The search for expression quantitative trait loci (eQTL) has traditionally centered entirely on the process of transcription, whereas variants with effects on mRNA translation have not been systematically studied. Here we present a high throughput approach for measuring translational cis-regulation in the human genome. Using ribosomal association as proxy for translational efficiency of polymorphic mRNAs, we test the ratio of polysomal/nonpolysomal mRNA level as a quantitative trait for association with single-nucleotide polymorphisms on the same mRNA transcript. We identify one important ribosomal-distribution effect, from rs1131017 in the 5’UTR of RPS26 , that is in high linkage disequilibrium (LD) with the 12q13 locus for susceptibility to type 1 diabetes. The effect on translation is confirmed at the protein level by quantitative Western blots, both ex vivo and after in vitro translation. Our results are a proof-of-principle that allelic effects on translation can be detected at a transcriptome-wide scale.
An elevated insulin resistance index (homeostasis model assessment of insulin resistance [HOMA-IR]) is more commonly seen in the Mexican American population than in European populations. We report quantitative ancestral effects within a Mexican American population, and we correlate ancestral components with HOMA-IR.
RESEARCH DESIGN AND METHODS
We performed ancestral analysis in 1,551 participants of the Cameron County Hispanic Cohort by genotyping 103 ancestry-informative markers (AIMs). These AIMs allow determination of the percentage (0–100%) ancestry from three major continental populations, i.e., European, African, and Amerindian.
We observed that predominantly Amerindian ancestral components were associated with increased HOMA-IR (β = 0.124, P = 1.64 × 10−7). The correlation was more significant in males (Amerindian β = 0.165, P = 5.08 × 10−7) than in females (Amerindian β = 0.079, P = 0.019).
This unique study design demonstrates how genomic markers for quantitative ancestral information can be used in admixed populations to predict phenotypic traits such as insulin resistance.
Background and Aims
Elevated alanine aminotransferase (ALT >40 IU/mL) is a marker of liver injury but provides little insight into etiology. We aimed to identify and stratify risk factors associated with elevated ALT in a randomly selected population with a high prevalence of elevated ALT (39%), obesity (49%) and diabetes (30%).
Two machine learning methods, the support vector machine (SVM) and Bayesian logistic regression (BLR), were used to capture risk factors in a community cohort of 1532 adults from the Cameron County Hispanic Cohort (CCHC). A total of 28 predictor variables were used in the prediction models. The recently identified genetic marker rs738409 on the PNPLA3 gene was genotyped using the Sequenom iPLEX assay.
The four major risk factors for elevated ALT were fasting plasma insulin level and insulin resistance, increased BMI and total body weight, plasma triglycerides and non-HDL cholesterol, and diastolic hypertension. In spite of the highly significant association of rs738409 in females, the role of rs738409 in the prediction model is minimal, compared to other epidemiological risk factors. Age and drug and alcohol consumption were not independent determinants of elevated ALT in this analysis.
The risk factors most strongly associated with elevated ALT in this population are components of the metabolic syndrome and point to nonalcoholic fatty liver disease (NAFLD). This population-based model identifies the likely cause of liver disease without the requirement of individual pathological diagnosis of liver diseases. Use of such a model can greatly contribute to a population-based approach to prevention of liver disease.
Alanine aminotransferase; Liver disease; Machine learning; NAFLD; PNPLA3 polymorphism; Public health
This study examined genetic associations of PNPLA3 polymorphisms and liver aminotransferases in an extensively documented, randomly recruited Mexican American population at high risk of liver disease.
Two single nucleotide polymorphisms (SNP) in the PNPLA3 gene, i.e. rs738409 and rs2281135, were genotyped in 1532 individuals. Population stratification was corrected by the genotyping of 103 ancestry informative markers (AIMs) for Mexican Americans.
Both PNPLA3 SNPs showed highly significant association with alanine aminotransferase (ALT) levels, but was also in males associated with aspartate aminotransferase (AST) levels. Haplotypic association test of the two SNPs suggested stronger genetic association with rs738409 than rs2281135. Obvious sex effects were characterized: rs738409-sex interaction in ALT levels had P=8.37×10−4; rs738409-sex interaction in AST levels had P=5.03×10−3.
This population study highlights a sex-specific association of PNPLA3 polymorphisms and elevated liver enzymes in a population-based study, independent of common pathological factors of the metabolic syndrome. The strong genetic association found in women≤50 years old, but not in women>50 years old, suggests that sex hormones may mediate the sex effect.
alanine aminotransferase; sex; genetic association; plasma liver enzymes; PNPLA3; metabolic syndrome; single nucleotide polymorphism
Previous studies in mice and humans observed down-regulation of the gene expression of ATP6V1H associated with type 2 diabetes. This study identified prospectively changes in ATP6V1H expression before and after overt diabetes.
Expression of ATP6V1H in peripheral blood was compared pre and post development of diabetes in nine individuals.
Considerable variation of ATP6V1H mRNA levels was observed between different individuals. However, within each individual the decrease in expression of ATP6V1H with the development of diabetes was highly statistically significant.
ATP6V1H may represent a critical molecular mechanism involved in the development of type 2 diabetes and its compilations through its important regulatory effect on vacuolar-ATPase activity.
ATP6V1H; diabetic complications; longitudinal study; type 2 diabetes; vacuolar ATPase
Tuberculosis (TB) remains a major global disease, and diabetes which is documented to increase susceptibility to TB threefold, is also becoming pandemic. This susceptibility has been attracting extensive research interest. The increased risk of TB in diabetes may serve as a unique model to understand host susceptibility to specific pathogens in humans. To examine this rationale, we investigated expression of reported TB candidate genes in a longitudinal diabetes study. Two genes HK2 and CD28 emerged as potential culprits in diabetes-increased TB susceptibility.
CD28; HK2; host susceptibility; type 2 diabetes; transcriptome; tuberculosis
Tuberculosis (TB) is a serious health issue in the developing world. Lack of knowledge on the etiological mechanisms of TB hinders the development of effective strategies for the treatment or prevention of TB disease. Human genetic study is an indispensable approach to understand the molecular basis of common diseases. Numerous efforts were made to screen the human genome for TB susceptibility by linkage mapping. A large number of candidate-based association studies of TB were performed to examine the association of predicted functional DNA variations in candidate genes. Recently, the first genome-wide association study (GWAS) on TB was reported. The GWAS is a proof-of-principle evidence which justifies the genetic approach to understand TB. Further hypothesis-free efforts on TB research may renovate the traditional idea of TB genetic susceptibility as none of the candidate genes with important roles in containing Mycobacterium tuberculosis (MTB) infection was identified of association with active TB, while the TB-associated loci in the GWAS harbors no gene with function in MTB infection.
tuberculosis; genetic susceptibility; GWAS; candidate gene; linkage mapping
The interferon regulatory factor 5 gene (IRF5) has been shown to play a crucial role in harmful immune responses by induction of proinflammatory cytokines. Functional genetic variants associated with increasd IRF5 expression of specific isoforms are associated with systemic lupus erythematosus (SLE) and it is possible that they may also predispose to other autoimmune disorders. We tested the association of two IRF5 SNPs, correlated with IRF5 expression and SLE risk, in 947 nuclear family trios type 1 diabetes (T1D) using the transmission disequilibrium test. Our results suggest that the functional IRF5 variations do not confer an obvious risk for T1D.
TCF7L2 belongs to a subfamily of TCF7-like HMG box-containing transcription factors, and maps to human chromosome 10q25.3. A recent study identified genetic association of type 2 diabetes (T2D) with this gene, correlated with diminished insulin secretion. This study aimed to investigate the possibility of genetic association between TCF7L2 and type 1 diabetes (T1D).
The SNP most significantly associated with T2D, rs7903146, was genotyped in 886 T1D nuclear family trios with ethnic backgrounds of mixed European descent.
This study found no T1D association with, and no age-of-onset effect from rs7903146.
This study suggests that a T2D mechanism mediated by TCF7L2 does not participate in the etiology of T1D.
Complementary single-nucleotide polymorphisms (SNPs) may not be distributed equally between two DNA strands if the strands are functionally distinct, such as in transcribed genes. In introns, an excess of A↔G over the complementary C↔T substitutions had previously been found and attributed to transcription-coupled repair (TCR), demonstrating the valuable functional clues that can be obtained by studying such asymmetry. Here we studied asymmetry of human synonymous SNPs (sSNPs) in the fourfold degenerate (FFD) sites as compared to intronic SNPs (iSNPs).
The identities of the ancestral bases and the direction of mutations were inferred from human-chimpanzee genomic alignment. After correction for background nucleotide composition, excess of A→G over the complementary T→C polymorphisms, which was observed previously and can be explained by TCR, was confirmed in FFD SNPs and iSNPs. However, when SNPs were separately examined according to whether they mapped to a CpG dinucleotide or not, an excess of C→T over G→A polymorphisms was found in non-CpG site FFD SNPs but was absent from iSNPs and CpG site FFD SNPs.
The genome-wide discrepancy of human FFD SNPs provides novel evidence for widespread selective pressure due to functional effects of sSNPs. The similar asymmetry pattern of FFD SNPs and iSNPs that map to a CpG can be explained by transcription-coupled mechanisms, including TCR and transcription-coupled mutation. Because of the hypermutability of CpG sites, more CpG site FFD SNPs are relatively younger and have confronted less selection effect than non-CpG FFD SNPs, which can explain the asymmetric discrepancy of CpG site FFD SNPs vs. non-CpG site FFD SNPs.
The rapid progress of genomic technologies has been providing new opportunities to address the need of maturity-onset diabetes of the young (MODY) molecular diagnosis. However, whether a new mutation causes MODY can be questionable. A number of in silico methods have been developed to predict functional effects of rare human mutations. The purpose of this study is to compare the performance of different bioinformatics methods in the functional prediction of nonsynonymous mutations in each MODY gene, and provides reference matrices to assist the molecular diagnosis of MODY. Our study showed that the prediction scores by different methods of the diabetes mutations were highly correlated, but were more complimentary than replacement to each other. The available in silico methods for the prediction of diabetes mutations had varied performances across different genes. Applying gene-specific thresholds defined by this study may be able to increase the performance of in silico prediction of disease-causing mutations.
Thirteen tag SNPs at the CASP8 and CASP10 loci in patients with advanced NSCLC were genotyped in a two-stage analysis consisting of a discovery set and an independent validation set. These SNPs were evaluated for their association with toxicity outcomes with platinum-based chemotherapy.
Caspase-8 and caspase-10 play crucial roles in both cancer development and chemotherapy efficacy. In this study, we aimed to comprehensively assess single nucleotide polymorphisms (SNPs) of the caspase-8 (CASP8) and caspase-10 (CASP10) genes in relation to toxicity outcomes with first-line platinum-based chemotherapy in patients with advanced non-small cell lung cancer (NSCLC). We genotyped 13 tag SNPs of CASP8 and CASP10 in 663 patients with advanced NSCLC treated with platinum-based chemotherapy regimens. Associations between SNPs and chemotherapy toxicity outcomes were identified in a discovery set of 279 patients and then validated in an independent set of 384 patients. In both the discovery and validation sets, variant homozygotes of CASP8 rs12990906 and heterozygotes of CASP8 rs3769827 and CASP10 rs11674246 and rs3731714 had a significantly lower risk for severe toxicity overall. However, only the association with the rs12990906 variant was replicated in the validation set for hematological toxicity risk. In a stratified analysis, we found that some other SNPs, including rs3769821, rs3769825, rs7608692, and rs12613347, were significantly associated with severe toxicity risk in some subgroups, such as in nonsmoking patients, patients with adenocarcinoma, and patients treated with cisplatin combinations. Consistent results were also found in haplotype analyses. Our results provide novel evidence that polymorphisms in CASP8 and CASP10 may modulate toxicity outcomes in patients with advanced NSCLC treated with platinum-based chemotherapy. If validated, the findings will facilitate the genotype-based selection of platinum-based chemotherapy regimens.
CASP8; CASP10; Polymorphisms; Platinum-based chemotherapy; Toxicity; Non-small cell lung cancer; Association
Syndrome of inappropriate antidiuretic hormone secretion (SIADH) is a common cause of hyponatremia in hospitalized patients and is often described in patients with small-cell carcinoma of the lung. In this report, we described both Castleman’s disease and lymphoma coexisting in one patient with SIADH.
A 70-year-old Chinese woman with a history of diabetes mellitus and insulin therapy had severe hyponatremia and gastrointestinal symptoms. Through a series of examinations, common causes such as pulmonary carcinoma were excluded. An abdominal mass was detected by computed tomography. Although the peripheral lymph node biopsy showed the pathological result as Castleman’s disease, the pathology of the abdominal lymph node revealed diffuse large B-cell lymphoma. After chemotherapy, the hyponatremia was treated during a period of follow-up.
This patient presented with the rare clinical condition of inappropriate antidiuretic hormone secretion alongside Castleman’s disease and lymphoma. Asymptomatic hyponatremia may persist for some time suggesting that clinical physicians should pay attention to the mild cases of hyponatremia. We also hypothesized that Castleman’s disease is a condition of pre-lymphoma with both having the ability to cause SIADH. The possibility of lymphoma as well as Castleman’s disease triggering the development of SIADH should also be taken into consideration for conducting recurrent biopsies.
Castleman’s disease; Hyponatremia; Lymphoma; Syndrome of inappropriate antidiuretic hormone secretion (SIADH)
According to the ramp model of mRNA translation, the first 50 codons favor rare codons and have slower speed of translation. This study aims to detect translational selection on coding synonymous single nucleotide polymorphisms (sSNP) to support the ramp theory. We investigated fourfold degenerate site (FFDS) sSNPs with A↔G or C↔T substitutions in human genome for distribution bias of synonymous codons (SC), grouped by CpG or non-CpG sites. Distribution bias of sSNPs between the 3rd ∼50th codons and the 51st ∼ remainder codons at non-CpG sites were observed. In the 3rd ∼50th codons, G→A sSNPs at non-CpG sites are favored than A→G sSNPs [P = 2.89×10−3], and C→T at non-CpG sites are favored than T→C sSNPs [P = 8.50×10−3]. The favored direction of SC usage change is from more frequent SCs to less frequent SCs. The distribution bias is more obvious in synonymous substitutions CG(G→A), AC(C→T), and CT(C→T). The distribution bias of sSNPs in human genome, i.e. frequent SCs to less frequent SCs is favored in the 3rd ∼50th codons, indicates translational selection on sSNPs in the ramp regions of mRNA templates.
Understanding molecular immunity against mycobacterial infection is critical for the development of effective strategies to control tuberculosis (TB), which is a major health issue in the developing world. Host immunogenetic studies represent an indispensable approach to understand the molecular mechanisms against mycobacterial infection. A superb paradigm is the identification of rare mutations causing Mendelian susceptibility to mycobacterial diseases (MSMD). Mutations in the interferon-gamma (IFN-γ) receptor genes are highly specific (although not exclusive) for mycobacterial infection. Only dominant negative mutations of STAT1 have specific susceptibility to mycobacterial infection. Mutations in the interleukin-12 (IL-12) signaling genes have phenotypes with non-specificity. Current studies highlight a complex molecular network in antimycobacterial immunity, centered on IFN-γ signaling.
Mendelian susceptibility to mycobacterial diseases; Interleukin-12; Interferon-gamma; IKBKG
The population of Han Chinese is ∼1.226 billion people. Genetic heterogeneity between northern Han Chinese (N-Han) and southern Han Chinese (S-Han) has been demonstrated by recent genome-wide studies. As an initial step toward health disparities and personalized medicine in Chinese population, this study developed a set of ancestry informative markers (AIM) for Han Chinese population.
ancestry informative marker; Han Chinese; genetic association study; population structure
The two genome-wide association studies published by us and by the Wellcome Trust Case-Control Consortium (WTCCC) revealed a number of novel loci but neither had the statistical power to elucidate all of the genetic components of type 1 diabetes risk, a task for which larger effective sample sizes are needed.
We analyzed data from two sources: 1) The previously published second stage of our study, with a total sample size of the two stages consisting of 1,046 Canadian case-parent trios and 538 multiplex families with 929 affected offspring from the Type 1 Diabetes Genetics Consortium (T1DGC); 2) The RR2 project of the T1DGC, which genotyped 4,417 individuals from 1,062 non-overlapping families, including 2,059 affected individuals (mostly sibling pairs) for the 1,536 markers with the highest statistical significance for type 1 diabetes in the WTCCC results.
One locus, mapping to an LD block at chr15q14, reached statistical significance by combining results from two markers (rs17574546 and rs7171171) in perfect linkage disequilibrium (LD) with each other (r2=1). We obtained a joint p value of 1.3 ×10−6, which exceeds by an order of magnitude the conservative threshold of 3.26×10−5 obtained by correcting for the 1,536 SNPs tested in our study. Meta-analysis with the original WTCCC genome-wide data produced a p value of 5.83×10−9.
A novel type 1 diabetes locus was discovered. It involves RASGRP1, a gene known to play a crucial role in thymocyte differentiation and TCR signaling by activating the Ras signaling pathway.
Etiology; Genetic susceptibility; Type 1 diabetes; RASGRP1
This study aimed to investigate whether the presence of autoantibodies specific for type 1 diabetes (T1D) is determined by the major genetic susceptibility locus for the disease at the HLA genes, using the T1D Genetics Consortium data.
We analyzed anti-IA-2 and anti-GAD 65 autoantibody data from 2,282 T1D patients from 1117 multiplex families. HLA genotyping was available for all cases and their parents and association with autoantibody positivity was tested by the transmission disequilibrium test.
Association of anti-IA-2 with the HLA genes was detected at high statistical signficance. HLA-DRB1*0401 confers both the strongest type 1 diabetes risk, and positive association of anti-IA-2, whereas the DRB1*03- DQA1*0501-DQB1*0201 haplotype, associated less strongly with T1D, showed a significant negative association with anti-IA-2 positivity. Interestingly, HLA-A*24 is also negatively associated with anti-IA-2, independently of the DRB1*03- DQA1*0501-DQB1*0201 haplotype. No statistically significant association was identified between anti-GAD65 and HLA.
This study highlights that IA-2 as an autoantigen depends on HLA genotype and suggests new insights into the mechanism of loss of immune tolerance.
autoantibody; GAD65; HLA; IA-2; Type 1 diabetes
Adiponectin and leptin play critical roles in the development of Metabolic Syndrome (MetS). The study was designed to assess circulating levels of adiponectin and leptin in early diagnosis of Metabolic Syndrome (MetS).
This cross-sectional study was performed on 367 participants randomly selected from a well-characterized cohort of Mexican-Americans living at the US-Mexico border.
Significant differences in circulating levels of adiponectin and leptin were observed between males and females. The adiponectin/leptin ratio significantly correlated with MetS in this population. A receiver-operator characteristic (ROC) analysis demonstrated that adiponectin/leptin ratio is a valuable biomarker for the diagnosis of MetS
Our study supported the central role of adiponectin and leptin in MetS, and demonstrated that adiponectin/leptin ratio can be used as a highly sensitive and specific biomarker for MetS.
adiponectin; computer-aided diagnosis; leptin; MetS; receiver operator characteristic
Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10−11) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10−9) resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10−9) lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
Despite the fact that there is clearly a large genetic component to type 1 diabetes (T1D), uncovering the genes contributing to this disease has proven challenging. However, in the past three years there has been relatively major progress in this regard, with advances in genetic screening technologies allowing investigators to scan the genome for variants conferring risk for disease without prior hypotheses. Such genome-wide association studies have revealed multiple regions of the genome to be robustly and consistently associated with T1D. More recent findings have been a consequence of combining of multiple datasets from independent investigators in meta-analyses, which have more power to pick up additional variants contributing to the trait. In the current study, we describe the largest meta-analysis of T1D genome-wide genotyped datasets to date, which combines six large studies. As a consequence, we have uncovered three new signals residing at the chromosomal locations 13q22, 2p23, and 6q27, which went on to be replicated in independent sample sets. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.
Study the prevalence of metabolic syndrome (MS) and risk factors for and association with elevated alanine aminotransferase (ALT) as markers of hepatic injury in a large Hispanic health disparity cohort with high rates of obesity.
Analysis of data from a prospective cross-sectional population based study. From 2004-7, we randomly recruited 2000 community participants to the Cameron County Hispanic Cohort collecting extensive socioeconomic, clinical and laboratory data. We excluded 153 subjects due to critical missing data. Pearson chi-square tests and Student's t-tests were used for categorical and continuous variable analysis, respectively. Logistic regression analysis was performed to determine the risk factors for elevated ALT.
The mean age of the cohort was 45 years and 67% were females. The majority of the cohort was either overweight (32.4%) or obese (50.7%). Almost half (43.7%) had MS and nearly one-third diabetes. Elevated ALT level was more prevalent in males than females. Obesity was a strong risk for abnormal ALT in both genders. Hypertriglyceridemia, hypercholesterolemia and young age were risks for elevated ALT in males only, whereas increased fasting plasma glucose was associated with elevated ALT in females only.
We identified high prevalence of MS and markers of liver injury in this large Mexican American cohort with gender differences in prevalence and risk factors, with younger males at greatest risk.
The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods.
This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance.
Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80.
Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.