Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Exome sequencing and analysis of 454,787 UK Biobank participants.

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.

External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems.

OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is fraught with challenges. Dataset shifts such as differences in clinical practice, new data acquisition devices, or changes in the electronic health record (EHR) implementation mean that the input data seen by a model can differ significantly from the data it was trained on. Validating models at multiple institutions is therefore critical. Here, using retrospective data, we demonstrate how Predicting Intensive Care Transfers and other UnfoReseen Events (PICTURE), a deterioration index developed at a single academic medical center, generalizes to a second institution with significantly different patient population. DESIGN: PICTURE is a deterioration index designed for the general ward, which uses structured EHR data such as laboratory values and vital signs. SETTING: The general wards of two large hospitals, one an academic medical center and the other a community hospital. SUBJECTS: The model has previously been trained and validated on a cohort of 165,018 general ward encounters from a large academic medical center. Here, we apply this model to 11,083 encounters from a separate community hospital. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The hospitals were found to have significant differences in missingness rates (> 5% difference in 9/52 features), deterioration rate (4.5% vs 2.5%), and racial makeup (20% non-White vs 49% non-White). Despite these differences, PICTURE's performance was consistent (area under the receiver operating characteristic curve [AUROC], 0.870; 95% CI, 0.861-0.878), area under the precision-recall curve (AUPRC, 0.298; 95% CI, 0.275-0.320) at the first hospital; AUROC 0.875 (0.851-0.902), AUPRC 0.339 (0.281-0.398) at the second. AUPRC was standardized to a 2.5% event rate. PICTURE also outperformed both the Epic Deterioration Index and the National Early Warning Score at both institutions. CONCLUSIONS: Important differences were observed between the two institutions, including data availability and demographic makeup. PICTURE was able to identify general ward patients at risk of deterioration at both hospitals with consistent performance (AUROC and AUPRC) and compared favorably to existing metrics.

Genomic Mismatch at LIMS1 Locus and Kidney Allograft Rejection.

BACKGROUND: In the context of kidney transplantation, genomic incompatibilities between donor and recipient may lead to allosensitization against new antigens. We hypothesized that recessive inheritance of gene-disrupting variants may represent a risk factor for allograft rejection. METHODS: We performed a two-stage genetic association study of kidney allograft rejection. In the first stage, we performed a recessive association screen of 50 common gene-intersecting deletion polymorphisms in a cohort of kidney transplant recipients. In the second stage, we replicated our findings in three independent cohorts of donor-recipient pairs. We defined genomic collision as a specific donor-recipient genotype combination in which a recipient who was homozygous for a gene-intersecting deletion received a transplant from a nonhomozygous donor. Identification of alloantibodies was performed with the use of protein arrays, enzyme-linked immunosorbent assays, and Western blot analyses. RESULTS: In the discovery cohort, which included 705 recipients, we found a significant association with allograft rejection at the LIMS1 locus represented by rs893403 (hazard ratio with the risk genotype vs. nonrisk genotypes, 1.84; 95% confidence interval [CI], 1.35 to 2.50; P = 9.8×10-5). This effect was replicated under the genomic-collision model in three independent cohorts involving a total of 2004 donor-recipient pairs (hazard ratio, 1.55; 95% CI, 1.25 to 1.93; P = 6.5×10-5). In the combined analysis (discovery cohort plus replication cohorts), the risk genotype was associated with a higher risk of rejection than the nonrisk genotype (hazard ratio, 1.63; 95% CI, 1.37 to 1.95; P = 4.7×10-8). We identified a specific antibody response against LIMS1, a kidney-expressed protein encoded within the collision locus. The response involved predominantly IgG2 and IgG3 antibody subclasses. CONCLUSIONS: We found that the LIMS1 locus appeared to encode a minor histocompatibility antigen. Genomic collision at this locus was associated with rejection of the kidney allograft and with production of anti-LIMS1 IgG2 and IgG3. (Funded by the Columbia University Transplant Center and others.).

An eQTL Landscape of Kidney Tissue in Human Nephrotic Syndrome.

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTL studies of human kidney. Here, we used whole-genome sequencing (WGS) and microdissected glomerular (GLOM) and tubulointerstitial (TI) transcriptomes from 187 individuals with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures. Using MatrixEQTL, we performed cis-eQTL analysis on GLOM (n = 136) and TI (n = 166). We used the Bayesian "Deterministic Approximation of Posteriors" (DAP) to fine-map these signals, eQTLBMA to discover GLOM- or TI-specific eQTLs, and single-cell RNA-seq data of control kidney tissue to identify the cell type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IgAN) GWAS to perform a transcriptome-wide association study (TWAS). We discovered 894 GLOM eQTLs and 1,767 TI eQTLs at FDR < 0.05. 14% and 19% of GLOM and TI eQTLs, respectively, had >1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM specific and TI specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IgAN TWAS identified significant GLOM and TI genes, primarily at the HLA region. In this study, we discovered GLOM and TI eQTLs, identified those that were tissue specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are available for browsing and download via our eQTL browser, "nephQTL."

Disruption of the exocyst induces podocyte loss and dysfunction.

Although the slit diaphragm proteins in podocytes are uniquely organized to maintain glomerular filtration assembly and function, little is known about the underlying mechanisms that participate in trafficking these proteins to the correct location for development and homeostasis. Identifying these mechanisms will likely provide novel targets for therapeutic intervention to preserve podocyte function following glomerular injury. Analysis of structural variation in cases of human nephrotic syndrome identified rare heterozygous deletions of EXOC4 in two patients. This suggested that disruption of the highly-conserved eight-protein exocyst trafficking complex could have a role in podocyte dysfunction. Indeed, mRNA profiling of injured podocytes identified significant exocyst down-regulation. To test the hypothesis that the exocyst is centrally involved in podocyte development/function, we generated homozygous podocyte-specific Exoc5 (a central exocyst component that interacts with Exoc4) knockout mice that showed massive proteinuria and died within 4 weeks of birth. Histological and ultrastructural analysis of these mice showed severe glomerular defects with increased fibrosis, proteinaceous casts, effaced podocytes, and loss of the slit diaphragm. Immunofluorescence analysis revealed that Neph1 and Nephrin, major slit diaphragm constituents, were mislocalized and/or lost. mRNA profiling of Exoc5 knockdown podocytes showed that vesicular trafficking was the most affected cellular event. Mapping of signaling pathways and Western blot analysis revealed significant up-regulation of the mitogen-activated protein kinase and transforming growth factor-ß pathways in Exoc5 knockdown podocytes and in the glomeruli of podocyte-specific Exoc5 KO mice. Based on these data, we propose that exocyst-based mechanisms regulate Neph1 and Nephrin signaling and trafficking, and thus podocyte development and function.

Exome-wide Association Study Identifies GREB1L Mutations in Congenital Kidney Malformations.

Renal agenesis and hypodysplasia (RHD) are major causes of pediatric chronic kidney disease and are highly genetically heterogeneous. We conducted whole-exome sequencing in 202 case subjects with RHD and identified diagnostic mutations in genes known to be associated with RHD in 7/202 case subjects. In an additional affected individual with RHD and a congenital heart defect, we found a homozygous loss-of-function (LOF) variant in SLIT3, recapitulating phenotypes reported with Slit3 inactivation in the mouse. To identify genes associated with RHD, we performed an exome-wide association study with 195 unresolved case subjects and 6,905 control subjects. The top signal resided in GREB1L, a gene implicated previously in Hoxb1 and Shha signaling in zebrafish. The significance of the association, which was p = 2.0 × 10-5 for novel LOF, increased to p = 4.1 × 10-6 for LOF and deleterious missense variants combined, and augmented further after accounting for segregation and de novo inheritance of rare variants (joint p = 2.3 × 10-7). Finally, CRISPR/Cas9 disruption or knockdown of greb1l in zebrafish caused specific pronephric defects, which were rescued by wild-type human GREB1L mRNA, but not mRNA containing alleles identified in case subjects. Together, our study provides insight into the genetic landscape of kidney malformations in humans, presents multiple candidates, and identifies SLIT3 and GREB1L as genes implicated in the pathogenesis of RHD.

Genetic Drivers of Kidney Defects in the DiGeorge Syndrome.

BACKGROUND: The DiGeorge syndrome, the most common of the microdeletion syndromes, affects multiple organs, including the heart, the nervous system, and the kidney. It is caused by deletions on chromosome 22q11.2; the genetic driver of the kidney defects is unknown. METHODS: We conducted a genomewide search for structural variants in two cohorts: 2080 patients with congenital kidney and urinary tract anomalies and 22,094 controls. We performed exome and targeted resequencing in samples obtained from 586 additional patients with congenital kidney anomalies. We also carried out functional studies using zebrafish and mice. RESULTS: We identified heterozygous deletions of 22q11.2 in 1.1% of the patients with congenital kidney anomalies and in 0.01% of population controls (odds ratio, 81.5; P=4.5×10-14). We localized the main drivers of renal disease in the DiGeorge syndrome to a 370-kb region containing nine genes. In zebrafish embryos, an induced loss of function in snap29, aifm3, and crkl resulted in renal defects; the loss of crkl alone was sufficient to induce defects. Five of 586 patients with congenital urinary anomalies had newly identified, heterozygous protein-altering variants, including a premature termination codon, in CRKL. The inactivation of Crkl in the mouse model induced developmental defects similar to those observed in patients with congenital urinary anomalies. CONCLUSIONS: We identified a recurrent 370-kb deletion at the 22q11.2 locus as a driver of kidney defects in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies. Of the nine genes at this locus, SNAP29, AIFM3, and CRKL appear to be critical to the phenotype, with haploinsufficiency of CRKL emerging as the main genetic driver. (Funded by the National Institutes of Health and others.).

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution.

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device.

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Transethnic, Genome-Wide Analysis Reveals Immune-Related Risk Alleles and Phenotypic Correlates in Pediatric Steroid-Sensitive Nephrotic Syndrome.

Background Steroid-sensitive nephrotic syndrome (SSNS) is a childhood disease with unclear pathophysiology and genetic architecture. We investigated the genomic basis of SSNS in children recruited in Europe and the biopsy-based North American NEPTUNE cohort.Methods We performed three ancestry-matched, genome-wide association studies (GWAS) in 273 children with NS (Children Cohort Nephrosis and Virus [NEPHROVIR] cohort: 132 European, 56 African, and 85 Maghrebian) followed by independent replication in 112 European children, transethnic meta-analysis, and conditional analysis. GWAS alleles were used to perform glomerular cis-expression quantitative trait loci studies in 39 children in the NEPTUNE cohort and epidemiologic studies in GWAS and NEPTUNE (97 children) cohorts.Results Transethnic meta-analysis identified one SSNS-associated single-nucleotide polymorphism (SNP) rs1063348 in the 3' untranslated region of HLA-DQB1 (P=9.3×10-23). Conditional analysis identified two additional independent risk alleles upstream of HLA-DRB1 (rs28366266, P=3.7×10-11) and in the 3' untranslated region of BTNL2 (rs9348883, P=9.4×10-7) within introns of HCG23 and LOC101929163 These three risk alleles were independent of the risk haplotype DRB1*07:01-DQA1*02:01-DQB1*02:02 identified in European patients. Increased burden of risk alleles across independent loci was associated with higher odds of SSNS. Increased burden of risk alleles across independent loci was associated with higher odds of SSNS, with younger age of onset across all cohorts, and with increased odds of complete remission across histologies in NEPTUNE children. rs1063348 associated with decreased glomerular expression of HLA-DRB1, HLA-DRB5, and HLA-DQB1.Conclusions Transethnic GWAS empowered discovery of three independent risk SNPs for pediatric SSNS. Characterization of these SNPs provide an entry for understanding immune dysregulation in NS and introducing a genomically defined classification.

An investigation of APOL1 risk genotypes and preterm birth in African American population cohorts.

BACKGROUND: Two genetic variants in apolipoprotein L1 (APOL1) are associated with increased risk of focal segmental glomerulosclerosis as well as other glomerular phenotypes. These risk variants are common in individuals of African ancestry but absent in other racial groups. Yet, the majority of individuals with two APOL1 risk alleles [high-risk (HR) genotype] do not have renal disease. It is critical to identify environmental and secondary genetic influences that, when combined with these alleles, lead to kidney disease. In a recent study of black children with glomerular disease enrolled in the Nephrotic Syndrome Study Network (NEPTUNE) and Chronic Kidney Disease in Children Study (n = 104), we found that subjects with an HR genotype had a 4.6-fold increase in the odds of preterm birth as compared to those with a low risk (LR) genotype [odds ratio 4.6 (CI 1.4-15.5)]. There are known racial disparities in preterm birth, which itself is a known risk factor for chronic kidney disease and focal segmental glomerulosclerosis. Thus, we questioned whether an HR APOL1 genotype is associated with prematurity in the general African American population. METHODS: We analyzed two publically available genetic datasets of preterm birth in African Americans, including 867 infants and 519 mothers from the Gene Environment Association Studies (GENEVA) study of preterm delivery and 960 mothers from the Boston Medical Center genome-wide association study of preterm birth. We performed multivariable analyses testing for association between HR APOL1 and birth outcomes. RESULTS: In both studies, there was no association between HR APOL1 in mothers and prematurity, gestational age or birthweight. Additionally, in the GENEVA study, we saw no association between infant HR APOL1 and prematurity, gestational age or birthweight. CONCLUSION: From these data, we conclude that the previously observed association between HR APOL1 and prematurity is specific to those with glomerular disease, suggesting prematurity may act as an additional risk factor in APOL1-associated renal disease.

APOL1-associated glomerular disease among African-American children: a collaboration of the Chronic Kidney Disease in Children (CKiD) and Nephrotic Syndrome Study Network (NEPTUNE) cohorts.

BACKGROUND: Individuals of African ancestry harboring two variant alleles within apolipoprotein L1 ( APOL1 ) are classified with a high-risk (HR) genotype. Adults with an HR genotype have increased risk of focal segmental glomerulosclerosis and chronic kidney disease compared with those with a low-risk (LR) genotype (0 or 1 variants). The role of APOL1 risk genotypes in children with glomerular disease is less well known. METHODS: This study characterized 104 African-American children with a glomerular disease by APOL1 genotype in two cohorts: the Chronic Kidney Disease in Children (CKiD) and Nephrotic Syndrome Study Network (NEPTUNE). RESULTS: Among these subjects, 46% had an HR genotype with a similar age at cohort enrollment. For APOL1 HR children, the median age of disease onset was older (CKiD: 4.5 versus 11.5 years for LR versus HR; NEPTUNE: 11 versus 14 years for LR versus HR, respectively) and preterm birth was more common [CKiD: 27 versus 4%; NEPTUNE: 26 versus 12%; combined odds ratio 4.6 (95% confidence interval: 1.4, 15.5)]. Within studies, HR children had lower initial estimated glomerular filtration rate (eGFR) (CKiD: 53 versus 69 mL/min/1.73 m 2 ; NEPTUNE: 74 versus 94 mL/min/1.73 m 2 ). Longitudinal eGFR decline was faster among HR children versus LR (CKiD: -18 versus -8% per year; NEPTUNE: -13 versus -3% per year). CONCLUSIONS: Children with an HR genotype in CKiD and NEPTUNE seem to have a more aggressive form of glomerular disease, in part due to a higher prevalence of focal segmental glomerulosclerosis. These consistent findings across independent cohorts suggest a common natural history for children with APOL1 -associated glomerular disease. Further study is needed to determine the generalizability of these findings.

Evaluating Mendelian nephrotic syndrome genes for evidence for risk alleles or oligogenicity that explain heritability.

BACKGROUND: More than 30 genes can harbor rare exonic variants sufficient to cause nephrotic syndrome (NS), and the number of genes implicated in monogenic NS continues to grow. However, outside the first year of life, the majority of affected patients, particularly in ancestrally mixed populations, do not have a known monogenic form of NS. Even in those children classified with a monogenic form of NS, there is phenotypic heterogeneity. Thus, we have only discovered a fraction of the heritability of NS-the underlying genetic factors contributing to phenotypic variation. Part of the "missing heritability" for NS has been posited to be explained by patients harboring coding variants across one or more previously implicated NS genes, insufficient to cause NS in a classical Mendelian manner, but that nonetheless have a sufficient impact on protein function to cause disease. However, systematic evaluation in patients with NS for rare or low-frequency risk alleles within single genes, or in combination across genes ("oligogenicity"), has not been reported. To determine whether, compared with a reference population, patients with NS have either a significantly increased burden of protein-altering variants ("risk-alleles"), or a unique combination of them ("oligogenicity"), in a set of 21 genes implicated in Mendelian forms of NS. METHODS: In 303 patients with NS enrolled in the Nephrotic Syndrome Study Network (NEPTUNE), we performed targeted amplification paired with next-generation sequencing of 21 genes implicated in monogenic NS. We created a high-quality variant call set and compared it with a variant call set of the same genes in a reference population composed of 2,535 individuals from phase 3 of the 1000 Genomes Project. We created both a "stringent" and a "relaxed" pathogenicity-filtering pipeline, applied them to both cohorts, and computed the burden of variants in the entire gene set per cohort, the burden of variants in the entire gene set per individual, the burden of variants within a single gene per cohort, and unique combinations of variants across two or more genes per cohort. RESULTS: With few exceptions when using the relaxed filter, and which are likely the result of confounding by population stratification, NS patients did not have a significantly increased burden of variants in Mendelian NS genes in comparison to a reference cohort, nor was there any evidence for oligogenicity. This was true when using both the relaxed and the stringent variant pathogenicity filter. CONCLUSION: In our study, there were no significant differences in the burden or particular combinations of low-frequency or rare protein-altering variants in a previously implicated Mendelian NS genes cohort between North American patients with NS and a reference population. Studies in larger independent cohorts or meta-analyses are needed to assess the generalizability of our discoveries and also address whether there is in fact small but significant enrichment of risk alleles or oligogenicity in NS cases that was undetectable with this current sample size. It is still possible that rare protein-altering variants in these genes, insufficient to cause Mendelian disease, still contribute to NS as risk alleles and/or via oligogenicity. However, we suggest that more accurate bioinformatic analyses and the incorporation of functional assays would be necessary to identify bona fide instances of this form of genetic architecture as a contributor to the heritability of NS.

Using Population Genetics to Interrogate the Monogenic Nephrotic Syndrome Diagnosis in a Case Cohort.

To maximize clinical benefits of genetic screening of patients with nephrotic syndrome (NS) to diagnose monogenic causes, reliably distinguishing NS-causing variants from the background of rare, noncausal variants prevalent in all genomes is vital. To determine the prevalence of monogenic NS in a North American case cohort while accounting for background prevalence of genetic variation, we sequenced 21 implicated monogenic NS genes in 312 participants from the Nephrotic Syndrome Study Network and 61 putative controls from the 1000 Genomes Project (1000G). These analyses were extended to available sequence data from approximately 2500 subjects from the 1000G. A typical pathogenicity filter identified causal variants for NS in 4.2% of patients and 5.8% of subjects from the 1000G. We devised a more stringent pathogenicity filtering strategy, reducing background prevalence of causal variants to 1.5%. When applying this stringent filter to patients, prevalence of monogenic NS was 2.9%; of these patients, 67% were pediatric, and 44% had FSGS on biopsy. The rate of complete remission did not associate with monogenic classification. Thus, we identified factors contributing to inaccurate monogenic classification of NS and developed a more accurate variant filtering strategy. The prevalence and clinical correlates of monogenic NS in this sporadically affected cohort differ substantially from those reported for patients referred for genetic analysis. Particularly in unselected, population-based cases, considering putative causal variants in known NS genes from a probabilistic rather than a deterministic perspective may be more precise. We also introduce GeneVetter, a web tool for monogenic assessment of rare disease.

Integrative Genomics Identifies Novel Associations with APOL1 Risk Genotypes in Black NEPTUNE Subjects.

APOL1 variants have been associated with renal phenotypes in blacks. To refine clinical outcomes and discover mechanisms of APOL1-associated kidney injury, we analyzed clinical and genomic datasets derived from 90 black subjects in the Nephrotic Syndrome Study Network (NEPTUNE), stratified by APOL1 risk genotype. Ninety subjects with proteinuria ≥0.5 g/d were enrolled at first biopsy for primary nephrotic syndrome and followed. Clinical outcomes were determined, and renal histomorphometry and sequencing of Mendelian nephrotic syndrome genes were performed. APOL1 variants were genotyped, and glomerular and tubulointerstitial transcriptomes from protocol renal biopsy cores were analyzed for differential and correlative gene expression. Analyses were performed under the recessive model (high-risk genotype defined by two risk alleles). APOL1 high-risk genotype was significantly associated with a 17 ml/min per 1.73 m(2) lower eGFR and a 69% reduction in the probability of complete remission at any time, independent of histologic diagnosis. Neither APOL1 risk group was enriched for Mendelian mutations. On renal biopsy, high-risk genotype was associated with increased fractional interstitial area, interstitial fibrosis, and tubular atrophy. Risk genotype was not associated with intrarenal APOL1 mRNA expression levels. Differential expression analysis demonstrated an increased steady-state level of five genes associated with the high-risk genotype (CXCL9, CXCL11, and UBD in glomerulus; SNOR14B and MUC13 in tubulointerstitium). APOL1 tubulointerstitial coexpression analysis showed coexpression of APOL1 mRNA levels with a group of intrarenal transcripts that together were associated with increased interstitial fibrosis and tubular atrophy. These data indicate the high-risk APOL1 genotype confers renal risk across histopathologic diagnoses.

tarSVM: Improving the accuracy of variant calls derived from microfluidic PCR-based targeted next generation sequencing using a support vector machine.

BACKGROUND: Targeted sequencing of discrete gene sets is a cost effective strategy to screen subjects for monogenic forms of disease. One method to achieve this pairs microfluidic PCR with next generation sequencing. The PCR step of this pipeline creates challenges in accurate variant calling. This includes that most reads targeting a specific exon are duplicates that have been amplified from the PCR step. To reduce false positive variant calls from these experiments, previous studies have used threshold-based filtering of alternative allele depth ratio and manual inspection of the alignments. However even after manual inspection and filtering, many variants fail to be validated via Sanger sequencing. To improve the accuracy of variant calling from these experiments, we are challenged to design a variant filtering strategy that sufficiently models microfluidic PCR-specific issues. RESULTS: We developed an open source variant filtering pipeline, targeted sequencing support vector machine ("tarSVM"), that uses a Support Vector Machine (SVM) and a new score the normalized allele dosage test to identify high quality variants from microfluidic PCR data. tarSVM maximizes training knowledge by selecting variants that are likely true and likely false variants by incorporating knowledge from the 1000 Genomes and the Exome Aggregation Consortium projects. tarSVM improves on previous approaches by synthesizing variant features from the Genome Analysis Toolkit and allele dosage information. We compared the accuracy of tarSVM versus existing variant quality filtering strategies on two cohorts (n = 474 and n = 1152), and validated our method on a third cohort (n = 75). In the first cohort, our method achieved 84.5 % accuracy of predicting whether or not a variant would be validated with Sanger sequencing versus 78.8 % for the second most accurate method. In the second cohort, our method had an accuracy of 73.3 %, versus 61.5 % for the second best method. Finally, our method had a false discovery rate of 5 % for the validation cohort. CONCLUSIONS: tarSVM increases the accuracy of variant calling when using microfluidic PCR based targeted sequencing approaches. This results in higher confidence downstream analyses, and ultimately reduces the costs Sanger validation. Our approach is less labor intensive than existing approaches, and is available as an open source pipeline for read trimming, aligning, variant calling, and variant quality filtering on GitHub at https://github.com/christopher-gillies/TargetSpecificGATKSequencingPipeline .

GeneVetter: a web tool for quantitative monogenic assessment of rare diseases.

UNLABELLED: When performing DNA sequencing to diagnose affected individuals with monogenic forms of rare diseases, accurate attribution of causality to detected variants is imperative but imperfect. Even if a gene has variants already known to cause a disease, rare disruptive variants predicted to be causal are not always so, mainly due to imperfect ability to predict the pathogenicity of variants. Existing population-scale sequence resources such as 1000 Genomes are useful to quantify the 'background prevalence' of an unaffected individual being falsely predicted to carry causal variants. We developed GeneVetter to allow users to quantify the 'background prevalence' of subjects with predicted causal variants within specific genes under user-specified filtering parameters. GeneVetter helps quantify uncertainty in monogenic diagnosis and design genetic studies with support for power and sample size calculations for specific genes with specific filtering criteria. GeneVetter also allows users to analyze their own sequence data without sending genotype information over the Internet. Overall, GeneVetter is an interactive web tool that facilitates quantifying and accounting for the background prevalence of predicted pathogenic variants in a population. AVAILABILITY AND IMPLEMENTATION: GeneVetter is available at http://genevetter.org/ CONTACT: mgsamps@med.umich.edu or hmkang@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A simulation to analyze feature selection methods utilizing gene ontology for gene expression classification.

Gene expression profile classification is a pivotal research domain assisting in the transformation from traditional to personalized medicine. A major challenge associated with gene expression data classification is the small number of samples relative to the large number of genes. To address this problem, researchers have devised various feature selection algorithms to reduce the number of genes. Recent studies have been experimenting with the use of semantic similarity between genes in Gene Ontology (GO) as a method to improve feature selection. While there are few studies that discuss how to use GO for feature selection, there is no simulation study that addresses when to use GO-based feature selection. To investigate this, we developed a novel simulation, which generates binary class datasets, where the differentially expressed genes between two classes have some underlying relationship in GO. This allows us to investigate the effects of various factors such as the relative connectedness of the underlying genes in GO, the mean magnitude of separation between differentially expressed genes denoted by δ, and the number of training samples. Our simulation results suggest that the connectedness in GO of the differentially expressed genes for a biological condition is the primary factor for determining the efficacy of GO-based feature selection. In particular, as the connectedness of differentially expressed genes increases, the classification accuracy improvement increases. To quantify this notion of connectedness, we defined a measure called Biological Condition Annotation Level BCAL(G), where G is a graph of differentially expressed genes. Our main conclusions with respect to GO-based feature selection are the following: (1) it increases classification accuracy when BCAL(G) ≥ 0.696; (2) it decreases classification accuracy when BCAL(G) ≤ 0.389; (3) it provides marginal accuracy improvement when 0.389