Integration of rare expression outlier-associated variants improves polygenic risk prediction.

Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual's likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10-14), 62.3% increase in risk for severe obesity (p = 1 × 10-6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10-15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing.

A functional genomic framework to elucidate novel causal metabolic dysfunction-associated fatty liver disease genes.

BACKGROUND AND AIMS: Metabolic dysfunction-associated fatty liver disease (MASLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for MASLD have been hampered by the relative paucity of human data from gold standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using MASLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined genome-wide association studies of MASLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for MASLD. APPROACH AND RESULTS: We used the UK Biobank to explore the associations of our novel MASLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study MASLD genes in vitro using CRISPRi. Our data identify VKORC1 , TNKS , LYPLAL1 , and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of MASLD. CONCLUSIONS: Complementary genetic and genomic approaches are useful for the identification of MASLD genes. Our data supports VKORC1 as a bona fide MASLD gene. We have established a functional genomic framework to study at scale putative novel MASLD genes from human genetic association studies.

Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.

We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

Survival analysis on rare events using group-regularized multi-response Cox regression.

MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. AVAILABILITYANDIMPLEMENTATION: https://github.com/rivas-lab/multisnpnet-Cox.

Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years.

Blood concentrations of triglycerides are influenced by genetic factors as well as a number of environmental factors, including adiposity and glucose homeostasis. The aim was to investigate the association between a serum triglyceride weighted genetic risk score (wGRS) and changes in fasting serum triglyceride level over 5 years and to test whether the effect of the wGRS was modified by 5 year changes of adiposity, insulin resistance, and lifestyle factors. A total of 3,474 nondiabetic individuals from the Danish Inter99 cohort participated in both the baseline and 5 year follow-up physical examinations and had information on the wGRS comprising 39 genetic variants. In a linear regression model adjusted for age, sex, and baseline serum triglyceride, the wGRS was associated with increased serum triglyceride levels over 5 years [per allele effect = 1.3% (1.0-1.6%); P = 1.0 × 10-17]. This triglyceride-increasing effect of the wGRS interacted with changes in insulin resistance (Pinteraction = 1.5 × 10-6). This interaction indicated that the effect of the wGRS was stronger in individuals who became more insulin resistant over 5 years. In conclusion, our findings suggest that increased genetic risk load is associated with a larger increase in fasting serum triglyceride levels in nondiabetic individuals during 5 years of follow-up. This effect of the wGRS is accentuated by increasing insulin resistance.

Solute carrier family 2 member 1 is involved in the development of nonalcoholic fatty liver disease.

UNLABELLED: Susceptibility to develop nonalcoholic fatty liver disease (NAFLD) has genetic bases, but the associated variants are uncertain. The aim of the present study was to identify genetic variants that could help to prognose and further understand the genetics and development of NAFLD. Allele frequencies of 3,072 single-nucleotide polymorphisms (SNPs) in 92 genes were characterized in 69 NAFLD patients and 217 healthy individuals. The markers that showed significant allele-frequency differences in the pilot groups were subsequently studied in 451 NAFLD patients and 304 healthy controls. Besides this, 4,414 type 2 diabetes mellitus (T2DM) cases and 4,567 controls were genotyped. Liver expression of the associated gene was measured and the effect of its potential role was studied by silencing the gene in vitro. Whole genome expression, oxidative stress (OS), and the consequences of oleic acid (OA)-enriched medium on lipid accumulation in siSLC2A1-THLE2 cells were studied by gene-expression analysis, dihydroethidium staining, BODIPY, and quantification of intracellular triglyceride content, respectively. Several SNPs of SLC2A1 (solute carrier family 2 [facilitated glucose transporter] member 1) showed association with NAFLD, but not with T2DM, being the haplotype containing the minor allele of SLC2A1 sequence related to the susceptibility to develop NAFLD. Gene-expression analysis demonstrated a significant down-regulation of SLC2A1 in NAFLD livers. Enrichment functional analyses of transcriptome profiles drove us to demonstrate that in vitro silencing of SLC2A1 induces an increased OS activity and a higher lipid accumulation under OA treatment. CONCLUSIONS: Genetic variants of SLC2A1 are associated with NAFLD, and in vitro down-regulation of this gene promotes lipid accumulation. Moreover, the oxidative response detected in siSLC2A1-THLE2 cells corroborated the antioxidant properties previously related to this gene and linked the most representative clinical characteristics of NAFLD patients: oxidative injury and increased lipid storage.

A functional genomic framework to elucidate novel causal non-alcoholic fatty liver disease genes.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for NAFLD have been hampered by the relative paucity of human data from gold-standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using NAFLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined GWAS of NAFLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for NAFLD. Approach & Results: We used the UK Biobank to explore the associations of our novel NAFLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study NAFLD genes in vitro using CRISPRi. Our data identify VKORC1, TNKS, LYPLAL1 and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of NAFLD. Conclusions: Complementary genetic and genomic approaches are useful for the identification of NAFLD genes. Our data supports VKORC1 as a bona fide NAFLD gene. We have established a functional genomic framework to study at scale putative novel NAFLD genes from human genetic association studies.

Interactions of physical activity, muscular fitness, adiposity, and genetic risk for NAFLD.

Genetic predisposition and unhealthy lifestyle are risk factors for nonalcoholic fatty liver disease (NAFLD). We investigated whether the genetic risk of NAFLD is modified by physical activity, muscular fitness, and/or adiposity. In up to 242,524 UK Biobank participants without excessive alcohol intake or known liver disease, we examined cross-sectional interactions and joint associations of physical activity, muscular fitness, body mass index (BMI), and a genetic risk score (GRS) with alanine aminotransferase (ALT) levels and the proxy definition for suspected NAFLD of ALT levels > 30 U/L in women and >40 U/L in men. Genetic predisposition to NAFLD was quantified using a GRS consisting of 68 loci known to be associated with chronically elevated ALT. Physical activity was assessed using accelerometry, and muscular fitness was estimated by measuring handgrip strength. We found that increased physical activity and grip strength modestly attenuate genetic predisposition to elevation in ALT levels, whereas higher BMI markedly amplifies it (all p values < 0.001). Among those with normal weight and high level of physical activity, the odds of suspected NAFLD were 1.6-fold higher in those with high versus low genetic risk (reference group). In those with high genetic risk, the odds of suspected NAFLD were 12-fold higher in obese participants with low physical activity versus those with normal weight and high physical activity (odds ratio for NAFLD = 19.2 and 1.6, respectively, vs. reference group). Conclusion: In individuals with high genetic predisposition for NAFLD, maintaining a normal body weight and increased physical activity may reduce the risk of NAFLD.

The minor C-allele of rs2014355 in ACADS is associated with reduced insulin release following an oral glucose load.

BACKGROUND: A genome-wide association study (GWAS) using metabolite concentrations as proxies for enzymatic activity, suggested that two variants: rs2014355 in the gene encoding short-chain acyl-coenzyme A dehydrogenase (ACADS) and rs11161510 in the gene encoding medium-chain acyl-coenzyme A dehydrogenase (ACADM) impair fatty acid ß-oxidation. Chronic exposure to fatty acids due to an impaired ß-oxidation may down-regulate the glucose-stimulated insulin release and result in an increased risk of type 2 diabetes (T2D). We aimed to investigate whether the two variants associate with altered insulin release following an oral glucose load or with T2D. METHODS: The variants were genotyped using KASPar® PCR SNP genotyping system and investigated for associations with estimates of insulin release and insulin sensitivity following an oral glucose tolerance test (OGTT) in a random sample of middle-aged Danish individuals (nACADS = 4,324; nACADM = 4,337). The T2D-case-control study involved a total of ~8,300 Danish individuals (nACADS = 8,313; nACADM = 8,344). RESULTS: In glucose-tolerant individuals the minor C-allele of rs2014355 of ACADS associated with reduced measures of serum insulin at 30 min following an oral glucose load (per allele effect (ß) = -3.8% (-6.3%;-1.3%), P = 0.003), reduced incremental area under the insulin curve (ß = -3.6% (-6.3%;-0.9%), P = 0.009), reduced acute insulin response (ß = -2.2% (-4.2%;0.2%), P = 0.03), and with increased insulin sensitivity ISIMatsuda (ß = 2.9% (0.5%;5.2%), P = 0.02). The C-allele did not associate with two other measures of insulin sensitivity or with a derived disposition index. The C-allele was not associated with T2D in the case-control analysis (OR 1.07, 95% CI 0.96-1.18, P = 0.21). rs11161510 of ACADM did not associate with any indices of glucose-stimulated insulin release or with T2D. CONCLUSIONS: In glucose-tolerant individuals the minor C-allele of rs2014355 of ACADS was associated with reduced measures of glucose-stimulated insulin release during an OGTT, a finding which in part may be mediated through an impaired ß-oxidation of fatty acids.

Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA2DS2-VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS). METHODS: Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. RESULTS: Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%-3.0%]) and fit (χ2P=0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06-1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, -0.018). CONCLUSIONS: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

Meta-analysis of exome array data identifies six novel genetic loci for lung function.

Background: Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. Methods: We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV 1), forced vital capacity (FVC) and the ratio of FEV 1 to FVC (FEV 1/FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. Results: We identified significant (P<2·8x10 -7) associations with six SNPs: a nonsynonymous variant in RPAP1, which is predicted to be damaging, three intronic SNPs ( SEC24C, CASC17 and UQCC1) and two intergenic SNPs near to LY86 and FGF10. Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including TYRO3 and PLAU. Conclusions: Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease.

Interactions of Lipid Genetic Risk Scores With Estimates of Metabolic Health in a Danish Population.

BACKGROUND: There are several well-established lifestyle factors influencing dyslipidemia and currently; 157 genetic susceptibility loci have been reported to be associated with serum lipid levels at genome-wide statistical significance. However, the interplay between lifestyle risk factors and these susceptibility loci has not been fully elucidated. We tested whether genetic risk scores (GRS) of lipid-associated single nucleotide polymorphisms associate with fasting serum lipid traits and whether the effects are modulated by lifestyle factors or estimates of metabolic health. METHODS AND RESULTS: The single nucleotide polymorphisms were genotyped in 2 Danish cohorts: inter99 (n=5961) for discovery analyses and Health2006 (n=2565) for replication. On the basis of published effect sizes of single nucleotide polymorphisms associated with circulating fasting levels of total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, or triglyceride, 4 weighted GRS were constructed. In a cross-sectional design, we investigated whether the effect of these weighted GRSs on lipid levels were modulated by diet, alcohol consumption, physical activity, and smoking or the individual metabolic health status as estimated from body mass index, waist circumference, and insulin resistance assessed using homeostasis model assessment of insulin resistance. All 4 lipid weighted GRSs associated strongly with their respective trait (from P=3.3×10(-69) to P=1.1×10(-123)). We found interactions between the triglyceride weighted GRS and body mass index and waist circumference on fasting triglyceride levels in Inter99 and replicated these findings in Health2006 (P(interaction)=9.8×10(-5) and 2.0×10(-5), respectively, in combined analysis). CONCLUSIONS: Our findings suggest that individuals who are obese may be more susceptible to the cumulative genetic burden of triglyceride single nucleotide polymorphisms. Therefore, it is suggested that especially these genetically at-risk individuals may benefit more from targeted interventions aiming at obesity prevention.

Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes.

Through whole-genome sequencing of 2,630 Icelanders and imputation into 11,114 Icelandic cases and 267,140 controls followed by testing in Danish and Iranian samples, we discovered 4 previously unreported variants affecting risk of type 2 diabetes (T2D). A low-frequency (1.47%) variant in intron 1 of CCND2, rs76895963[G], reduces risk of T2D by half (odds ratio (OR) = 0.53, P = 5.0 × 10(-21)) and is correlated with increased CCND2 expression. Notably, this variant is also associated with both greater height and higher body mass index (1.17 cm per allele, P = 5.5 × 10(-12) and 0.56 kg/m(2) per allele, P = 6.5 × 10(-7), respectively). In addition, two missense variants in PAM, encoding p.Asp563Gly (frequency of 4.98%) and p.Ser539Trp (frequency of 0.65%), confer moderately higher risk of T2D (OR = 1.23, P = 3.9 × 10(-10) and OR = 1.47, P = 1.7 × 10(-5), respectively), and a rare (0.20%) frameshift variant in PDX1, encoding p.Gly218Alafs*12, associates with high risk of T2D (OR = 2.27, P = 7.3 × 10(-7)).

Type 2 diabetes risk alleles near BCAR1 and in ANK1 associate with decreased ß-cell function whereas risk alleles near ANKRD55 and GRB14 associate with decreased insulin sensitivity in the Danish Inter99 cohort.

CONTEXT: Recently, 10 novel type 2 diabetes (T2D) susceptibility single nucleotide polymorphisms (SNPs) in ZMIZ1, ANK1, KLHDC5, TLE1, ANKRD55, CILP2, MC4R, BCAR1, HMG20A, and GRB14 loci were discovered in MetaboChip-genotyped populations of European ancestry. OBJECTIVE: The aim of the present study was to characterize prediabetic quantitative traits underlying these SNP associations and to calculate the amount of interindividual variation in glycemic traits explained by these and previous T2D susceptibility variants. DESIGN AND PARTICIPANTS: A total of 5739 Danish individuals naive to glucose-lowering medication were included in quantitative trait studies, and case-control analyses were performed in 1892 patients with T2D and 6603 normoglycemic control subjects. Participants without known T2D underwent an oral glucose tolerance test, and measures of insulin release and sensitivity were estimated from insulinogenic, disposition, BIGTT, and Matsuda indexes. RESULTS: We confirmed associations of ZMIZ1, KLHDC5, CILP2, HMG20A, ANK1, ANKRD55, and BCAR1 with T2D. The risk T allele of BCAR1 rs7202877 associated with decreased disposition index (P = .02). The C allele of ANK1 rs516946 associated with decreased insulinogenic (P = .005) and disposition (P = .002) indexes. The G allele of ANKRD55 rs459193 associated with decreased Matsuda index (P = .02) adjusted for waist circumference. The C allele of GRB14 rs13389219 associated with both increased insulinogenic (P = .04) and decreased Matsuda (P = .05) indexes. All validated European T2D variants still only explained a few percentage points of glycemic trait variation. CONCLUSIONS: BCAR1 rs7202877 may mediate its diabetogenic impact through impaired ß-cell function, but this finding needs to be replicated in independent studies. In addition, we substantiated previous evidence that ANK1 rs516946 confers impaired insulin release and that ANKRD55 rs459193 and GRB14 rs13389219 associate with insulin resistance.

Genetic risk score of 46 type 2 diabetes risk variants associates with changes in plasma glucose and estimates of pancreatic ß-cell function over 5 years of follow-up.

More than 40 genetic risk variants for type 2 diabetes have been validated. We aimed to test whether a genetic risk score associates with the incidence of type 2 diabetes and with 5-year changes in glycemic traits and whether the effects were modulated by changes in BMI and lifestyle. The Inter99 study population was genotyped for 46 variants, and a genetic risk score was constructed. During a median follow-up of 11 years, 327 of 5,850 individuals developed diabetes. Physical examinations and oral glucose tolerance tests were performed at baseline and after 5 years (n = 3,727). The risk of incident type 2 diabetes was increased with a hazard ratio of 1.06 (95% CI 1.03-1.08) per risk allele. While the population in general had improved glucose regulation during the 5-year follow-up period, each additional allele in the genetic risk score was associated with a relative increase in fasting, 30-min, and 120-min plasma glucose values and a relative decrease in measures of ß-cell function over the 5-year period, whereas indices of insulin sensitivity were unaffected. The effect of the genetic risk score on 5-year changes in fasting plasma glucose was stronger in individuals who increased their BMI. In conclusion, a genetic risk score based on 46 variants associated strongly with incident type 2 diabetes and 5-year changes in plasma glucose and ß-cell function. Individuals who gain weight may be more susceptible to the cumulative impact of type 2 diabetes risk variants on fasting plasma glucose.

Implications of central obesity-related variants in LYPLAL1, NRXN3, MSRA, and TFAP2B on quantitative metabolic traits in adult Danes.

BACKGROUND: Two meta-analyses of genome-wide association studies (GWAS) have suggested that four variants: rs2605100 in lysophospholipase-like 1 (LYPLAL1), rs10146997 in neuroxin 3 (NRXN3), rs545854 in methionine sulfoxide reductase A (MSRA), and rs987237 in transcription factor activating enhancer-binding protein 2 beta (TFAP2B) associate with measures of central obesity. To elucidate potential underlying phenotypes we aimed to investigate whether these variants associated with: 1) quantitative metabolic traits, 2) anthropometric measures (waist circumference (WC), waist-hip ratio, and BMI), or 3) type 2 diabetes, and central and general overweight and obesity. METHODOLOGY/PRINCIPAL FINDINGS: The four variants were genotyped in Danish individuals using KASPar®. Quantitative metabolic traits were examined in a population-based sample (n = 6,038) and WC and BMI were furthermore analyzed in a combined study sample (n = 13,507). Case-control studies of diabetes and adiposity included 15,326 individuals. The major G-allele of LYPLAL1 rs2605100 associated with increased fasting serum triglyceride concentrations (per allele effect (ß) = 3%(1;5(95%CI)), p(additive) = 2.7×10(-3)), an association driven by the male gender (p(interaction) = 0.02). The same allele associated with increased fasting serum insulin concentrations (ß = 3%(1;5), p(additive) = 2.5×10(-3)) and increased insulin resistance (HOMA-IR) (ß = 4%(1;6), p(additive) = 1.5×10(-3)). The minor G-allele of rs10146997 in NRXN3 associated with increased WC among women (ß = 0.55cm (0.20;0.89), p(additive) = 1.7×10(-3), p(interaction) = 1.0×10(-3)), but showed no associations with obesity related metabolic traits. The MSRA rs545854 and TFAP2B rs987237 showed nominal associations with central obesity; however, no underlying metabolic phenotypes became obvious, when investigating quantitative metabolic traits. None of the variants influenced the prevalence of type 2 diabetes. CONCLUSION/SIGNIFICANCE: We demonstrate that several of the central obesity-associated variants in LYPLAL1, NRXN3, MSRA, and TFAP2B associate with metabolic and anthropometric traits in Danish adults. However, analyses were made without adjusting for multiple testing, and further studies are needed to confirm the putative role of LYPLAL1, NRXN3, MSRA, and TFAP2B in the pathophysiology of obesity.

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

OBJECTIVE: Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes. RESEARCH DESIGN AND METHODS: By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS). RESULTS: 273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations. CONCLUSIONS: Using a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.