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Genetic Studies in Type 2 Diabetes Mellitus: A time to rethink our strategies!
Assessing the function of genetic variants involved in Type 2 Diabetes mellitus : In Silico
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Dwaipayan Bharadwaj Institute of Genomics and Integrative Biology (CSIR), Sreenivas Chavali, Anubha Mahajan, Rubina Tabassum, Amitabh Sharma,
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db{at}igib.res.in Dwaipayan Bharadwaj, et al.
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Genetic Studies in Type 2 Diabetes Mellitus: A time to rethink our strategies! The whole study was based on the “available literature” on association of variants with Type 2 Diabetes Mellitus (T2DM). By establishing the network for the T2DM associated genes, we have shown that genetic research over the years has concentrated on only one biochemical aspect of the disease, creating a void between the biochemical and genetic knowledge. A quick catch up of genetic studies with that of biochemical is strongly called for, to make functional candidate based genetic dissection much more effective and meaningful. Though powerful, regrettably, linkage mapping of genes has not met with great success. In this scenario, genome wide association studies seem reliable, though practically there are concerns [1]. Agreeably, most of the rare monogenetic forms of T2DM are caused by defects in insulin secretion, which is also an integral part of syndromic T2DM. This comes with a lesson and a challenge, which cannot be ignored. The lesson being that a single gene variation is sufficient to bring about a phenotype, which mimics that of a common multifactorial disorder and the challenge, is to decipher the complex mixture of factors that cause a similar phenotype. Slow progress in gene identification of the multifactorial T2DM could be attributed to the way the disease genetic susceptibility was hypothesized. In direct association studies where the susceptible or causal alleles are evaluated, the allele effect size and frequencies would dominantly influence association. We present a qualitative overview of the combined effects of these factors in genetic dissection of complex traits (Table 1). Many common variants might have small influences on the susceptibility of a common disease. The scale of genetic studies and the sample size might act as stymie in identifying these. It is highly probable that the data available as of now mostly pertains to the alleles of moderate effect size, detectable in terms of differences in allele frequencies. Our study focuses on in silico determination of the allele effect size for the associations derived on the basis of allele frequencies. We observe that small number of common variants and large number of rare variants interact, leading to our ‘Mosaic Model’ of allele interactions. It takes into account the role of common variants and brings into the big picture the role of rare variants, hitherto defining the allelic space in terms of the number and loci of rare and common variants that might involve in the pathogenesis of the disease. Association of rare variants with intermediate phenotypes of diabetes [2] and the indication that there is a sudden increase in the number of functional polymorphisms below a minor allele frequency of ~6% in mixed human population provides evidence for the increasing knowledge on the role of rare variants [3]. This shift in the paradigm of allele architecture of T2DM might complicate the understanding of an already complicated complex disorder. We emphasize that Mosaic Model stands as a tradeoff between the common disease common variants model and the genetic heterogeneity model. This knowledge would act as a platform in designing future studies. We maintain that the polymorphisms considered for the study were associated with T2DM and/or its intermediate phenotypes. Association of certain variants with diabetes as well as its complications implies that an overlapping allelic spectrum might confer susceptibility to both conditions. Though the genetic effects might be consistent across populations, differences in allelic frequencies might not allow perfect visualization of associations. The differences in allelic frequencies across populations might be accredited to the selection pressure each population is subjected to. The report by Thompson et al. [4] provides evidence for this. They have shown that variants which influence salt homeostasis were targets of a shared selective pressure that resulted from an environmental variable correlated with latitude, an inference obtained from the remarkable interpopulation differences observed with regard to frequency spectrum and haplotype structure. Hence, in T2DM emphasis on selection pressure with respect to temporal differences in the life style adoption resulting in variable expressivity needs to be given for observed differences in variation statistics. This might be a plausible explanation of high incidence of T2DM in ancient populations. The failure to replicate the associations could also be attributed to the difficulties in accommodating gene-gene and gene-environment interactions in the existing genetic models. Whether these problems could be solved by increasing either the sample size or the number of phenotypes is yet to be investigated [5]. Large-cohort based studies assessing defined polymorphisms and phenotypes might provide a better insight. It is not clear as to whether mutation rates for Mendelian disorders will necessary be representative of the complex-disease loci. Further, there might also be a publication bias toward loci with high mutation rates, since the corresponding diseases will be more common at mutation- selection balance [6]. It might be naive to draw a generalization for mutation rates for human genes. Additionally, in late-onset diseases the propagation of an allele in population seems very much likely, depending on the effect size. The current population based genetic studies of common diseases relate DNA sequence variants to the disease or to the contributing intermediate traits. This in itself is powerful but not sufficient enough to enable complete comprehension of the disease. To obtain better explanations for the late onset, progressive and quantitative nature and the role of environment in disease development, it is necessary to integrate genetic, biochemical, epigenetic, epidemiological and clinical approaches. References: 1. Wang, W. Y., Barratt, B. J., Clayton, D. G., and Todd, J. A. (2005) Genome-wide association studies: theoretical and practical concerns. Nat. Rev. Genet. 6, 109-118. 2. Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., and Hobbs, H. H. (2004) Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 305, 869-872. 3. Wong, G. K., Yang, Z., Passey, D. A., Kibukawa, M., Paddock, M., Liu, C. R., Bolund, L., and Yu, J. (2003) A population threshold for functional polymorphisms. Genome Res. 13, 1873-1879. 4. Thompson, E. E., Kuttab-Boulos, H., Witonsky, D., Yang, L., Roe, B. A., and Di Rienzo, A. (2004) CYP3A variation and the evolution of salt- sensitivity variants. Am. J. Hum. Genet. 75, 1059-1069. 5. Permutt, M. A., Wasson, J., and Cox, N. (2005) Genetic epidemiology of diabetes. J. Clin. Invest. 115, 1431-1439. 6. Pritchard JK. (2001) Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124-137.
Table 1: Qualitative overview of the combined effects of allele frequencies and allele effect size on the genetic dissection of complex traits.
From Sreenivas Chavali, Anubha Mahajan, Rubina Tabassum, Amitabh Sharma, Dwaipayan Bharadwaj* Functional Genomics Unit, Institute of Genomics and Integrative Biology (CSIR), Delhi- 110 007, India *db@i |
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Swapan K Das, Research Scientist, Institute of Genomics and Integrative Biology, Mall Road, Delhi-110 007, India
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skdas{at}igib.res.in Swapan K Das
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Dear Editor, In a recently published article in Molecular and Cellular Proteomics, Sharma A et. al. (published on May 10th, 2005 as Manuscript M500024-MCP200) analyzed the effect of nonsynonymous variations on the structure and functions of proteins and have attempted to determine their possible role in Type 2 Diabetes mellitus by in silico methods. This bioinformatics approach is one of the areas of current research interest in complex disease genetics research [1] and multiple different web servers have been developed towards this direction. The authors have selected 29 "Disease causing variations" (DCVs) and 45 "Disease associated variations" (DAVs) after a key word search in Pubmed. They concluded from pathway analysis that the majority of the genes harboring these variations are clustered in or near genes of the insulin signaling network. In most of these published Type 2 diabetes genetic association studies, the investigators initially selected these variations in putative functional candidate genes. Hence, investigators selected for their genetic association studies genes which were likely to alter the insulin signaling cascade directly or indirectly. Due to lack of high throughput genotyping techniques and data analysis algorithm until recently, none of these studies have implemented unbiased genome wide association analysis. Recent findings have revived interest in the role played by the brain in both glucose homeostasis and the mechanism linking obesity to type 2 diabetes. Also there is mounting evidence for changes in beta-cell mass and concomitant change of insulin secretion in the pathogenesis of type 2 diabetes. Only a whole genome association study followed by pathway analysis can lead to an unbiased conclusion about the genes involved and their mode of interactions in T2DM pathogenesis. Type 2 diabetes is likely to be a heterogeneous disorder that may result from defects in one or more diverse molecular pathways. Rare monogenic forms of T2DM (mostly MODY) account for only 5% of diabetes. Most monogenic forms of diabetes are caused by defects in insulin secretion as compared to syndromic or common multifactorial T2DM in which insulin resistance and obesity plays the major role. Progress in gene identification for more common, multifactorial forms of type 2 diabetes has been slower, but there is now compelling evidence that common variants in the PPARG, KCNJ11, CAPN10 and HNF4-alpha genes influence T2DM susceptibility. The authors have selected 45 DAVs for comparison and found 80% of them as rare variants (leading to mosaic model). Associations of many of those variants have not been replicated in a second population or in another cohort, and experimental evidence elucidating the role of most of those variants in common multifactorial form of T2DM pathogenesis is lacking. Additionally, the authors have selected some of the DAVs that actually show an association with diabetes complications rather than T2DM. The role of rare mutations in most complex genetic diseases cannot be ignored, but due to the selection of a heterogeneous data set the authors have ignored the major role of common variants and overemphasized rare variants in syndromic T2DM pathogenesis. In this context the authors also concluded that failure to replicate these associations across different populations must be the result of variable expressivity resulting from selection pressure that has occurred in accordance with the temporal differences in the life style adoption. In a recent study [2], Ioannidis et al (2004), examined the genetic effects for 43 validated gene-disease associations across 697 study populations of various descents and proved that racial differences in genetic risk should be scrutinized cautiously. They argued that genetic effects are usually consistent across human populations. Small sample size, study design flaws or other biases may be more common reasons than true 'racial' heterogeneity for the observed discrepancies between studies addressing genetic risks. T2DM is a lifestyle disorder with the highest prevalence seen in populations that have a heightened genetic susceptibility; environmental factors associated with lifestyle unmask the disease. Critical evaluation of the 45 DAV data set including disease heterogeneity, sample size, and possible population stratification in these cohorts may lead to more useful conclusions. Sharma et. al. also concluded from their interpretation that 80% of DAVs were rare variants that the T2DM phenotype evolved recently. Because the highest mutation rates for any human gene are only around 10(-5) per generation, the expectation is that only deleterious genes with prevalence below 10(-5) could be sustained within a population by recurrent mutations alone. The actual incidence of T2DM is up to 50,000 times higher. Furthermore, the high prevalence of the disease in many large, ancient, well-mixed populations rules out explanations in terms of the founder effects or genetic drift [3]. Whole genome disease association studies in large cohorts and its replication in cohorts of different ethnic groups may lead to significant findings regarding the role of common and rare variants in the etiology of T2DM and evolution of those variants. References: Rebbeck, T.R., Spitz, M., Wu, X. (2004) Assessing the function of genetic variants in candidate gene association studies. Nat. Rev. Genet. 5, 589-597. Ioannidis, J.P.A., Ntzani, E.E., and Trikalinos, T.A. (2004) 'Racial' differences in genetic effects for complex diseases. Nat. Genet. 36, 1312-1318 Diamond, J. (2003) The double puzzle of diabetes. Nature. 423, 599-602 |
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