Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes

遗 传 学 报 ISSN 0379-4172 Acta Genetica Sinica, July 2006, 33 (7):573–589 Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabe...
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遗 传 学 报

ISSN 0379-4172

Acta Genetica Sinica, July 2006, 33 (7):573–589

Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes HUANG Qing-Yang1,2,①, CHENG Meng-Rong1, JI Sen-Lin1 1. College of Life Science, Central China Normal University,Wuhan 430079, China; 2. Department of Medicine, the University of Hong Kong, Hong Kong, China Abstract: Type 2 diabetes mellitus (T2DM) is a complex disease characterized by hyperglycemia, insulin resistance, and impaired insulin secretion. T2DM is under strong genetic control. Identification and characterization of genes involved in determining T2DM will contribute to a greater understanding of the pathogenesis of T2DM, and ultimately might lead to the development of better diagnosis, prevention and treatment strategies. Efforts to identify T2DM susceptibility genes have focused on candidate gene approach (association studies) and genome-wide scans (linkage analyses). In this article, we review the current status for mapping and identification of genes for T2DM, with a focus on some promising regions (or genes) and future prospects. Key words: type 2 diabetes; genetics; association studies; candidate genes; genome-wide scans

1

The Role of Genetic Factors in Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a metabolic disease in which hyperglycemia is caused by insulin resistance and defects in islet beta cell insulin secretion. T2DM is a complex, polygenic disease that results from the interplay of genetic and environmental factors. Genetic factors play a major role in the pathogenesis of T2DM [1]. First, the obvious familial aggregation is one of the features of T2DM. Moreover, the prevalence varies widely across populations, from 5% or less in white and Asian populations to 50% or more among Pima Indians. Second, concordance rates for T2DM are consistently higher in monozygotic than in dizygotic twin pairs. Third, λs (the ratio of disease prevalence in the siblings of affected individuals compared with that in the general population) for T2DM is approximately 3.5–4. Fourth, the heritability of T2DM is estimated to be 70%–80%. High genetic determinants of T2DM have greatly

fueled its genetic research. The final evidence of genetic factors in the development of T2DM comes when the susceptibility genes for T2DM have successfully been identified.

2

The Search for Type 2 Diabetes Genes

Two major approaches in identifying genes for T2DM have been adopted: candidate gene approach (association studies) and genome-wide scans (linkage analyses) (Table 1). The candidate gene approach tests for the association between a particular gene variant and T2DM, and depends on linkage disequilibrium of markers with functional mutations. It is generally prone to population admixture/stratification in yielding false positive or false negative results. To overcome this problem, transmission disequilibrium test (TDT) is employed to test specific candidate genes for both association and linkage. Genome-wide scans test only linkage and are robust to population admixture/stratification. A disadvantage is that they

Received: 2005-10-08; Accepted: 2005-12-09 This work was supported by the National Natural Sciences Foundation of China (No.30340068) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. ①

Corresponding author. E-mail: [email protected]

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have relatively low statistical power to detect genes with modest effects unless the sample size as reflected by the informative relative pairs is large. Genome scan not only guides candidate gene research Table 1

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Vol.33 No.7 2006

by according greater priority to candidates that are located within regions of linkage, but also identifies novel chromosomal regions within which no known candidates have been recognized.

Comparison of linkage and association analysis Linkage analysis

Association analysis

Tests for co-segregation of marker and disease locus alleles in families

Tests for association between marker and disease locus alleles in population

Significant result indicates physical linkage between marker and disease loci

Significant result can indicate physical linkage with linkage disequilibrium between marker and disease loci

Requires family data with at least two affected children

Can use population or family data

Can detect disease locus even if it is not close to a marker

Can detect disease locus only if it is close to a marker and if linkage disequilibrium exists

Fewer markers needed for genome scan

Many more markers needed for genome scan

Less powerful for detecting common disease susceptibility alleles of modest effect

More powerful for detecting common disease susceptibility alleles of modest effect

2. 1

Whole-genome scans for T2DM

To date, genome-wide scans for T2DM have been performed in over 20 different populations, including European and U.S. Caucasians, Mexican Americans, native American Indians, African Americans, and Asians. Results from these studies have indicated that T2DM susceptibility loci reside on a number of different chromosomes. Genome-wide linkage studies showing significant and suggestive linkage with T2DM are indicated in Tables 2 and 3, respectively. 2. 1. 1 Chromosome 1 The most consistent evidence for linkage to T2DM is found in the vicinity of 1q21-q25. Studies have reported linkage to this region in at least seven different populations. The initial report of linkage to this region was in Pima Indians. The investigators detected a suggestive linkage to marker D1S1677 on chromosome 1q23.3 (LOD = 2.48) based on an analysis of sibpairs concordant and discordant for early-onset T2DM (age of onset 1.0 are listed to their right. a: among early-onset DM; b: recessive model; c: among families with low BMI; d: among subjects with lowest mean 30-minute insulin levels in OGGT; e: among subjects with highest BMI.

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Acta Genetica Sinica

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Table 3 Genome-wide linkage studies showing suggestive linkage with T2DM Population

Location

Marker

LOD a

Reference

Supportive

Population

LOD

Finnish Caucasians

1q42.2

D1S3462

2.4

[40]

French Caucasians

2p21

D2S2259

2.3

[4]

Australian aborigines

2q24.3

D2S2345

3.0

[24]

[4]

French Caucasians

1.2b

French Caucasians

4q34.1

D4S1539

2.1

[4]

[29]

Finnish Caucasians

NPL2.5

[31]

Ashkenazi Jews

1.3

[28]

[5]

U.K. Caucasians

1.2

[5]

U.K. Caucasians

1.2

American Caucasians

5q13.3

D5S1404

2.8 c

Finnish Caucasians

5q31.1

D5S816

2.4

[40]

Pima Indians

7q32.3

D7S1804-D7S500

2.0

[62]

Mexican-Americans

9p24.2

D9S288-D9S295

2.4

[30]

Finnish Caucasians

9q21.12

D9S166

3.3

[29]

[15]

Chinese

NPL2.9

African-Americans

10p14

D10S1412

2.4

[28]

[15]

Chinese

NPL2.0

Japanese

11p13

D11S935

3.1

[26]

[36]

American sians

[15]

Chinese

NPL1.5

Finnish Caucasians

11q13-q14

D11S4172

3.0

[45]

American Caucasians

12q15

D12S375

3.1

[42]

[28]

American Caucasians

2.8

Finnish Caucasians

14q23

D14S290

2.7

[45]

Finnish Caucasians

20p12.3

D20S905

2.0

[44]

[31]

Ashkenazi Jews

0.9

American Caucasians

Xq23

GATA172D05

3.0

[28]

[26]

Japanese

1.7

[45]

Finnish Caucasians

1.3

Cauca-

NPL1.9

Studies showing near-suggestive linkage (LOD>2.0) are listed on the left side of the Table; studies supporting linkage in that region at LOD>1.0 are listed to their right. a: among subjects with highest waist-to-hip ratio; b: among subjects with lowest BMI; c :among early-onset DM.

Four additional genome scan studies also reported evidence for linkage to this region on chromosome 1.

Study reported evidence for linkage (LOD = 2.81) to D1S1589 in this region [9].

A study of an Amish population from Pennsylvania

The multiple linkages on chromosome 1q21-q25

(U.S.) found evidence for linkage of T2DM or the trait

provide compelling evidence for the existence of a

impaired glucose homeostasis (LOD = 2.35) to this

T2DM susceptibility gene in this region. T2DM suscep-

same region on chromosome 1, with a peak linkage

tibility gene(s) located in this region is (are) likely to

signal occurring at marker D1S2715, located approxi-

relate to the pathogenesis of the T2DM in multiple

mately 6–12 cM from the peak linkage signals reported

populations. Recent fine-mapping indicated that there

in the Utah Mormon and French families

[6]

. In a ge-

were more than one linkage peak in this region. Consis-

nome scan of early onset T2DM in families of Han

tent replication is likely to be due to multiple genes that

ethnicity from China, a maximum LOD score = 8.91

reside in the region [10]. The region contains at least 133

occurred near marker D1S2815

[7]

. Recently, ge-

known genes, 145 putative transcripts with homology to

nome-wide scan for type 2 diabetes loci in Hong Kong

known genes, and as many as 393 other potentially ex-

Chinese confirmed the presence of a susceptibility lo-

pressed sequences. Several of these genes are excellent

[8]

. A genome scan for

positional candidates. They include insulin recep-

the trait, glycated hemoglobin levels, in T2DM and

tor-related receptor (INSRR), hepatic pyruvate kinase

nondiabetic subjects from the Framingham Offspring

(PKLR), lamin A/C (LMNA), apolipoprotein A2. Elbein

cus on chromosome 1q21-q25

HUANG Qing-Yang et al.: Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes

et al. [11] documented an association of non-coding SNP

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this marker was strengthened and NIDDM1 was fur-

insulin-stimulated glucose turnover, suggestive of insulin resistance [23]. Insulin resistance in carriers of the ‘at risk’ allele has also been reported in Finns [22]. Additional functional and genetic epidemiological studies will be required to discern the means through which variants in calpain-10 increase risk of T2DM and to determine the relevance of these variants in other populations. 2. 1. 3 Chromosome 3 Evidence for linkage to T2DM has been found

ther narrowed to 7 cM (about 1.7 Mb) when these

on both the long and short arms of chromosome 3. In

researchers took into account of an interaction with a

a large pedigree of indigenous Australians, linkage to

of PKLR with T2DM. A collaborative effort in fine-mapping and association analysis of this region is ongoing among several groups. 2. 1. 2 Chromosome 2 In 1996, Hanis et al.

[12]

reported significant

linkage of T2DM to marker D2S125 in the 2q37 region (LOD = 4.1, named NIDDM1) in 330 affected sibpairs from Mexican American families. Linkage at

locus on chromosome 15

[13]

. Evidence suggesting

linkage to a similar region has also been found in Indo-Mauritians

[24]

. Strong evidence was also

found telomeric to this marker in French families

that consisted of the leanest fami-

(MLS = 3.56 at 3q27-qter near D3S1580) when the

[17]

lies. Horikawa et al.

LOD = 1.8) on 3q24.3

and

American Caucasians [16]

T2DM was found at marker D3S1311 (two-point

[3]

, French

[14]

, Chinese

[15]

typed 20 markers over the 7 cM

analysis was restricted to sibpairs with age of diabetes [4]

. Genome wide scans of insulin

and identified a haplotype combination of three SNPs

onset 1 000 individuals) showing statistically convincing associations Altshuler et al.

[68]

[65-67]

.

have studied mutations of 16 genes

that were previously reported to be associated with

estimated 25% in European populations. Whether individuals with different PPARγ genotypes respond differently to TZD treatment still needs to be demonstrated. 2. 2. 2

Beta-cell adenosine triphosphate-sensitive potassium channel The beta-cell adenosine triphosphate-sensitive potassium (KATP) channel plays a critical role in insulin secretion. The channel is composed of two subunits: the sulfonylurea receptor-1 (SUR1) and an inward rectifying potassium channel (Kir6.2) that are encoded on chromosome 11p15.1 by genes ABCC8 and KCNJ11, respectively. The two genes are located 5 kb from each other. An E23K variant in the Kir6.2 gene has been associated with T2DM in several studies and suggested to explain about 12% of the population risk [69-71]. A meta-analysis has confirmed that a modest, positive association does exist between the E23K variant of KCNJ11 and T2DM [70]. Functional studies have shown that the E23K mutation increases the threshold ATP concentration at which the beta cell ATP-sensitive channels function thus inducing overactivity of these channels that result in impaired insulin secretion [72]. SUR1 is the drug target for a widely used class of hypoglycemic medications, and the ABCC8 gene is mutated in the monogenic disorder familial hyperinsulinism. ABCC8 carries several polymorphisms, including E1506K, C/T in exon 18, c/t at the −3 posi-

PGC-1α were identified in a Japanese population, and the T394T-G482S haplotype of two of these polymorphisms was found to be strongly associated with T2DM

[77]

. The G482S of PGC-1α gene was also re-

ported to be associated with T2DM in studies of Danish Caucasians and Australians [78,79], but not in a study of French Caucasians, probably due to its relatively small sample size

[80]

. Comparison of gene ex-

pression profiles in muscle from subjects with and without T2DM revealed a set of genes involved in oxidative phosphorylation in human diabetes, all of which are regulated by PGC-1α

[81]

. Decreases in

PGC-1α expression or structural variants caused by genetic polymorphisms in PGC-1α might therefore alter expression of a set of coordinated genes, leading to changes in transcriptional activity of several proteins that result in the variety of defects seen in T2DM. Despite a number of reports of linkage and association between genes and T2DM, most of them cannot be replicated. KCNJ11 and PPARγ genes seem to be the most reproducible candidate genes for T2DM. Reasons for this include false positive or false negative results, small sample sizes and low statistical power, different sets of genes operating in different populations, small effect of individual gene and its interaction with genetic background and environmental factors. To deliver robust results, some guidelines have been suggested [82]. These include (1) significantly increased sample sizes. Large sample stud-

HUANG Qing-Yang et al.: Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes

ies are more likely to find association between true susceptibility genetic variants and T2DM, and probability of false positive or false negative results is low. They also provide more accurate estimation of T2DM risk measurement; (2) incorporation of diverse study designs including case-control, family-based association studies and intermediate phenotype data sets; (3) replication of findings in additional study groups of similar ethnic origin.

3 3. 1

Prospects for Gene Discovery in T2DM Fine mapping

The most crucial future aim is positional cloning of causal genes and identification of sequence variants within the coding or controlling regions of such genes. To achieve this, it will be essential to refine and to narrow the existing QTL to ~1 cM, a requisite size at which positional cloning becomes feasible. The chromosomal regions described so far are quite broad. It is recognized that the saturation of a candidate interval with ever more markers contribute very little to its narrowing by linkage [83,84]. Now, increased attention is turning to techniques of linkage disequilibrium (LD)-based association mapping with SNPs. SNPs allow the unification of the candidate gene approach and association-based fine mapping to identify gene(s) of interest. However, it is important to keep in mind that, even in the region narrowed, there is still the challenge of identifying the actual gene involved. There may be a lack of LD even between polymorphic loci that mapped to the same gene. On the other hand, even where association is demonstrated it might not indicate a contribution of that gene, but might rather reflect LD with polymorphisms in a neighboring gene. Recently, haplotype blocks were discovered in the human genome [85-87]. The existence of haplotype blocks raises hope that whole genome association studies can be carried out with reasonable cost by genotyping only a small fraction of SNPs that represent most haplotype diversity within the blocks. However, we currently know little about the pattern of haplotype and LD across various populations. Efficiency of association study based on

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LD will be greatly increased as the international haplotype project is completed. 3. 2

DNA microarray analysis and proteomics

Oligonucleotide and cDNA microarrays have revolutionized the study of differential gene expression in cells and tissues, enabling genome-wide screening of gene transcript variations. Failure to find mutations in candidate gene coding regions does not rule out a possible contribution of altered gene expression to T2DM. The identification of differentially expressed transcripts in normal versus affected tissues may add to the process of gene discovery in T2DM. In the simplest case, the target gene of interest might be identified directly by characteristic changes in expression levels across a series of samples. Alternatively, statistical analysis of microarray data might aid gene discovery by detecting new metabolic disease pathways related to the target gene and facilitating identification of candidate genes [88]. Of course, some of the gene expression changes identified in this way may be a result of environmental factors, chance, or other confounding variables (false positives). Nevertheless, combining positional information and expressional information will simplify the process of moving from putative linkage to gene identification. For example, microarray analysis led to the generation of a list of 175 cDNAs underexpressed by 2.5-fold or more in the fibroblasts of an affected individual (the Tangier disease, TD). By combining these data with linkage information that localized the disease gene to chromosome 9q between markers WI-14706 and WI-4062, the candidate list was narrowed sufficiently to identify the gene ABC1, which indeed did carry mutations [89]. Likewise, comparison of protein expression between normal and disease states would identify proteins relevant to the disease process, and provide obvious candidate genes as the source of inherited variation in susceptibility. Numerous alterations may occur in proteins that are not reflected in changes at the RNA level. The correlation among DNA sequence, mRNA and protein is low due to transcriptional control, translational control, and post-translational

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modification. Since genes ultimately influence disease states through the protein products they encode, the field of proteomics could be a powerful means to help identify candidate genes that underlie genetic variation [90]. Strategies to incorporate DNA microarray and proteomics data into traditional linkage or candidate gene studies would improve the efficiency and capability of gene discovery in T2DM and in illuminating the functions of the genes and the pathways in which the genes and/or their products are involved. 3. 3

Investigation of gene-gene and gene-environment interactions

For complex human diseases such as T2DM, which are determined by the joint action of multiple genes and environmental factors, most current models treat separate disease loci as if they were independent of each other. Even though the individual effect of a gene may appear to be small, interactions with other genes and/or environment could make a substantial contribution to the final manifestation of the disease. Failure to recognize and accommodate such interactions may often mask the effects of an individual gene. For example, Cox et al. [91] described an approach to assessing statistical interactions between different chromosomal regions where evidence for linkage at one region is taken into account in assessing the evidence for linkage elsewhere in the genome. Using this approach, they showed an interaction between loci on chromosomes 2 and 15 that increases the susceptibility to non-insulin-dependent diabetes (NIDD1). Interestingly, conventional linkage analysis failed to detect linkage to chromosome 15 in the initial genome scan [13]. In addition, Cordell et al. [92] described a multi-locus linkage method. They showed that multi-locus analysis not only increased power to detect linkage, but also assisted in determining the nature of the relation between disease loci (i.e. genetic heterogeneity versus epistasis). One of the most important goals of the next generation of genetic studies of T2DM is to determine which multi-locus genotypes create the highest risk for development of T2DM. Investigation of gene–gene interaction and additive or synergistic effect of multiple genes is very

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likely to elucidate the small and important contributions of some candidate genes to T2DM. 3. 4

International collaborations

Over 20 whole genome-wide linkage scans for T2DM have been completed. Sample sizes have generally been modest. The relatively small numbers studied would have tended both to limit the power of these genome screens to detect linkage and to increase the possibility of false positives. A common approach to enhance the power of any study is to utilize a larger sample size. Large samples may augment weak linkage signals found in small data sets and are less susceptible to random statistical fluctuations that may lead to false positive results in smaller samples. The most expedient approach to further progress in the identification of genes for T2DM would be through the efforts of a consortium to merge and jointly analyze all extant data sets for linkage. The feasibility of this approach has been demonstrated in search of genes for type 1 diabetes [93]. However, since there may be considerable differences in sampling strategies, in phenotypic measurement, marker sets, and expected etiologic heterogeneity, sometimes it is not possible to directly pool the data from studies that are conducted independently without standardization. In this case, meta-analyses of multiple independent data sets have been proposed as an alternative [94]. We advocate that both significant and non-significant results of whole genome scans should be published to facilitate meta-analyses [61]. On the other hand, multi-center genetic and family studies are rapidly evolving as a means of generating large samples of family data collected by using standardized protocols, for example, the Family Blood Pressure Program (FBPP) [95].

4

Conclusions

Considerable efforts have been made recently to investigate the genetic basis of T2DM. Numerous candidate genes have been tested for association and linkage with T2DM. However, single candidate gene is often neither essential nor sufficient to produce T2DM on its own. Whole genome scans have identi-

HUANG Qing-Yang et al.: Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes

fied some regions that may harbor QTLs contributing to T2DM. However, the transition from QTL detection to gene identification has proven difficult. Nevertheless, the successful identification of calpain 10 susceptiblity locus for T2DM [17] and replication of some of the linkage findings across multiple studies are encouraging. With the anatomy of the human genome at hand, sequence-based gene discovery is complementing, and will eventually replace map-based gene discovery. Identifying sequence variations responsible for T2DM and understanding how these variations regulate the phenotypes will still be the major challenges in the future. We can be optimistic concerning the future of gene discovery for T2DM by the use of a combination of functional, positional, and expression information. References: [1] O’Rahilly S, Barroso I, Wareham N J. Genetic factors in type 2 diabetes: the end of the beginning? Science, 2005, 307 : 370-373. [2] Hanson R L, Ehm M G, Pettitt D J, Prochazka M, Thompson D B, Timberlake D, Foroud T, Kobes S, Baier L, Burns D K, Almasy L, Blangero J, Garvey W T, Bennett P H, Knowler W C. An autosomal genomic scan for loci linked to typeⅡdiabetes mellitus and body-mass index in Pima Indians. Am J Hum Genet, 1998, 63(4) : 1130-1138. [3] Elbein S C, Hoffman M D, Teng K, Leppert M F, Hasstedt S J. A genome-wide search for type 2 diabetes susceptibility genes in Utah Caucasians. Diabetes, 1999, 48(5) : 1175-1182. [4] Vionnet N, Hani El-H, Dupont S, Gallina S, Francke S, Dotte S, De Matos F, Durand E, Lepretre F, Lecoeur C, Gallina P, Zekiri L, Dina C, Froguel P.Genomewide search for type 2 diabetes-susceptibility genes in French Whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21-q24. Am J Hum Genet, 2000, 67(6) : 1470-1480. [5] Wiltshire S, Hattersley A T, Hitman G A, Walker M, Levy J C, Sampson M, O'Rahilly S, Frayling T M, Bell J I, Lathrop G M, Bennett A, Dhillon R, Fletcher C, Groves C J, Jones E, Prestwich P, Simecek N, Rao P V, Wishart M, Bottazzo G F, Foxon R, Howell S, Smedley D, Cardon L R, Menzel S, McCarthy M I. A genomewide scan for loci predisposing to type 2 diabetes in a U.K.

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population (the Diabetes UK Warren 2 Repository): analysis of 573 pedigrees provides independent replication of a susceptibility locus on chromosome 1q. Am J Hum Genet, 2001, 69(3) : 553-569. [6] Hsueh W C, St Jean P L, Mitchell B D, Pollin T I, Knowler W C, Ehm M G, Bell C J, Sakul H, Wagner M J, Burns D K, Shuldiner A R. Genome-wide and fine-mapping linkage studies of type 2 diabetes and glucose traits in the old order Amish: evidence for a new diabetes locus on chromosome 14q11 and confirmation of a locus on chromosome 1q21-q24. Diabetes, 2003, 52(2) : 550-557. [7] Xiang K, Wang Y, Zheng T, Jia W, Li J, Chen L, Shen K, Wu S, Lin X, Zhang G, Wang C, Wang S, Lu H, Fang Q, Shi Y, Zhang R, Xu J, Weng Q. Genome-wide search for type 2 diabetes/impaired glucose homeostasis susceptibility genes in the Chinese: significant linkage to chromosome 6q21-q23 and chromosome 1q21-q24. Diabetes, 2004, 53(1) : 228-234. [8] Ng M C, So W Y, Cox N J, Lam V K, Cockram C S, Critchley J A, Bell G I, Chan J C. Genome-wide scan for type 2 diabetes loci in Hong Kong Chinese and confirmation of a susceptibility locus on chromosome 1q21-q25.Diabetes, 2004, 53(6) : 1609-1613. [9] Meigs J B, Panhuysen C I, Myers R H, Wilson P W, Cupples L A. A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: The Framingham Offspring Study. Diabetes, 2002, 51(3) : 833-840. [10] Zhao J Y, Xiong M M, Huang W, Wang H, Zuo J, Wu G D, Chen Z, Qiang B Q, Zhang M L, Chen J L, Ding W, Yuan W T, Xu H Y, Jin L, Li Y X, Sun Q, Liu Q Y, Boerwinkle E, Fang F D. An autosomal genomic scan for loci linked to type 2 diabetes in northern Han Chinese. J Mol Med, 2005, 83(3) : 209-215. [11] Wang H, Chu W, Das S K, Ren Q, Hasstedt S J, Elbein S C. Liver pyruvate kinase polymorphisms are associated with 2 diabetes in Northern European Caucasians. Diabetes, 2002, 51(9) : 2861-2865. [12] Hanis C L, Boerwinkle E, Chakraborty R, Ellsworth D L, Concannon P, Stirling B, Morrison V A, Wapelhorst B, Spielman R S, Gogolin-Ewens K J, Shepard J M, Williams S R, Risch N, Hinds D, Iwasaki N, Ogata M, Omori Y, Petzold C, Rietzch H, Schroder H E, Schulze J, Cox N J, Menzel S, Boriraj V V, Chen X, Lim L R, Lindner T, Mereu L E, Wang Y Q, Xiang K, Yamagata K, Yang Y, Bell G I. A genome-wide search for human non-insulin-dependent (type 2) diabetes genes reveals a major susceptibility locus on chromosome 2. Nat Genet, 1996, 13(2) : 161-166.

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[13] Cox N J, Frigge M, Nicolae D L, Concannon P, Hanis C L, Bell G I, Kong A.Loci on chromosomes 2 (NIDDM1) and 15 interact to increase susceptibility to diabetes in Mexican Americans. Nat Genet, 1999, 21(2) : 21-215. [14] Hani E H, Hager J, Philippi A, Demenais F, Froguel P, Vionnet N. Mapping NIDDM susceptibility loci in French families: studies with markers in the region of NIDDM1 on chromosome 2q. Diabetes, 1997, 46(7) : 1225-1226. [15] Luo T H, Zhao Y, Li G, Yuan W T, Zhao J J, Chen J L, Huang W, Luo M. A genome-wide search for type II diabetes susceptibility genes in Chinese Hans. Diabetologia, 2001, 44(4) : 501-506. [16] Francke S, Manraj M, Lacquemant C, Lecoeur C, Lepretre F, Passa P, Hebe A, Corset L, Yan S L, Lahmidi S, Jankee S, Gunness T K, Ramjuttun U S, Balgobin V, Dina C, Froguel P. A genome-wide scan for coronary heart disease suggests in Indo-Mauritians a susceptibility locus on 16p13 and replicates linkage with the metabolic syndrome on 3q27. Hum Mol Genet, 2001, 10(24) : 2751-2765. [17] Horikawa Y, Oda N, Cox N J, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner T H, Mashima H, Schwarz P E, del Bosque-Plata L, Horikawa Y, Oda Y, Yoshiuchi I, Colilla S, Polonsky K S, Wei S, Concannon P, Iwasaki N, Schulze J, Baier L J, Bogardus C, Groop L, Boerwinkle E, Hanis C L, Bell G I. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet, 2000, 26(2) : 163-175. [18] Garant M J, Kao W H, Brancati F, Coresh J, Rami T M, Hanis C L, Boerwinkle E, Shuldiner A R; Atherosclerosis Risk in Communities Study. SNP43 of CAPN10 and the risk of type 2 diabetes in African-Americans: the Atherosclerosis Risk in Communities Study. Diabetes, 2002, 51(1) : 231-237. [19] Evans J C, Frayling T M, Cassell P G, Saker P J, Hitman G A, Walker M, Levy J C, O'Rahilly S, Rao P V, Bennett A J, Jones E C, Menzel S, Prestwich P, Simecek N, Wishart M, Dhillon R, Fletcher C, Millward A, Demaine A, Wilkin T, Horikawa Y, Cox N J, Bell G I, Ellard S, McCarthy M I, Hattersley A T. Studies of association between the gene for calpain-10 and type 2 diabetes mellitus in the United Kingdom. Am J Hum Genet, 2001, 69(3) : 544-552. [20] Cassell P G, Jackson A E, North B V, Evans J C, Syndercombe-Court D, Phillips C, Ramachandran A, Snehalatha C, Gelding S V, Vijayaravaghan S, Curtis D, Hitman G A. Haplotype combination of calpain 10 gene polymorphisms associate with increased risk of impaired glucose tolerance and type 2 diabetes in south Indians. Diabetes,

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HUANG Qing-Yang et al.: Linkage and Association Studies of the Susceptibility Genes for Type 2 Diabetes

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2 型糖尿病易感基因的连锁和关联研究 黄青阳 1,2,程孟荣 1,姬森林 1 1. 华中师范大学生命科学学院,武汉 430079; 2. 香港大学内科系,香港 摘 要:2 型糖尿病(T2DM)是由于胰岛素抵抗和 β 细胞分泌缺陷导致高血糖的一种复杂多基因疾病。遗传因素在 T2DM 的发生发展中起着重要的作用,其遗传率估计为 70%~80%。鉴定 2 型糖尿病基因将有助于阐明其发病机制, 发展更好的诊断、预防和治疗策略。2 型糖尿病易感基因的鉴定方法主要有候选基因关联研究和全基因组连锁分析。 有 3 种类型的候选基因:功能候选基因、图位候选基因和表达候选基因。虽然许多候选基因与 T2DM 的关联分析已经 进行,但多数都没有得到一致的重复,过氧化物酶体增殖物激活受-γ 体和 β-细胞 ATP 敏感性钾通道基因是目前最好 重复的基因。迄今为止,T2DM 的全基因组扫描已在 20 多个不同的群体中进行,包括欧洲人、美国白人、墨西哥裔 美国人、美国本地印度人、非洲裔美国人和亚洲人,这些研究鉴定了一些与 T2DM 相关的 QTLs 区域。与 T2DM 显著 和证实连锁的区域包括 1q25、2q37、3q28、3p24、6q22、8p23、10q26、12q24、18p11、20q13 等, 与 T2DM 提示连 锁的区域有 1q42、2p21、2q24、4q34、5q13、5q31、7q32、9p24、9q21、10p14、11p13、11q13、12q15、14q23、20p12、 Xq23 等。鉴定这些区域的 T2DM QTLs 基因及其作用机制是未来的主要挑战。把 DNA 微阵列和蛋白质组学技术结合 起来应用于传统的连锁分析和关联研究,研究基因-基因间、基因-环境间的互作和多个基因对 T2DM 的加性效应和综 合作用,进一步加强国际协作,T2DM 的遗传机制可望在不远的将来得到阐明。本文总结了 2 型糖尿病基因鉴定的现 状,重点在一些得到重复的区域和未来的展望。 关键词:2 型糖尿病;遗传学;关联研究;候选基因;全基因组扫描 作者简介:黄青阳(1963-),男,博士,教授,研究方向:分子遗传与基因组学。E-mail: [email protected]

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