Unlike case-control studies, family-based tests for association are protected against population stratification. Complex genetic traits are often governed by quantitative precursors and it has been argued that it may be a more powerful strategy to analyze these quantitative precursors instead of the clinical end point trait. Although methods have been developed for family-based association tests for single quantitative traits, it is of interest to develop such methods for multivariate phenotypes. We propose a novel transmission-based approach based on a trio design using a simple logistic regression to test for association with a multivariate phenotype. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. However, we find that the bivariate analysis of the two phenotypes did not provide more promising results compared to univariate analyses, suggesting a possibility of a different set of major genetic variants modulating the two phenotypes.
Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait.
It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses.
Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes–associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10−9). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10−12) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D.
To report a case of corneal graft failure due to epithelial ingrowth after an uneventful combined Descemet stripping automated endothelial keratoplasty (DSAEK) and phacoemulsification cataract surgery with intraocular lens implant treated successfully with a repeat DSAEK.
A 77-year-old male patient underwent combined DSAEK and phacoemulsification with intraocular lens implant implantation for Fuchs’ endothelial dystrophy plus cataract in the right eye. The donor cornea was cut on the Moria ALTK system and introduced using a suture pull-through technique. After an episode of endothelial rejection, the graft failed, with signs suggesting epithelial ingrowth. It was stripped from the host cornea using a Descemet’s membrane stripper, and a Simcoe irrigation-aspiration cannula was used to remove all traces of interface material. The excised lenticule was examined histologically using a hematoxylin and eosin stain.
The patient regained and maintained excellent visual acuity with no sign of recurrence of epithelial ingrowth. Histopathological evaluation of the donor tissue of the first graft showed epithelial ingrowth on the stromal surface of the graft and very few endothelial cells, in keeping with the diagnosis of graft failure.
Epithelial ingrowth is a possible cause of endothelial graft failure, but histologically proven cases are rare. Surgical intervention can achieve successful clearance, with the potential for cure and an excellent outcome.
epithelial ingrowth; Descemet stripping automated endothelial keratoplasty; graft failure
It is now well established that population stratification can result in spurious association findings in genetic case-control studies. However, very few studies have addressed similar issues for mapping quantitative traits. Since quantitative phenotypes are often precursors of clinical end-point traits and carry more information on within-genotype trait variability, it has been argued that studying these quantitative traits may be a more powerful strategy to map genes than the binary clinical end-points. Thus, it is of interest to evaluate the adverse effects of population stratification on the analyses of quantitative traits. The popular statistical tests of association for quantitative traits using population level data are ANOVA, linear regression with an additive allelic effect and Kruskal-Wallis. We have theoretically studied the marginal effects of genetic heterogeneity and phenotypic heterogeneity as well as their joint effects on the false positive rate of the three tests mentioned above. We have carried out extensive simulations under different genetic models and probability distributions of quantitative traits to assess the rate of false positives in the presence of population stratification. We find that the rate of false positives increases at a very fast rate with simultaneous increase in differences in the standardized phenotypic means and marker allele frequencies in the subpopulations.
allelic additivity; ANOVA; genetic heterogeneity; Kruskal-Wallis test; phenotypic heterogeneity
The increasing prevalence of obesity in urban Indian children is indicative of an impending crisis of metabolic disorders. Although perturbations in the secretion of adipokines and inflammatory molecules in childhood obesity are well documented, the contribution of common variants of genes encoding them is not well investigated. We assessed the association of 125 common variants from 21 genes, encoding adipocytokines and inflammatory markers in 1,325 urban Indian children (862 normal weight [NW group] and 463 overweight/obese [OW/OB group]) and replicated top loci in 1,843 Indian children (1,399 NW children and 444 OW/OB children). Variants of four genes (PBEF1 [rs3801266] [P = 4.5 × 10−4], IL6 [rs2069845] [P = 8.7 × 10−4], LEPR [rs1137100] [P = 1.8 × 10−3], and IL6R [rs7514452] [P = 2.1 × 10−3]) were top signals in the discovery sample. Associations of rs2069845, rs1137100, and rs3801266 were replicated (P = 7.9 × 10−4, 8.3 × 10−3, and 0.036, respectively) and corroborated in meta-analysis (P = 2.3 × 10−6, 3.9 × 10−5, and 4.3 × 10−4, respectively) that remained significant after multiple testing corrections. These variants also were associated with quantitative measures of adiposity (weight, BMI, and waist and hip circumferences). Allele dosage analysis of rs2069845, rs1137100, and rs3801266 revealed that children with five to six risk alleles had an approximately four times increased risk of obesity than children with less than two risk alleles (P = 1.2 × 10−7). In conclusion, our results demonstrate the association of the common variants of IL6, LEPR, and PBEF1 with obesity in Indian children.
Polymorphic variants of DNA repair and damage response genes play major role in carcinogenesis. These variants are suspected as predisposition factors to Oral Squamous Cell Carcinoma (OSCC). For identification of susceptible variants affecting OSCC development in Indian population, the “maximally informative” method of SNP selection from HapMap data to non-HapMap populations was applied. Three hundred twenty-five SNPs from 11 key genes involved in double strand break repair, mismatch repair and DNA damage response pathways were genotyped on a total of 373 OSCC, 253 leukoplakia and 535 unrelated control individuals. The significantly associated SNPs were validated in an additional cohort of 144 OSCC patients and 160 controls. The rs12515548 of MSH3 showed significant association with OSCC both in the discovery and validation phases (discovery P-value: 1.43E-05, replication P-value: 4.84E-03). Two SNPs (rs12360870 of MRE11A, P-value: 2.37E-07 and rs7003908 of PRKDC, P-value: 7.99E-05) were found to be significantly associated only with leukoplakia. Stratification of subjects based on amount of tobacco consumption identified SNPs that were associated with either high or low tobacco exposed group. The study reveals a synergism between associated SNPs and lifestyle factors in predisposition to OSCC and leukoplakia.
FTO variants are robustly associated with obesity and related traits in many population and shown to have variable impact during life course. Although studies have shown association of FTO variants with adiposity in adult Indian, its association in Indian children is yet to be confirmed.
Here we examined association of FTO variants (rs9939609 and rs8050136) with obesity and related anthropometric and biochemical traits in 3,126 Indian children (aged 11–17 years) including 2,230 normal-weight and 896 over-weight/obese children. We also compared effects observed in the present study with that observed in previous studies on South Asian adults and children of other ethnic groups.
The variant rs9939609 showed significant association with risk of obesity [OR = 1.21, P = 2.5×10−3] and its measures BMI, weight, waist circumference and hip circumference [β range = 0.11 to 0.14 Z-score units; P range = 1.3×10−4 to 1.6×10−7] in children. The observed effect sizes in Indian children were similar to those reported for European children. Variant rs9939609 explained 0.88% of BMI variance in Indian children. The effect sizes of rs9939609 on BMI and WC were ∼2 fold higher in children than adults. Interestingly rs9939609 was also associated with serum levels of thyroid stimulating hormone (TSH) [β = 0.10 Z-score, P = 5.8×10−3]. The other variant rs8050136 was in strong linkage disequilibrium with rs9939609 (r2 = 0.97) and provided similar association results.
The study provides first report of association of FTO variants with obesity and related anthropometric traits in Indian children with higher impact in children compared to adults. We also demonstrated association of FTO variant with serum levels of TSH, indicating putative influence of FTO in hypothalamic-pituitary-thyroid axis.
Background & objectives:
N-acetyltransferases 1 and 2 (NAT1 and NAT2) are important enzymes for metabolism of tobacco carcinogens. Due to polymorphisms, improper activities of these enzymes might lead to the formation of DNA adducts that may modulate risk of tobacco related oral precancer and cancer. Previously, it was shown that NAT2 polymorphisms did not modulate the risk of oral precancer and cancer. We undertook this study to check whether polymorphisms at NAT1 can modulate the risk of oral leukoplakia and cancer either alone or in combination with NAT2.
Genotypes at four SNPs on NAT1 were determined by TaqMan method in 389 controls, 224 leukoplakia and 310 cancer patients. Genotype data were analyzed to know haplotypes and acetylation status of individuals and, then to estimate the risk of diseases. Using our previously published NAT2 data, combination of NAT1 and NAT2 acetylation genotypes of patients and controls were also analyzed to estimate the risk of diseases.
Analysis of NAT1 genotype data revealed that 1088T and 1095C alleles exist in strong linkage disequilibrium (r2=0.97, P<0.0001) and SNPs are in Hardy-Weinberg Equilibrium (P=0.1). Wild type or normal acetylating and variant or rapid acetylating alleles were two major alleles (frequencies 0.62 and 0.36, respectively) present in the control population. NAT1 rapid acetylation could not modulate the risk of leukoplakia and cancer (OR=0.9, 95% CI: 0.6-1.3; OR=1.0, 95% CI: 0.7-1.4, respectively). Analysis of combined NAT1 and NAT2 acetylating data also showed no significant enhancement of the risk of diseases.
Interpretation & conclusions:
NAT1 rapid acetylation alone as well as combination of NAT1 rapid-NAT2 slow acetylation did not modulate the risk of oral precancer and cancer in our patient population. So, NAT1/NAT2 metabolized carcinogen products may not be involved in tobacco related oral precancer and cancer. It may be interpreted that large sample size as well as combination of polymorphisms at other candidate loci may be important to estimate the risk of a complex disease like oral cancer.
Combination of polymorphisms; leukoplakia; NAT1; NAT2; oral cancer
Hyperhomocysteinemia is regarded as a risk factor for cardiovascular diseases, diabetes and obesity. Manifestation of these chronic metabolic disorders starts in early life marked by increase in body mass index (BMI). We hypothesized that perturbations in homocysteine metabolism in early life could be a link between childhood obesity and adult metabolic disorders. Thus here we investigated association of common variants from homocysteine metabolism pathway genes with obesity in 3,168 urban Indian children.
We genotyped 90 common variants from 18 genes in 1,325 children comprising of 862 normal-weight (NW) and 463 over-weight/obese (OW/OB) children in stage 1. The top signal obtained was replicated in an independent sample set of 1843 children (1,399 NW and 444 OW/OB) in stage 2. Stage 1 association analysis revealed association between seven variants and childhood obesity at P<0.05, but association of only rs2796749 in AMD1 [OR = 1.41, P = 1.5×10-4] remained significant after multiple testing correction. Association of rs2796749 with childhood obesity was validated in stage 2 [OR = 1.28, P = 4.2×10-3] and meta-analysis [OR = 1.35, P = 1.9×10-6]. AMD1 variant rs2796749 was also associated with quantitative measures of adiposity and plasma leptin levels that was also replicated and corroborated in combined analysis.
Our study provides first evidence for the association of AMD1 variant with obesity and plasma leptin levels in children. Further studies to confirm this association, its functional significance and mechanism of action need to be undertaken.
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
Clinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies in integrating the constituent phenotypes into a reduced univariate phenotype for association analyses. We assess the performances of certain reduced phenotypes using analysis of variance and a model-free quantile-based approach. We find that analysis of variance is more powerful than the quantile-based approach in detecting association, particularly for rare variants. We also find that using a principal component of the quantitative phenotypes and the residual of a logistic regression of the binary phenotype on the quantitative phenotypes may be an optimal method for integrating a binary phenotype with quantitative phenotypes to define a reduced univariate phenotype.
Hyperhomocysteinemia, a risk factor for cardiovascular disorder, obesity, and type 2 diabetes, is prevalent among Indians who are at high risk of these metabolic disorders. We evaluated association of common variants of genes involved in homocysteine metabolism or its levels with type 2 diabetes, obesity, and related traits in North Indians. We genotyped 90 variants in initial phase (2.115 subjects) and replicated top signals in an independent sample set (2.085 subjects). The variant MTHFR-rs1801133 was the top signal for association with type 2 diabetes (OR = 0.78 (95% CI = 0.67–0.92), P = 0.003) and was also associated with 2 h postload plasma glucose (P = 0.04), high-density lipoprotein cholesterol (P = 0.004), and total cholesterol (P = 0.01) in control subjects. These associations were neither replicated nor significant after meta-analysis. Studies involving a larger study population and different ethnic groups are required before ruling out the role of these important candidate genes in type 2 diabetes, obesity, and related traits.
High sensitivity C-reactive protein (hsCRP) levels are shown to be influenced by genetic variants in Europeans; however, little is explored in Indian population.
Herein, we comprehensively evaluated association of all previously reported genetic determinants of hsCRP levels, including 18 cis (proximal to CRP gene) and 73 trans-acting (distal to CRP gene) variants in 4,200 North Indians of Indo-European ethnicity. First, we evaluated association of 91 variants from 12 candidate loci with hsCRP levels in 2,115 North Indians (1,042 non-diabetic subjects and 1,073 patients with type 2 diabetes). Then, cis and trans-acting variants contributing maximally to hsCRP level variation were further replicated in an independent 2,085 North Indians (1,047 patients with type 2 diabetes and 1,038 non-diabetic subjects).
We found association of 12 variants from CRP, LEPR, IL1A, IL6, and IL6R with hsCRP levels in non-diabetic subjects. However, only rs3093059-CRP [β = 0.33, P = 9.6×10−5] and the haplotype harboring rs3093059 risk allele [β = 0.32 µg/mL, P = 1.4×10−4/Pperm = 9.0×10−4] retained significance after correcting for multiple testing. The cis-acting variant rs3093059-CRP had maximum contribution to the variance in hsCRP levels (1.14%). Among, trans-acting variants, rs1892534-LEPR was observed to contribute maximally to hsCRP level variance (0.59%). Associations of rs3093059-CRP and rs1892534-LEPR were confirmed by replication and attained higher significance after meta-analysis [βmeta = 0.26/0.22; Pmeta = 4.3×10−7/7.4×10−3 and βmeta = −0.15/−0.12; Pmeta = 2.0×10−6/1.6×10−6 for rs3093059 and rs1892534, respectively in non-diabetic subjects and all subjects taken together].
In conclusion, we identified rs3093059 in CRP and rs1892534 in LEPR as major cis and trans-acting contributor respectively, to the variance in hsCRP levels in North Indian population.
There has been no systematic evaluation of the association between genetic variants of type 2 receptor for TNFα (TNFR2) and type 2 diabetes, despite strong biological evidence for the role of this receptor in the pathogenesis of this complex disorder. In view of this, we performed a comprehensive association analysis of TNFRSF1B variants with type 2 diabetes in 4,200 Indo-European subjects from North India.
The initial phase evaluated association of seven SNPs viz. rs652625, rs496888, rs6697733, rs945439, rs235249, rs17883432 and rs17884213 with type 2 diabetes in 2,115 participants (1,073 type 2 diabetes patients and 1,042 control subjects). Further, we conducted replication analysis of three associated SNPs in 2,085 subjects (1,047 type 2 diabetes patients and 1,038 control subjects).
We observed nominal association of rs945439, rs235249 and rs17884213 with type 2 diabetes (P < 0.05) in the initial phase. Haplotype CC of rs945439 and rs235249 conferred increased susceptibility for type 2 diabetes [OR = 1.19 (95%CI 1.03-1.37), P = 0.019/Pperm = 0.076] whereas, TG haplotype of rs235249 and rs17884213 provided protection against type 2 diabetes [OR = 0.83 (95%CI 0.72-0.95, P = 7.2 × 10-3/Pperm = 0.019]. We also observed suggestive association of rs496888 with plasma hsCRP levels [P = 0.042]. However, the association of rs945439, rs235249 and rs17884213 with type 2 diabetes was not replicated in the second study population. Meta-analysis of the two studies also failed to detect any association with type 2 diabetes.
Our two-stage association analysis suggests that TNFRSF1B variants are not the determinants of genetic risk of type 2 diabetes in North Indians.
Common variants in PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 genes have been shown to be associated with type 2 diabetes in European populations by genome-wide association studies. We have studied the association of common variants in these eight genes with type 2 diabetes and related traits in Indians by combining the data from two independent case–control studies.
RESEARCH DESIGN AND METHODS
We genotyped eight single nucleotide polymorphisms (PPARG-rs1801282, KCNJ11-rs5219, TCF7L2-rs7903146, SLC30A8-rs13266634, HHEX-rs1111875, CDKN2A-rs10811661, IGF2BP2-rs4402960, and CDKAL1-rs10946398) in 5,164 unrelated Indians of Indo-European ethnicity, including 2,486 type 2 diabetic patients and 2,678 ethnically matched control subjects.
We confirmed the association of all eight loci with type 2 diabetes with odds ratio (OR) ranging from 1.18 to 1.89 (P = 1.6 × 10−3 to 4.6 × 10−34). The strongest association with the highest effect size was observed for TCF7L2 (OR 1.89 [95% CI 1.71–2.09], P = 4.6 × 10−34). We also found significant association of PPARG and TCF7L2 with homeostasis model assessment of β-cell function (P = 6.9 × 10−8 and 3 × 10−4, respectively), which looked consistent with recessive and under-dominant models, respectively.
Our study replicates the association of well-established common variants with type 2 diabetes in Indians and shows larger effect size for most of them than those reported in Europeans.
There are two major classes of genetic association analyses: population based and family based. Population-based case–control studies have been the method of choice due to the ease of data collection. However, population stratification is one of the major limitations of case–control studies, while family-based studies are protected against stratification. In this study, we carry out extensive simulations under different disease models (both Mendelian as well as complex) to evaluate the relative powers of the two approaches in detecting association.
MATERIALS AND METHODS:
The power comparisons are based on a case–control design comprising 200 cases and 200 controls versus a Transmission Disequilibrium Test (TDT) or Pedigree Disequilibrium Test (PDT) design with 200 informative trios. We perform the allele-level test for case–control studies, which is based on the difference of allele frequencies at a single nucleotide polymorphism (SNP) between unrelated cases and controls. The TDT and the PDT are based on preferential allelic transmissions at a SNP from heterozygous parents to the affected offspring. We considered five disease modes of inheritance: (i) recessive with complete penetrance (ii) dominant with complete penetrance and (iii), (iv) and (v) complex diseases with varying levels of penetrances and phenocopies.
We find that while the TDT/PDT design with 200 informative trios is in general more powerful than a case–control design with 200 cases and 200 controls (except when the heterozygosity at the marker locus is high), it may be necessary to sample a very large number of trios to obtain the requisite number of informative families.
The current study provides insights into power comparisons between population-based and family-based association studies.
Allelic association; informative trios; complex genetic disorder
In this report, we present results of a genome-wide linkage scan using as a phenotype the number of externalizing symptoms associated with alcohol use disorders. Subjects were collected by the Collaborative Study on the Genetics of Alcoholism project from families in which at least three first degree relatives were affected by alcohol dependence. We use a novel non-parametric regression method based on kernel smoothing for our analysis. We report a statistically significant linkage close to the ADH gene cluster on Chromosome 4. We also obtain evidence for epistatic interaction between a region on Chromosome 1 and one on Chromosome 15. Although alcoholism as a covariate does not have any effect on the linkage scan, it has an effect on the epistatic interaction.
non-parametric regression; linkage; externalizing symptoms
The group that formed on the theme of genome-wide association of quantitative traits (Group 2) in the Genetic Analysis Workshop 16 comprised eight sets of investigators. Three data sets were available: one on auto-antibodies related to rheumatoid arthritis provided by the North American Rheumatoid Arthritis Consortium; the second on anthropometric, lipid, and biochemical measures provided by the Framingham Heart Study (FHS); and the third a simulated data set modeled after FHS. The different investigators in the group addressed a large set of statistical challenges and applied a wide spectrum of association methods in analyzing quantitative traits at the genome-wide level. While some previously reported genes were validated, some novel chromosomal regions provided significant evidence of association in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect association.
linkage disequilibrium mapping; model-free genetic analyses; rheumatoid arthritis; cardiovascular disorder
Type 2 diabetes is a complex metabolic disorder with obesity being a major contributing factor in its development. Susceptibility loci for type 2 diabetes and obesity have been localized on different chromosomal regions by various genome-wide linkage scans. Of these chromosomal regions, 20q13 is one of the strongest linked regions for type 2 diabetes as well as obesity. On 20q13 lies DOK5 that seems to be a strong functional and positional candidate for type 2 diabetes and obesity because of its involvement in insulin signaling and immune responses. Hence, for the first time, we explored DOK5 as a potential type 2 diabetes and obesity susceptibility gene.
We sequenced 43 subjects for polymorphisms in functionally relevant regions of DOK5. A total of 10 SNPs that included 5 that were identified by sequencing and 5 additional SNPs from NCBI Variation Database were genotyped in 2,115 participants comprising of 1,073 patients with type 2 diabetes and 1,042 controls of Indo-European ethnicity from North India.
We identified a novel variant in intron 7 referred to as DK176673. We found nominal association of three SNPs-rs6064099 (OR = 0.75, P = 0.019), rs873079 (OR = 0.76, P = 0.036) and DK176673 (OR = 1.55, P = 0.037) with type 2 diabetes among normal-weight subjects [BMI < 23 kg/m2]. The haplotype GGC harboring rs6068916, rs6064099 and rs873079 showed strong association with type 2 diabetes among normal-weight subjects (OR = 1.37, P/Pperm = 5.8 × 10-3/0.037). Association analysis with obesity revealed that rs6064099 is associated with reduced susceptibility for obesity (OR = 0.48, P = 6.8 × 10-3). Also, haplotype GGC conferred increased susceptibility for obesity (OR = 1.27, P/Pperm = 9.0 × 10-3/0.039). Also, rs6064099 was significantly associated with reduced BMI [median(IQR) = 24.0(20.7-27.1) vs 23.9(20.2-26.8) vs 21.8(19.2-24.7) for GG vs GC vs CC, P = 7.0 × 10-3].
We identified DOK5 as a novel susceptibility gene for obesity and type 2 diabetes in North Indian subjects. Association of DOK5 variants both with obesity and type 2 diabetes suggests that these variants might modulate type 2 diabetes susceptibility through obesity.
Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common disorders are hypothesized to be highly complex with interaction among genes and other risk factors playing a major role in the disease process. This complexity is further magnified because such interactions may be with or without a strong independent effect and are thus difficult to detect using traditional statistical methodologies. The main challenge to analyze such gene × gene and gene × environment interaction is attributed to a phenomenon referred to as the "curse of dimensionality." Several combinatorial methodologies have been proposed to tackle this analytical challenge. Because quantitative traits underlie complex phenotypes and contain more information on the trait variation within genotypes than qualitative dichotomy, analyzing quantitative traits correlated with the affection status is a more powerful tool for mapping such trait genes. Recently, a generalized multifactor dimensionality reduction method was proposed that allows for adjustment for discrete and quantitative traits and can be used to analyze qualitative and quantitative phenotypes in a population based study design.
In this report, we evaluate the efficiency of the generalized multifactor dimensionality reduction statistical suite to decipher small interacting factors that contribute to RA disease pathogenesis.
Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA using correlations between allele frequencies in the different quantile intervals of the quantitative trait and the quantile values. We performed genome-wide association scans on anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M, two quantitative traits correlated with rheumatoid arthritis, using the data provided in Genetic Analysis Workshop 16. Both the quantitative traits exhibited significant evidence of association on Chromosome 6, although not in the human leukocyte antigen region which is known to harbor a major gene predisposing to rheumatoid arthritis. We found that while a majority of the significant findings using the asymptotic thresholds of ANOVA was not validated using permutations, a relatively higher proportion of the significant findings using the asymptotic cut-offs of the correlation statistic were validated using permutations.