Glycoprotein Acetyls Is a Novel Biomarker Predicting Cardiovascular Complications in Rheumatoid Arthritis.

The relationship between rheumatoid arthritis (RA) and early onset atherosclerosis is well depicted, each with an important inflammatory component. Glycoprotein acetyls (GlycA), a novel biomarker of inflammation, may play a role in the manifestation of these two inflammatory conditions. The present study examined a potential mediating role of GlycA within the RA-atherosclerosis relationship to determine whether it accounts for the excess risk of cardiovascular disease over that posed by lipid risk factors. The UK Biobank dataset was acquired to establish associations among RA, atherosclerosis, GlycA, and major lipid factors: total cholesterol (TC), high- and low-density lipoprotein (HDL, LDL) cholesterol, and triglycerides (TGs). Genome-wide association study summary statistics were collected from various resources to perform genetic analyses. Causality among variables was tested using Mendelian Randomization (MR) analysis. Genes of interest were identified using colocalization analysis and gene enrichment analysis. MR results appeared to indicate that the genetic relationship between GlycA and RA and also between RA and atherosclerosis was explained by horizontal pleiotropy (p-value = 0.001 and <0.001, respectively), while GlycA may causally predict atherosclerosis (p-value = 0.017). Colocalization analysis revealed several functionally relevant genes shared between GlycA and all the variables assessed. Two loci were apparent in all relationships tested and included the HLA region as well as SLC22A1. GlycA appears to mediate the RA-atherosclerosis relationship through several possible pathways. GlycA, although pleiotropically related to RA, appears to causally predict atherosclerosis. Thus, GlycA is suggested as a significant factor in the etiology of atherosclerosis development in RA.

Analysis of the Associations of Measurements of Body Composition and Inflammatory Factors with Cardiovascular Disease and Its Comorbidities in a Community-Based Study.

The associations of cardiovascular disease (CVD) with comorbidities and biochemical and body composition measurements are repeatedly described but have not been studied simultaneously. In the present cross-sectional study, information on CVD and comorbidities [type 2 diabetes mellitus (T2DM), hypertension (HTN), and hyperlipidemia (HDL)], body composition, levels of soluble markers, and other measures were collected from 1079 individuals. When we examined the association of each comorbidity and CVD, controlling for other comorbidities, we observed a clear pattern of the comorbidity-related specific associations with tested covariates. For example, T2DM was significantly associated with GDF-15 levels and the leptin/adiponectin (L/A) ratio independently of two other comorbidities; HTN, similarly, was independently associated with extracellular water (ECW) levels, L/A ratio, and age; and HDL was independently related to age only. CVD showed very strong independent associations with each of the comorbidities, being associated most strongly with HTN (OR = 10.89, 6.46-18.38) but also with HDL (2.49, 1.43-4.33) and T2DM (1.93, 1.12-3.33). An additive Bayesian network analysis suggests that all three comorbidities, particularly HTN, GDF-15 levels, and ECW content, likely have a main role in the risk of CVD development. Other factors, L/A ratio, lymphocyte count, and the systemic inflammation response index, are likely indirectly related to CVD, acting through the comorbidities and ECW.

Relationships between Circulating Biomarkers and Body Composition Parameters in Patients with Metabolic Syndrome: A Community-Based Study.

Metabolic syndrome (MetS) is a complex disease involving multiple physiological, biochemical, and metabolic abnormalities. The search for reliable biomarkers may help to better elucidate its pathogenesis and develop new preventive and therapeutic strategies. In the present population-based study, we looked for biomarkers of MetS among obesity- and inflammation-related circulating factors and body composition parameters in 1079 individuals (with age range between 18 and 80) belonging to an ethnically homogeneous population. Plasma levels of soluble markers were measured by using ELISA. Body composition parameters were assessed using bioimpedance analysis (BIA). Statistical analysis, including mixed-effects regression, with MetS as a dependent variable, revealed that the most significant independent variables were mainly adipose tissue-related phenotypes, including fat mass/weight (FM/WT) [OR (95% CI)], 2.77 (2.01-3.81); leptin/adiponectin ratio (L/A ratio), 1.50 (1.23-1.83); growth and differentiation factor 15 (GDF-15) levels, 1.32 (1.08-1.62); inflammatory markers, specifically monocyte to high-density lipoprotein cholesterol ratio (MHR), 2.53 (2.00-3.15), and a few others. Additive Bayesian network modeling suggests that age, sex, MHR, and FM/WT are directly associated with MetS and probably affect its manifestation. Additionally, MetS may be causing the GDF-15 and L/A ratio. Our novel findings suggest the existence of complex, age-related, and possibly hierarchical relationships between MetS and factors associated with obesity.

Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source.

BACKGROUND: Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources. METHODS: We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus. RESULTS: Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa. CONCLUSIONS: Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens.

Antibiotics are drugs that kill some bacteria. Antibiotic resistant bacteria are bacteria that continue to grow despite the presence of an antibiotic drug. These bacteria are a major problem in healthcare, particularly if the bacteria are resistant to multiple drugs. Here, we study bacteria that are resistant to several antibiotics that are present in patients in hospital. We find that patterns of cross-resistance differ between the location bacteria were sampled from, such as blood or urine. Our results highlight the importance of considering sample sources when assessing the likelihood that bacteria is resistant to multiple antibiotics. The information and methods described in our study should enable further analysis and prediction of the presence of cross-resistant bacteria, enabling appropriate antibiotic treatments to be used.

Insights into the pleiotropic relationships between chronic back pain and inflammation-related musculoskeletal conditions: rheumatoid arthritis and osteoporotic abnormalities.

ABSTRACT: The ageing process includes the development of debilitating musculoskeletal (MSK) conditions, including chronic back pain (CBP), rheumatoid arthritis (RA), and osteoporosis (OP). The mechanisms involved in the genetic-epidemiological relationships between these MSK phenotypes are controversial and limited and thus require clarification, in particular, between CBP and the other MSK phenotypes. A cross-sectional statistical analysis was conducted using Europeans from the UK Biobank data collection, including 73,794 CBP, 4883 RA, and 7153 OP cases as well as 242,216 calcaneus bone mineral density scores. C-reactive protein (CRP) was measured for 402,165 subjects in this sample. Genetic correlations were assessed to evaluate shared genetic background between traits. Mendelian randomization was performed to assess a causal relationship between CBP and RA and OP along with other risk factors, such as CRP. Colocalization analysis was conducted to identify shared pleiotropic regions between the examined traits. Bayesian modelling was performed to determine a potential pathway that may explain the interrelationships among these traits. Mendelian randomization analyses revealed that CRP causally predicts CBP only (ß = 0.183, 95% CI = 0.077-0.290, P -value = 0.001). Horizontally pleiotropy appeared to explain the relationship between CBP and RA and OP. Through colocalization analysis, several genomic regions emerged describing common genetic influences between CBP and its proposed risk factors, including HLA-DQA1/HLA-DQB1, APOE , SOX5, and MYH7B as well as Histone 1 genes. We speculate that among other factors, CBP and its MSK comorbidities may arise from common inflammatory mechanisms. Colocalized identified genes may aid in advancing or improving the mode of treatment in patients with CBP.

Exploring potential shared genetic influences between rheumatoid arthritis and blood lipid levels.

BACKGROUND AND AIMS: The association between rheumatoid arthritis (RA) and blood lipid levels has often been described as paradoxical, despite the strong association between RA and cardiovascular disease (CVD) risk. We aimed to clarify the genetic architecture that would explain the relationship between RA and blood-lipid levels, while considering inflammation as measured by C-reactive protein (CRP). METHODS: Genome-wide association study (GWAS) summary statistics were collected from the CHARGE Consortium and Global Lipids Genetics Consortium. Blood-lipid levels includes HDL-C, LDL-C, triglycerides (TG), and total cholesterol (TC). Causality was examined by assessing Mendelian Randomization (MR) analysis. Pleiotropy, the identification of shared causal variants between traits, was assessed by conducting colocalization analyses. RESULTS: Using the MR Egger method, RA did not appear to causally predict alterations in lipid factors, rather the MR Egger intercept revealed that the genetic relationship between RA and HDL-C, LDL-C and TC may be explained by horizontal pleiotropy (p=0.003, 0.006, and 0.018, respectively). MR was suggestive of a horizontally pleiotropic relationship between CRP and lipid factors, while a causal relationship could not be ruled out. Recurring genes arising from shared causal genetic variants between RA and varying lipid factors included NAT2/PSD3, FADS2/FADS1, SH2B3, and YDJC. CONCLUSIONS: Horizontal pleiotropy appears to explain the genetic relationship between RA and blood-lipid levels. In addition, blood-lipid levels appear to suggest a horizontally pleiotropic relationship to CRP, if not mediated through RA as well. Consideration of the pleiotropic genes between RA and blood lipid levels may aid in enhancing diagnostic means to predict CVD.

Genome-wide association meta-analysis identifies 48 risk variants and highlights the role of the stria vascularis in hearing loss.

Hearing loss is one of the top contributors to years lived with disability and is a risk factor for dementia. Molecular evidence on the cellular origins of hearing loss in humans is growing. Here, we performed a genome-wide association meta-analysis of clinically diagnosed and self-reported hearing impairment on 723,266 individuals and identified 48 significant loci, 10 of which are novel. A large proportion of associations comprised missense variants, half of which lie within known familial hearing loss loci. We used single-cell RNA-sequencing data from mouse cochlea and brain and mapped common-variant genomic results to spindle, root, and basal cells from the stria vascularis, a structure in the cochlea necessary for normal hearing. Our findings indicate the importance of the stria vascularis in the mechanism of hearing impairment, providing future paths for developing targets for therapeutic intervention in hearing loss.

Genetic and environmental correlational structure among metabolic syndrome endophenotypes.

Metabolic syndrome (MetS) is diagnosed by the presence of high scores on three or more metabolic traits, including systolic and diastolic blood pressure (SBP, DBP), glucose and insulin levels, cholesterol and triglyceride (TG) levels, and central obesity. A diagnosis of MetS is associated with increased risk of cardiovascular disease and type 2 diabetes. The components of MetS have long been demonstrated to have substantial genetic components, but their genetic overlap is less well understood. The present paper takes a multi-prong approach to examining the extent of this genetic overlap. This includes the quantitative genetic and additive Bayesian network modeling of the large TwinsUK project and examination of the results of genome-wide association study (GWAS) of UK Biobank data through use of LD score regression and examination of the number of genes and pathways identified in the GWASes which overlap across MetS traits. Results demonstrate a modest genetic overlap, and the genetic correlations obtained from TwinsUK and UK Biobank are nearly identical. However, these correlations imply more genetic dissimilarity than similarity. Furthermore, examination of the extent of overlap in significant GWAS hits, both at the gene and pathway level, again demonstrates only modest but significant genetic overlap. This lends support to the idea that in clinical treatment of MetS, treating each of the components individually may be an important way to address MetS.

Understanding the complex genetic architecture connecting rheumatoid arthritis, osteoporosis and inflammation: discovering causal pathways.

Rheumatoid arthritis (RA) and osteoporosis (OP) are two comorbid complex inflammatory conditions with evidence of shared genetic background and causal relationships. We aimed to clarify the genetic architecture underlying RA and various OP phenotypes while additionally considering an inflammatory component, C-reactive protein (CRP). Genome-wide association study summary statistics were acquired from the GEnetic Factors for OSteoporosis Consortium, Cohorts for Heart and Aging Research Consortium and UK Biobank. Mendelian randomization (MR) was used to detect the presence of causal relationships. Colocalization analysis was performed to determine shared genetic variants between CRP and OP phenotypes. Analysis of pleiotropy between traits owing to shared causal single nucleotide polymorphisms (SNPs) was performed using PL eiotropic A nalysis under CO mposite null hypothesis (PLACO). MR analysis was suggestive of horizontal pleiotropy between RA and OP traits. RA was a significant causal risk factor for CRP (ß = 0.027, 95% confidence interval = 0.016-0.038). There was no evidence of CRP→OP causal relationship, but horizontal pleiotropy was apparent. Colocalization established shared genomic regions between CRP and OP, including GCKR and SERPINA1 genes. Pleiotropy arising from shared causal SNPs revealed through the colocalization analysis was all confirmed by PLACO. These genes were found to be involved in the same molecular function 'protein binding' (GO:0005515) associated with RA, OP and CRP. We identified three major components explaining the epidemiological relationship among RA, OP and inflammation: (1) Pleiotropy explains a portion of the shared genetic relationship between RA and OP, albeit polygenically; (2) RA contributes to CRP elevation and (3) CRP, which is influenced by RA, demonstrated pleiotropy with OP.

Deciphering the Causal Relationships Between Low Back Pain Complications, Metabolic Factors, and Comorbidities.

PURPOSE: Low back pain (LBP) is one of the major disabling health conditions in aging societies presenting significant cost burdens to health and social care systems. Its complications and associated disability are often accompanied by mental disorders, metabolic comorbidities, changed body composition, and inflammation. However, their mutual relationships in LBP-associated disability remain unclear. METHODS: In the present case-control study, a self-report validated questionnaire was used to collect LBP disability data in an ethnically homogeneous Israeli Arab sample (489 males and 589 females). Body composition parameters were assessed through bioelectrical impedance analysis and plasma levels of soluble markers by EISA. Comorbidity status was assessed in personal interview and from the individual medical files. RESULTS: Our mixed multiple regression analysis identified that GDF-15 (ß = 0.160, p = 2.95×10-8), vaspin (ß = 0.085, p = 0.003), follistatin (ß = 0.076, p = 0.001), extracellular water (ß = 0.096, p = 0.001), waist hip ratio (ß = 0.072, p = 0.009), mental disorders (ß = 0.077, p = 0.001), and metabolic comorbidities (ß = 0.059, p = 0.02) were significantly associated with LBP disability scores, when controlling for age and sex effects. Additive Bayesian network modelling further suggests that LBP disability appears to be directly influenced by age, sex, GDF-15, and extracellular water, and indirectly by mental and metabolic disorders, waist-hip ratio, and follistatin. LBP, in turn, seems to affect the vaspin levels directly. CONCLUSION: Our data suggest the existence of a complex, age-associated, and probably hierarchical, relationship between LBP disability and mental and metabolic disorders, inflammation-related soluble markers, and body composition parameters.

Potential role of regulatory DNA variants in modifying the risk of severe cutaneous reactions induced by aromatic anti-seizure medications.

OBJECTIVE: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe cutaneous adverse drug reactions. Antiseizure medications (ASMs) with aromatic ring structure, including carbamazepine, are among the most common culprits. Screening for human leukocyte antigen (HLA) allele HLA-B*15:02 is recommended prior to initiating treatment with carbamazepine in Asians, but this allele has low positive predictive value. METHODS: We performed whole genome sequencing and analyzed 6 199 696 common variants among 113 aromatic ASM-induced SJS/TEN cases and 84 tolerant controls of Han Chinese ethnicity. RESULTS: In the primary analysis, nine variants reached genome-wide significance (p < 5e-08), one in the carbamazepine subanalysis (85 cases vs. 77 controls) and a further eight identified in HLA-B*15:02-negative subanalysis (35 cases and 53 controls). Interaction analysis between each novel variant from the primary analysis found that five increased risk irrespective of HLA-B*15:02 status or zygosity. HLA-B*15:02-positive individuals were found to have reduced risk if they also carried a chromosome 12 variant, chr12.9426934 (heterozygotes: relative risk = .71, p = .001; homozygotes: relative risk = .23, p < .001). All significant variants lie within intronic or intergenic regions with poorly understood functional consequence. In silico functional analysis of suggestive variants (p < 5e-6) identified through the primary and subanalyses (stratified by HLA-B*15:02 status and drug exposure) suggests that genetic variation within regulatory DNA may contribute to risk indirectly by disrupting the regulation of pathology-related genes. The genes implicated were specific either to the primary analysis (CD9), HLA-B*15:02 carriers (DOCK10), noncarriers (ABCA1), carbamazepine exposure (HLA-E), or phenytoin exposure (CD24). SIGNIFICANCE: We identified variants that could explain why some carriers of HLA-B*15:02 tolerate treatment, and why some noncarriers develop ASM-induced SJS/TEN. Additionally, this analysis suggests that the mixing of HLA-B*15:02 carrier status in previous studies might have masked variants contributing to susceptibility, and that inheritance of risk for ASM-induced SJS/TEN is complex, likely involving multiple risk variants.

Shared Genetic Architecture Between Rheumatoid Arthritis and Varying Osteoporotic Phenotypes.

Rheumatoid arthritis (RA) and low bone mineral density (BMD), an indicator of osteoporosis (OP), appear epidemiologically associated. Shared genetic factors may explain this association. This study aimed to investigate the presence of pleiotropy to clarify the potential genetic association between RA and OP. We examined BMDs at varying skeletal sites reported in UK Biobank as well as OP fracture acquired from the Genetic Factors for Osteoporosis (GEFOS) Consortium and the TwinsUK study. PRSice-2 was used to assess the potential shared genetic overlap between RA and OP. The presence of pleiotropy was examined using colocalization analysis. PRSice-2 revealed that RA was significantly associated with OP fracture (ß = 351.6 ± 83.9, p value = 2.76E-05), total BMD (ß = -1763.5 ± 612.8, p = 4.00E-03), spine BMD (ß = -919.8 ± 264.6, p value = 5.09E-04), and forearm BMD (ß = -66.09 ± 31.40, p value = 3.53E-02). Through colocalization analysis, the same causal genetic variants, associated with both RA and OP, were apparent in 12 genes: PLCL1, BOLL, AC011997.1, TNFAIP3, RP11-158I9.1, CDK6, CHCHD4P2, RP11-505C13.1, PHF19, TRAF1, C5, and C11orf49 with moderate posterior probabilities (>50%). Pleiotropy is involved in the association between RA and OP phenotypes. These findings contribute to the understanding of disease mechanisms and provide insight into possible therapeutic advancements and enhanced screening measures. © 2021 American Society for Bone and Mineral Research (ASBMR).

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases.

Identifying rare variants that contribute to complex diseases is challenging because of the low statistical power in current tests comparing cases with controls. Here, we propose a novel and powerful rare variants association test based on the deviation of the observed mutation burden of a gene in cases from a baseline predicted by a weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association tests and has reasonable type 1 error rates even for stratified populations or in small samples. Applied to real case-control data, RUNNER recapitulates known genes of Hirschsprung disease and Alzheimer's disease missed by current methods and detects promising new candidate genes for both disorders. In a case-only study, RUNNER successfully detected a known causal gene of amyotrophic lateral sclerosis. The present study provides a powerful and robust method to identify susceptibility genes with rare risk variants for complex diseases.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling.

OBJECTIVES: Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage. METHODS: Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415). RESULTS: All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage. CONCLUSIONS: Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.

Statistical Power and the Classical Twin Design.

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

The Genes We Inherit and Those We Don't: Maternal Genetic Nurture and Child BMI Trajectories.

Recently, methods have been introduced using polygenic scores (PGS) to estimate the effects of genetic nurture, the environmentally-mediated effects of parental genotypes on the phenotype of their child above and beyond the effects of the alleles which are transmitted to the child. We introduce a simplified model for estimating genetic nurture effects and show, through simulation and analytical derivation, that our method provides unbiased estimates and offers an increase in power to detect genetic nurture of up to 1/3 greater than that of previous methods. Subsequently, we apply this method to data from the Avon Longitudinal Study of Parents and Children to estimate the effects of maternal genetic nurture on childhood body mass index (BMI) trajectories. Through mixed modeling, we observe a statistically significant age-dependent effect of maternal PGS on child BMI, such that the influence of maternal genetic nurture appears to increase throughout development.

Self-reported hearing loss questions provide a good measure for genetic studies: a polygenic risk score analysis from UK Biobank.

Age-related hearing impairment (ARHI) is very common in older adults and has major impact on quality of life. The heritability of ARHI has been estimated to be around 50%. The present study aimed to estimate heritability and environmental contributions to liability of ARHI and the extent to which a polygenic risk score (PRS) derived from a recent genome-wide association study of questionnaire items regarding hearing loss using the UK Biobank is predictive of hearing loss in other samples. We examined (1) a sample from TwinsUK who have had hearing ability measured by pure-tone audiogram and the speech-to-noise ratio test as well as questionnaire measures that are comparable with the UK Biobank questionnaire items and (2) European and non-European samples from the UK Biobank which were not part of the original GWAS. Results indicated that the questionnaire items were over 50% heritable in TwinsUK and comparable with the objective hearing measures. In addition, we found very high genetic correlation (0.30-0.84) between the questionnaire responses and objective hearing measures in the TwinsUK sample. Finally, PRS computed from weighted UK Biobank GWAS results were predictive of both questionnaire and objective measures of hearing loss in the TwinsUK sample, as well as questionnaire-measured hearing loss in Europeans but not non-European subpopulations. These results demonstrate the utility of questionnaire-based methods in genetic association studies of hearing loss in adults and highlight the differences in genetic predisposition to ARHI by ethnic background.

A random forest-based framework for genotyping and accuracy assessment of copy number variations.

Detection of copy number variations (CNVs) is essential for uncovering genetic factors underlying human diseases. However, CNV detection by current methods is prone to error, and precisely identifying CNVs from paired-end whole genome sequencing (WGS) data is still challenging. Here, we present a framework, CNV-JACG, for Judging the Accuracy of CNVs and Genotyping using paired-end WGS data. CNV-JACG is based on a random forest model trained on 21 distinctive features characterizing the CNV region and its breakpoints. Using the data from the 1000 Genomes Project, Genome in a Bottle Consortium, the Human Genome Structural Variation Consortium and in-house technical replicates, we show that CNV-JACG has superior sensitivity over the latest genotyping method, SV2, particularly for the small CNVs (≤1 kb). We also demonstrate that CNV-JACG outperforms SV2 in terms of Mendelian inconsistency in trios and concordance between technical replicates. Our study suggests that CNV-JACG would be a useful tool in assessing the accuracy of CNVs to meet the ever-growing needs for uncovering the missing heritability linked to CNVs.