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Emerging face of genetics, genomics and diabetes

G. R. Sridhar, Ravindranath Duggirala, Sandosh Padmanabhan
2013 International Journal of Diabetes in Developing Countries  
Type 2 diabetes mellitus (T2DM) has become a global public health issue encompassing even children and youth in recent decades. It is a complex disease influenced by genetic and environmental factors. Although much attention is paid to environmental factors since they are identifiable and potentially modifiable, beginning the turn of the 21st century, there has been an explosion of activities that are aimed to screen the genome for identifying T2DM susceptibility genes. This gained momentum
more » ... a confluence of different scientific disciplines that was showcased by the millennial Human Genome Project and its aftermath. Currently, the next generation sequencing accelerated the identification of potential causal genes/variants (especially low frequency and rare variants) influencing complex diseases such as T2DM. Also, the other technologies such as transcriptomics, proteomics, metabolomics and epigenomics have emerged as additional tools to identify the molecular factors underling the phenotypic expression of T2DM. The voluminous biological data generated by these studies has necessitated or contributed to bioinformatics, a confluence of biology and its various flavours, information technology, computational biology, algorithms, matching, statistics, mathematics, nanotechnology, ethics among others. The areas of analysis using bioinformatics in diabetes can be approached as employing datasets from genome or amino acid sequences, structures of biological molecules and functional genomics experiments. Nevertheless analyses extend to other types of data including evolutionary trees, metabolic pathways and the semantics of published literature. Mathematical models and analysis and statistical analyses are required to obtain critical information from the large amounts of data that is generated by genomic and other approaches. Newer algorithms to cluster data from a wide scatter will be relevant: for example, K-means can select best density in a self-adoptive manner and initialize r empirically; an improved method over K means, the Automatic Generation of Distance for Density based Clustering (AGDDC) was developed by Karteeka Pavan et al. to optimize number of clusters [1]. This was developed to find initial optimum centroids based on density clustering. It can be applied to relevant genes related to the pathogenesis of T2DM. The challenge in modern biology and of modern diabetes, is a profusion of data. Bioinformatic methods aid in curating and synthesizing the deluge [2]. The Human Genomics Project (HGP) which heralded the situation where data overran the capacity to cull out the information, simultaneously saw sequencing of genes of other species including vertebrates, invertebrates, fungi, bacteria and plants. The underlying theme was to know how genetic architecture and genes evolved over time. Considering that the basic building blocks were the four nucleotides across all life forms, it was logical to compare the genomes of different species to identify where and how divergence of genes occurred and how newer metabolic pathways emerged [3, 4] . The initial steps in using bioinformatics techniques in diabetes consisted of identifying known genes from literature and from genomic databases that were related to different aspects of diabetes or its complications. Phylogenetic trees were then constructed using the nucleotide or amino acid
doi:10.1007/s13410-013-0164-9 fatcat:h4c72ttbnvcqvjmvga5vpdcgvm

Do rare variant genotypes predict common variant genotypes?

Jack W Kent, Vidya Farook, Harald HH Göring, Thomas D Dyer, Laura Almasy, Ravindranath Duggirala, John Blangero
2011 BMC Proceedings  
The synthetic association hypothesis proposes that common genetic variants detectable in genome-wide association studies may reflect the net phenotypic effect of multiple rare polymorphisms distributed broadly within the focal gene rather than, as often assumed, the effect of common functional variants in high linkage disequilibrium with the focal marker. In a recent study, Dickson and colleagues demonstrated synthetic association in simulations and in two well-characterized, highly polymorphic
more » ... human disease genes. The converse of this hypothesis is that rare variant genotypes must be correlated with common variant genotypes often enough to make the phenomenon of synthetic association possible. Here we used the exome genotype data provided for Genetic Analysis Workshop 17 to ask how often, how well, and under what conditions rare variant genotypes predict the genotypes of common variants within the same gene. We found nominal evidence of correlation between rare and common variants in 21-30% of cases examined for unrelated individuals; this rate increased to 38-44% for related individuals, underscoring the segregation that underlies synthetic association.
doi:10.1186/1753-6561-5-s9-s87 pmid:22373504 pmcid:PMC3287928 fatcat:7qgf5ay7wzafdfwuni2ihq6gka

Pedigree-based random effect tests to screen gene pathways

Marcio Almeida, Juan M Peralta, Vidya Farook, Sobha Puppala, John W Kent, Ravindranath Duggirala, John Blangero
2014 BMC Proceedings  
The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable,
more » ... uggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers.
doi:10.1186/1753-6561-8-s1-s100 pmid:25519354 pmcid:PMC4143680 fatcat:zswfdrb2cbf5vazt6chnvg32zq

Birth weight and the Metabolic Syndrome: thrifty phenotype or thrifty genotype?

Michael P. Stern, Mary Bartley, Ravindranath Duggirala, Benjamin Bradshaw
2000 Diabetes/Metabolism Research Reviews  
Stern Mary Bartley^ Ravindranath Duggirala^ Benjamin Bradshaw^ ^Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA ^University  ...  Duggirala R, Stem MP, Mitchell BD, et al. Quantitative variation in obesity-related traits and insulin precursors linked to the OB gene region on human chromosome 7.  ... 
doi:10.1002/(sici)1520-7560(200003/04)16:2<88::aid-dmrr81>;2-m pmid:10751748 fatcat:zqpyr6wserh5pkqjrxrd3aa2ca

Genetic Factors Influence Serological Measures of Common Infections

Rohina Rubicz, Charles T. Leach, Ellen Kraig, Nikhil V. Dhurandhar, Ravindranath Duggirala, John Blangero, Robert Yolken, Harald H.H. Göring
2011 Human Heredity  
0.5480.18 HSV-1 0.2680.05 0.2280.07 0.2680.11 0.5580.14 HSV-2 0.1880.05 0.1080.07 0.0780.07 0.0480.11 HAV 0.3880.06 0.2480.07 0.4880.11 0.5880.14 Rubicz /Leach /Kraig /Dhurandhar / Duggirala  ... 
doi:10.1159/000331220 pmid:21996708 pmcid:PMC3214928 fatcat:rojz6fkoabam3khqqgcuoku7p4

Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype

Christopher P. Jenkinson, Harald H.H. Göring, Rector Arya, John Blangero, Ravindranath Duggirala, Ralph A. DeFronzo
2016 Genomics Data  
Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related
more » ... ical data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples.
doi:10.1016/j.gdata.2015.12.001 pmid:27114903 pmcid:PMC4832048 fatcat:s7rfcu7szrdbleq5bh2mh3omoq

A performance assessment of relatedness inference methods using genome-wide data from thousands of relatives [article]

Monica Ramstetter, Thomas D Dyer, Donna M Lehman, Joanne E Curran, Ravindranath Duggirala, John Blangero, Jason G Mezey, Amy L Williams
2017 bioRxiv   pre-print
Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We find that all methods
more » ... ve high accuracy (92%-99%) when detecting first and second degree relationships, but their accuracy dwindles to less than 43% for seventh degree relationships. However, most IBD segment-based methods inferred seventh degree relatives correct to within one relatedness degree for more than 76% of relative pairs. Overall, the most accurate methods were ERSA and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches--such as new methods that leverage relatedness signals from multiple samples--are needed to achieve a sizeable jump in performance.
doi:10.1101/106013 fatcat:tru4ah65j5b7jm7o3pcckuiply

Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives

Monica D. Ramstetter, Thomas D. Dyer, Donna M. Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G. Mezey, Amy L. Williams
2017 Genetics  
assess these methods, we used SNP array genotypes from Mexican American individuals contained in large pedigrees from the San Antonio Mexican American Family Studies (SAMAFS) (Mitchell et al. 1996; Duggirala  ... 
doi:10.1534/genetics.117.1122 pmid:28739658 pmcid:PMC5586387 fatcat:pedm4abrxrbl3ly5555lfk7psy

Physical activity and FTO genotype by physical activity interactive influences on obesity

Joon Young Kim, Jacob T. DeMenna, Sobha Puppala, Geetha Chittoor, Jennifer Schneider, Ravindranath Duggirala, Lawrence J. Mandarino, Gabriel Q. Shaibi, Dawn K. Coletta
2016 BMC Genetics  
Although the effect of the fat mass and obesity-associated (FTO) gene on adiposity is well established, there is a lack of evidence whether physical activity (PA) modifies the effect of FTO variants on obesity in Latino populations. Therefore, the purpose of this study was to examine PA influences and interactive effects between FTO variants and PA on measures of adiposity in Latinos. Results: After controlling for age and sex, participants who did not engage in regular PA exhibited higher BMI,
more » ... fat mass, HC, and WC with statistical significance (P < 0.001). Although significant associations between the three FTO genotypes and adiposity measures were found, none of the FTO genotype by PA interaction assessments revealed nominally significant associations. However, several of such interactive influences exhibited considerable trend towards association. Conclusions: These data suggest that adiposity measures are associated with PA and FTO variants in Latinos, but the impact of their interactive influences on these obesity measures appear to be minimal. Future studies with large sample sizes may help to determine whether individuals with specific FTO variants exhibit differential responses to PA interventions.
doi:10.1186/s12863-016-0357-6 pmid:26908368 pmcid:PMC4765034 fatcat:mlilzqbxsbfrbpovvak4yrjvte

Effects of covariates and interactions on a genome-wide association analysis of rheumatoid arthritis

Rector Arya, Elizabeth Hare, Inmaculada del Rincon, Christopher P Jenkinson, Ravindranath Duggirala, Laura Almasy, Agustin Escalante
2009 BMC Proceedings  
While genetic and environmental factors and their interactions influence susceptibility to rheumatoid arthritis (RA), causative genetic variants have not been identified. The purpose of the present study was to assess the effects of covariates and genotype × sex interactions on the genome-wide association analysis (GWAA) of RA using Genetic Analysis Workshop 16 Problem 1 data and a logistic regression approach as implemented in PLINK. After accounting for the effects of population
more » ... , effects of covariates and genotype × sex interactions on the GWAA of RA were assessed by conducting association and interaction analyses. We found significant allelic associations, covariate, and genotype × sex interaction effects on RA. Several top single-nucleotide polymorphisms (SNPs) (~22 SNPs) showed significant associations with strong p-values (p < 1 × 10 -4 -p < 1 × 10 -24 ). Only three SNPs on chromosomes 4, 13, and 20 were significant after Bonferroni correction, and none of these three SNPs showed significant genotype × sex interactions. Of the 30 top SNPs with significant (p < 1 × 10 -4 -p < 1 × 10 -6 ) interactions,~23 SNPs showed additive interactions and~5 SNPs showed only dominance interactions. Those SNPs showing significant associations in the regular logistic regression failed to show significant interactions. In contrast, the SNPs that showed significant interactions failed to show significant associations in models that did not incorporate interactions. It is important to consider interactions of genotype × sex in addition to associations in a GWAA of RA. Furthermore, the association between SNPs and RA susceptibility varies significantly between men and women.
doi:10.1186/1753-6561-3-s7-s84 pmid:20018080 pmcid:PMC2795987 fatcat:g6ja5cz66ngp7fe73gqmepssvu

Inferring identical by descent sharing of sample ancestors promotes high resolution relative detection [article]

Monica D Ramstetter, Sushila A Shenoy, Thomas D Dyer, Donna M Lehman, Joanne E. Curran, Ravindranath Duggirala, John Blangero, Jason G Mezey, Amy L Williams
2018 bioRxiv   pre-print
As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID -- Deep Relatedness Utilizing Identity by Descent -- a method that works by inferring the identical by descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining
more » ... on from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses a novel approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7-31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3-58.3% of tenth degree relatives in this way compared to 79.6-96.7% using DRUID.
doi:10.1101/243048 fatcat:lzpr7lrwcjcwvignzbyn5vd7ji

Effect of genotype × alcoholism interaction on linkage analysis of an alcoholism-related quantitative phenotype

Rector Arya, Thomas D Dyer, Diane M Warren, Christopher P Jenkinson, Ravindranath Duggirala, Laura Almasy
2005 BMC Genetics  
Studies have shown that genetic and environmental factors and their interactions affect several alcoholism phenotypes. Genotype × alcoholism (G×A) interaction refers to the environmental (alcoholic and non-alcoholic) influences on the autosomal genes contributing to variation in an alcoholism-related quantitative phenotype. The purpose of this study was to examine the effects of G×A interaction on the detection of linkage for alcoholism-related phenotypes. from Genetic Analysis Workshop 14:
more » ... osatellite and single-nucleotide polymorphism Noordwijkerhout,
doi:10.1186/1471-2156-6-s1-s120 pmid:16451578 pmcid:PMC1866817 fatcat:c3fzg3twh5ht5jemslfbncjdce

On the genetic architecture of cortical folding and brain volume in primates

Jeffrey Rogers, Peter Kochunov, Karl Zilles, Wendy Shelledy, Jack Lancaster, Paul Thompson, Ravindranath Duggirala, John Blangero, Peter T. Fox, David C. Glahn
2010 NeuroImage  
Understanding the evolutionary forces that produced the human brain is a central problem in neuroscience and human biology. Comparisons across primate species show that both brain volume and gyrification (the degree of folding in the cerebral cortex) have progressively increased during primate evolution and there is a strong positive correlation between these two traits across primate species. The human brain is exceptional among primates in both total volume and gyrification, and therefore
more » ... rstanding the genetic mechanisms influencing variation in these traits will improve our understanding of a landmark feature of our species. Here we show that individual variation in gyrification is significantly heritable in both humans and an Old World monkey (baboons, Papio hamadryas). Furthermore, contrary to expectations based on the positive phenotypic correlation across species, the genetic correlation between cerebral volume and gyrification within both humans and baboons is estimated as negative. These results suggest that the positive relationship between cerebral volume and cortical folding across species cannot be explained by one set of selective pressures or genetic changes. Our data suggest that one set of selective pressures favored the progressive increase in brain volume documented in the primate fossil record, and that a second independent selective process, possibly related to parturition and neonatal brain size, may have favored brains with progressively greater cortical folding. Without a second separate selective pressure, natural selection favoring increased brain volume would be expected to produce less folded, more lissencephalic brains. These results provide initial evidence for the heritability of gyrification, and possibly a new perspective on the evolutionary mechanisms underlying long-term changes in the nonhuman primate and human brain.
doi:10.1016/j.neuroimage.2010.02.020 pmid:20176115 pmcid:PMC3137430 fatcat:ijmomwl2qrebvhzgzyjj3w72wm

Evidence for bivariate linkage of obesity and HDL-C levels in the Framingham Heart Study

Rector Arya, Donna Lehman, Kelly J Hunt, Jennifer Schneider, Laura Almasy, John Blangero, Michael P Stern, Ravindranath Duggirala
2003 BMC Genetics  
Epidemiological studies have indicated that obesity and low high-density lipoprotein (HDL) levels are strong cardiovascular risk factors, and that these traits are inversely correlated. Despite the belief that these traits are correlated in part due to pleiotropy, knowledge on specific genes commonly affecting obesity and dyslipidemia is very limited. To address this issue, we first conducted univariate multipoint linkage analysis for body mass index (BMI) and HDL-C to identify loci influencing
more » ... variation in these phenotypes using Framingham Heart Study data relating to 1702 subjects distributed across 330 pedigrees. Subsequently, we performed bivariate multipoint linkage analysis to detect common loci influencing covariation between these two traits. Results: We scanned the genome and identified a major locus near marker D6S1009 influencing variation in BMI (LOD = 3.9) using the program SOLAR. We also identified a major locus for HDL-C near marker D2S1334 on chromosome 2 (LOD = 3.5) and another region near marker D6S1009 on chromosome 6 with suggestive evidence for linkage (LOD = 2.7). Since these two phenotypes have been independently mapped to the same region on chromosome 6q, we used the bivariate multipoint linkage approach using SOLAR. The bivariate linkage analysis of BMI and HDL-C implicated the genetic region near marker D6S1009 as harboring a major gene commonly influencing these phenotypes (bivariate LOD = 6.2; LOD eq = 5.5) and appears to improve power to map the correlated traits to a region, precisely. Conclusions: We found substantial evidence for a quantitative trait locus with pleiotropic effects, which appears to influence both BMI and HDL-C phenotypes in the Framingham data.
doi:10.1186/1471-2156-4-s1-s52 pmid:14975120 pmcid:PMC1866489 fatcat:64amdras5ndrvlok3xrlo5dpbm

Association of Genetic Variation inENPP1With Obesity-related Phenotypes

Christopher P. Jenkinson, Dawn K. Coletta, Marion Flechtner-Mors, Shirley L. Hu, Marcel J. Fourcaudot, Lenore M. Rodriguez, Jennifer Schneider, Rector Arya, Michael P. Stern, John Blangero, Ravindranath Duggirala, Ralph A. DeFronzo
2008 Obesity  
Ectonucleotide pyrophosphatase phosphodiesterase (ENPP1) is a positional candidate gene at chromosome 6q23 where we previously detected strong linkage with fasting-specific plasma insulin and obesity in Mexican Americans from the San Antonio Family Diabetes Study (SAFDS). We genotyped 106 single-nucleotide polymorphisms (SNPs) within ENPP1 in all 439 subjects from the linkage study, and measured association with obesity and metabolic syndrome (MS)related traits. Of 72 polymorphic SNPs, 24 were
more » ... ssociated, using an additive model, with at least one of eight key metabolic traits. Three traits were associated with at least four SNPs. They were high-density lipoprotein cholesterol (HDL-C), leptin, and fasting plasma glucose (FPG). HDL-C was associated with seven SNPs, of which the two most significant P values were 0.0068 and 0.0096. All SNPs and SNP combinations were analyzed for functional contribution to the traits using the Bayesian quantitative-trait nucleotide (BQTN) approach. With this SNP-prioritization analysis, HDL-C was the most strongly associated trait in a four-SNP model (P = 0.00008). After accounting for multiple testing, we conclude that ENPP1 is not a major contributor to our previous linkage peak with MS-related traits in Mexican Americans. However, these results indicate that ENPP1 is a genetic determinant of these traits in this population, and are consistent with multiple positive association findings in independent studies in diverse human populations. In a genome scan of Mexican Americans from the San Antonio Family Diabetes Study (SAFDS), we previously found a strong linkage signal with several obesity-related
doi:10.1038/oby.2008.262 pmid:18464750 pmcid:PMC4889449 fatcat:w5gdkdhpzrac3cpp32miilkblm
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