotype (ALP) is characterized by increased levels of triglyceride-rich lipoproteins, reduction in HDL and a 3-fold increased risk of MI. A close linkage was found between the ALP and the LDL receptor locus on chromosome 19 (24,25). Recently, linkage has been shown also between total cholesterol concentrations and this locus in a genome scan in Pima Indians (26). Extended pedigrees from Finland with familial combined hyperlipidemia (FCHL) as well as German and Chinese families showed linkage with chromosome 1q21–q23 (27,28). However, Dutch families with FCHL failed to replicate the chromosome 1q but detected a new locus on 11p (29). Pajukanta et al. (30) revealed in FCHL Finnish families several quantitative trait loci (QTLs) influencing plasma levels of triglycerides (on 2q31 and 10p11.2), of total cholesterol (on 10q11.2-qter), and of ApoB (on 21q21). With regard to HBP, a linkage was found in the region of the angiotensinogen gene on 1q42–43 in Caucasian families (31) and a locus near but distinct from ACE on chromosome 17q was detected in French, British and American families (32,33). To our knowledge, no full genome scan of families ascertained for premature CHD was published so far, probably due to the difficulty recruiting a cohort large enough in a population with relatively low risk like Caucasians.
As atherosclerosis and CHD are likely to be extremely heterogeneous traits, choosing a very extreme phenotype in an isolated population could reduce the genetic complexity and could increase the power to detect susceptibility genes (34). In Mauritius, an island in the Indian Ocean, several Indo-Mauritian ethnicities, Creoles and Franco-Mauritians live together with a rare admixture between the different ethnicities. For communities of Indian descent, their ancestors, who migrated during different periods to Mauritius could be traced back to distinct ports of immigration (Bombay, Calcutta, Madras). Indo-Mauritian people have one of the highest rates of T2DM and mortality from CHD (35,36), which is higher than in any other ethnic group in Mauritius (37). Moreover, this population seems to be enriched for the genetic background predisposing to insulin resistance and its premature macrovascular complications (38). Indeed, comparison of positive family history for CHD between patients affected by CHD before 60 years of age and a control group matched for ethnicity in the North-Indian population (whose ancestors migrated from the port of Calcutta) from Mauritius showed an odds ratio of 7.42 [95% confidence interval (CI) 3.34–17.13] for cases having another sib affected by CHD (M.Manraj, unpublished data). Therefore, we have undertaken a genome-wide search in a subsection of the Mauritian population for genetic loci linked to qualitative and intermediate quantitative traits expressing CHD and its risk factors.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIAL AND METHODSREFERENCES |
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First-stage genome scan
We considered any region with a LOD score
1.17 (P 
0.01) as ‘potentially interesting’, and applied the criteria of Lander and Kruglyak (34) to further define regions of significance (LOD 3.6, P 0.00002) or suggestive (LOD 2.2, P 0.0007) linkage. Results of the Mapmarker-Sibs (MLS) and maximum-likelihood-binomial (MLB) multipoint analysis of the initial genome scan for selected qualitative traits (T2DM, CHD, HBP, categorized trait CHD_MI_cat) are summarized in Table 1. Figure 1 presents MLS multipoint analysis results of all chromosomes for T2DM, CHD and HBP qualitative traits. The highest LOD score for CHD was detected on chromosome 10q23 (LOD = 2.06, P = 0.00188). A potentially interesting region linked to the categorized trait CHD_MI_cat was found on 3q27 (LOD = 1.62, P = 0.0031). Interestingly, a LOD = 1.90, P = 0.00279 with CHD was found on 16p13-pter, partially overlapping with the HBP locus. On chromosome 8q23, a partially overlapping linkage was detected with CHD (LOD = 1.38, P = 0.00989) and T2DM (LOD = 1.73, P = 0.00421). Suggestive evidence for linkage with HBP was found on chromosome 10p14 using MLS statistics (LOD = 2.51, P = 0.00065). Best results for T2DM were found q-terminal on chromosome 1q44 (LOD = 2.14, P = 0.00156) and on chromosome 3q22 (LOD = 2.06, P = 0.0019). No linkage to any qualitative trait was detected on chromosome X.
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In order to try to replicate in another population having Indian origin the most interesting results found in the Mauritian genome scan, we analysed markers in 35 T2DM Pondicherian families, as shown in Table 2. On 3q22, nominal indication for linkage to T2DM was detected at marker D3S1292 (LOD = 1.36, P = 0.0106). In contrast, we found no linkage on 3q27 for markers D3S1262 and D3S1580 (data not shown). On 16p13-pter, we detected a weak evidence for linkage with T2DM at D16S407 (LOD = 1.15, P = 0.01796). In contrast, analysis did not reveal linkage on 1q44, 8q23 and 10q23 to T2DM in the Pondicherian population, compared to the Mauritius results for qualitative traits.
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Table 3 summarizes MLB multipoint LOD scores >1.17 of the initial genome scan for diabetes and CHD-related quantitative traits for all Mauritian subjects (diabetics and non-diabetics together). We detected a suggestive linkage for ageCHD on 1p36 (LOD = 2.62, P = 0.0003). The region 10q23 showed overlapping linkage at marker D10S185 with ageCHD (LOD = 2.03, P = 0.0011), as well as with HDL cholesterol (LOD = 1.48, P = 0.0045), and LDL/HDL ratio (LOD = 1.34, P = 0.0065) (Fig. 2A). On chromosome 2q12–p11, overlapping linkage was detected with LDL/HDL ratio (LOD = 1.34, P = 0.0064) and TG/HDL ratio (LOD = 1.94, P = 0.0014) (Fig. 2B). The best linkage for TG/HDL ratio was found at marker D3S1271 on 3q12.3 (LOD = 2.10, P = 0.0009). This region was linked to T2DM at marker D3S1292 in the Pondicherian population. None of the peaks for ageT2DM exceeded a better P-value than P < 0.005 nor showed substantial overlap with one of the other analysed traits. We detected no linkage to diabetes and CHD-related quantitative traits on the chromosome X.
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Results of the ordered-subset analysis for CHD or T2DM-associated quantitative traits in the Mauritian population are presented in Table 4. Ordered-subset analysis based on family body mass index (BMI) ranking showed a suggestive evidence for linkage with T2DM (MLS of 3.03) at position 273 cM on chromosome 2q37 including 24 T2DM families with the lowest BMI. Other interesting results were also observed on chromosome 8q23, 16q12 and 19p13.3. Ordered-subset analysis based on family triglyceride levels showed suggestive evidence for linkage with CHD on chromosome 6q22.1 (21 CHD families) and on 8q23 (35 CHD families) the same region which was already found to be linked to both CHD and T2DM in the whole sample.
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Second-stage mapping of chromosomes 3q27, 8q23 and 16p13
Three regions of interest were chosen after the first stage for partial saturation with additional markers (3q27, 8q23 and 16p13). Markers were added between the initial markers to increase map density (10, 8 and 10, respectively). The average density was 4.2, 6.1 and 5.5 cM, respectively. Results for the saturation maps are given in Table 5.
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The region on chromosome 3q27 immediately caught our attention due to our own previous results (39). With a categorized trait, paying attention to the different affection status of CHD and MI (CHD_MI_cat), we obtained a LOD score of 2.13 (P = 0.0009) between D3S1571 and D3S3686 (Fig. 3A). This peak is shifted
2 cM to the centromere, compared with the results from the initial genome scan (D3S1262–D3S1580). Importantly, at this locus, a linkage was detected for the most severe CHD-related phenotype, MI (LOD = 2.37, P = 0.00089) in the 39 sib pairs concordant for MI.
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With regard to chromosome 16p13-pter, the initial overlap between CHD and HBP peaks mostly disappeared (Fig. 3B). Moreover the LOD score for HBP (at 20.69 cM) faded to 1.44 (P = 0.00853). On the contrary, the increased density of the microsatellite map led to a LOD score of 3.06 (P = 0.00017) for linkage with CHD at marker D16S3027 (8.69 cM). The saturation decreased the 1 LOD unit CI (40) on chromosomes 3q from 24.17 to 16.84 cM and on 16p from 18.41 to 4.19 cM.
Only the locus on chromosome 8q23 gave evidence for overlapping linkage with T2DM and CHD in the initial 10 cM genome screen. Additional markers on chromosome 8q23 improved the LOD score for T2DM at 121.98 cM (LOD = 2.55, P = 0.00058). Nevertheless, we were not able to confirm linkage with CHD. By contrast, genotyping additional markers on 8q23 revealed a linkage for HBP with marker D8S556 (126.96 cM) (LOD = 1.69, P = 0.00459) which was not detected in the initial 10 cM genome scan.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIAL AND METHODSREFERENCES |
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We report here the first genome-wide search for susceptibility genes in CHD. Our results show strong evidence for linkage between CHD and a locus on chromosome 16p13.3. We were able to replicate previous linkage to a region 3q27 as well. This region was recently linked by our group to T2DM in a collection of French Caucasian families (39) and in another large Caucasian collection to the metabolic syndrome (41), both traits considered as potent risk factors for CHD. We couldn’t exclude that type 1 errors lead to false positive linkage, especially as several phenotypes have been used for analysis. Replication in other ethnic groups ascertained for CHD will be necessary to clarify these issues. We performed a genome-wide analysis to find genes involved in CHD occurrence. Although this status was our primary interest, we thought it was of great importance to give results for other, more or less correlated traits. These results can be used in a global meta-analysis project, together with results of other studies on the same subject. In this context, although it is possible to use simulations to derive an empirical P-value for the number of analyses performed, we think that this is beyond the scope of this article.
We estimated a 95% power of our study to detect at least a locus with a LOD score >3 and a 
of 3. is the sibling recurrence ratio defined as Ks/K, where, assuming a binary phenotype, K is the population frequency of the disease and Ks is the risk for a sib of an affected proband to be also affected. expresses the genetic contribution to the phenotypic trait. Interethnic marriage was very rare until recently due to the strict social rules in Mauritius. Families were selected on the basis of common sharing of geographic origin of ancestors and common dialect, avoiding a population admixture. Therefore, we expect a very homogenous genetic background in our studied population, the importance of which is reflected in the ethnic-specific phenotype also observed by others (42,43).
With a LOD score of 3.06 (P = 0.00017) we had our best linkage result with CHD on chromosome 16p13-pter at marker D16S3027. With the increased map density in this region the peak linked to HBP gave a maximal LOD score 12 cM distal from the region linked to CHD. Whereas the LOD score for CHD increased with additional markers, the linkage results for HBP decreased. However, the two 1 LOD CI overlap and we couldn’t exclude that they correspond to the same gene(s). At the same locus, a nominal indication for linkage with T2DM was found in families from Pondichery also having Indian origin. However, linkage with CHD couldn’t been assessed as data were not available in this population. Our results are supported by other genome-wide searches focused on metabolic phenotypes. Recently, a genome scan in native Canadian Indian population showed linkage between T2DM and 16p13.3 at D16S2616 (44). Linkage analysis in the GK rat, a T2DM model, revealed disease-related QTLs on the rat chromosome 10. Parts of the syntenic regions in humans map to chromosome 16p (45). A genome-wide search for genes modulating fat-free mass in human (which is closely correlated to insulin sensitivity) showed linkage to 16p13.2–D16S748 (46) and a genome-wide search for T2DM susceptibility genes in Chinese Hans showed linkage to the same marker D16S423 as our approach (47). Our Mauritian genome scan did not detect any evidence for linkage with T2DM in this region. It is noteworthy that our ascertainment based on CHD criteria as well as the generally high prevalence of T2DM in the Mauritian population, decreased the power to detect linkage with T2DM in this relatively small cohort. It remains that the 16p13 seems to be repeatedly linked to the insulin resistance syndrome and its complications. Several interesting candidate genes for metabolic diseases are located in region 16p13. The SOSC1 gene (or SSI1) is an inhibitor of STAT activation and probably provides a feedback mechanism responsible for switching off signals from cytokine receptors and the JAK/STAT complex, a signal transduction also known from the leptin receptor (48). Within the same regions the SA gene was mapped in humans. The SA mRNA was differentially expressed between SHR and WKY rats and related to sodium metabolism or blood pressure control (49). The somatostatin-5 receptor (SSTR5) is also located in the p-terminal region of chromosome 16, which showed linkage to CHD. Somatostatin inhibits the secretion of glucagon and insulin. Recently, it has been shown that SSTR5 and dopamine receptors can form hetero-oligomers with enhanced functional activity (50).
We also showed indication for linkage with CHD on chromosome 10q23 (LOD = 2.06, P = 0.00188), as well as with the age of onset for CHD (LOD = 2.03, P = 0.0011). This locus probably harbours a susceptible region for the early onset of CHD in our Mauritian population of Indian ancestry. Moreover, the clustering of linkage to lipid values like HDL cholesterol and the LDL/HDL ratio in the same region supports these results and suggests that the underlying genetic factors predisposing to CHD in this region may be related to lipid metabolism. CYP2C8, a member of the cytochrome P450 IIC subfamily is located within a range of 2 Mb q-terminal to this marker. CYP2C8 might act as synthase for a coronary endothelium-derived hyperpolarization factor. This hyperpolarization factor mediates the dilator response of vascular smooth muscle cells (51). The LIPA gene (lipase A) is located within the same distance, but in the opposite direction. Mutations in the LIPA gene are responsible for the Wolman- and the cholesteryl storage diseases, which lead to the progressive accumulation of triglycerides and cholesterol esters in lysosomes in the tissues of affected persons. The accumulation of neutral fats and cholesterol esters in the arteries predispose affected persons to atherosclerosis. Our region does not overlap with a reported region on 10q linked to T2DM and age of onset of T2DM found previously (52).
Early multipoint analysis showed linkage with CHD and T2DM on different regions of chromosome 3. The linkage to CHD at the interval D3S1262–D3S1580 was the second region which underwent saturation with additional markers. In addition to the linkage with early onset of T2DM (39) and to the metabolic syndrome (41), the region 3q27 is also linked to renal function in hypertensive subjects (53), which may suggest a vascular role of the causative gene(s). Although the initial evidence for linkage with CHD trait was not that striking (LOD = 1.75), a LOD of 2.37 was obtained when only using the 39 concordant sib pairs for MI. As this number may lack sufficient power for such an analysis, we also combined the two phenotypes CHD and MI as a categorized trait (CHD_MI_cat). Chromosome 3q27 was the only region which showed linkage with this categorized trait over the whole genome. Chromosome 3q27 harbours some possible candidate genes. ACRP30/adiponectin is an abundant adipocyte-secreted protein which is involved in lipid oxidation in various tissues (54). Two additional good positional candidate genes are located close to D3S1571. KIAA0604 shows a high similarity to endothelin converting enzymes, which are vasoconstriction regulators (55). The gene for kininogen (KNG) is located less than 2 Mb distal to D3S1571. KNG gene product is, due to differential splicing, involved in the blood coagulation cascade, as well as after enzymatic cleavage, in the vasodilation branch of the blood pressure regulation system (bradykinin). Other candidate genes in this region are somatostatin (SST), apolipoprotein D (APOD), type 1 protein phosphatase I-2 (PPP1R2) and the peroxisomal bifunctional enzyme (PBFE).
Clearly separated from this part of the chromosome, we mapped a region linked to T2DM in our study. This region around D3S1292–3q21.3 overlaps distally with a region found to be linked to fasting glucose levels in prediabetic individuals (56). We replicated this linkage to T2DM with the same marker in our Indian families from Pondichery. The chromosomal region 8q23 was the only region from the initial genome scan which showed overlapping linkage to our qualitative traits T2DM, CHD and HBP. Fine-mapping in this region did not strengthen the observed linkage. Nevertheless, our ordered-subset analysis revealed in the same region linkage with CHD in the 35 families with the lowest plasma triglyceride, as well as with T2DM in the 45 families with the highest BMI. In the region 8q23 is located the gene FOG-2 encoding a multi-zinc finger transcriptional corepressor protein that binds specifically to GATA4. KO mice studies provided a role of FOG-2 gene in induction of coronary vasculature by myocardium in the developing heart (57).
T2DM as well as CHD are clinically and most likely genetically heterogeneous diseases. Therefore, we thought that a reduction in the phenotypic and genetic complexity through statistical stratification analysis could be useful. The best MLS (LOD = 3.03) with T2DM was obtained on chromosome 2q37 at marker D2S125 in the 24 T2DM families having the lowest BMI. This region was already identified as a susceptibility locus to T2DM in Mexican Americans (58). Recently, a genome-wide search for T2DM susceptibility genes in Chinese Hans showed linkage to the same region with the marker D2S126 (47). In this region is located the CAPN10 gene encoding a cysteine protease calpain with ubiquitous expression. Recently, an association between polymorphisms in CAPN10 gene and T2DM was found in both Mexican American and Northern European populations (59). Further studies will determine if CAPN10 is a putative diabetes gene in the Indo-Mauritian population as well.
As our positive results somehow confirm results from similar approaches in complex metabolic diseases, it seemed interesting to have a closer look into two genome-wide scans related to microvascular complications and lipid levels. Imperatore et al. (60) have undertaken a genome-wide scan in a Pima Indian population for microvascular complications like nephropathy and retinopathy. Besides other loci, they found linkage with both traits at D3S3053–3q26.31. Their region is located proximally to our CHD_MI_cat region. Their peak of D3S3053 on chromosome 3 shows no overlap to the region for CHD_MI_cat (even if it is close), nor to the region for which we have shown linkage with T2DM. Interestingly, they found recently for the same marker on chromosome 3, in a second genome scan, linkage to HDL cholesterol concentration, with a LOD score of 2.64 (26).
A genome-wide search for loci linked with TG concentrations or with the ratio between TG and HDL (61) tried to shed light on the fact that low HDL and high TG concentrations are one of the major risk factors for CHD. Families were chosen randomly from the Framingham study and represent a normal population. In this study a LOD score of 1.8 was shown for marker D3S2460 on chromosome 3q21.1. Again, this region does not overlap with our 3q27 region, nor with the region linked to retinopathy and nephropathy in Pima Indians (60) or regions detected with our lipid traits. This illustrates the difficulties in comparing different populations and different trait definitions. However, the occurrence of interesting findings in an agglomeration of different studies at a similar locus, might show the presence of a common disease-underlying gene and the modification of the disease phenotype, due to different genetic backgrounds.
We were not able to show significant linkage around accepted candidate genes for CHD like the ACE gene. The probability of detecting a disease locus by linkage studies depends on its contribution to the disease phenotype. Linkage studies are usually able to detect loci with a major impact on the disease phenotype. When the genotype risk ratio for a locus drops below 2, linkage studies are usually not applicable, due to the high numbers of families necessary to reach sufficient power (62). In this case association studies on large numbers can be more powerful. Furthermore, meta-analysis could reveal the importance of genes with a small individual risk for metabolic diseases (63). Nevertheless, a meta-analysis of previously published studies on the ACE ID polymorphism found the risk ratio for MI with the DD genotype to lie between 1.0 and 1.1 (64). Moreover, only one (61) out of five genome scans performed with blood pressure or related traits detected linkage within the region of the ACE gene (53,66–68).
According to other known candidate genes for CHD, only the cluster of genes ApoAI/CIII/AIV is located at about 1 Mb from the region 11q23 linked to HBP in our study. ApoA-I levels were studied in families ascertained through cases of HBP or early CHD (69). Data supported a major effect of a single genetic locus on the variation of plasma ApoA-I. However, further complex segregation analysis suggested heterogeneous aetiologies for individual differences for ApoA-I levels with a strong genetic component (70).
In conclusion, we are presenting here the first genome-wide search for genomic region linked to CHD. We showed evidence for a major gene located on chromosome 16p13-pter linked to CHD. The region at 10q23 can harbour another possible candidate gene. Ordered subset analysis pointed to the importance of the NIDDM1 region at 2q37 in the Mauritian population, as well as the results on chromosome 3q27 are replicating our own findings in a French family collection affected by T2DM. These results are also supported by findings from a genome scan in metabolic diseases (41), indicating that the underlying susceptibility gene may have a variable phenotype from CHD over T2DM to obesity, depending on the genetic background of the population studied.
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MATERIAL AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIAL AND METHODSREFERENCES |
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Subjects
Families belonging to all ethnic groups were initially ascertained in Mauritius through an index subject who survived a coronary event occurring before 60 years of age. After they had given informed written consent, family participants were submitted to detailed personal and medical family history questionnaires, and research clinic evaluations were performed such as fasting blood samples (uricemia and lipid measurements: total cholesterol, triglycerides, HDL cholesterol), an oral glucose tolerance test (OGTT) with fasting and 2 h blood samples for glucose and insulin measurements, anthropometric and blood pressure measurements, electrocardiography (ECG), physician interview and physical examination. All participants were screened in a fasting state in the morning, after 12–14 h fasting. Blood pressure measurements were carried out after a minimum of 15 min rest, in a sitting position, using a mercurial sphygmomanometer (Accoson). Blood pressure was measured from both arms and the average of two measurements was obtained for each participant. OGTT was carried out only for those who had no known diabetes; 10–20 ml blood was collected on EDTA from all the participants for DNA extraction.
Selection of families was carried out on the basis of ethnicity, of age of onset of CHD for the index subject and the existence of multiple affected for CHD and/or abnormal glucose metabolism. 558 individuals belonging to 103 families of North-Indian origin (Hindu or Muslim, whose ancestors had migrated from the port of Calcutta) were thus selected in 1998 for the initial genome scan. Families were ascertained through an index case with CHD whose age of onset was earlier than 52 years of age and at least one additional sib affected by CHD or two members of the sibship affected by T2DM. All individuals affected by CHD or abnormal glucose metabolism belonging to the selected families were included as well as living parents and one non-affected sibling. Prior to genotyping of all 400 markers for the genome scan, 18 markers for the chromosome X (panel 28) were analysed to check for DNA inconsistencies and Mendelian inheritance. From the primary selection of 103 families, four were discarded from the analysis because of recurrent Mendelian incompatibilities. Thus, all analyses presented in this report were obtained in 99 complex families, corresponding to 155 nuclear families with approximately four sibs. Accordingly, the individuals included in the study comprised 240 individuals affected for CHD (Table 6). Details on the affection status related to CHD or MI, T2DM and HBP are given in Table 7.
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Fifty multiplex Tamil Indian families were selected for T2DM and ascertained in Pondichery through at least two T2DM affected individuals in the sibship. All available siblings and parents were included, whether they were currently living in Pondichery or in France. We discarded 15 families because of clinical data or DNA not available. The 35 complex families corresponded to 99 nuclear families with approximately three sibs. The 372 individuals included 134 affected sib pairs with T2DM. The mean age at diagnosis of T2DM was 44.8 ± 13.9 years. The clinical details of familial patterns of T2DM in the Pondicherian population we studied have been previously published (71).
Phenotyping
Biochemical and insulin measurements. Blood glucose, total cholesterol, triglycerides, uric acid and HDL cholesterol were assayed by enzymatic colorimetric methods using a multiparametric auto-analyser. LDL concentrations were derived from lipid values above, according to the Friedewald formula (72). Quantitative measurements of plasma insulin were carried out using double-antibody radioimmunoassays. DNA was extracted by the salting out method.
Characterization of CHD status. In most of the cases diagnosis of CHD was already documented prior to recruitment. We considered as positive for CHD the following: previous diagnosis of MI (based on the existence of at least two of the following: chest pain of more than 30 min duration, ECG patterns of acute MI, elevation of cardiac enzymes), history of revascularization procedures such as angioplasty or coronary artery bypass grafts, or patients following treatment for angina pectoris. When patients previously undiagnosed as having CHD presented with clinical symptoms suggestive of ischaemic heart disease and/or ECG patterns suggestive of myocardial ischaemia or of semi-recent or old MI, they were referred to a cardiologist who either confirmed or rejected the diagnosis of CHD [usually supported by an exercise (treadmill) ECG test and echocardiography findings].
Characterization of glucose metabolism status. We used a combination of personal history, fasting plasma glucose (FPG) and 2 h plasma glucose (2HPG) measurements: normal glucose tolerance if FPG < 7 mmol/l and 2HPG < 7.8 mmol/l. Impaired glucose tolerance (IGT) if FPG < 7 mmol/l and 2HPG 7.8 mmol/l and < 11.1 mmol/l. T2DM: if either there was a previous history of diabetes, if FPG > 7 mmol/l, or if FPG < 7 mmol/l and 2HPG 11.1 mmol/l (see Table 6 for clinical characterization of individuals).
Models of affectedness
Qualitative traits linkage analysis. Type 2 diabetes: individuals were labelled as affected or not, given the definition for T2DM above, except for individuals with IGT who were labelled as ‘unknown’. High blood pressure: an individual was declared affected if he had a known history of HBP or if his systolic blood pressure was higher than 160 mmHg and/or his diastolic blood pressure was higher than 95 mmHg. All other individuals were declared as ‘unknown’. Coronary heart disease: all individuals considered as positive for CHD were labelled as affected while the rest were labelled as unaffected. Myocardial infarction: individuals were labelled as affected or not following the definition above for MI, while individuals with positive diagnosis for CHD were labelled as ‘unknown’. Categorized trait: we used in our study a categorized trait related to CHD and/or MI (CHD_MI_cat) for the following purpose: we were able to combine qualitative traits with ‘broad’ definition together with the corresponding ‘strict’ trait. This is of particular interest, as the few numbers of sib pairs with MI would just account for ‘anecdotal’ results. Individuals were categorized for the CHD_MI_cat trait as unaffected (1), CHD (2) or MI (3), respectively. The probabilities for the three categories, required by the MlbQtl method (see below) were given by the reported prevalence in the Mauritian population.
Ordered subsets analysis. Because of the phenotypic heterogeneity underlying CHD associated to T2DM in the context of the insulin resistance syndrome, we performed ordered-subsets analysis (73). In this analysis, we sought to identify more-homogeneous groups of families, on the basis of their mean levels for CHD or diabetes-associated quantitative trait. For this purpose, we ranked families according to their mean sibship value for the quantitative trait of interest. For example, starting with the family with the lowest triglyceride level mean, family-specific LOD scores were added in, one family at a time, in rank order, until all families were included. We did 500 permutations of family orders to give several probabilities of observing the ordered subsets analysis results. We give the probability to exceed the highest LOD score (LODmax) on a chromosome whatever the number of families and the position on the chromosome. After each family was added, we determined the MLS for the current subset of families. The highest LOD score observed was finally reported as well as the number of families for which this LOD score is reached. We are particularly interested by the increase in LOD score at the LODmax locus. We then repeated the analysis, starting with the family with the highest mean triglyceride level.
We performed this ordered-subset analysis on the basis of mean values for the following CHD-associated quantitative traits: plasma triglyceride and LDL concentrations, and age at diagnosis. Diabetes and CHD are closely associated in our Mauritian population. Therefore, diabetes-associated traits might be underlying the development of CHD in this population. Thus, we performed an ordered-subset analysis on the basis of mean values for the following diabetes-associated quantitative traits: BMI and age at diagnosis.
QTL linkage analysis. A categorized traits-based approach was used in our QTL genome scan for age of onset of CHD and T2DM (ageCHD and ageT2DM) and plasma lipids for the Mauritian population. The advantage of categorized trait is the robustness when the quantitative trait does not follow a Gaussian law. Moreover, we reduce the bias of misclassification due to errors in plasma measurements or age at diagnosis. Our population is particularly characterized by early-onset CHD associated to T2DM. This population may represent a subset of insulin-resistant cases enriched for genetic factors as known for early-onset T2DM (MODY). Therefore, in order to increase the detection power of genetic factors responsible for this complex metabolic disease we decided to study ages of onset as QTL. The traits related to age at diagnosis of CHD and T2DM were categorized in deciles by age and sex when necessary. Lipid-related traits used in our study are HDL, triglycerides/HDL ratio and LDL/HDL ratio. CHD risk is positively associated with increased plasma triglycerides and decreased levels of plasma HDL. Concurrent hypertriglyceridemia and low HDL are characteristic of insulin-resistant subjects and may represent a single inherited phenotype (74). Triglycerides and HDL levels are inversely correlated, their metabolism may be closely interrelated and combined information on these two variables may be a more precise CHD risk factor (75). Therefore, we defined a lipid parameter consisting of the ratio of triglycerides and HDL cholesterol levels. Each lipid quantitative value was corrected in regression analysis when necessary, because of differences in lipid traits distribution according to age, sex, smoking behaviour, alcohol consumption, diabetes status or treatment. Individuals treated with lipid-lowering drugs were excluded prior to these calculations (n = 12). The corrected lipid traits were then categorized in deciles.
Genotyping
Initial 10 cM genome-wide screen. Genomic DNA was extracted from peripheral blood cells using standard protocols (salting out method). Genotyping was performed using a fluorescently labelled human linkage mapping set (PE-LMSV2 for the Mauritian population and PE-LMSV1 for the Pondicherian population). Multiplex PCR conditions were set up for each of the 28 panels in a manner so as to amplify the 400 markers in 87 PCRs. PCR (95°C for 12 min; 30 cycles of 94°C for 15 s, 55°C for 15 s, 72°C for 30 s, 72°C for 10 min) was performed on GeneAmp PCR system 9700 (Perkin-Elmer): 10 µl reactions; 40 ng of genomic DNA, 2.5 mM MgCl2, 0.25 mM dNTPs (Pharmacia), a variable amount from 0.2 to 1.5 pmol of 5' and 3' primers, 0.4 U Amplitaq Gold DNA polymerase (Perkin-Elmer) in 1x PCR Buffer II (Perkin-Elmer).
Pooled amplification products were electrophoresed through 5% polyacrylamide gels (Long RangerR SingelTM pack; Perkin-Elmer) for 1.5 h at 2000 V on 24 cm plates on an ABI 377 DNA sequencer. An automated 96-channel pipettor MultimekTM96 (Beckman) was used for all the pipetting steps. Semiautomated fragment sizing was performed by using Genescan 3.0 software (ABI) followed by allele calling with Genotyper 2.1 software (ABI). Each genotype was reviewed independently by two members of the research team to confirm the accuracy of allele calling. Three additional markers were added to the 400 to fill in gaps >20 cM (D5S429, D6S282 and D8S279). The distance between the 403 markers of the initial set comprises 9.24 ± 3.4 cM (mean ± SD). The average heterozygosity for the 403 markers used in the first-stage genome search was 0.79. Incompatibilities were searched for with the PED-CHECK 1.1 program (76) and inconsistencies resolved.
Second-stage mapping of chromosomes 3q, 8q and 16p.
Following the first-stage genome search in the Mauritian families, we observed that linkage was primarily supported, by most statistical methods, with markers on 3q27 and 16p13-pter in relation to CHD and/or MI, and on 8q23 in relation to T2DM. We subsequently saturated these regions with 10, 8 and 10 additional microsatellite markers in the region of chromosomes 3, 8 and 16, respectively, to confirm and further locate the linkage results. Each of the 28 markers was amplified individually by PCR and genotyping was performed similarly to the genome-wide scan. The information content for markers used in the multipoint analysis exceeded 0.80 for the three regions.
Statistical analysis
Clinical and biological data analysis. Quantitative data are reported as mean ± SD. Differences between means were subjected to an ANOVA test. Qualitative data were compared between groups by the 
2 test. Statistical analyses were performed with the JMP software.
Linkage analysis. As no model of inheritance is known for CHD, model-free methods were used to detect linkage. Marker allele frequencies were estimated by using data from all independent individuals (founders) by the program DOWNFREQ V1.1 from J.D.Terwilliger. To establish a map of the markers (order and genetic distances) two-by-two recombination fractions were estimated by using the program MLINK V5.2 implemented in the vitesse linkage package (77).
For multipoint analysis of qualitative traits a ‘maximum likelihood score’ method, implemented in the Genehunter 2.1 package was used (78). Using affected sib pairs the test statistic maximizes a likelihood ratio as a function of the IBD probabilities p (ibd = 0), p (ibd = 1) and p (ibd = 2) (z0, z1 and z2, respectively). The results are compared with the likelihood values under null hypothesis of no linkage (z0 = 0.25, z1 = 0.5 and z2 = 0.25, respectively). This ratio follows a mixture of 2 with 1 and 2 degrees of freedom.
The MLB method (79) was used to assess linkage for binary (affection), categorical and quantitative traits. This linkage test is based on the probability (
) that sibship members of the same phenotypic category received the same allele from an heterozygous parent. Under the null hypothesis of no linkage, this probability is equal to 0.5. For binary traits, this category is the affection status. For other traits, a latent binary variable (Y = 0 or 1) is created and each sib is classified under each category with a probability dependent of its trait value (quantitative or categorical). All 2k combinations (with k the number of sibs) are tested and the likelihood is weighted by the combination probability. The traits were categorized in deciles with the first decile having P(Y = 1) = 0.9 and the last decile P(Y = 1) = 0.1. For the CHD_MI_cat phenotype, population prevalences were used. This method implemented in the MlbGh package tests the deviation from = 0.5. The test for linkage is based on a likelihood ratio which follows a mixture of 0.52 with 0 and 1 degree of freedom. The null hypothesis ( = 0.5) will be rejected when > 0.5 is significantly higher.
In the present study we used the significance levels corrected for multiple testing proposed by Lander and Kruglyak (80). Significance evidence for linkage: P < 0.00002 for non-parametric allele-sharing methods and a maximum LOD score of 3.6 for LOD score analysis. Suggestive evidence for linkage: P < 0.0007 for non-parametric allele-sharing methods and a maximum LOD score of 2.2 for LOD score analysis.
Electronic data information
Data on chromosomic location of markers in this article were obtained as follows: CEDAR Database (http://cedar. genetics.soton.ac.uk); Genome Database (http://www.gdb.org); Human Genome Browser (http://genome.ucsc.edu/goldenPath/hgTracks.html).
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ACKNOWLEDGEMENTS |
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We present our deepest thanks to all the Mauritian and Pondicherian families who participated in this project, to the cardiologists and officers of the Ministry of Health (special thanks to Mr P.Bissoondyal from Victoria Hospital and Mr Goolaub from SSRN Hospital). We also thank Mr P.K.Das (Vector Control Research Center, Pondichery) for performing DNA extraction and to sisters and nurses from Saint Joseph of Cluny hospital for their help in clinical data collection. We are grateful to the Ministry of Health in Mauritius for administrative support and access to the outpatients’ departments. We thank the University of Mauritius for its administrative support throughout the project, for its financial contribution to the phenotyping of all individuals in the project and the DNA extractions at the SSR Centre in Mauritius. We wish to thank Dr P.Doolub for his help in confirming phenotypes and the laboratory technical assistants in SSR Centre for Medical Studies and Research. This work was supported by grants from the administration of Nord-Pas-de-Calais Region and by Eli Lilly Pharmaceutical Company.
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FOOTNOTES |
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+ To whom correspondence should be addressed at: Institute of Biology of Lille, UPRES A 8090, Pasteur Institute of Lille, 1, rue du Pr. Calmette, B.P. 447, 59021 Lille Cedex, France. Tel: +33 3 20 87 79 54; Fax: +33 3 20 87 72 29; Email: froguel@mail-good.pasteur-lille.frThe authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors
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REFERENCES |
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