CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal.

A deep learning transformer model predicts high rates of undiagnosed rare disease in large electronic health systems

It is estimated that as many as 1 in 16 people worldwide suffer from rare diseases. Rare disease patients face difficulty finding diagnosis and treatment for their conditions, including long diagnostic odysseys, multiple incorrect diagnoses, and unavailable or prohibitively expensive treatments. As a result, it is likely that large electronic health record (EHR) systems include high numbers of participants suffering from undiagnosed rare disease. While this has been shown in detail for specific diseases, these studies are expensive and time consuming and have only been feasible to perform for a handful of the thousands of known rare diseases. The bulk of these undiagnosed cases are effectively hidden, with no straightforward way to differentiate them from healthy controls. The ability to access them at scale would enormously expand our capacity to study and develop drugs for rare diseases, adding to tools aimed at increasing availability of study cohorts for rare disease. In this study, we train a deep learning transformer algorithm, RarePT (Rare-Phenotype Prediction Transformer), to impute undiagnosed rare disease from EHR diagnosis codes in 436,407 participants in the UK Biobank and validated on an independent cohort from 3,333,560 individuals from the Mount Sinai Health System. We applied our model to 155 rare diagnosis codes with fewer than 250 cases each in the UK Biobank and predicted participants with elevated risk for each diagnosis, with the number of participants predicted to be at risk ranging from 85 to 22,000 for different diagnoses. These risk predictions are significantly associated with increased mortality for 65% of diagnoses, with disease burden expressed as disability-adjusted life years (DALY) for 73% of diagnoses, and with 72% of available disease-specific diagnostic tests. They are also highly enriched for known rare diagnoses in patients not included in the training set, with an odds ratio (OR) of 48.0 in cross-validation cohorts of the UK Biobank and an OR of 30.6 in the independent Mount Sinai Health System cohort. Most importantly, RarePT successfully screens for undiagnosed patients in 32 rare diseases with available diagnostic tests in the UK Biobank. Using the trained model to estimate the prevalence of undiagnosed disease in the UK Biobank for these 32 rare phenotypes, we find that at least 50% of patients remain undiagnosed for 20 of 32 diseases. These estimates provide empirical evidence of a high prevalence of undiagnosed rare disease, as well as demonstrating the enormous potential benefit of using RarePT to screen for undiagnosed rare disease patients in large electronic health systems.

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.

Phenome-wide Mendelian randomization study of plasma triglyceride levels and 2,600 disease traits

Background: Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems.

Methods: Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2,600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p < 0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels.

Results: Our results identified 7 disease traits reaching Bonferroni-corrected significance in both the discovery (p < 1.92 × 10^-5) and replication analyses (p < 2.26 × 10^-4), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p = 2.47 × 10^-13). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p < 0.05), identifying plasma TG levels as a novel potential risk factor for 9 non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p = 1.17 × 10^-5).

Conclusions: Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies.

Funding: RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).

Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts

BACKGROUND

Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model.

METHODS

In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes-namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae.

FINDINGS

Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94-0·95; sensitivity of 0·94 [0·94-0·95] and specificity of 0·82 [0·81-0·83]) and 0·93 (0·92-0·93; sensitivity of 0·90 [0·89-0·90] and specificity of 0·88 [0·87-0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91-0·91; sensitivity of 0·84 [0·83-0·84] and specificity of 0·83 [0·82-0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0-1·0], 0·2% prevalence; decile 6: 11 [3·9-31], 3·1% prevalence; and decile 10: 56 [20-158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines.

INTERPRETATION

Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals.

FUNDING

National Institutes of Health.

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Population-Based Penetrance of Deleterious Clinical Variants

IMPORTANCE

Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches.

OBJECTIVE

To evaluate the population-based disease risk of clinical variants in known disease predisposition genes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 72 434 individuals with 37 780 clinical variants who were enrolled in the BioMe Biobank from 2007 onwards with follow-up until December 2020 and the UK Biobank from 2006 to 2010 with follow-up until June 2020. Participants had linked exome and electronic health record data, were older than 20 years, and were of diverse ancestral backgrounds.

EXPOSURES

Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants).

MAIN OUTCOMES AND MEASURES

The primary outcome was the disease risk associated with clinical variants. The risk difference (RD) between the prevalence of disease in individuals with a variant allele (penetrance) vs in individuals with a normal allele was measured.

RESULTS

Among 72 434 study participants, 43 395 were from the UK Biobank (mean [SD] age, 57 [8.0] years; 24 065 [55%] women; 2948 [7%] non-European) and 29 039 were from the BioMe Biobank (mean [SD] age, 56 [16] years; 17 355 [60%] women; 19 663 [68%] non-European). Of 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with an RD less than or equal to 0.05. Mean penetrance was 6.9% (95% CI, 6.0%-7.8%) for pathogenic variants and 0.85% (95% CI, 0.76%-0.95%) for benign variants reported in ClinVar (difference, 6.0 [95% CI, 5.6-6.4] percentage points), with a median of 0% for both groups due to large numbers of nonpenetrant variants. Penetrance of pathogenic/loss-of-function variants for late-onset diseases was modified by age: mean penetrance was 10.3% (95% CI, 9.0%-11.6%) in individuals 70 years or older and 8.5% (95% CI, 7.9%-9.1%) in individuals 20 years or older (difference, 1.8 [95% CI, 0.40-3.3] percentage points). Penetrance of pathogenic/loss-of-function variants was heterogeneous even in known disease predisposition genes, including BRCA1 (mean [range], 38% [0%-100%]), BRCA2 (mean [range], 38% [0%-100%]), and PALB2 (mean [range], 26% [0%-100%]).

CONCLUSIONS AND RELEVANCE

In 2 large biobank cohorts, the estimated penetrance of pathogenic/loss-of-function variants was variable but generally low. Further research of population-based penetrance is needed to refine variant interpretation and clinical evaluation of individuals with these variant alleles.

Overcoming constraints on the detection of recessive selection in human genes from population frequency data

The identification of genes that evolve under recessive natural selection is a long-standing goal of population genetics research that has important applications to the discovery of genes associated with disease. We found that commonly used methods to evaluate selective constraint at the gene level are highly sensitive to genes under heterozygous selection but ubiquitously fail to detect recessively evolving genes. Additionally, more sophisticated likelihood-based methods designed to detect recessivity similarly lack power for a human gene of realistic length from current population sample sizes. However, extensive simulations suggested that recessive genes may be detectable in aggregate. Here, we offer a method informed by population genetics simulations designed to detect recessive purifying selection in gene sets. Applying this to empirical gene sets produced significant enrichments for strong recessive selection in genes previously inferred to be under recessive selection in a consanguineous cohort and in genes involved in autosomal recessive monogenic disorders.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

An integrative multiomic network model links lipid metabolism to glucose regulation in coronary artery disease

Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery. By integrating gene expression, genotype, metabolomic, and clinical data, we identify a glucose and lipid determining (GLD) regulatory network showing inverse relationships with lipid and glucose traits. Master regulators of the GLD network also impact lipid and glucose levels in inverse directions. Experimental inhibition of one of the GLD network master regulators, lanosterol synthase (LSS), in mice confirms the inverse relationships to glucose and lipid levels as predicted by our model and provides mechanistic insights.

HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1%) explain less than 10% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

No causal effects of serum urate levels on the risk of chronic kidney disease: A Mendelian randomization study

BACKGROUND

Studies have shown strong positive associations between serum urate (SU) levels and chronic kidney disease (CKD) risk; however, whether the relation is causal remains uncertain. We evaluate whether genetic data are consistent with a causal impact of SU level on the risk of CKD and estimated glomerular filtration rate (eGFR).

METHODS AND FINDINGS

We used Mendelian randomization (MR) methods to evaluate the presence of a causal effect. We used aggregated genome-wide association data (N = 110,347 for SU, N = 69,374 for gout, N = 133,413 for eGFR, N = 117,165 for CKD), electronic-medical-record-linked UK Biobank data (N = 335,212), and population-based cohorts (N = 13,425), all in individuals of European ancestry, for SU levels and CKD. Our MR analysis showed that SU has a causal effect on neither eGFR level nor CKD risk across all MR analyses (all P > 0.05). These null associations contrasted with our epidemiological association findings from the 4 population-based cohorts (change in eGFR level per 1-mg/dl [59.48 μmol/l] increase in SU: -1.99 ml/min/1.73 m2; 95% CI -2.86 to -1.11; P = 8.08 × 10(-6); odds ratio [OR] for CKD: 1.48; 95% CI 1.32 to 1.65; P = 1.52 × 10(-11)). In contrast, the same MR approaches showed that SU has a causal effect on the risk of gout (OR estimates ranging from 3.41 to 6.04 per 1-mg/dl increase in SU, all P < 10-3), which served as a positive control of our approach. Overall, our MR analysis had >99% power to detect a causal effect of SU level on the risk of CKD of the same magnitude as the observed epidemiological association between SU and CKD. Limitations of this study include the lifelong effect of a genetic perturbation not being the same as an acute perturbation, the inability to study non-European populations, and some sample overlap between the datasets used in the study.

CONCLUSIONS

Evidence from our series of causal inference approaches using genetics does not support a causal effect of SU level on eGFR level or CKD risk. Reducing SU levels is unlikely to reduce the risk of CKD development.

Using Full Genomic Information to Predict Disease: Breaking Down the Barriers Between Complex and Mendelian Diseases

While sequence-based genetic tests have long been available for specific loci, especially for Mendelian disease, the rapidly falling costs of genome-wide genotyping arrays, whole-exome sequencing, and whole-genome sequencing are moving us toward a future where full genomic information might inform the prognosis and treatment of a variety of diseases, including complex disease. Similarly, the availability of large populations with full genomic information has enabled new insights about the etiology and genetic architecture of complex disease. Insights from the latest generation of genomic studies suggest that our categorization of diseases as complex may conceal a wide spectrum of genetic architectures and causal mechanisms that ranges from Mendelian forms of complex disease to complex regulatory structures underlying Mendelian disease. Here, we review these insights, along with advances in the prediction of disease risk and outcomes from full genomic information.

A literature review at genome scale: improving clinical variant assessment

PURPOSE

Over 150,000 variants have been reported to cause Mendelian disease in the medical literature. It is still difficult to leverage this knowledge base in clinical practice, as many reports lack strong statistical evidence or may include false associations. Clinical laboratories assess whether these variants (along with newly observed variants that are adjacent to these published ones) underlie clinical disorders.

METHODS

We investigated whether citation data-including journal impact factor and the number of cited variants (NCV) in each gene with published disease associations-can be used to improve variant assessment.

RESULTS

Surprisingly, we found that impact factor is not predictive of pathogenicity, but the NCV score for each gene can provide statistical support for prediction of pathogenicity. When this gene-level citation metric is combined with variant-level evolutionary conservation and structural features, classification accuracy reaches 89.5%. Further, variants identified in clinical exome sequencing cases have higher NCVs than do simulated rare variants from the Exome Aggregation Consortium database within the same set of genes and functional consequences (P < 2.22  ×  10^-16).

CONCLUSION

Aggregate citation data can complement existing variant-based predictive algorithms, and can boost their performance without the need to access and review large numbers of papers. The NCV is a slow-growing metric of scientific knowledge about each gene's association with disease.

When "N of 2" is not enough: integrating statistical and functional data in gene discovery

The expanding use of genomic sequencing promises to improve clinical diagnostics and to drive the discovery of new disease genes. Candidate genes are increasingly being identified through recurrent cases (e.g., two or more independent cases [“N of 2”] in which variants are present in the same gene). These second case hits provide statistical evidence of an association, which may then be combined with functional validation or familial segregation studies to bolster the evidence that a gene is truly causal. Here, we discuss how to integrate different forms of functional evidence with human genetics case and segregation data to improve the significance of new disease–gene associations.

Mitigating false-positive associations in rare disease gene discovery

Clinical sequencing is expanding, but causal variants are still not identified in the majority of cases. These unsolved cases can aid in gene discovery when individuals with similar phenotypes are identified in systems such as the Matchmaker Exchange. We describe risks for gene discovery in this growing set of unsolved cases. In a set of rare disease cases with the same phenotype, it is not difficult to find two individuals with the same phenotype that carry variants in the same gene. We quantify the risk of false-positive association in a cohort of individuals with the same phenotype, using the prior probability of observing a variant in each gene from over 60,000 individuals (Exome Aggregation Consortium). Based on the number of individuals with a genic variant, cohort size, specific gene, and mode of inheritance, we calculate a P value that the match represents a true association. A match in two of 10 patients in MECP2 is statistically significant (P = 0.0014), whereas a match in TTN would not reach significance, as expected (P > 0.999). Finally, we analyze the probability of matching in clinical exome cases to estimate the number of cases needed to identify genes related to different disorders. We offer Rare Disease Match, an online tool to mitigate the uncertainty of false-positive associations.

Excess of Deleterious Mutations around HLA Genes Reveals Evolutionary Cost of Balancing Selection

Deleterious mutations are expected to evolve under negative selection and are usually purged from the population. However, deleterious alleles segregate in the human population and some disease-associated variants are maintained at considerable frequencies. Here, we test the hypothesis that balancing selection may counteract purifying selection in neighboring regions and thus maintain deleterious variants at higher frequency than expected from their detrimental fitness effect. We first show in realistic simulations that balancing selection reduces the density of polymorphic sites surrounding a locus under balancing selection, but at the same time markedly increases the population frequency of the remaining variants, including even substantially deleterious alleles. To test the predictions of our simulations empirically, we then use whole-exome sequencing data from 6,500 human individuals and focus on the most established example for balancing selection in the human genome, the major histocompatibility complex (MHC). Our analysis shows an elevated frequency of putatively deleterious coding variants in nonhuman leukocyte antigen (non-HLA) genes localized in the MHC region. The mean frequency of these variants declined with physical distance from the classical HLA genes, indicating dependency on genetic linkage. These results reveal an indirect cost of the genetic diversity maintained by balancing selection, which has hitherto been perceived as mostly advantageous, and have implications both for the evolution of recombination and also for the epidemiology of various MHC-associated diseases.

Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease

Large genome-wide association studies (GWAS) have identified many genetic loci associated with risk for myocardial infarction (MI) and coronary artery disease (CAD). Concurrently, efforts such as the National Institutes of Health (NIH) Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) Consortium have provided unprecedented data on functional elements of the human genome. In the present study, we systematically investigate the biological link between genetic variants associated with this complex disease and their impacts on gene function. First, we examined the heritability of MI/CAD according to genomic compartments. We observed that single nucleotide polymorphisms (SNPs) residing within nearby regulatory regions show significant polygenicity and contribute between 59–71% of the heritability for MI/CAD. Second, we showed that the polygenicity and heritability explained by these SNPs are enriched in histone modification marks in specific cell types. Third, we found that a statistically higher number of 45 MI/CAD-associated SNPs that have been identified from large-scale GWAS studies reside within certain functional elements of the genome, particularly in active enhancer and promoter regions. Finally, we observed significant heterogeneity of this signal across cell types, with strong signals observed within adipose nuclei, as well as brain and spleen cell types. These results suggest that the genetic etiology of MI/CAD is largely explained by tissue-specific regulatory perturbation within the human genome.

Coronary artery disease (CAD) and its subcomponent, myocardial infarction (MI), are the leading causes of infirmity and death worldwide. Large-scale genetic association studies have identified many genetic markers associated with CAD and MI. However, it has been difficult to determine the precise functional effects of these markers. Furthermore, it is unknown which cell types are biologically important in the development of MI/CAD. By intersecting findings from large-scale genetic association studies with functional genomic annotations, we show that genetic markers located in genomic regions that regulate expression of genes make up a large proportion of the genetic risk of MI/CAD. Furthermore, we show that this effect is particularly strong in certain tissues, including adipose, brain and spleen tissue. These results highlight the role of tissue-specific regulatory mechanisms in the genetic etiology of MI/CAD.

Widespread macromolecular interaction perturbations in human genetic disorders

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

Large numbers of genetic variants considered to be pathogenic are common in asymptomatic individuals

It is now affordable to order clinically interpreted whole-genome sequence reports from clinical laboratories. One major component of these reports is derived from the knowledge base of previously identified pathogenic variants, including research articles, locus-specific, and other databases. While over 150,000 such pathogenic variants have been identified, many of these were originally discovered in small cohort studies of affected individuals, so their applicability to asymptomatic populations is unclear. We analyzed the prevalence of a large set of pathogenic variants from the medical and scientific literature in a large set of asymptomatic individuals (N = 1,092) and found 8.5% of these pathogenic variants in at least one individual. In the average individual in the 1000 Genomes Project, previously identified pathogenic variants occur on average 294 times (σ = 25.5) in homozygous form and 942 times (σ = 68.2) in heterozygous form. We also find that many of these pathogenic variants are frequently occurring: there are 3,744 variants with minor allele frequency (MAF) ≥ 0.01 (4.6%) and 2,837 variants with MAF ≥ 0.05 (3.5%). This indicates that many of these variants may be erroneous findings or have lower penetrance than previously expected.

Predicting functional effect of human missense mutations using PolyPhen-2

PolyPhen-2 (Polymorphism Phenotyping v2), available as software and via a Web server, predicts the possible impact of amino acid substitutions on the stability and function of human proteins using structural and comparative evolutionary considerations. It performs functional annotation of single-nucleotide polymorphisms (SNPs), maps coding SNPs to gene transcripts, extracts protein sequence annotations and structural attributes, and builds conservation profiles. It then estimates the probability of the missense mutation being damaging based on a combination of all these properties. PolyPhen-2 features include a high-quality multiple protein sequence alignment pipeline and a prediction method employing machine-learning classification. The software also integrates the UCSC Genome Browser's human genome annotations and MultiZ multiple alignments of vertebrate genomes with the human genome. PolyPhen-2 is capable of analyzing large volumes of data produced by next-generation sequencing projects, thanks to built-in support for high-performance computing environments like Grid Engine and Platform LSF.

Development and validation of a computational method for assessment of missense variants in hypertrophic cardiomyopathy

Assessing the significance of novel genetic variants revealed by DNA sequencing is a major challenge to the integration of genomic techniques with medical practice. Many variants remain difficult to classify by traditional genetic methods. Computational methods have been developed that could contribute to classifying these variants, but they have not been properly validated and are generally not considered mature enough to be used effectively in a clinical setting. We developed a computational method for predicting the effects of missense variants detected in patients with hypertrophic cardiomyopathy (HCM). We used a curated clinical data set of 74 missense variants in six genes associated with HCM to train and validate an automated predictor. The predictor is based on support vector regression and uses phylogenetic and structural features specific to genes involved in HCM. Ten-fold cross validation estimated our predictor's sensitivity at 94% (95% confidence interval: 83%-98%) and specificity at 89% (95% confidence interval: 72%-100%). This corresponds to an odds ratio of 10 for a prediction of pathogenic (95% confidence interval: 4.0-infinity), or an odds ratio of 9.9 for a prediction of benign (95% confidence interval: 4.6-21). Coverage (proportion of variants for which a prediction was made) was 57% (95% confidence interval: 49%-64%). This performance exceeds that of existing methods that are not specifically designed for HCM. The accuracy of this predictor provides support for the clinical use of automated predictions alongside family segregation and population frequency data in the interpretation of new missense variants and suggests future development of similar tools for other diseases.

Human allelic variation: perspective from protein function, structure, and evolution

It is widely anticipated that the coming year will be marked by the complete characterization of DNA sequence of protein-coding regions of thousands of human individuals. A number of existing computational methods use comparative protein sequence analysis and analysis of protein structure to predict the functional effect of coding human alleles. Functional and structural analysis of coding allelic variants can inform various aspects of research on human genetic variation. In population and evolutionary genetics it helps estimate the strength of purifying selection against deleterious missense mutations and study the imprint of demographic history on deleterious genetic variation. In medical genetics it may assist in the interpretation of uncharacterized mutations in genes involved in monogenic and oligogenic diseases. It has a potential to facilitate medical sequencing studies searching for genes underlying Mendelian diseases or harboring rare alleles involved in complex traits.

Parameterization of peptide 13C carbonyl chemical shielding anisotropy in molecular dynamics simulations

NMR chemical shielding anisotropy (CSA) relaxation is an important tool in the study of dynamical processes in proteins and nucleic acids in solution. Herein, we investigate how dynamical variations in local geometry affect the chemical shielding anisotropy relaxation of the carbonyl carbon nucleus, using the following protocol: 1) Using density functional theory, the carbonyl (13)C' CSA is computed for 103 conformations of the model peptide group N-methylacetamide (NMA). 2) The variations in computed (13)C' CSA parameters are fitted against quadratic hypersurfaces containing cross terms between the variables. 3) The predictive quality of the CSA hypersurfaces is validated by comparing the predicted and de novo calculated (13)C' CSAs for 20 molecular dynamics snapshots. 4) The CSA fluctuations and their autocorrelation and cross correlation functions due to bond-length and bond-angle distortions are predicted for a chemistry Harvard molecular mechanics (CHARMM) molecular dynamics trajectory of Ca(2+)-saturated calmodulin and GB3 from the hypersurfaces, as well as for a molecular dynamics (MD) simulation of an NMA trimer using a quantum mechanically correct forcefield. We find that the fluctuations can be represented by a 0.93 scaling factor of the CSA tensor for both R(1) and R(2) relaxations for residues in helix, coil, and sheet alike. This result is important, as it establishes that (13)C' relaxation is a valid tool for measurement of interesting dynamical events in proteins.

Topiramate vs placebo in painful diabetic neuropathy: analgesic and metabolic effects

BACKGROUND

Using identical methods, three simultaneous placebo-controlled trials of topiramate for painful diabetic neuropathy (PDN) did not reach significance. This independent yet concurrent placebo-controlled trial used different methods to assess topiramate efficacy and tolerability in PDN.

METHODS

This 12-week, multicenter, randomized, double-blind trial included 323 subjects with PDN and pain visual analog (PVA) score of at least 40 on a scale from 0 (no pain) to 100 (worst possible pain). Topiramate (n = 214) or placebo (n = 109) was titrated to 400 mg daily or maximum tolerated dose. Short-acting rescue analgesics were permitted only during the first 6 weeks.

RESULTS

Baseline characteristics were comparable between groups except for mean body weight (topiramate, 101.4 kg; placebo, 95.7 kg; p = 0.028). Twelve weeks of topiramate treatment reduced PVA scale score (from 68.0 to 46.2 mm) more effectively than placebo (from 69.1 to 54.0 mm; p = 0.038). Fifty percent of topiramate-treated subjects and 34% of placebo-treated subjects responded to treatment, defined as >30% reduction in PVA scale score (p = 0.004). Topiramate monotherapy also reduced worst pain intensity (p = 0.003 vs placebo) and sleep disruption (p = 0.020 vs placebo). Diarrhea, loss of appetite, and somnolence were the most commonly reported adverse events in the topiramate group. Topiramate reduced body weight (-2.6 vs +0.2 kg for placebo; p < 0.001) without disrupting glycemic control.

CONCLUSIONS

Topiramate monotherapy reduced pain and body weight more effectively than placebo in patients with painful diabetic neuropathy.

Preliminary results of a pilot program on depression in patients with congestive heart failure

While the sample size is too small to warrant conclusions, these preliminary results suggest that assessment of depression would be worthwhile for patients diagnosed with congestive heart failure. 32 out of 54 patients with congestive heart failure scored positive for depression. When psychiatric treatment was given, there was a decrease in depressive symptoms for four of the six patients at the 6-mo, retest. A decrease in depressive symptoms was found for two of the six untreated patients, and the remaining four patients had worse scores on the Zung Depression Inventory. Primary care physicians, who typically meet with such patients regularly, are encouraged to screen for depression, as their clinical assessments in this study were associated with scores on the Zung Depression Inventory. These observations support a full scale investigation with a much larger sample size and a requisite medical cost comparison.

Detection of Lawsonia intracellularis in swine using polymerase chain reaction methodology

The polymerase chain reaction (PCR) was evaluated for its usefulness as a diagnostic tool to detect Lawsonia (ileal symbiont) intracellularis. Porcine ilea were collected from swine cases submitted to the Iowa State University Veterinary Diagnostic Laboratory between December 1, 1994, and June 30, 1995. Sampling was random, with no regard to health status. There were 621 ileum scrapings evaluated using the PCR technique. Thirty-five of the samples were positive, either by PCR or conventional diagnostic methods such as histology and Warthin-Starry silver stain. These 35 samples were further evaluated by Warthin-Starry silver stain and indirect immunofluorescent antibody test (IFAT) to confirm the presence of L. intracellularis in the tissue sections. Of the 26 samples positive by PCR, 22 were positive by IFAT. Sixteen of the 22 were also positive when stained with Warthin-Starry and evaluated microscopically for typical bacteria. Nine of the original samples were negative by all 3 techniques. PCR appears more sensitive and specific for L. intracellularis detection than Warthin-Starry stain and IFAT. This study provides evidence that PCR may be useful as a reference standard for the detection of L. intracellularis. PCR may be an appropriate monitoring tool for swine herds because it is a rapid procedure that could be applied to batch testing. Although the test is currently too laborious and expensive for routine diagnostic use, there may be situations in which it is justified because of the advantages of greater sensitivity and specificity.

Evaluation of antemortem polymerase chain reaction and serologic methods for detection of Lawsonia intracellularis-exposed pigs

OBJECTIVE

To evaluate polymerase chain reaction (PCR) for detection of Lawsonia intracellularis DNA in feces and an indirect fluorescent antibody test (IFAT) for detecting serum IgG antibodies in pigs exposed to L intracellularis.

ANIMALS

15 seven-week-old pigs and 42 three-week-old pigs.

PROCEDURE

During 3 experiments, 23 pigs were inoculated with a pure culture of L intracellularis, 31 pigs served as noninoculated controls, and 3 pigs were used as sentinels. Fecal shedding of L intracellularis was monitored by use of PCR analysis at 7-day intervals. At euthanasia, the ileum was obtained for PCR and histologic analyses. Serum was obtained at 7-day intervals for use in the IFAT.

RESULTS

Polymerase chain reaction analysis detected L intracellularis DNA in the feces of 39% of the inoculated pigs; by postinoculation days 21 to 28, 90% of inoculated pigs developed IgG antibodies detected by IFAT. Neither L intracellularis DNA nor IgG antibodies were detected in any of the noninoculated control pigs at euthanasia. Sera from pigs inoculated with enteric pathogens other than L intracellularis did not contain detectable antibodies that reacted with L intracellularis by use of the IFAT.

CONCLUSION

The IFAT for L intracellularis IgG antibody detection appeared to be a more sensitive antemortem test for detecting pigs experimentally infected with L intracellularis than was a PCR method for direct detection of the organism in the feces.

CLINICAL RELEVANCE

Not all animals that are infected with L intracellularis shed the organism in feces at detectable amounts.

Effect of light-tip distance on polymerization of resin composite

The inability to place a light tip in close approximation to a resin restoration may affect the resultant polymerization and clinical durability of the restoration. This research measured light intensity at the surface of a resin composite, as well as 2 mm within its bulk, as the tip-to-resin distance is moved from 0 to 10 mm. The polymerization of the resin composite at both locations was measured for the various tip distances using exposure durations of 10, 20, 40, and 60 seconds. Light intensity did not decrease with the inverse square of the tip distance. The polymerization on the surface was greatly dependent upon the duration of exposure. The extent of polymerization 2 mm below the surface was still dependent primarily upon exposure duration, but intensity had a significant affect. For exposure durations of 10, 20, and 40 seconds, a tip distance greater than 4 mm demonstrated a significant decrease in resin polymerization 2 mm below the resin composite surface.