Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies

Balancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study

OBJECTIVE

Phenotypic misclassification in genetic association analyses can impact the accuracy of PRS-based prediction models. The bias reduction method proposed by Tong et al. (2019) has demonstrated its efficacy in reducing the effects of bias on the estimation of association parameters between genotype and phenotype while minimizing variance by employing chart reviews on a subset of the data for validating phenotypes, however its improvement of subsequent PRS prediction accuracy remains unclear. Our study aims to fill this gap by assessing the performance of simulated PRS models and estimating the optimal number of chart reviews needed for validation.

METHODS

To comprehensively assess the efficacy of the bias reduction method proposed by Tong et al. in enhancing the accuracy of PRS-based prediction models, we simulated each phenotype under different correlation structures (an independent model, a weakly correlated model, a strongly correlated model) and introduced error-prone phenotypes using two distinct error mechanisms (differential and non-differential phenotyping errors). To facilitate this, we used genotype and phenotype data from 12 case-control datasets in the Alzheimer's Disease Genetics Consortium (ADGC) to produce simulated phenotypes. The evaluation included analyses across various misclassification rates of original phenotypes as well as quantities of validation set. Additionally, we determined the median threshold, identifying the minimal validation size required for a meaningful improvement in the accuracy of PRS-based predictions across a broad spectrum.

RESULTS

This simulation study demonstrated that incorporating chart review does not universally guarantee enhanced performance of PRS-based prediction models. Specifically, in scenarios with minimal misclassification rates and limited validation sizes, PRS models utilizing debiased regression coefficients demonstrated inferior predictive capabilities compared to models using error-prone phenotypes. Put differently, the effectiveness of the bias reduction method is contingent upon the misclassification rates of phenotypes and the size of the validation set employed during chart reviews. Notably, when dealing with datasets featuring higher misclassification rates, the advantages of utilizing this bias reduction method become more evident, requiring a smaller validation set to achieve better performance.

CONCLUSION

This study highlights the importance of choosing an appropriate validation set size to balance between the efforts of chart review and the gain in PRS prediction accuracy. Consequently, our study establishes a valuable guidance for validation planning, across a diverse array of sensitivity and specificity combinations.

Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks

Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.

Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.

Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies

Calculating the sample size in scientific studies is one of the critical issues as regards the scientific contribution of the study. The sample size critically affects the hypothesis and the study design, and there is no straightforward way of calculating the effective sample size for reaching an accurate conclusion. Use of a statistically incorrect sample size may lead to inadequate results in both clinical and laboratory studies as well as resulting in time loss, cost, and ethical problems. This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.

Longevity Relatives Count score identifies heritable longevity carriers and suggests case improvement in genetic studies

Loci associated with longevity are likely to harbor genes coding for key players of molecular pathways involved in a lifelong decreased mortality and decreased/compressed morbidity. However, identifying such loci is challenging. One of the most plausible reasons is the uncertainty in defining long‐lived cases with the heritable longevity trait among long‐living phenocopies. To avoid phenocopies, family selection scores have been constructed, but these have not yet been adopted as state of the art in longevity research. Here, we aim to identify individuals with the heritable longevity trait by using current insights and a novel family score based on these insights. We use a unique dataset connecting living study participants to their deceased ancestors covering 37,825 persons from 1,326 five‐generational families, living between 1788 and 2019. Our main finding suggests that longevity is transmitted for at least two subsequent generations only when at least 20% of all relatives are long‐lived. This proves the importance of family data to avoid phenocopies in genetic studies.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Genetic risk for dengue hemorrhagic fever and dengue fever in multiple ancestries

Background

Genetic risk factors for dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS) and dengue fever (DF) are limited, in particular there are sparse data on genetic risk across diverse populations.

Methods

We conducted a genome-wide association study (GWAS) in a derivation and validation sample of 7, 460 participants of Latin American, South Asian, and South East Asian ancestries. We then developed a weighted polygenic risk score (PRS) for each participant in each of the validation cohorts of the three ancestries to predict the risk of DHF/DSS compared to DF, DHF/DSS compared to controls, and, DF compared to controls.

Findings

The risk of DHF/DSS was significantly increased, odds ratio [OR] 1.84 (95%CI 1.47 to 2.31) (195 SNPs), compared to DF, fourth PRS quartile versus first quartile, in the validation cohort. The risk of DHF/DSS compared to controls was increased (OR=3.94; 95% CI 2.84 to 5.45) (278 SNPs), as was the risk of DF compared to controls (OR=1.97; 95%CI 1.63 to 2.39) (251 SNPs). Risk increased in a dose-dependent manner with increase in quartiles of PRS across comparisons. Significant associations persisted for PRS built within ancestries and applied to the same or different ancestries as well as for PRS built for one outcome (DHF/DSS or DF) and applied to the other.

Interpretation

There is a strong genetic effect that predisposes to risk of DHF/DSS and DF. The genetic risk for DHF/DSS is higher than that for DF when compared to controls, and this effect persists across multiple ancestries.

Genomic Regions Associated with IgE Levels against Culicoides spp. Antigens in Three Horse Breeds

Insect bite hypersensitivity (IBH), which is a cutaneous allergic reaction to antigens from Culicoides spp., is the most prevalent skin disorder in horses. Misdiagnosis is possible, as IBH is usually diagnosed based on clinical signs. Our study is the first to employ IgE levels against several recombinant Culicoides spp. allergens as an objective, independent, and quantitative phenotype to improve the power to detect genetic variants that underlie IBH. Genotypes of 200 Shetland ponies, 127 Icelandic horses, and 223 Belgian Warmblood horses were analyzed while using a mixed model approach. No single-nucleotide polymorphism (SNP) passed the Bonferroni corrected significance threshold, but several regions were identified within and across breeds, which confirmed previously identified regions of interest and, in addition, identifying new regions of interest. Allergen-specific IgE levels are a continuous and objective phenotype that allow for more powerful analyses when compared to a case-control set-up, as more significant associations were obtained. However, the use of a higher density array seems necessary to fully employ the use of IgE levels as a phenotype. While these results still require validation in a large independent dataset, the use of allergen-specific IgE levels showed value as an objective and continuous phenotype that can deepen our understanding of the biology underlying IBH.

Statistical power in genome-wide association studies and quantitative trait locus mapping

Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.

Replication, reanalysis, and gene expression: ME2 and genetic generalized epilepsy

OBJECTIVE

Genetic generalized epilepsy (GGE) consists of epileptic syndromes with overlapping symptoms and is considered to be largely genetic. Previous cosegregation and association studies have pointed to malic enzyme 2 (ME2) as a candidate susceptibility gene for adolescent-onset GGE. In this article, we present new evidence supporting ME2's involvement in GGE.

METHODS

To definitively test ME2's influence on GGE, we used 3 different approaches. First, we compared a newly recruited GGE cohort with an ethnically matched reference sample from 1000 Genomes Project, using an efficient test of association (POPFAM+). Second, we used POPFAM+ to reanalyze a previously collected data set, wherein the original controls were replaced with ethnically matched reference samples to minimize the confounding effect of population stratification. Third, in a post hoc analysis of expression data from healthy human prefrontal cortex, we identified single nucleotide polymorphisms (SNPs) influencing ME2 messenger RNA (mRNA) expression; and then we tested those same SNPs for association with GGE in a large case-control cohort.

RESULTS

First, in the analysis of our newly recruited GGE Cohort, we found a strong association between an ME2 SNP and GGE (P = 0.0006 at rs608781). Second, in the reanalysis of previously collected data, we confirmed the Greenberg et al (2005) finding of a GGE-associated ME2 risk haplotype. Third, in the post hoc ME2 expression analysis, we found evidence for a possible link between GGE and ME2 gene expression in human brain.

SIGNIFICANCE

Overall, our research, and the research of others, provides compelling evidence that ME2 influences susceptibility to adolescent-onset GGE.

Identification of a novel locus on chromosome 2q13, which predisposes to clinical vertebral fractures independently of bone density

OBJECTIVES

To identify genetic determinants of susceptibility to clinical vertebral fractures, which is an important complication of osteoporosis.

METHODS

Here we conduct a genome-wide association study in 1553 postmenopausal women with clinical vertebral fractures and 4340 controls, with a two-stage replication involving 1028 cases and 3762 controls. Potentially causal variants were identified using expression quantitative trait loci (eQTL) data from transiliac bone biopsies and bioinformatic studies.

RESULTS

A locus tagged by rs10190845 was identified on chromosome 2q13, which was significantly associated with clinical vertebral fracture (P=1.04×10-9) with a large effect size (OR 1.74, 95% CI 1.06 to 2.6). Bioinformatic analysis of this locus identified several potentially functional SNPs that are associated with expression of the positional candidate genes TTL (tubulin tyrosine ligase) and SLC20A1 (solute carrier family 20 member 1). Three other suggestive loci were identified on chromosomes 1p31, 11q12 and 15q11. All these loci were novel and had not previously been associated with bone mineral density or clinical fractures.

CONCLUSION

We have identified a novel genetic variant that is associated with clinical vertebral fractures by mechanisms that are independent of BMD. Further studies are now in progress to validate this association and evaluate the underlying mechanism.

Informatics and machine learning to define the phenotype

INTRODUCTION

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies

Diagnostic codes within electronic health record systems can vary widely in accuracy. It has been noted that the number of instances of a particular diagnostic code monotonically increases with the accuracy of disease phenotype classification. As a growing number of health system databases become linked with genomic data, it is critically important to understand the effect of this misclassification on the power of genetic association studies. Here, I investigate the impact of this diagnostic code misclassification on the power of genetic association studies with the aim to better inform experimental designs using health informatics data. The trade-off between (i) reduced misclassification rates from utilizing additional instances of a diagnostic code per individual and (ii) the resulting smaller sample size is explored, and general rules are presented to improve experimental designs.

Genome-wide association study and meta-analysis in multiple populations identifies new loci for peanut allergy and establishes C11orf30/EMSY as a genetic risk factor for food allergy

BACKGROUND

Peanut allergy (PA) is a complex disease with both environmental and genetic risk factors. Previously, PA loci were identified in filaggrin (FLG) and HLA in candidate gene studies, and loci in HLA were identified in a genome-wide association study and meta-analysis.

OBJECTIVE

We sought to investigate genetic susceptibility to PA.

METHODS

Eight hundred fifty cases and 926 hyper-control subjects and more than 7.8 million genotyped and imputed single nucleotide polymorphisms (SNPs) were analyzed in a genome-wide association study to identify susceptibility variants for PA in the Canadian population. A meta-analysis of 2 phenotypes (PA and food allergy) was conducted by using 7 studies from the Canadian, American (n = 2), Australian, German, and Dutch (n = 2) populations.

RESULTS

An SNP near integrin α6 (ITGA6) reached genome-wide significance with PA (P = 1.80 × 10^-8), whereas SNPs associated with Src kinase-associated phosphoprotein 1 (SKAP1), matrix metallopeptidase 12 (MMP12)/MMP13, catenin α3 (CTNNA3), rho GTPase-activating protein 24 (ARHGAP24), angiopoietin 4 (ANGPT4), chromosome 11 open reading frame (C11orf30/EMSY), and exocyst complex component 4 (EXOC4) reached a threshold suggestive of association (P ≤ 1.49 × 10^-6). In the meta-analysis of PA, loci in or near ITGA6, ANGPT4, MMP12/MMP13, C11orf30, and EXOC4 were significant (P ≤ 1.49 × 10^-6). When a phenotype of any food allergy was used for meta-analysis, the C11orf30 locus reached genome-wide significance (P = 7.50 × 10^-11), whereas SNPs associated with ITGA6, ANGPT4, MMP12/MMP13, and EXOC4 and additional C11orf30 SNPs were suggestive (P ≤ 1.49 × 10^-6). Functional annotation indicated that SKAP1 regulates expression of CBX1, which colocalizes with the EMSY protein coded by C11orf30.

CONCLUSION

This study identifies multiple novel loci as risk factors for PA and food allergy and establishes C11orf30 as a risk locus for both PA and food allergy. Multiple genes (C11orf30/EMSY, SKAP1, and CTNNA3) identified by this study are involved in epigenetic regulation of gene expression.

ANLN truncation causes a familial fatal acute respiratory distress syndrome in Dalmatian dogs

Acute respiratory distress syndrome (ARDS) is the leading cause of death in critical care medicine. The syndrome is typified by an exaggerated inflammatory response within the lungs. ARDS has been reported in many species, including dogs. We have previously reported a fatal familial juvenile respiratory disease accompanied by occasional unilateral renal aplasia and hydrocephalus, in Dalmatian dogs. The condition with a suggested recessive mode of inheritance resembles acute exacerbation of usual interstitial pneumonia in man. We combined SNP-based homozygosity mapping of two ARDS-affected Dalmatian dogs and whole genome sequencing of one affected dog to identify a case-specific homozygous nonsense variant, c.31C>T; p.R11* in the ANLN gene. Subsequent analysis of the variant in a total cohort of 188 Dalmatians, including seven cases, indicated complete segregation of the variant with the disease and confirmed an autosomal recessive mode of inheritance. Low carrier frequency of 1.7% was observed in a population cohort. The early nonsense variant results in a nearly complete truncation of the ANLN protein and immunohistochemical analysis of the affected lung tissue demonstrated the lack of the membranous and cytoplasmic staining of ANLN protein in the metaplastic bronchial epithelium. The ANLN gene encodes an anillin actin binding protein with a suggested regulatory role in the integrity of intercellular junctions. Our study suggests that defective ANLN results in abnormal cellular organization of the bronchiolar epithelium, which in turn predisposes to acute respiratory distress. ANLN has been previously linked to a dominant focal segmental glomerulosclerosis in human without pulmonary defects. However, the lack of similar renal manifestations in the affected Dalmatians suggest a novel ANLN-related pulmonary function and disease association.

"Noisy beets": impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris

BACKGROUND

Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding.

RESULTS

In this study, the sensitivity to noise in the data of five classification methods (K-nearest neighbours-KNN, random forest-RF, ridge logistic regression-LR, and support vector machines with linear or radial basis function kernels) was investigated. A sugar beet population of 123 plants phenotyped for a binary trait and genotyped for 192 SNP (single nucleotide polymorphism) markers was used. Labels (0/1 phenotype) were randomly sampled to generate noise. From the base scenario without errors in the labels, increasing proportions of noisy labels-up to 50 %-were generated and introduced in the data.

CONCLUSIONS

Local classification methods-KNN and RF-showed higher tolerance to noisy labels compared to methods that leverage global data properties-LR and the two SVM models. In particular, KNN outperformed all other classifiers with AUC (area under the ROC curve) higher than 0.95 up to 20 % noisy labels. The runner-up method, RF, had an AUC of 0.941 with 20 % noise.

GNAS1 T393C Polymorphism is Associated With Migraine

Migraineurs have an interictal sympathetic nervous system (SNS) hypofunctionality and hypersensitivity to adrenergic amines. The GNAS1 T393C polymorphism has been associated with a distinct SNS sensitivity in healthy subjects. We tested GNAS1 T393C variant in two independent sets of subjects. In the case-control subset, 365 migraine patients [194 with aura (MA)] vs. 347 healthy controls were studied. A significant excess of the CC genotype was found in migraneurs (31.2%) as opposed to controls (20.2%; P = 0.003). Using a logistic regression model corrected for sex, the CC genotype conferred a general risk for migraine twice [odds ratio (OR) 1.79, 95% confidence interval (CI) 1.27-2.53; P = 0.001] higher than CT/TT genotypes. Using parents from 117 migraine families, a marginally significant trend for association could be observed ( P = 0.025), but the transmission disequilibrium test for alleles maternally transmitted failed to demonstrate familial association. In this subgroup, CC genotype conferred a risk for migraine over twice (OR 2.20; 95% CI 1.14-4.40; P = 0.019) higher than TT/TC genotypes. In conclusion, the GNAS1 T393C variant is associated with migraine, which suggests a genetic basis for its higher SNS sensitivity.

Associations of prodynorphin sequence variation with alcohol dependence and related traits are phenotype-specific and sex-dependent

We previously demonstrated that prodynorphin (PDYN) haplotypes and single nucleotide polymorphism (SNP) rs2281285 are associated with alcohol dependence and the propensity to drink in negative emotional states, and recent studies suggest that PDYN gene effects on substance dependence risk may be sex-related. We examined sex-dependent associations of PDYN variation with alcohol dependence and related phenotypes, including negative craving, time until relapse after treatment and the length of sobriety episodes before seeking treatment, in discovery and validation cohorts of European ancestry. We found a significant haplotype-by-sex interaction (p  =  0.03), suggesting association with alcohol dependence in males (p = 1E-4) but not females. The rs2281285 G allele increased risk for alcohol dependence in males in the discovery cohort (OR = 1.49, p = 0.002), with a similar trend in the validation cohort (OR = 1.35, p = 0.086). However, rs2281285 showed a trend towards association with increased negative craving in females in both the discovery (beta = 10.16, p = 0.045) and validation samples (OR = 7.11, p = 0.066). In the discovery cohort, rs2281285 was associated with time until relapse after treatment in females (HR = 1.72, p = 0.037); in the validation cohort, it was associated with increased length of sobriety episodes before treatment in males (beta = 13.49, p = 0.001). Our findings suggest that sex-dependent effects of PDYN variants in alcohol dependence are phenotype-specific.

Genetic variants in long non-coding RNA MIAT contribute to risk of paranoid schizophrenia in a Chinese Han population

The heritability of schizophrenia has been reported to be as high as ~80%, but the contribution of genetic variants identified to this heritability remains to be estimated. Long non-coding RNAs (LncRNAs) are involved in multiple processes critical to normal cellular function and dysfunction of lncRNA MIAT may contribute to the pathophysiology of schizophrenia. However, the genetic evidence of lncRNAs involved in schizophrenia has not been documented. Here, we conducted a two-stage association analysis on 8 tag SNPs that cover the whole MIAT locus in two independent Han Chinese schizophrenia case-control cohorts (discovery sample from Shanxi Province: 1093 patients with paranoid schizophrenia and 1180 control subjects; replication cohort from Jilin Province: 1255 cases and 1209 healthy controls). In discovery stage, significant genetic association with paranoid schizophrenia was observed for rs1894720 (χ(2)=74.20, P=7.1E-18), of which minor allele (T) had an OR of 1.70 (95% CI=1.50-1.91). This association was confirmed in the replication cohort (χ(2)=22.66, P=1.9E-06, OR=1.32, 95%CI 1.18-1.49). Besides, a weak genotypic association was detected for rs4274 (χ(2)=4.96, df=2, P=0.03); the AA carriers showed increased disease risk (OR=1.30, 95%CI=1.03-1.64). No significant association was found between any haplotype and paranoid schizophrenia. The present studies showed that lncRNA MIAT was a novel susceptibility gene for paranoid schizophrenia in the Chinese Han population. Considering that most lncRNAs locate in non-coding regions, our result may explain why most susceptibility loci for schizophrenia identified by genome wide association studies were out of coding regions.

The genetics of diabetic complications

The rising global prevalence of diabetes mellitus is accompanied by an increasing burden of morbidity and mortality that is attributable to the complications of chronic hyperglycaemia. These complications include blindness, renal failure and cardiovascular disease. Current therapeutic options for chronic hyperglycaemia reduce, but do not eradicate, the risk of these complications. Success in defining new preventative and therapeutic strategies hinges on an improved understanding of the molecular processes involved in the development of these complications. This Review explores the role of human genetics in delivering such insights, and describes progress in characterizing the sequence variants that influence individual predisposition to diabetic kidney disease, retinopathy, neuropathy and accelerated cardiovascular disease. Numerous risk variants for microvascular complications of diabetes have been reported, but very few have shown robust replication. Furthermore, only limited evidence exists of a difference in the repertoire of risk variants influencing macrovascular disease between those with and those without diabetes. Here, we outline the challenges associated with the genetic analysis of diabetic complications and highlight ongoing efforts to deliver biological insights that can drive translational benefits.

Genome-wide association scan for variants associated with early-onset prostate cancer

Prostate cancer is the most common non-skin cancer and the second leading cause of cancer related mortality for men in the United States. There is strong empirical and epidemiological evidence supporting a stronger role of genetics in early-onset prostate cancer. We performed a genome-wide association scan for early-onset prostate cancer. Novel aspects of this study include the focus on early-onset disease (defined as men with prostate cancer diagnosed before age 56 years) and use of publically available control genotype data from previous genome-wide association studies. We found genome-wide significant (p<5×10⁻⁸) evidence for variants at 8q24 and 11p15 and strong supportive evidence for a number of previously reported loci. We found little evidence for individual or systematic inflated association findings resulting from using public controls, demonstrating the utility of using public control data in large-scale genetic association studies of common variants. Taken together, these results demonstrate the importance of established common genetic variants for early-onset prostate cancer and the power of including early-onset prostate cancer cases in genetic association studies.

Impact of measurement error on testing genetic association with quantitative traits

Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (p<10⁻⁵) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.

The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases

Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.

Enhancing the power of genetic association studies through the use of silver standard cases derived from electronic medical records

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network

BACKGROUND

Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats.

OBJECTIVE

To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies.

MATERIALS AND METHODS

The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University.

RESULTS

By validating EMR-derived phenotypes we learned that: (1) multisite validation improves phenotype algorithm accuracy; (2) targets for validation should be carefully considered and defined; (3) specifying time frames for review of variables eases validation time and improves accuracy; (4) using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied; (5) patient movement in and out of the health plan (transience) can result in incomplete or fragmented data; (6) the review scope should be defined carefully; (7) particular care is required in combining EMR and research data; (8) medication data can be assessed using claims, medications dispensed, or medications prescribed; (9) algorithm development and validation work best as an iterative process; and (10) validation by content experts or structured chart review can provide accurate results.

CONCLUSIONS

Despite the diverse structure of the five EMRs of the eMERGE sites, we developed, validated, and successfully deployed 13 electronic phenotype algorithms. Validation is a worthwhile process that not only measures phenotype performance but also strengthens phenotype algorithm definitions and enhances their inter-institutional sharing.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Correction of phenotype misclassification based on high-discrimination genetic predictive risk models

Misclassification of phenotype status can seriously affect accuracy in association studies, including studies of genetic risk factors. A common problem is the classification of participants as nondiseased because of insufficient diagnostic workup or because participants have not been followed up long enough to develop disease. Some validated predictive models may have high discrimination in predicting disease. We suggest that information from such models can be used to predict the risk that a nondiseased participant will eventually develop disease and to recode the status of participants predicted to be at highest risk. We evaluate conditions under which recoding results in a maximal net improvement in the accuracy of phenotype classification. Net improvement is expected only when the positive likelihood ratio of the predictive model is larger than the inverse of the odds of disease among apparently nondiseased controls. We conducted simulations to probe the impact of reclassification on the power to detect new risk factors under several scenarios of classification accuracy of the previously developed models. We also apply this framework to a validated model of progression to advanced age-related macular degeneration that uses genetic and nongenetic variables (area under the curve = 0.915). In the training cohort (n = 2,937) and a separate validation cohort (n = 1,227), 195-272 and 78-91 nonprogressor participants, respectively, were reclassified as progressors. Correction of phenotype misclassification based on highly informative predictive models may be helpful in identifying additional genetic and other risk factors, when there are validated risk factors that provide strong discriminating ability.

Inflammation and immune-related candidate gene associations with acute lung injury susceptibility and severity: a validation study

Introduction

Common variants in genes related to inflammation, innate immunity, epithelial cell function, and angiogenesis have been reported to be associated with risks for Acute Lung Injury (ALI) and related outcomes. We tested whether previously-reported associations can be validated in an independent cohort at risk for ALI.

Methods

We identified 37 genetic variants in 27 genes previously associated with ALI and related outcomes. We prepared allelic discrimination assays for 12 SNPs from 11 genes with MAF>0.05 and genotyped these SNPs in Caucasian subjects from a cohort of critically ill patients meeting criteria for the systemic inflammatory response syndrome (SIRS) followed for development of ALI, duration of mechanical ventilation, and in-hospital death. We tested for associations using additive and recessive genetic models.

Results

Among Caucasian subjects with SIRS (n = 750), we identified a nominal association between rs2069832 in IL6 and ALI susceptibility (OR_adj 1.61; 95% confidence interval [CI], 1.04–2.48, P = 0.03). In a sensitivity analysis limiting ALI cases to those who qualified for the Acute Respiratory Distress Syndrome (ARDS), rs61330082 in NAMPT was nominally associated with risk for ARDS. In terms of ALI outcomes, SNPs in MBL2 (rs1800450) and IL8 (rs4073) were nominally associated with fewer ventilator-free days (VFDs), and SNPs in NFE2L2 (rs6721961) and NAMPT (rs61330082) were nominally associated with 28-day mortality. The directions of effect for these nominal associations were in the same direction as previously reported but none of the associations survived correction for multiple hypothesis testing.

Conclusion

Although our primary analyses failed to statistically validate prior associations, our results provide some support for associations between SNPs in IL6 and NAMPT and risk for development of lung injury and for SNPs in IL8, MBL2, NFE2L2 and NAMPT with severity in ALI outcomes. These associations provide further evidence that genetic factors in genes related to immunity and inflammation contribute to ALI pathogenesis.

Limited contribution of common genetic variants to risk for liver injury due to a variety of drugs

BACKGROUND AND AIMS

Drug-induced liver injury (DILI) is a serious adverse drug event that is suspected to have a heritable component. We carried out a genome-wide association study of 783 individuals of European ancestry who experienced DILI due to more than 200 implicated drugs.

METHODS

DILI patients from the US-based Drug-Induced Liver Injury Network (n=401) and three international registries (n=382) were genotyped with the Illumina 1Mduo BeadChip and compared with population controls (n=3001). Potential associations were tested in 307 independent Drug-Induced Liver Injury Network cases.

RESULTS

After accounting for known major histocompatibility complex risk alleles for flucloxacillin-DILI and amoxicillin/clavulanate-DILI, there were no genome-wide significant associations, including in the major histocompatibility complex region. Stratification of DILI cases according to clinical phenotypes (injury type, latency, age of onset) also did not show significant associations. An analysis of hepatocellular DILI (n=285) restricted to 193 single-nucleotide polymorphisms previously associated with autoimmune disease showed a trend association for rs7574865, in the vicinity of signal transducer and activator of transcription 4 (STAT4) (P=4.5×10(-4)). This association was replicated in an independent cohort of 168 hepatocellular DILI cases (P=0.011 and 1.5×10(-5) for combined cohorts). No significant associations were found with stratification by other clinical or demographic variables.

CONCLUSION

Although not significant at the genome-wide level, the association between hepatocellular DILI and STAT4 is consistent with the emerging role of the immune system in DILI. However, the lack of genome-wide association study findings supports the idea that strong genetic determinants of DILI may be largely drug-specific or may reflect rare genetic variations, which were not assessed in our study.

Association of markers at chromosome 15q14 in Chinese patients with moderate to high myopia

PURPOSE

To investigate the association of two reported regions on chromosome 15 with moderate to high myopia in two Chinese cohorts from southern China.

METHODS

Two candidate regions on 15q14 and 15q25 were selected based on reported association with refractive error in the literature. Five single nucleotide polymorphisms (SNPs) were genotyped in 300 university students with high myopia at Guangzhou and 308 without refractive error, and 96 university students of Chaoshan Chinese origin with moderate to high myopia and 96 without refractive error. Genotypes were evaluated using direct sequencing and analyzed with chi-square, Armitage trend, and Mantel-Haenszel tests, and regression analysis.

RESULTS

Of the five SNPs screened, alleles of rs634990 and rs524952 in the 15q14 region showed evidence of allelic association with moderate to high myopia (p<8.81×10(-7) and p<1.57×10(-6), respectively) in the Guangzhou group, but not in the Chaoshan group. The SNPs at 15q25 did not show significant association in any group. Association of rs634990 and rs524952 were still significant when both groups were combined into a single analysis (p<1.66×10(-6) and p<2.72×10(-6), respectively), and for genotypic, additive, and dominant models.

CONCLUSIONS

This study confirms the significant association of rs634990 and rs524952 on chromosome 15q14 previously reported in European and Japanese populations with high myopia in the Guangzhou but not the Chaoshan Chinese populations, suggesting that genetic contributors to high myopia in the Chaoshan population might be different from other Chinese populations.

Power and sample size calculations for SNP association studies with censored time-to-event outcomes

For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided.

The use of chronic gingivitis as reference status increases the power and odds of periodontitis genetic studies: a proposal based in the exposure concept and clearer resistance and susceptibility phenotypes definition

AIM

Current literature on chronic periodontitis genetics encompasses numerous single nucleotide polymorphisms-focused case-control studies with inconsistent and controversial results, which typically disregards the exposure concept embraced by case-control definition. Herein, we propose a case-control design reappraisal by clear phenotype selection, where chronic gingivitis represents a genetically resistant phenotype/genotype opposing the susceptible cohort.

MATERIAL AND METHODS

The hypothesis was tested in healthy, chronic periodontitis and gingivitis groups through Real-time PCR-based allelic discrimination of classic variants IL1B-3954, IL6-174, TNFA-308, IL10-592 and TLR4-299.

RESULTS

Observed allele/genotype frequencies characterize the healthy group with an intermediate genetic profile between periodontitis and gingivitis cohorts. When comparing genotype/allele frequencies in periodontitis versus healthy and periodontitis versus gingivitis scenarios, the number of positive associations (2-4) and the degree of association (p and odds ratio values) were significantly increased by the new approach proposed (periodontitis versus gingivitis), suggesting the association of IL1B-3954, TNFA-308, IL10-592 and TLR4-299 with periodontitis risk. Power study was also significantly improved by the new study design proposed when compared to the traditional approach.

CONCLUSIONS

The data presented herein support the use of new case-control study design based on the case-control definition and clear resistance/susceptibility phenotypes selection, which can significantly impact the study power and odds of identification of genetic factors involved in PD.

Genome-wide association studies of antidepressant outcome: a brief review

Genome-wide association studies (GWAS) of antidepressant treatment outcome have been at the forefront of psychiatric pharmacogenetics. Such studies may ultimately help match medications with patients, maximizing efficacy while minimizing adverse effects. The hypothesis-free approach of the GWAS has the advantage of interrogating genes that otherwise would have not been considered as candidates due to our limited understanding of their function, and may also uncover important regulatory variation within the large regions of the genome that do not contain protein-coding genes. Three independent samples have so far been studied using a genome-wide approach: The Sequenced Treatment Alternatives to Relieve Depression sample (STAR*D) (n=1953), the Munich Antidepressant Response Signature (MARS) sample (n=339) and the Genome-based Therapeutic Drugs for Depression (GENDEP) sample (n=706). None of the studies reported results that achieved genome-wide significance, suggesting that larger samples and better outcome measures will be needed. This review discusses the published GWAS studies, their strengths, limitations, and possible future directions.

Electronic medical records for genetic research: results of the eMERGE consortium

Clinical data in electronic medical records (EMRs) are a potential source of longitudinal clinical data for research. The Electronic Medical Records and Genomics Network (eMERGE) investigates whether data captured through routine clinical care using EMRs can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98% and negative predictive values of 98 to 100%. Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.

Genetic susceptibility to tuberculosis associated with cathepsin Z haplotype in a Ugandan household contact study

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), causes 9 million new cases worldwide and 2 million deaths annually. Genetic linkage and association analyses have suggested several chromosomal regions and candidate genes involved in TB susceptibility. This study examines the association of TB disease susceptibility with a selection of biologically relevant genes on regions on chromosomes 7 (IL6 and CARD11) and 20 (CTSZ and MC3R) and fine mapping of the chromosome 7p22-p21 region identified through our genome scan. We analyzed 565 individuals from Kampala, Uganda, who were previously included in our genome-wide linkage scan. Association analyses were conducted for 1,417 single-nucleotide polymorphisms (SNP) that passed quality control. None of the candidate gene or fine mapping SNPs was significantly associated with TB susceptibility (p > 0.10). When we restricted the analysis to HIV-negative individuals, 2 SNPs on chromosome 7 were significantly associated with TB susceptibility (p < 0.05). Haplotype analyses identified a significant risk haplotype in cathepsin X (CTSZ; p = 0.0281, odds ratio = 1.5493, 95% confidence interval [1.039, 2.320]).

GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits

Background

In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed.

Results

This paper presents GWAPower, a statistical software package of power calculation designed for GWA studies with quantitative traits, where genetic effect is defined as heritability. Based on several popular one-degree-of-freedom genetic models, this method avoids the need to specify the non-centrality parameter of the F-distribution under the alternative hypothesis. Therefore, it can use heritability information directly without approximation. In GWAPower, the power calculation can be easily adjusted for adding covariates and linkage disequilibrium information. An example is provided to illustrate GWAPower, followed by discussions.

Conclusions

GWAPower is a user-friendly free software package for calculating statistical power based on heritability in GWA studies with quantitative traits. The software is freely available at: http://dl.dropbox.com/u/10502931/GWAPower.zip

Genetic epidemiology of tuberculosis susceptibility: impact of study design

Several candidate gene studies have provided evidence for a role of host genetics in susceptibility to tuberculosis (TB). However, the results of these studies have been very inconsistent, even within a study population. Here, we review the design of these studies from a genetic epidemiological perspective, illustrating important differences in phenotype definition in both cases and controls, consideration of latent M. tuberculosis infection versus active TB disease, population genetic factors such as population substructure and linkage disequilibrium, polymorphism selection, and potential global differences in M. tuberculosis strain. These considerable differences between studies should be accounted for when examining the current literature. Recommendations are made for future studies to further clarify the host genetics of TB.

Tuberculosis as a complex trait: impact of genetic epidemiological study design

Several studies have suggested a role for human genetic risk factors in the susceptibility to developing tuberculosis (TB). However, results of these studies have been inconsistent, and one potential reason for these inconsistencies is variation in aspects of study design. Specifically, phenotype definitions and population genetic factors have varied dramatically. Since TB is a complex trait, there are many challenges in designing studies to assess appropriately human genetic risk factors for the development of TB as opposed to the acquisition of latent M. tuberculosis infection. In this review we summarize these important study design differences, with illustrations from the TB genetics literature. We cite specific examples of studies of the NRAMP1 (SLC11A1) gene and present Fisher's combined p values for different stratifications of these studies to further illustrate the impact of study design differences. Finally, we provide suggestions for the design of future genetic epidemiological studies of TB.

Using public control genotype data to increase power and decrease cost of case-control genetic association studies

Genome-wide association (GWA) studies are a powerful approach for identifying novel genetic risk factors associated with human disease. A GWA study typically requires the inclusion of thousands of samples to have sufficient statistical power to detect single nucleotide polymorphisms that are associated with only modest increases in risk of disease given the heavy burden of a multiple test correction that is necessary to maintain valid statistical tests. Low statistical power and the high financial cost of performing a GWA study remains prohibitive for many scientific investigators anxious to perform such a study using their own samples. A number of remedies have been suggested to increase statistical power and decrease cost, including the utilization of free publicly available genotype data and multi-stage genotyping designs. Herein, we compare the statistical power and relative costs of alternative association study designs that use cases and screened controls to study designs that are based only on, or additionally include, free public control genotype data. We describe a novel replication-based two-stage study design, which uses free public control genotype data in the first stage and follow-up genotype data on case-matched controls in the second stage that preserves many of the advantages inherent when using only an epidemiologically matched set of controls. Specifically, we show that our proposed two-stage design can substantially increase statistical power and decrease cost of performing a GWA study while controlling the type-I error rate that can be inflated when using public controls due to differences in ancestry and batch genotype effects.

Accuracy of phenotyping of autistic children based on Internet implemented parent report

While strong familial evidence supports a substantial genetic contribution to the etiology of autism spectrum disorders (ASD), specific genetic abnormalities have been identified in only a small minority of all cases. In order to comprehensively delineate the genetic components of autism including the identification of rare and common variants, overall sample sizes an order of magnitude larger than those currently under study are critically needed. This will require rapid and scalable subject assessment paradigms that obviate clinic-based time-intensive behavioral phenotyping, which is a rate-limiting step. Here, we test the accuracy of a web-based approach to autism phenotyping implemented within the Interactive Autism Network (IAN). Families who were registered with the IAN and resided near one of the three study sites were eligible for the study. One hundred seven children ascertained from this pool who were verbal, age 4-17 years, and had Social Communication Questionnaire (SCQ) scores > or =12 (a profile that characterizes a majority of ASD-affected children in IAN) underwent a clinical confirmation battery. One hundred five of the 107 children were ASD positive (98%) by clinician's best estimate. One hundred four of these individuals (99%) were ASD positive by developmental history using the Autism Diagnostic Interview-Revised (ADI-R) and 97 (93%) were positive for ASD by developmental history and direct observational assessment (Autism Diagnostic Observational Schedule or expert clinician observation). These data support the reliability and feasibility of the IAN-implemented parent-report paradigms for the ascertainment of clinical ASD for large-scale genetic research.

Association between anal sac gland carcinoma and dog leukocyte antigen-DQB1 in the English Cocker Spaniel

Anal sac gland carcinomas occur frequently in English Cocker Spaniels and, to a lesser extent, in other spaniel breeds. The disease typically presents in dogs aged 8 years or older and frequently metastasises to the local lymph nodes. The association between anal sac gland carcinoma in English Cocker Spaniels and the major histocompatibility complex class II loci (the dog leukocyte antigen loci DLA-DRB1, -DQA1, -DQB1) was investigated in 42 cases and 75 controls. Based on a corrected error rate of 0.017 for each test, the allele distribution in DLA-DRB1 showed no significant difference between cases and controls (P value = 0.019), while a significant difference was obtained for DLA-DQA1 and -DQB1 alleles (P values are 0.010 and 3.3 × 10⁻⁵). The DLA-DQB1*00701 allele was the most common in both cases and controls, but it had a higher frequency among the former (0.89) than in the latter (0.61), while the second most common allele had a higher frequency in the controls (0.23) than in the cases (0.07). Haplotype distributions were also significantly different between the two groups (P value = 1.61 × 10⁻⁴). This is the second disease in English Cocker Spaniels for which the most common DLA-DQB1 allele in the breed has been shown to have a higher frequency in cases than controls, while the second most common allele in the breed (*02001) has a significantly higher frequency in the controls, compared with the cases.

Common functional genetic variants in catecholamine storage vesicle protein promoter motifs interact to trigger systemic hypertension

OBJECTIVES

The purpose of this study is to understand whether naturally occurring genetic variation in the promoter of chromogranin B (CHGB), a major constituent of catecholamine storage vesicles, is functional and confers risk for cardiovascular disease.

BACKGROUND

CHGB plays a necessary (catalytic) role in catecholamine storage vesicle biogenesis. Previously, we found that genetic variation at CHGB influenced autonomic function, with association maximal toward the 5' region.

METHODS

Here we explored transcriptional mechanisms of such effects, characterizing 2 common variants in the proximal promoter, A-296C and A-261T, using transfection/cotransfection, electrophoretic mobility shift assay (EMSA), and chromatin immunoprecipitation (ChIP). We then tested the effects of promoter variation on cardiovascular traits.

RESULTS

The A-296C disrupted a c-FOS motif, exhibiting differential mobility shifting to chromaffin cell nuclear proteins during EMSA, binding of endogenous c-FOS on ChIP, and differential response to exogenous c-FOS. The A-261T disrupted motifs for SRY and YY1, with similar consequences for EMSA, endogenous factor binding, and responses to exogenous factors. The 2-SNP CHGB promoter haplotypes had a profound (p=3.16E-20) effect on blood pressure (BP) in the European ancestry population, with a rank order of CT<AA<<CA<AT on both systolic blood pressure (SBP) and diastolic blood pressure (DBP), accounting for approximately 2.3% to approximately 3.4% of SBP/DBP variance; the haplotype effects on BP in vivo paralleled those on promoter activity in cella. Site-by-site interactions at A-296C and A-261T yielded highly nonadditive effects on SBP/DBP. The CHGB haplotype effects on BP were also noted in an independent (African ancestry) sample. In normotensive twins, parallel effects were noted for a pre-hypertensive phenotype, BP response to environmental stress.

CONCLUSIONS

The common CHGB promoter variants A-296C and A-261T, and their consequent haplotypes, alter binding of specific transcription factors to influence gene expression in cella as well as BP in vivo. Such variation contributes substantially to risk for human hypertension. Involvement of the sex-specific factor SRY suggests a novel mechanism for development of sexual dimorphism in BP.

Intermediate sized CGG repeats are not a common cause of idiopathic premature ovarian failure

BACKGROUND

It is recognized that FMR1 premutation expansions are associated with premature ovarian failure (POF), but the role of smaller repeats at the boundary of premutation and normal is less clear.

METHODS

We have therefore investigated the incidence of these intermediate sized FMR1 CGG repeats (35-58 repeats) in a series of 366 women ascertained because of menopause before the age of 40.

RESULTS

We found no significant difference in the incidence of intermediates in cases compared with controls. Thus, we were unable to replicate previous studies showing a positive association, despite a significantly larger sample size.

CONCLUSIONS

We therefore conclude that intermediate sized FMR1 CGG repeat alleles should not be considered a high-risk factor for POF based on current evidence.

The association of the dopamine transporter gene and the dopamine receptor 2 gene with delirium, a meta-analysis

Delirium is the most common neuropsychiatric syndrome in elderly ill patients. Previously, associations between delirium and the dopamine transporter gene (solute carrier family 6, member 3 (SLC6A3)) and dopamine receptor 2 gene (DRD2) were found. The aim of this study was to validate whether markers of the SLC6A3 and DRD2 genes are were associated with delirium in independent populations. Six European populations collected DNA of older delirious patients. Associations were determined per population and results were combined in a meta-analysis. In total 820 medical inpatients, 185 cardiac surgery patients, 134 non-cardiac surgery patients and 502 population-based elderly subjects were included. Mean age was 82 years (SD 7.5 years), 598 (36%) were male, 665 (41%) had pre-existing cognitive impairment, and 558 (34%) experienced delirium. The SLC6A3 rs393795 homozygous AA genotype was more frequent in patients without delirium in all populations. The meta-analysis showed an Odds Ratio (OR) for delirium of 0.4 (95% confidence interval (C.I.) 0.2-0.6, P = 0.0003) for subjects with AA genotype compared to the AG and GG genotypes. SLC6A3 marker rs1042098 showed no association with delirium. In meta-analysis the DRD2 rs6276 homozygous GG genotype showed an OR of 0.8 for delirium (95% C.I. 0.6-1.1, P = 0.24). When subjects were stratified for cognitive status the rs6276 GG genotype showed ORs of 0.6 (95% C.I. 0.4-1.0, P = 0.06) and 0.8 (95% C.I. 0.5-1.5, P = 0.51) for delirium in patients with and without cognitive impairment, respectively. In independent cohorts, a variation in the SLC6A3 gene and possibly the DRD2 gene were found to protect for delirium.

The association between delirium and the apolipoprotein E epsilon 4 allele: new study results and a meta-analysis

OBJECTIVES

To determine a possible association between Apolipoprotein E (APOE)sigma4-allele and delirium in a large cohort and combining these current data with former studies in a meta-analysis.

DESIGN

Combination of a new prospective cohort study and meta-analysis.

SETTING

Medical department and orthopedic/traumatology department of University hospital from 2003 to 2007.

PARTICIPANTS

A total of 656 patients aged 65 years and older acutely admitted with a medical diagnosis or after hip fracture.

MEASUREMENTS

Confusion Assessment Method for delirium, Informant Questionnaire on Cognitive Decline-short form for predelirium global cognitive impairment, and Katz Index of Activities of Daily Living for functionality. APOE was genotyped by mass spectrometer. A meta-analysis was performed combining the current data with published studies analyzing the association between the APOE sigma4-allele and the delirium.

RESULTS

: The 49% of the 76 surgical patients and 35% of the 580 medical patients experienced delirium. Delirious patients were significantly older (82 versus 77 years) and had more frequently functional (66% versus 26%) and cognitive impairment (86% versus 29%) than nondelirious patients. The odds ratio (OR) for delirium adjusted for age, cognitive, and functional impairment of sigma4 carriers compared with non-sigma4 carriers was 1.7 (95% confidence interval [CI]: 1.1-2.6). Four studies were added to the meta-analysis, which included 1,099 patients in total. The OR for delirium in the meta-analysis was 1.6 (95% CI: 0.9-2.7) of sigma4 carriers compared with non-sigma4 carriers.

CONCLUSIONS

This study and meta-analysis suggest an association between delirium and the APOE sigma4 allele.