Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

ABSTRACT

The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here, we performed a retrospective EHR study based on a cohort of 3,652 individuals with self-reported sleep behaviors documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m²) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was 1 hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p < 10^?12). Sleep duration varied considerably as a function of age and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q < 0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n = 555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p < 0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.     

Combinations of Susceptibility Genes Are Associated with Higher Risk for Multiple Sclerosis and Imply Disease Course Specificity

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that predominantly affects young adults. The genetic contributions to this multifactorial disease were underscored by a genome wide association study (GWAS) conducted by the International Multiple Sclerosis Genetic Consortium in a multinational cohort prompting the discovery of 57 non-MHC MS-associated common genetic variants. Hitherto, few of these newly reported variants have been replicated in larger independent patient cohorts. We genotyped a cohort of 1033 MS patients and 644 healthy controls with a consistent genetic background for the 57 non-MHC variants reported to be associated with MS by the first large GWAS as well as the HLA DRB1*1501 tagging SNP rs3135388. We robustly replicated three of the 57 non-MHC reported MS-associated single nucleotide polymorphisms (SNPs). In addition, our study revealed several genotype-genotype combinations with an evidently higher degree of disease association than the genotypes of the single SNPs. We further correlated well-defined clinical phenotypes, i.e. ataxia, visual impairment due to optic neuritis and paresis with single SNPs and genotype combinations, and identified several associations. The results may open new avenues for clinical implications of the MS associated genetic variants reported from large GWAS.     

Rare Genomic Structural Variants in Complex Disease: Lessons from the Replication of Associations with Obesity

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the ‘missing heritability’. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10⁻⁴ (95% confidence interval [9.6×10⁻⁵–3.1×10⁻⁴]); accounts overall for 0.5% [0.19%–0.82%] of severe childhood obesity cases (P = 3.8×10⁻¹⁰; odds ratio = 25.0 [9.9–60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m⁻² [1.8–10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.     

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.     

Evaluating human autosomal loci for sexually antagonistic viability selection in two large biobanks

Sex and sexual differentiation are pervasive across the tree of life. Because females and males often have substantially different functional requirements, we expect selection to differ between the sexes. Recent studies in diverse species, including humans, suggest that sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci. However, theory and population-level simulations indicate that sex-specific differences in viability would need to be very large to produce and maintain reported levels of between-sex allelic differentiation. We address this contradiction between theoretical predictions and empirical observations by evaluating evidence for sexually antagonistic viability selection on autosomal loci in humans using the largest cohort to date (UK Biobank, n = 487,999) along with a second large, independent cohort (BioVU, n = 93,864). We performed association tests between genetically ascertained sex and autosomal loci. Although we found dozens of genome-wide significant associations, none replicated across cohorts. Moreover, closer inspection revealed that all associations are likely due to cross-hybridization with sex chromosome regions during genotyping. We report loci with potential for mis-hybridization found on commonly used genotyping platforms that should be carefully considered in future genetic studies of sex-specific differences. Despite being well powered to detect allele frequency differences of up to 0.8% between the sexes, we do not detect clear evidence for this signature of sexually antagonistic viability selection on autosomal variation. These findings suggest a lack of strong ongoing sexually antagonistic viability selection acting on single locus autosomal variation in humans.     

Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease

Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%-7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.     

Non-replication and inconsistency in the genome-wide association setting

Non-replication and inconsistency had been common features in the search for common variants of candidate genes affecting the risk of complex diseases. They may continue to require attention in the current era, when massive hypothesis-free testing of genetic variants is feasible. An empirical evaluation of the early experience with genome-wide association (GWA) studies suggests several examples where proposed associations have failed to be replicated by subsequent investigations. Non-replication and inconsistency is defined here in the framework of cumulative meta-analysis. Ideally, associations exist, GWA finds them, and subsequent investigations should replicate them. However, a number of other possibilities need to be considered. No common genetic variants may associate with the phenotype of interest and GWA may find nothing; or associations may exist, but GWA may miss them. Associations that do not exist may be falsely selected by the GWA and subsequent studies may appropriately refute them or falsely replicate them. Finally, GWA may find true associations that are nevertheless falsely non-replicated in the subsequent studies; or associations may be genuinely inconsistent across study populations. A list of options is presented for consideration in each of these scenarios.     

The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits

The integrated analysis of genotypic and expression data for association with complex traits could identify novel genetic pathways involved in complex traits. We profiled 19,573 expression probes in Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from 299 twins and correlated these with 44 quantitative traits (QTs). For 939 expressed probes correlating with more than one QT, we investigated the presence of eQTL associations in three datasets of 57 CEU HapMap founders and 86 unrelated twins. Genome-wide association analysis of these probes with 2.2 m SNPs revealed 131 potential eQTLs (1,989 eQTL SNPs) overlapping between the HapMap datasets, five of which were in cis (58 eQTL SNPs). We then tested 535 SNPs tagging the eQTL SNPs, for association with the relevant QT in 2,905 twins. We identified nine potential SNP-QT associations (P<0.01) but none significantly replicated in five large consortia of 1,097-16,129 subjects. We also failed to replicate previous reported eQTL associations with body mass index, plasma low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides levels derived from lymphocytes, adipose and liver tissue. Our results and additional power calculations suggest that proponents may have been overoptimistic in the power of LCLs in eQTL approaches to elucidate regulatory genetic effects on complex traits using the small datasets generated to date. Nevertheless, larger tissue-specific expression data sets relevant to specific traits are becoming available, and should enable the adoption of similar integrated analyses in the near future.     

Genome-wide association analysis of circulating vitamin D levels in children with asthma

Vitamin D deficiency is becoming more apparent in many populations. Genetic factors may play a role in the maintenance of vitamin D levels. The objective of this study was to perform a genome-wide analysis (GWAS) of vitamin D levels, including replication of prior GWAS results. We measured 25-hydroxyvitamin D (25(OH)D) levels in serum collected at the time of enrollment and at year 4 in 572 Caucasian children with asthma, who were part of a multi-center clinical trial, the Childhood Asthma Management Program. Replication was performed in a second cohort of 592 asthmatics from Costa Rica and a third cohort of 516 Puerto Rican asthmatics. In addition, we attempted replication of three SNPs that were previously identified in a large GWAS of Caucasian individuals. The setting included data from a clinical trial of childhood asthmatics and two cohorts of asthmatics recruited for genetic studies of asthma. The main outcome measure was circulating 25(OH)D levels. The 25(OH)D levels at the two time-points were only modestly correlated with each other (intraclass correlation coefficient?=?0.33) in the CAMP population. We identified SNPs that were nominally associated with 25(OH)D levels at two time-points in CAMP, and replicated four SNPs in the Costa Rican cohort: rs11002969, rs163221, rs1678849, and rs4864976. However, these SNPs were not significantly associated with 25(OH)D levels in a third population of Puerto Rican asthmatics. We were able to replicate the SNP with the strongest effect, previously reported in a large GWAS: rs2282679 (GC), and we were able to replicate another SNP, rs10741657 (CYP2R1), to a lesser degree. We were able to replicate two of three prior significant findings in a GWAS of 25(OH)D levels. Other SNPs may be additionally associated with 25(OH)D levels in certain populations.     

Genome-wide association of bipolar disorder suggests an enrichment of replicable associations in regions near genes

Although a highly heritable and disabling disease, bipolar disorder''s (BD) genetic variants have been challenging to identify. We present new genotype data for 1,190 cases and 401 controls and perform a genome-wide association study including additional samples for a total of 2,191 cases and 1,434 controls. We do not detect genome-wide significant associations for individual loci; however, across all SNPs, we show an association between the power to detect effects calculated from a previous genome-wide association study and evidence for replication (P = 1.5×10⁻⁷). To demonstrate that this result is not likely to be a false positive, we analyze replication rates in a large meta-analysis of height and show that, in a large enough study, associations replicate as a function of power, approaching a linear relationship. Within BD, SNPs near exons exhibit a greater probability of replication, supporting an enrichment of reproducible associations near functional regions of genes. These results indicate that there is likely common genetic variation associated with BD near exons (±10 kb) that could be identified in larger studies and, further, provide a framework for assessing the potential for replication when combining results from multiple studies.     

A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data

Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into “ancestry groups” and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.     

Predicting disease associations via biological network analysis

Background

Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.

Results

We took advantage of diverse biological data including disease-gene associations and a large-scale molecular network to gain novel insights into disease relationships. We analysed and compared four publicly available disease-gene association datasets, then applied three disease similarity measures, namely annotation-based measure, function-based measure and topology-based measure, to estimate the similarity scores between diseases. We systematically evaluated disease associations obtained by these measures against a statistical measure of comorbidity which was derived from a large number of medical patient records. Our results show that the correlation between our similarity measures and comorbidity scores is substantially higher than expected at random, confirming that our similarity measures are able to recover comorbidity associations. We also demonstrated that our predicted disease associations correlated with disease associations generated from genome-wide association studies significantly higher than expected at random. Furthermore, we evaluated our predicted disease associations via mining the literature on PubMed, and presented case studies to demonstrate how these novel disease associations can be used to enhance our current knowledge of disease relationships.

Conclusions

We present three similarity measures for predicting disease associations. The strong correlation between our predictions and known disease associations demonstrates the ability of our measures to provide novel insights into disease relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-304) contains supplementary material, which is available to authorized users.     

High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants

Genome-wide association studies (GWAS) have detected many disease associations. However, the reported variants tend to explain small fractions of risk, and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease. Studying the degree of sharing of disease-associated variants across populations can help in solving these issues. We present a comprehensive survey of GWAS replicability across 28 diseases. Most loci and SNPs discovered in Europeans for these conditions have been extensively replicated using peoples of European and East Asian ancestry, while the replication with individuals of African ancestry is much less common. We found a strong and significant correlation of Odds Ratios across Europeans and East Asians, indicating that underlying causal variants are common and shared between the two ancestries. Moreover, SNPs that failed to replicate in East Asians map into genomic regions where Linkage Disequilibrium patterns differ significantly between populations. Finally, we observed that GWAS with larger sample sizes have detected variants with weaker effects rather than with lower frequencies. Our results indicate that most GWAS results are due to common variants. In addition, the sharing of disease alleles and the high correlation in their effect sizes suggest that most of the underlying causal variants are shared between Europeans and East Asians and that they tend to map close to the associated marker SNPs.     

Assessing and managing risk when sharing aggregate genetic variant data

Access to genetic data across studies is an important aspect of identifying new genetic associations through genome-wide association studies (GWASs). Meta-analysis across multiple GWASs with combined cohort sizes of tens of thousands of individuals often uncovers many more genome-wide associated loci than the original individual studies; this emphasizes the importance of tools and mechanisms for data sharing. However, even sharing summary-level data, such as allele frequencies, inherently carries some degree of privacy risk to study participants. Here we discuss mechanisms and resources for sharing data from GWASs, particularly focusing on approaches for assessing and quantifying the privacy risks to participants that result from the sharing of summary-level data.     

Empirical evaluation of cytonuclear models incorporating genetic drift and tests for neutrality of mtDNA variants: data from experimental Gambusia hybrid zones

Statistical tests of genetic drift and of the neutrality of mtDNA are presented using empirical time‐series data on multi‐generational changes in cytonuclear disequilibria within replicated experimental hybrid populations of two species of live‐bearing Poeciliid fishes (Gambusia holbrooki and G.affinis) which were monitored over a period of two years (three generations). Cytonuclear disequilibria D and D (which measure departures from random associations of cytoplasmic and nuclear genotypes) over the three generations of the experiment were non‐zero for all replicate populations. For each of five nuclear loci, the observed measures of D and D were highly concordant between replicates during each generation. Significant departures from expectations were observed after one and two generations. A statistical measure of goodness of fit of observed changes in cytonuclear disequilibria (and implicitly of the neutrality of the mtDNA markers) was calculated for each nuclear locus. When the results for the replicates were combined into an overall test of neutrality, the fit to the random union of zygotes (RUZ) model was rejected for four of the five nuclear loci (P < 0.05). A simple genetic drift model does not explain the temporal changes in composite cytonuclear genotypic frequencies. Frequencies of parental G. holbrooki mitochondrial alleles and nuclear genotypes exceeded expected values during most time periods, implying some selective advantage of offspring produced by G. holbrooki females. Expansion of cytonuclear models to explicitly address questions of genetic drift and neutrality have general relevance to studies of natural populations. This revised version was published online in July 2006 with corrections to the Cover Date.     

Impact of scoring error and reproducibility RAPD data on RAPD based estimates of genetic distance

RAPD band reproducibility and scoring error were evaluated for RAPDs generated by 50 RAPD primers among ten snap bean (Phaseolus vulgaris L.) genotypes. Genetic distances based on different sets of RAPD bands were compared to evaluate the impact of scoring error, reproducibility, and differences in relative amplification strength on the reproducibility of RAPD based genetic distance estimates. The measured RAPD data scoring error was 2%. Reproducibility, expressed as the percentage of RAPD bands scored that are also scored in replicate data, was 76%. The results indicate that the probability of a scored RAPD band being scored in replicate data is strongly dependent on the uniformity of amplification conditions between experiments, as well as the relative amplification strength of the RAPD band. Significant improvement in the reproducibility of scored bands and some reduction in scoring error was achieved by reducing differences in reaction conditions between replicates. Observed primer variability for the reproducibility of scored RAPDs may also facilitate the selection of primers, resulting in dramatic improvements in the reproducibility of RAPD data used in germplasm studies. Variance of genetic distances across replicates due to sampling error was found to be more than six times greater than that due to scoring error for a set of 192 RAPD bands. Genetic distance matrices computed from the RAPD bands scored in replicated data and RAPD bands that failed to be scored in replicated data were not significantly different. Differences in the ethidium bromide staining intensity of RAPD bands were not associated with significant differences in resulting genetic distance matrices. The assumption of sampling error as the only source of error was sufficient to account for the observed variation in genetic distance estimates across independent sets of RAPD bands.     

On the replication of genetic associations: timing can be everything!

The failure of researchers to replicate genetic-association findings is most commonly attributed to insufficient statistical power, population stratification, or various forms of between-study heterogeneity or environmental influences.(1) Here, we illustrate another potential cause for nonreplications that has so far not received much attention in the literature. We illustrate that the strength of a genetic effect can vary by age, causing "age-varying associations." If not taken into account during the design and the analysis of a study, age-varying genetic associations can cause nonreplication. By using the 100K SNP scan of the Framingham Heart Study, we identified an age-varying association between a SNP in ROBO1 and obesity and hypothesized an age-gene interaction. This finding was followed up in eight independent samples comprising 13,584 individuals. The association was replicated in five of the eight studies, showing an age-dependent relationship (one-sided combined p = 3.92 x 10(-9), combined p value from pediatric cohorts = 2.21 x 10(-8), combined p value from adult cohorts = 0.00422). Furthermore, this study illustrates that it is difficult for cross-sectional study designs to detect age-varying associations. If the specifics of age- or time-varying genetic effects are not considered in the selection of both the follow-up samples and in the statistical analysis, important genetic associations may be missed.