Genotypic spectrum of 21-hydroxylase deficiency in an endogamous population

BACKGROUND

Congenital adrenal hyperplasia (CAH) due to autosomal recessive 21-hydroxylase deficiency (21-OHD) is caused by defects in the CYP21 (CYP21A2) gene. Several mutations have been identified in the CYP21 (CYP21A2) gene of patients with 21-OHD. We aimed at determining the frequency of these mutations among a group of Egyptian patients and studying the genotype-phenotype correlation.

METHODS

Forty-seven patients with CAH due to 21-OHD from 42 different families diagnosed by clinical and hormonal evaluation and classified accordingly into salt wasting (SW) and simple virilizing (SV) phenotypes were enrolled. Their ages ranged between 1.78 and 18.99 years. Molecular analysis of the CYP21 (CYP21A2) gene was performed for the detection of eleven common mutations: P30L, I2 splice (I2 G), Del 8 bp E3 (G110del8nt), I172N, cluster E6 (I236N, V237E, M239K), V281L, L307 frameshift (F306 + T), Q318X, R356W, P453S, R483P by polymerase chain reaction (PCR) and reverse hybridization.

RESULTS

Disease-causing mutations were identified in 47 patients, 55.31% of them were compound heterozygous. The most frequent mutations were I2 splice (25.43%), followed by cluster E6 (16.66%) and P30L (15.78%). Two point mutations (P453S, R483P) were not identified in any patient. In the SW patients, genotypes were more compatible with their phenotypes.

CONCLUSION

Molecular characterization should be considered along with clinical and biochemical diagnosis of CAH since it could confirm the diagnosis, outline the treatment strategy and morbidity, and ensure proper genetic counseling.

Challenges of CYP21A2 genotyping in children with 21-hydroxylase deficiency: determination of genotype-phenotype correlation using next generation sequencing in Southeastern Anatolia

BACKGROUND/PURPOSE

Although it is known that there is generally a good correlation between genotypes and phenotypes, the number of studies reporting discrepancies has recently increased, exclusively between milder genotypes and their phenotypes due to the complex nature of the CYP21A2 gene and methodological pitfalls. This study aimed to assess CYP21A2 genotyping in children with 21-hydroxylase deficiency (21-OHD) and establish their predictive genotype-phenotype correlation features using a large cohort in Southeastern Anatolia's ethnically diverse population.

METHODS

The patients were classified into three groups: salt-wasting (SW), simple virilizing (SV) and non-classical (NC). The genotypes were categorized into six groups due to residual enzyme activity: null-A-B-C-D-E. CYP21A2 genotyping was performed by sequence-specific primer and sequenced with next generation sequencing (NGS), and the expected phenotypes were compared to the observed phenotypes.

RESULTS

A total of 118 unrelated children with 21-OHD were included in this study (61% SW, 24.5% SV and 14.5% NC). The pathogenic variants were found in 79.5% of 171 mutated alleles (60.2%, 22.2%, and 17.6% in SW, SV and NC, respectively). Patient distribution based on genotype groups was as follows: null-16.1%, A-41.4%, B-6.0%, C-14.4%, E-22%). In2G was the most common pathogenic variant (33.9% of all alleles) and the most common variant in the three phenotype groups (SW-38.8%, SV-22.2% and NC-23.3%). The total genotype-phenotype correlation was 81.5%. The correlations of the null and A groups were 100% and 76.1%, respectively, while it was lower in group B and poor in group C (71.4% and 23.5%, respectively).

CONCLUSION

This study revealed that the concordance rates of the severe genotypes with their phenotypes were good, while those of the milder genotypes were poor. The discrepancies could have resulted from the complex characteristics of 21-OHD genotyping and the limitations of using NGS alone without integrating with other comprehensive methods.

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer's Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer's disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10-13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

New and sex-specific migraine susceptibility loci identified from a multiethnic genome-wide meta-analysis

Migraine is a common disabling primary headache disorder that is ranked as the most common neurological cause of disability worldwide. Women present with migraine much more frequently than men, but the reasons for this difference are unknown. Migraine heritability is estimated to up to 57%, yet much of the genetic risk remains unaccounted for, especially in non-European ancestry populations. To elucidate the etiology of this common disorder, we conduct a multiethnic genome-wide association meta-analysis of migraine, combining results from the GERA and UK Biobank cohorts, followed by a European-ancestry meta-analysis using public summary statistics. We report 79 loci associated with migraine, of which 45 were novel. Sex-stratified analyses identify three additional novel loci (CPS1, PBRM1, and SLC25A21) specific to women. This large multiethnic migraine study provides important information that may substantially improve our understanding of the etiology of migraine susceptibility.

Determinants of hepatic insulin clearance - Results from a Mendelian Randomization study

AIMS/HYPOTHESIS

Besides insulin resistance, type 2 diabetes associates with decreased hepatic insulin clearance (HIC). We now tested for causal relationship of HIC to liver fat accumulation or features of the metabolic syndrome.

METHODS

HIC was derived from oral glucose tolerance tests with the "Oral C-peptide and Insulin Minimal Models" (n = 3311). Liver fat was quantified by magnetic resonance spectroscopy (n = 1211). Mendelian Randomization was performed using established single nucleotide polymorphisms (SNPs; 115 for liver fat, 155 alanine-aminotransferase, 37 insulin sensitivity, 37 insulin secretion, 72 fasting insulin, 5285 BMI, 163 visceral fat, 270 waist circumference, 442 triglycerides, 620 HDL-Cholesterol, 193 C-reactive protein, 53 lipodystrophy-like phenotypes).

RESULTS

HIC associated inversely with liver fat (p < 0.003) and insulin sensitivity (p < 0.0001). Both liver fat and HIC were independently associated with insulin sensitivity (p < 0.0001). Neither liver fat nor alanine-aminotransferase were causally linked to HIC, as indicated by Mendelian Randomization (N_{liver fat} = 1054, N_HIC = 2254; N_{alanineaminotranferase} = 1985, N_HIC = 2251). BMI-related SNPs were causally associated with HIC (N_BMI = 2772, N_HIC = 2259, p < 0.001) but not waist circumference-SNPs (N_{SNPs-waist circumference} = 2751, N_HIC = 2280). Genetically determined insulin sensitivity was not causally related to HIC (N_{insulin sensitivity} = 2752, N_HIC = 2286). C-reactive protein and HDL were causally associated with HIC, with higher C-reactive protein and lower HDL leading to higher HIC (N_{C-reactive protein} = 2660, N_HIC = 2240; N_HDL = 2694, N_HIC = 2275).

CONCLUSIONS

This Mendelian Randomization analysis does not support a causal link between hepatic steatosis and HIC. Other components of the metabolic syndrome seem to compensate peripheral hyperinsulinemia by increasing hepatic insulin extraction.

Associations between Vascular Endothelial Growth Factor Gene Polymorphisms and Different Types of Diabetic Retinopathy Susceptibility: A Systematic Review and Meta-Analysis

BACKGROUND

Vascular endothelial growth factor (VEGF) gene polymorphisms have been shown to be associated with the risk of diabetic retinopathy (DR), but the results were inconsistent. The aim of this study was to systematically assess the associations between VEGF gene polymorphisms and different types of DR (nonproliferative DR and proliferative DR).

METHODS

Electronic databases PubMed, Embase, Web of Science, CNKI, and WANFANG DATA were searched for articles on the associations between VEGF gene polymorphisms and different types of DR up to November 6, 2019. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and subgroup analyses were conducted by ethnicity. Sensitivity analysis was conducted to assess the stability of the results. Publication bias was assessed by using the Egger regression asymmetry test and visualization of funnel plots. A systematic review was conducted for polymorphisms with a high degree of heterogeneity (I ² > 75%) or studied in only one study.

RESULTS

A total of 13 and 18 studies analyzed the associations between VEGF SNPs and nonproliferative DR (NPDR) as well as proliferative DR (PDR), respectively. There were significant associations between rs2010963 and NPDR in Asian (dominant model: OR = 1.29, 95%CI = 1.04 - 1.60); and rs2010963 is associated with PDR in total population (dominant model: OR = 1.20, 95%CI = 1.03 - 1.41), either Asian (recessive model: OR = 1.57, 95%CI = 1.04 - 2.35) or Caucasian (recessive model: OR = 1.83, 95%CI = 1.28 - 2.63). Rs833061 is associated with PDR in Asian (recessive model: OR = 1.58, 95%CI = 1.11 - 2.26). Rs699947 is associated with NPDR in the total population (dominant model: OR = 2.04, 95%CI = 1.30 - 3.21) and associated with PDR in Asian (dominant model: OR = 1.72, 95%CI = 1.05 - 2.84).

CONCLUSIONS

Rs2010963, rs833061, and rs699947 are associated with NPDR or PDR, which may be involved in the occurrence and development of DR.

Dedicated transcriptomics combined with power analysis lead to functional understanding of genes with weak phenotypic changes in knockout lines

Systematic knockout studies in mice have shown that a large fraction of the gene replacements show no lethal or other overt phenotypes. This has led to the development of more refined analysis schemes, including physiological, behavioral, developmental and cytological tests. However, transcriptomic analyses have not yet been systematically evaluated for non-lethal knockouts. We conducted a power analysis to determine the experimental conditions under which even small changes in transcript levels can be reliably traced. We have applied this to two gene disruption lines of genes for which no function was known so far. Dedicated phenotyping tests informed by the tissues and stages of highest expression of the two genes show small effects on the tested phenotypes. For the transcriptome analysis of these stages and tissues, we used a prior power analysis to determine the number of biological replicates and the sequencing depth. We find that under these conditions, the knockouts have a significant impact on the transcriptional networks, with thousands of genes showing small transcriptional changes. GO analysis suggests that A930004D18Rik is involved in developmental processes through contributing to protein complexes, and A830005F24Rik in extracellular matrix functions. Subsampling analysis of the data reveals that the increase in the number of biological replicates was more important that increasing the sequencing depth to arrive at these results. Hence, our proof-of-principle experiment suggests that transcriptomic analysis is indeed an option to study gene functions of genes with weak or no traceable phenotypic effects and it provides the boundary conditions under which this is possible.

Knockout mice benefit the understanding of gene functions in mammals. However, it has proven difficult for many genes to identify clear phenotypes, related due to lack of sufficient assays. As Lewis Wolpert put it in a famous quote “But did you take them to the opera?”, thus metaphorically alluding to the need to extend phenotyping efforts. This insight led to the establishment of phenotyping pipelines that are nowadays routinely used to characterize knock-out lines. However, transcriptomic approaches based on RNA-Seq have been much less explored for such deep-level studies. We conducted here both, a theoretical power analysis and practical RNA-Seq experiments on two knockout lines with small phenotypic effects to investigate the parameters including sample size, sequencing depth, fold change, and dispersion. Our dedicated RNA-Seq studies discovered thousands of genes with small transcriptional changes and enriched in specific functions in both knockout lines. We find that it is more important to increase the number of samples than to increase the sequencing depth. Our work shows that a deep RNA-Seq study on knockouts is powerful for understanding gene functions in cases of weak phenotypic effects, and provides a guideline for the experimental design of such studies.

An Overview of the Main Genetic, Epigenetic and Environmental Factors Involved in Autism Spectrum Disorder Focusing on Synaptic Activity

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects social interaction and communication, with restricted interests, activity and behaviors. ASD is highly familial, indicating that genetic background strongly contributes to the development of this condition. However, only a fraction of the total number of genes thought to be associated with the condition have been discovered. Moreover, other factors may play an important role in ASD onset. In fact, it has been shown that parental conditions and in utero and perinatal factors may contribute to ASD etiology. More recently, epigenetic changes, including DNA methylation and micro RNA alterations, have been associated with ASD and proposed as potential biomarkers. This review aims to provide a summary of the literature regarding ASD candidate genes, mainly focusing on synapse formation and functionality and relevant epigenetic and environmental aspects acting in concert to determine ASD onset.

Association of the MTHFR 677C>T and 1298A>C polymorphisms and male infertility risk: a meta-analysis

BACKGROUND AND OBJECTIVES

One of the possible male sterility risk factors are polymorphisms of Methylenetetrahydrofolate reductase (MTHFR). However, the epidemiologic investigations described inconsistent results regarding MTHFR polymorphism and the risk of male infertility. For that reason, we carried out a meta-analysis of published case-control studies to re-examine the controversy.

METHODS

Electronic searches of Cochrane, EMBASE, Google Scholar, and PubMed were conducted to select eligible studies for this meta-analysis (updated to May 2019). According to our exclusion and inclusion criteria, only high-quality studies that remarked the association between MTHFR polymorphisms and male infertility risk were included. The Crude odds ratio (OR) with a confidence interval of 95% (CI) was used to assess the relationship between MTHFR polymorphism and male infertility risk.

RESULTS

Thirty-four case-control studies with 9662 cases and 9154 controls concerning 677C/T polymorphism and 22 case-control studies with 5893 cases and 6303 controls concerning 1298A/C polymorphism were recruited. Both MTHFR polymorphisms had significant associations with male infertility risk (CT + TT vs. CC: OR = 1.37, 95% CI: 1.21-1.55, P = 0.00, I² = 41.9%); (CC vs. CA + AA: OR = 0.82, 95% CI: 0.52-1.30, P = 0.04, I² = 50.1%). Further, when stratified by ethnicity, the significant association results were observed in Asians and Caucasians for 677C/T and just Asians for 1298A/C.

CONCLUSIONS

Some of MTHFR polymorphisms like MTHFR 677C > T are associated with an elevated male infertility risk. To confirm our conclusion and to provide more accurate and complete gene-environment communication with male infertility risk, more analytical studies are needed.

Analysis of secondary phenotypes in multigroup association studies

Although case-control association studies have been widely used, they are insufficient for many complex diseases, such as Alzheimer's disease and breast cancer, since these diseases may have multiple subtypes with distinct morphologies and clinical implications. Many multigroup studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have been undertaken by recruiting subjects based on their multiclass primary disease status, while extensive secondary outcomes have been collected. The aim of this paper is to develop a general regression framework for the analysis of secondary phenotypes collected in multigroup association studies. Our regression framework is built on a conditional model for the secondary outcome given the multigroup status and covariates and its relationship with the population regression of interest of the secondary outcome given the covariates. Then, we develop generalized estimation equations to estimate the parameters of interest. We use both simulations and a large-scale imaging genetic data analysis from the ADNI to evaluate the effect of the multigroup sampling scheme on standard genome-wide association analyses based on linear regression methods, while comparing it with our statistical methods that appropriately adjust for the multigroup sampling scheme. Data used in preparation of this article were obtained from the ADNI database.

Association analysis in a Latin American population revealed ethnic differences in rheumatoid arthritis-associated SNPs in Caucasian and Asian populations

Large genome-wide association studies (GWAS) have increased our knowledge of the genetic risk factors of rheumatoid arthritis (RA). However, little is known about genetic susceptibility in populations with a large admixture of Amerindian ancestry. The aim of the present study was to test the generalizability of previously reported RA loci in a Latin American (LA) population with admixed ancestry. We selected 128 single nucleotide polymorphisms (SNPs) in linkage equilibrium, with high association to RA in multiple populations of non-Amerindian origin. Genotyping of 118 SNPs was performed in 313 RA patients/487 healthy control subjects by mid-density arrays of polymerase chain reaction (PCR). Some of the identified associations were validated in an additional cohort (250 cases/290 controls). One marker, the SNP rs2451258, located upstream of T Cell Activation RhoGTPase Activating Protein (TAGAP) gene, showed significant association with RA (p = 5 × 10-3), whereas 18 markers exhibited suggestive associations (p < 0.05). Haplotype testing showed association of some groups of adjacent SNPs around the signal transducer and activator of transcription 4 (STAT4) gene (p = 9.82 × 10-3 to 2.04 × 10-3) with RA. Our major finding was little replication of previously reported genetic associations with RA. These results suggest that performing GWAS and admixture mapping in LA populations has the potential to reveal novel loci associated with RA. This in turn might help to gain insight into the 'pathogenomics' of this disease and to explore trans-population differences for RA in general.

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Phenogenon: Gene to phenotype associations for rare genetic diseases

As high-throughput sequencing is increasingly applied to the molecular diagnosis of rare Mendelian disorders, a large number of patients with diverse phenotypes have their genetic and phenotypic data pooled together to uncover new gene-phenotype relations. We introduce Phenogenon, a statistical tool that combines, Human Phenotype Ontology (HPO) annotated patient phenotypes, gnomAD allele population frequency, and Combined Annotation Dependent Depletion (CADD) score for variant pathogenicity, in order to jointly predict the mode of inheritance and gene-phenotype associations. We ran Phenogenon on our cohort of 3,290 patients who had undergone whole exome sequencing. Among the top associations, we recapitulated previously known, such as "SRD5A3—Abnormal full-field electroretinogram—recessive" and "GRHL2 –Nail dystrophy—recessive", and discovered one potentially novel, “RRAGA–Abnormality of the skin—dominant”. We also developed an interactive web interface available at https://phenogenon.phenopolis.org to visualise and explore the results.

Identification of novel biomarkers in ischemic stroke: a genome-wide integrated analysis

BACKGROUND

Ischemic Stroke (IS) is the most common neurological emergency disease and has become the second most frequent cause of death after coronary artery disease in 2015. Owing to its high fatality rate and narrow therapeutic time window, early identification and prevention of potential stroke is becoming increasingly important.

METHODS

We used meta-analysis and bioinformatics mining to explore disease-related pathways and regulatory networks after combining messengerRNA (mRNA) and miRNA expression analyses. The purpose of our study was to screen for candidate target genes and microRNA(miRNA) for early diagnosis of potential stroke.

RESULTS

Five datasets were collected from the Gene Expression Omnibus (GEO) database by systematical retrieval, which contained three mRNA datasets (102 peripheral blood samples in total) and two miRNA dataset (59 peripheral blood samples). Approximately 221 different expression(DE) mRNAs (155 upregulated and 66 downregulated mRNAs) and 185 DE miRNAs were obtained using the metaDE package and GEO2R tools. Further functional enrichments of DE-mRNA, DE-miRNA and protein-protein interaction (PPI) were performed and visualized using Cytoscape.

CONCLUSION

Our study identified six core mRNAs and two regulated miRNAs in the pathogenesis of stroke, and we elaborated the intrinsic role of systemic lupus erythematosus (SLE) and atypical infections in stroke, which may aid in the development of precision medicine for treating ischemic stroke. However, the role of these novel biomarkers and the underlying molecular mechanisms in IS require further fundamental experiments and further clinical evidence.

Association between resting heart rate and incident diabetes risk: a Mendelian randomization study

AIMS

Observational studies indicated that resting heart rate (RHR) was associated with diabetes mellitus (DM) risk; however, it remains unclear whether the association between RHR and DM is causal. We aimed to examine whether there was causal association of RHR with DM risk.

METHODS

A prospective study including 16,201 middle-aged and older Chinese (7031 males and 9170 females) derived from the Dongfeng-Tongji cohort was performed. Cox proportional hazard regression models were conducted to estimate the associations between RHR and incident DM risk. In 7481 participants, 65 single nucleotide polymorphisms related to RHR were genotyped. A genetic risk score (GRS) of RHR was calculated based on the RHR-associated variants. The causal associations of RHR with DM risk were investigated by Mendelian randomization analysis.

RESULTS

During a mean (SD) follow-up of 4.5 (0.5) years, 1110 diabetes were identified. Compared with the referential RHR group (≤ 60 beats per minute [bpm]), individuals with RHR > 80 bpm have a higher incident diabetes risk, with a hazard ratio of 1.40 (95% confidence interval [CI], 1.05-1.88). With per SD increase in the weighted genetic risk score, the resting heart rate increased by 0.71 bpm (95% CI 0.49-0.93). By using the GRS to estimate the unconfounded effect, we found that higher resting heart rate did not have a causal effect on diabetes risk (OR 1.00 [95% CI 0.95-1.05]).

CONCLUSIONS

The present study supported a positive but not a causal association of RHR with incident diabetes risk. More studies are needed to verify our findings.

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.

Genetic polymorphisms of the endocannabinoid system in obesity and diabetes

The endocannabinoid system (ECS) is involved in many physiological processes including fertility, pain and energy regulation. The aim of this systematic review was to examine the contribution of single nucleotide polymorphisms (SNPs) of the ECS to adiposity and glucose metabolism. Database searches identified 734 articles, of which 65 were included; these covered 70 SNPs in genes coding for cannabinoid receptors 1 and 2 (CB₁ , CB₂ ), fatty acid amide hydrolase (FAAH) and N-acyl phosphatidylethanolamine phospholipase D (NAPE-PLD). No studies included SNPs relating to monoacylglycerol lipase or diacylglycerol lipase. The CB₁ receptor SNP rs1049353 showed 17 associations with lower body mass index (BMI) and fat mass (five studies). It also showed three associations with lower insulin levels (one study). Conversely, the CB₁ receptor SNP rs806368 was associated with increased BMI and waist circumference (two studies). The FAAH SNP rs324420 was associated with increased obesity (three studies). A haplotype of NAPE-PLD was associated with decreased BMI (one study). A total of 60 SNPs showed no association with any measured outcome. This review suggests a complex but important role of ECS SNPs in energy and glucose metabolism.

Family-based association tests for rare variants with censored traits

We propose a set of family-based burden and kernel tests for censored traits (FamBAC and FamKAC). Here, censored traits refer to time-to-event outcomes, for instance, age-at-onset of a disease. To model censored traits in family-based designs, we used the frailty model, which incorporated not only fixed genetic effects of rare variants in a region of interest but also random polygenic effects shared within families. We first partitioned genotype scores of rare variants into orthogonal between- and within-family components, and then derived their corresponding efficient score statistics from the frailty model. Finally, FamBAC and FamKAC were constructed by aggregating the weighted efficient scores of the within-family components across rare variants and subjects. FamBAC collapsed rare variants within subject first to form a burden test that followed a chi-squared distribution; whereas FamKAC was a variant component test following a mixture of chi-squared distributions. For FamKAC, p-values can be computed by permutation tests or for computational efficiency by approximation methods. Through simulation studies, we showed that type I error was correctly controlled by FamBAC for various variant weighting schemes (0.0371 to 0.0527). However, FamKAC type I error rates based on approximation methods were deflated (max 0.0376) but improved by permutation tests. Our simulations also demonstrated that burden test FamBAC had higher power than kernel test FamKAC when high proportion (e.g. ≥ 80%) of causal variants had effects in the same direction. In contrast, when the effects of causal variants on the censored trait were in mixed directions, FamKAC outperformed FamBAC and had comparable or higher power than an existing method, RVFam. Our proposed framework has the flexibility to accommodate general nuclear families, and can be used to analyze sequence data for censored traits such as age-at-onset of a complex disease of interest.

A Bayesian approach for analysis of ordered categorical responses subject to misclassification

Ordinal categorical responses are frequently collected in survey studies, human medicine, and animal and plant improvement programs, just to mention a few. Errors in this type of data are neither rare nor easy to detect. These errors tend to bias the inference, reduce the statistical power and ultimately the efficiency of the decision-making process. Contrarily to the binary situation where misclassification occurs between two response classes, noise in ordinal categorical data is more complex due to the increased number of categories, diversity and asymmetry of errors. Although several approaches have been presented for dealing with misclassification in binary data, only limited practical methods have been proposed to analyze noisy categorical responses. A latent variable model implemented within a Bayesian framework was proposed to analyze ordinal categorical data subject to misclassification using simulated and real datasets. The simulated scenario consisted of a discrete response with three categories and a symmetric error rate of 5% between any two classes. The real data consisted of calving ease records of beef cows. Using real and simulated data, ignoring misclassification resulted in substantial bias in the estimation of genetic parameters and reduction of the accuracy of predicted breeding values. Using our proposed approach, a significant reduction in bias and increase in accuracy ranging from 11% to 17% was observed. Furthermore, most of the misclassified observations (in the simulated data) were identified with a substantially higher probability. Similar results were observed for a scenario with asymmetric misclassification. While the extension to traits with more categories between adjacent classes is straightforward, it could be computationally costly. For traits with high heritability, the performance of the methodology would be expected to improve.

Association of CD1 and FcγR gene polymorphisms with Guillain-Barré syndrome susceptibility: a meta-analysis

CD1 and immunoglobulin G Fc receptor (FcγR) genes have been proposed to be involved in the pathogenesis of Guillain-Barré syndrome (GBS). However, results of different studies are conflicting. This meta-analysis aimed to systematically examine the association between CD1 and FcγR gene polymorphisms and GBS. A comprehensive literature search through PubMed, EmBase, ScienceDirect, and Cochrane Library was performed to identify all eligible studies. The strength of association was assessed by pooled odds ratios (ORs) and corresponding 95% confidence intervals (95% CI) in allelic, dominant, recessive, homozygous and heterozygous genetic models. Four case-control studies about polymorphisms of exon 2 in CD1A and CD1E genes and GBS risk and five studies (six cohorts) about FcγR gene polymorphisms and GBS risk were included in this meta-analysis. The association between exon 2 of CD1E gene polymorphism and GBS was marginally significant in Caucasians in allelic model (OR = 1.193, 95% CI = 1.001-1.423, P = 0.049). FcγRIIA gene polymorphism was significantly associated with GBS risk in Caucasians under allelic model (OR = 1.553, 95% CI = 1.018-2.368, P = 0.041) and dominant model (OR = 1.320, 95% CI = 1.027-1.697, P = 0.030). However, no significant association was found between polymorphisms in exon 2 of CD1A, FcγRIIIA and FcγRIIIB genes and GBS susceptibility. This meta-analysis suggested that FcγRIIA gene polymorphism may contribute to GBS risk in Caucasians and revealed a certain trend toward significance in the association of exon 2 of CD1E gene with GBS in Caucasians. Further studies with larger sample size are required to validate these results.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Multi-trait analysis of genome-wide association summary statistics using MTAG

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression

Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Genotype-phenotype association study via new multi-task learning model

Research on the associations between genetic variations and imaging phenotypes is developing with the advance in high-throughput genotype and brain image techniques. Regression analysis of single nucleotide polymorphisms (SNPs) and imaging measures as quantitative traits (QTs) has been proposed to identify the quantitative trait loci (QTL) via multi-task learning models. Recent studies consider the interlinked structures within SNPs and imaging QTs through group lasso, e.g. ℓ2, 1-norm, leading to better predictive results and insights of SNPs. However, group sparsity is not enough for representing the correlation between multiple tasks and ℓ2, 1-norm regularization is not robust either. In this paper, we propose a new multi-task learning model to analyze the associations between SNPs and QTs. We suppose that low-rank structure is also beneficial to uncover the correlation between genetic variations and imaging phenotypes. Finally, we conduct regression analysis of SNPs and QTs. Experimental results show that our model is more accurate in prediction than compared methods and presents new insights of SNPs.

Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies

In genome-wide association studies (GWAS), it has been increasingly recognized that, as a complementary approach to standard single SNP analyses, it may be beneficial to analyze a group of functionally related SNPs together. Among the existent population-based SNP-set association tests, two adaptive tests, the aSPU test and the aSPUpath test, offer a powerful and general approach at the gene- and pathway-levels by data-adaptively combining the results across multiple SNPs (and genes) such that high statistical power can be maintained across a wide range of scenarios. We extend the aSPU and the aSPUpath test to familial data under the framework of the generalized linear mixed models (GLMMs), which can take account of both subject relatedness and possible population structure. As in population-based GWAS, the proposed aSPU and aSPUpath tests require only fitting a single and common GLMM (under the null hypothesis) for all the SNPs, thus are computationally efficient and feasible for large GWAS data. We illustrate our approaches in identifying genes and pathways associated with alcohol dependence in the Minnesota Twin Family Study. The aSPU test detected a gene associated with the trait, in contrast to none by the standard single SNP analysis. Our aSPU test also controlled Type I errors satisfactorily in a small simulation study. We provide R code to conduct the aSPU and aSPUpath tests for familial and other correlated data.

Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences

Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tended to rely on comparisons of fit metrics like C-statistics. However, for many data adaptive modelling procedures such approaches are not suitable. In these cases, statisticians have used cross-validation, which can make inference challenging. In this paper we propose a general approach that focuses on the "conditional" risk difference (conditional on the model fits being fixed) for the improvement in prediction risk. Specifically, we derive a Wald-type test statistic and associated confidence intervals for cross-validated test sets utilizing the independent validation within cross-validation in conjunction with a test for multiple comparisons. We show that this test maintains proper Type I Error under the null fit, and can be used as a general test of relative fit for any semi-parametric model alternative. We apply the test to a candidate gene study to test for the association of a set of genes in a genetic pathway.

Oncologist use and perception of large panel next-generation tumor sequencing

BACKGROUND

Genomic profiling is increasingly incorporated into oncology research and the clinical care of cancer patients. We sought to determine physician perception and use of enterprise-scale clinical sequencing at our center, including whether testing changed management and the reasoning behind this decision-making.

PATIENTS AND METHODS

All physicians who consented patients to MSK-IMPACT, a next-generation hybridization capture assay, in tumor types where molecular profiling is not routinely performed were asked to complete a questionnaire for each patient. Physician determination of genomic 'actionability' was compared to an expertly curated knowledgebase of somatic variants. Reported management decisions were compared to chart review.

RESULTS

Responses were received from 146 physicians pertaining to 1932 patients diagnosed with 1 of 49 cancer types. Physicians indicated that sequencing altered management in 21% (331/1593) of patients in need of a treatment change. Among those in whom treatment was not altered, physicians indicated the presence of an actionable alteration in 55% (805/1474), however, only 45% (362/805) of these cases had a genomic variant annotated as actionable by expert curators. Further evaluation of these patients revealed that 66% (291/443) had a variant in a gene associated with biologic but not clinical evidence of actionability or a variant of unknown significance in a gene with at least one known actionable alteration. Of the cases annotated as actionable by experts, physicians identified an actionable alteration in 81% (362/445). In total, 13% (245/1932) of patients were enrolled to a genomically matched trial.

CONCLUSION

Although physician and expert assessment differed, clinicians demonstrate substantial awareness of the genes associated with potential actionability and report using this knowledge to inform management in one in five patients.

CLINICAL TRIAL NUMBER

NCT01775072.

DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease

Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the "common disease-common variant" paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer's disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.

Census and evaluation of p53 target genes

The tumor suppressor p53 functions primarily as a transcription factor. Mutation of the TP53 gene alters its response pathway, and is central to the development of many cancers. The discovery of a large number of p53 target genes, which confer p53's tumor suppressor function, has led to increasingly complex models of p53 function. Recent meta-analysis approaches, however, are simplifying our understanding of how p53 functions as a transcription factor. In the survey presented here, a total set of 3661 direct p53 target genes is identified that comprise 3509 potential targets from 13 high-throughput studies, and 346 target genes from individual gene analyses. Comparison of the p53 target genes reported in individual studies with those identified in 13 high-throughput studies reveals limited consistency. Here, p53 target genes have been evaluated based on the meta-analysis data, and the results show that high-confidence p53 target genes are involved in multiple cellular responses, including cell cycle arrest, DNA repair, apoptosis, metabolism, autophagy, mRNA translation and feedback mechanisms. However, many p53 target genes are identified only in a small number of studies and have a higher likelihood of being false positives. While numerous mechanisms have been proposed for mediating gene regulation in response to p53, recent advances in our understanding of p53 function show that p53 itself is solely an activator of transcription, and gene downregulation by p53 is indirect and requires p21. Taking into account the function of p53 as an activator of transcription, recent results point to an unsophisticated means of regulation.

X-Linked and Autosomal Recessive Alport Syndrome: Pathogenic Variant Features and Further Genotype-Phenotype Correlations

Alport syndrome results from mutations in the COL4A5 (X-linked) or COL4A3/COL4A4 (recessive) genes. This study examined 754 previously- unpublished variants in these genes from individuals referred for genetic testing in 12 accredited diagnostic laboratories worldwide, in addition to all published COL4A5, COL4A3 and COL4A4 variants in the LOVD databases. It also determined genotype-phenotype correlations for variants where clinical data were available. Individuals were referred for genetic testing where Alport syndrome was suspected clinically or on biopsy (renal failure, hearing loss, retinopathy, lamellated glomerular basement membrane), variant pathogenicity was assessed using currently-accepted criteria, and variants were examined for gene location, and age at renal failure onset. Results were compared using Fisher’s exact test (DNA Stata). Altogether 754 new DNA variants were identified, an increase of 25%, predominantly in people of European background. Of the 1168 COL4A5 variants, 504 (43%) were missense mutations, 273 (23%) splicing variants, 73 (6%) nonsense mutations, 169 (14%) short deletions and 76 (7%) complex or large deletions. Only 135 of the 432 Gly residues in the collagenous sequence were substituted (31%), which means that fewer than 10% of all possible variants have been identified. Both missense and nonsense mutations in COL4A5 were not randomly distributed but more common at the 70 CpG sequences (p<10⁻⁴¹ and p<0.001 respectively). Gly>Ala substitutions were underrepresented in all three genes (p< 0.0001) probably because of an association with a milder phenotype. The average age at end-stage renal failure was the same for all mutations in COL4A5 (24.4 ±7.8 years), COL4A3 (23.3 ± 9.3) and COL4A4 (25.4 ± 10.3) (COL4A5 and COL4A3, p = 0.45; COL4A5 and COL4A4, p = 0.55; COL4A3 and COL4A4, p = 0.41). For COL4A5, renal failure occurred sooner with non-missense than missense variants (p<0.01). For the COL4A3 and COL4A4 genes, age at renal failure occurred sooner with two non-missense variants (p = 0.08, and p = 0.01 respectively). Thus DNA variant characteristics that predict age at renal failure appeared to be the same for all three Alport genes. Founder mutations (with the pathogenic variant in at least 5 apparently- unrelated individuals) were not necessarily associated with a milder phenotype. This study illustrates the benefits when routine diagnostic laboratories share and analyse their data.

Epidemiological and Mendelian Randomization Studies of Dihydrotestosterone and Estradiol and Leukocyte Telomere Length in Men

CONTEXT

Advancing age is accompanied by an accumulation of ill health and shortening of chromosomal telomeres signifying biological aging. T is metabolized to DHT by 5α-reductase (SRD5A2) and to estradiol (E2) by aromatase (CYP19A1). Telomerase preserves telomeres, and T and E2 regulate telomerase expression and activity in vitro.

OBJECTIVE

The objective of the study was to establish whether circulating T or its metabolites, DHT or E2, and single-nucleotide polymorphisms in SRD5A2 or CYP19A1 associate with leucocyte telomere length (LTL) in men.

PARTICIPANTS AND METHODS

Early-morning serum T, DHT, and E2 were assayed using mass spectrometry, and SRD5A2 and CYP19A1 single-nucleotide polymorphisms and LTL analyzed by PCR in 980 men from the Western Australian Busselton Health Survey who participated in the study. LTL was expressed as the T/S ratio.

RESULTS

Men were aged (mean ± SD) 53.7 ± 15.6 years. LTL decreased linearly with age, from the T/S ratio of 1.89 ± 0.41 at younger than 30 years to 1.50 ± 0.49 at 70 to younger than 80 years (r = -0.225, P < .0001). After adjustment for age, DHT and E2 were positively correlated with LTL (DHT, r = 0.069, P = .030; E2, r = 0.068, P = .034). The SRD5A2 rs9282858 polymorphism was associated with serum DHT but not with LTL. Three dominant alleles of CYP19A1 were each associated with lower serum E2 and shorter LTL: rs2899470 T (E2, 59.3 vs 68.6 pmol/L, P < .0001; T/S ratio, 1.54 vs 1.62, P = .045), rs10046 C (60.5 vs 68.1 pmol/L, P = .0005, 1.54 vs 1.62, P = .035), and rs700518 A (59.9 vs 68.9 pmol/L, P < .0001, 1.54 vs 1.63, P = .020). A single-copy haplotype C/T/I/A/T rs10046/rs2899470/rs11575899/rs700518/rs17703883 (52% prevalence) was associated with both lower E2 and shorter LTL.

CONCLUSIONS

In men, serum DHT and E2 correlate with LTL independently of age. Aromatase gene polymorphisms include three dominant alleles that are associated with both lower serum E2 and shorter LTL. E2 influences telomere length in vivo, thus warranting further studies to examine whether hormonal interventions might slow biological aging in men.

False Appearance of Gene-Environment Interactions in Genetic Association Studies

Under the assumption of gene-environment independence, unknown/unmeasured environmental factors, irrespective of what they may be, cannot confound the genetic effects. This may lead many people to believe that genetic heterogeneity across different levels of the studied environmental exposure should only mean gene-environment interaction--even though other environmental factors are not adjusted for. However, this is not true if the odds ratio is the effect measure used for quantifying genetic effects. This is because the odds ratio is a "noncollapsible" measure--a marginal odds ratio is not a weighted average of the conditional odds ratios, but instead has a tendency toward the null. In this study, the authors derive formulae for gene-environment interaction bias due to noncollapsibility. They use computer simulation and real data example to show that the bias can be substantial for common diseases. For genetic association study of nonrare diseases, researchers are advised to use collapsible measures, such as risk ratio or peril ratio.

Using local multiplicity to improve effect estimation from a hypothesis-generating pharmacogenetics study

We propose a multiple estimation adjustment (MEA) method to correct effect overestimation due to selection bias from a hypothesis-generating study (HGS) in pharmacogenetics. MEA uses a hierarchical Bayesian approach to model individual effect estimates from maximal likelihood estimation (MLE) in a region jointly and shrinks them toward the regional effect. Unlike many methods that model a fixed selection scheme, MEA capitalizes on local multiplicity independent of selection. We compared mean square errors (MSEs) in simulated HGSs from naive MLE, MEA and a conditional likelihood adjustment (CLA) method that model threshold selection bias. We observed that MEA effectively reduced MSE from MLE on null effects with or without selection, and had a clear advantage over CLA on extreme MLE estimates from null effects under lenient threshold selection in small samples, which are common among 'top' associations from a pharmacogenetics HGS.

REPRODUCIBLE AND SHAREABLE QUANTIFICATIONS OF PATHOGENICITY

There are now hundreds of thousands of pathogenicity assertions that relate genetic variation to disease, but most of this clinically utilized variation has no accepted quantitative disease risk estimate. Recent disease-specific studies have used control sequence data to reclassify large amounts of prior pathogenic variation, but there is a critical need to scale up both the pace and feasibility of such pathogenicity reassessments across human disease. In this manuscript we develop a shareable computational framework to quantify pathogenicity assertions. We release a reproducible "digital notebook" that integrates executable code, text annotations, and mathematical expressions in a freely accessible statistical environment. We extend previous disease-specific pathogenicity assessments to over 6,000 diseases and 160,000 assertions in the ClinVar database. Investigators can use this platform to prioritize variants for reassessment and tailor genetic model parameters (such as prevalence and heterogeneity) to expose the uncertainty underlying pathogenicity-based risk assessments. Finally, we release a website that links users to pathogenic variation for a queried disease, supporting literature, and implied disease risk calculations subject to user-defined and disease-specific genetic risk models in order to facilitate variant reassessments.

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES

Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.

[Correct statistical analysis of genotype frequencies for UCP and PPAR gene families in residents of besieged Leningrad and the control group]

Correct harmonized statistical re-analysis of the data published in this Journal by I.V.Polyakova et al. (2014) clearly shows that, contrary to the authors' opinion, the distribution of genotypes among residents of besieged Leningrad and the residents of the North-West region of Russia appeared to be statistically indistinguishable in all five genes studied. The main causes of the erroneous conclusions of the authors are neglecting the problem of multiple comparisons and fundamental impossibility of sampling adequate control group. A scheme for harmonized statistical analysis of such data is presented. It implies not only frequentist but Bayesian point and interval estimates for genotype proportions and their differences, for fixation index (coefficient of inbreeding) FIS, for the effect size φ based on χ2 statistic (contingency coefficient) and for the achieved power (1 - β), as well as estimates of posterior probabilities for the null hypothesis P(H_0 |D), Bayes factors 〖BF〗_01, observed p-values, p_obs, with the prediction intervals, and p-values adjusted for the multiplicity of null hypotheses tested (P_S).

PHENOME-WIDE INTERACTION STUDY (PheWIS) IN AIDS CLINICAL TRIALS GROUP DATA (ACTG)

Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genomewide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient's response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.

TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS

Previous candidate gene and genome-wide association studies have identified common genetic variants in LPA associated with the quantitative trait Lp(a), an emerging risk factor for cardiovascular disease. These associations are population-specific and many have not yet been tested for association with the clinical outcome of interest. To fill this gap in knowledge, we accessed the epidemiologic Third National Health and Nutrition Examination Surveys (NHANES III) and BioVU, the Vanderbilt University Medical Center biorepository linked to de-identified electronic health records (EHRs), including billing codes (ICD-9-CM) and clinical notes, to test population-specific Lp(a)-associated variants for an association with myocardial infarction (MI) among African Americans. We performed electronic phenotyping among African Americans in BioVU≥40 years of age using billing codes. At total of 93 cases and 522 controls were identified in NHANES III and 265 cases and 363 controls were identified in BioVU. We tested five known Lp(a)-associated genetic variants (rs1367211, rs41271028, rs6907156, rs10945682, and rs1652507) in both NHANES III and BioVU for association with myocardial infarction. We also tested LPA rs3798220 (I4399M), previously associated with increased levels of Lp(a), MI, and coronary artery disease in European Americans, in BioVU. After meta-analysis, tests of association using logistic regression assuming an additive genetic model revealed no significant associations (p<0.05) for any of the five LPA variants previously associated with Lp(a) levels in African Americans. Also, I4399M rs3798220 was not associated with MI in African Americans (odds ratio = 0.51; 95% confidence interval: 0.16 - 1.65; p=0.26) despite strong, replicated associations with MI and coronary artery disease in European American genome-wide association studies. These data highlight the challenges in translating quantitative trait associations to clinical outcomes in diverse populations using large epidemiologic and clinic-based collections as envisioned for the Precision Medicine Initiative.

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer's Disease

Results from Genome-Wide Association Studies (GWAS) have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer's Disease (AD) ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.

Association of LEPR Gln223Arg polymorphism with T2DM: A meta-analysis

A meta-analysis was conducted to evaluate the association of LEPR Gln223Arg polymorphism with type 2diabetes (T2DM). Sixteen individual studies with 7827 subjects were included into the meta-analysis. Current studies suggest that LEPR Gln223Arg polymorphism may not affect the susceptibility with type 2diabetes (T2DM).

Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies

The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.

A Meta-Analysis of Somatic KCNJ5 K(+) Channel Mutations In 1636 Patients With an Aldosterone-Producing Adenoma

CONTEXT

Due to selection biases and inadequate statistical power, individual studies may fail to identify the clinical features of patients with an aldosterone-producing adenoma (APA) harboring KCNJ5 mutations. When this failure occurs, meta-analysis can provide significant outcome data.

OBJECTIVE

The objective was to determine the clinical features of these APA patients.

DESIGN

We systematically searched the PubMed, Scopus, Web of Science, and Cochrane databases library in January 2015 applying the Population, Intervention, Comparison, and Outcome (PICO) strategy. The standardized differences in mean and corresponding 95% confidence interval of continuous variables were computed by random-effects modeling.

SETTING

We performed a meta-analysis of all available studies on somatic KCNJ5 mutations in APA.

PATIENTS

We could identify 13 studies that recruited 1636 patients (age 49 ± 4 years; 55% females).

MAIN OUTCOMES AND MEASURES

Differences between APA with and without KCNJ5 mutations in gender, plasma renin activity, plasma aldosterone, tumor size, serum potassium, and blood pressure were investigated.

RESULTS

The overall prevalence of KCNJ5 mutations was 43% (range = 12-80%). Their rate was lower (P < .003) in the studies done in Europe, the United States, and Australia (35%) than in Japan and China (63%); it correlated (r = 0.60, P = .029) with the mean daily urinary sodium excretion. Compared with the wild-type, the mutated APA patients were younger (45 ± 3 vs 52 ± 5 yrs), had higher plasma aldosterone (42 ± 8 vs 33 ± 8 ng/dl), larger tumors (16.1 ± 6.4 versus 14.9 ± 7.4 mm), and were more often females (67% vs 44%) (all P < .05).

CONCLUSIONS

Meta-analysis showed that more pronounced hyperaldosteronism, young age, female gender, and larger tumors are the phenotypic features of APA patients with KCNJ5 mutations. No significant differences in blood pressure and serum K(+) was found, which suggests that these clinical features do not help in identifying mutated APA patients.

Measures of exposure impact genetic association studies: an example in vitamin K levels and VKORC1

Studies assessing the impact of gene-environment interactions on common human diseases and traits have been relatively few for many reasons. One often acknowledged reason is that it is difficult to accurately measure the environment or exposure. Indeed, most large-scale epidemiologic studies use questionnaires to assess and measure past and current exposure levels. While questionnaires may be cost-effective, the data may or may not accurately represent the exposure compared with more direct measurements (e.g., self-reported current smoking status versus direct measurement for cotinine levels). Much like phenotyping, the choice in how an exposure is measured may impact downstream tests of genetic association and gene-environment interaction studies. As a case study, we performed tests of association between five common VKORC1 SNPs and two different measurements of vitamin K levels, dietary (n=5,725) and serum (n=348), in the Third National Health and Nutrition Examination Studies (NHANES III). We did not replicate previously reported associations between VKORC1 and vitamin K levels using either measure. Furthermore, the suggestive associations and estimated genetic effect sizes identified in this study differed depending on the vitamin K measurement. This case study of VKORC1 and vitamin K levels serves as a cautionary example of the downstream consequences that the type of exposure measurement choices will have on genetic association and possibly gene-environment studies.

A systematic appraisal of field synopses in genetic epidemiology: a HuGE review

Evidence from genetic association studies is accumulating rapidly. Field synopses have recently arisen as an unbiased way of systematically synthesizing this evidence. We performed a systematic review and appraisal of published field synopses in genetic epidemiology and assessed their main findings and methodological characteristics. We identified 61 eligible field synopses, published between January 1, 2007, and October 31, 2013, on 52 outcomes reporting 734 significant associations at the P < 0.05 level. The median odds ratio for these associations was 1.25 (interquartile range, 1.15-1.43). Egger's test was the most common method (n = 30 synopses) of assessing publication bias. Only 12 synopses (20%) used the Venice criteria to evaluate the epidemiologic credibility of their findings (n = 449 variants). Eleven synopses (18%) were accompanied by an online database that has been regularly updated. These synopses received more citations (P = 0.01) and needed a larger research team (P = 0.02) than synopses without an online database. Overall, field synopses are becoming a valuable tool for the identification of common genetic variants, especially when researchers follow relevant methodological guidelines. Our work provides a summary of the current status of the field synopses published to date and may help interested readers efficiently identify the online resources containing the relevant genetic evidence.

Size matters: how population size influences genotype-phenotype association studies in anonymized data

OBJECTIVE

Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.

METHODS

We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.

RESULTS

Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.

CONCLUSIONS

This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

Functional analysis of variance for association studies

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.

Variant association tools for quality control and analysis of large-scale sequence and genotyping array data

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT's data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

Application of nontargeted metabolite profiling to discover novel markers of quality traits in an advanced population of malting barley

The process of breeding superior varieties for the agricultural industry is lengthy and expensive. Plant metabolites may act as markers of quality traits, potentially expediting the appraisal of experimental lines during breeding. Here, we evaluated the utility of metabolites as markers by assessing metabolic variation influenced by genetic and environmental factors in an advanced breeding setting and in relation to the phenotypic distribution of 20 quality traits. Nontargeted liquid chromatography-mass spectrometry metabolite profiling was performed on barley (Hordeum vulgare L.) grain and malt from 72 advanced malting barley lines grown at two distinct but climatically similar locations, with 2-row and 6-row barley as the main genetic factors. 27 420 molecular features were detected, and the metabolite and quality trait profiles were similarly influenced by genotype and environment; however, malt was more influenced by genotype compared with barley. An O2PLS model characterized molecular features and quality traits that covaried, and 1319 features associated with at least one of 20 quality traits. An indiscriminant MS/MS acquisition and novel data analysis method facilitated the identification of metabolites. The analysis described 216 primary and secondary metabolites that correlated with multiple quality traits and included amines, amino acids, alkaloids, polyphenolics and lipids. The mechanisms governing quality trait-metabolite associations were interpreted based on colocalization to genetic markers and their gene annotations. The results of this study support the hypothesis that metabolism and quality traits are co-influenced by relatively narrow genetic and environmental factors and illustrate the utility of grain metabolites as functional markers of quality traits.