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Objective: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic.Design: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density.Measurements: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured.Results: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%.Conclusion: A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance. [ABSTRACT FROM AUTHOR]

Deep mutational scanning enables high-throughput functional assessment of genetic variants. While phenotypic measurements from screening assays generally align with clinical outcomes, experimental noise may affect the accuracy of individual variant estimates. We developed the FUSE (functional substitution estimation) pipeline, which leverages measurements collectively within screening assays to improve the estimation of variant impacts. Drawing data from 115 published functional assays, FUSE assesses the mean functional effect per amino acid position and makes estimates for individual allelic variants. It enhances the correlation of variant functional effects from different assay platforms and increases the classification accuracy of missense variants in ClinVar across 29 genes (area under the receiver operating characteristic [ROC] curve [AUC] from 0.83 to 0.90). In UK Biobank patients with rare missense variants in BRCA1, LDLR, or TP53, FUSE improves the classification accuracy of associated phenotypes. FUSE can also impute variant effects for substitutions not experimentally screened. This approach improves accuracy and broadens the utility of data from functional screening., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR , we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization., Competing Interests: Competing interests L.P. has financial interests in Edilytics, Inc., Excelsior Genomics, and SeQure Dx, Inc. L.P.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The remaining authors declare no competing interests.

While pathogenic variants can significantly increase disease risk, it is still challenging to estimate the clinical impact of rare missense variants more generally. Even in genes such as BRCA2 or PALB2, large cohort studies find no significant association between breast cancer and rare missense variants collectively. Here, we introduce REGatta, a method to estimate clinical risk from variants in smaller segments of individual genes. We first define these regions by using the density of pathogenic diagnostic reports and then calculate the relative risk in each region by using over 200,000 exome sequences in the UK Biobank. We apply this method in 13 genes with established roles across several monogenic disorders. In genes with no significant difference at the gene level, this approach significantly separates disease risk for individuals with rare missense variants at higher or lower risk (BRCA2 regional model OR = 1.46 [1.12, 1.79], p = 0.0036 vs. BRCA2 gene model OR = 0.96 [0.85, 1.07] p = 0.4171). We find high concordance between these regional risk estimates and high-throughput functional assays of variant impact. We compare our method with existing methods and the use of protein domains (Pfam) as regions and find REGatta better identifies individuals at elevated or reduced risk. These regions provide useful priors and are potentially useful for improving risk assessment for genes associated with monogenic diseases., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.0, ACMG SF v2.0). DeMAG improves performance over existing VEPs by reaching balanced specificity (82%) and sensitivity (94%) on clinical data, and includes a novel epistatic feature, the 'partners score', which leverages evolutionary and structural partnerships of residues. The 'partners score' provides a general framework for modeling epistatic interactions, integrating both clinical and functional information. We provide our tool and predictions for all missense variants in 316 clinically actionable disease genes (demag.org) to facilitate the interpretation of variants and improve clinical decision-making., (© 2023. The Author(s).)

Deep mutational scanning assays enable the functional assessment of variants in high throughput. Phenotypic measurements from these assays are broadly concordant with clinical outcomes but are prone to noise at the individual variant level. We develop a framework to exploit related measurements within and across experimental assays to jointly estimate variant impact. Drawing from a large corpus of deep mutational scanning data, we collectively estimate the mean functional effect per AA residue position within each gene, normalize observed functional effects by substitution type, and make estimates for individual allelic variants with a pipeline called FUSE ( Fu nctional S ubstitution E stimation). FUSE improves the correlation of functional screening datasets covering the same variants, better separates estimated functional impacts for known pathogenic and benign variants (ClinVar BRCA1 , p=2.24×10 -51 ), and increases the number of variants for which predictions can be made (2,741 to 10,347) by inferring additional variant effects for substitutions not experimentally screened. For UK Biobank patients who carry a rare variant in TP53 , FUSE significantly improves the separation of patients who develop cancer syndromes from those without cancer (p=1.77×10 -6 ). These approaches promise to improve estimates of variant impact and broaden the utility of screening data generated from functional assays.

Purpose: This study aimed to explore whether evidence of pathogenicity from prior variant classifications in ClinVar could be used to inform variant interpretation using the American College of Medical Genetics and Genomics/Association for Molecular Pathology clinical guidelines., Methods: We identified distinct single-nucleotide variants (SNVs) that are either similar in location or in functional consequence to pathogenic variants in ClinVar and analyzed evidence in support of pathogenicity using 3 interpretation criteria., Results: Thousands of variants, including many in clinically actionable disease genes (American College of Medical Genetics and Genomics secondary findings v3.0), have evidence of pathogenicity from existing variant classifications, accounting for 2.5% of nonsynonymous SNVs within ClinVar. Notably, there are many variants with uncertain or conflicting classifications that cause the same amino acid substitution as other pathogenic variants (PS1, N = 323), variants that are predicted to cause different amino acid substitutions in the same codon as pathogenic variants (PM5, N = 7692), and loss-of-function variants that are present in genes in which many loss-of-function variants are classified as pathogenic (PVS1, N = 3635). Most of these variants have similar computational predictions of pathogenicity and splicing effect as their associated pathogenic variants., Conclusion: Broadly, for >1.4 million SNVs exome wide, information from previously classified variants could be used to provide evidence of pathogenicity. We have developed a pipeline to identify variants meeting these criteria that may inform interpretation efforts., Competing Interests: Conflict of Interest C.A.C. has served as a consultant or received honoraria from gWell Health, Athenahealth, and Data Sentry Solutions. All other authors declare no conflicts of interest., (Copyright © 2022 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.)

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis -linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.', Competing Interests: NC, SN, DL, HS, JS, SC, CC, CC, SS No competing interests declared, (© 2022, Connally et al.)

Background and Aims: Very early onset inflammatory bowel disease [VEOIBD] is characterized by intestinal inflammation affecting infants and children less than 6 years of age. To date, over 60 monogenic aetiologies of VEOIBD have been identified, many characterized by highly penetrant recessive or dominant variants in underlying immune and/or epithelial pathways. We sought to identify the genetic cause of VEOIBD in a subset of patients with a unique clinical presentation., Methods: Whole exome sequencing was performed on five families with ten patients who presented with a similar constellation of symptoms including medically refractory infantile-onset IBD, bilateral sensorineural hearing loss and, in the majority, recurrent infections. Genetic aetiologies of VEOIBD were assessed and Sanger sequencing was performed to confirm novel genetic findings. Western analysis on peripheral blood mononuclear cells and functional studies with epithelial cell lines were employed., Results: In each of the ten patients, we identified damaging heterozygous or biallelic variants in the Syntaxin-Binding Protein 3 gene [STXBP3], a protein known to regulate intracellular vesicular trafficking in the syntaxin-binding protein family of molecules, but not associated to date with either VEOIBD or sensorineural hearing loss. These mutations interfere with either intron splicing or protein stability and lead to reduced STXBP3 protein expression. Knock-down of STXBP3 in CaCo2 cells resulted in defects in cell polarity., Conclusion: Overall, we describe a novel genetic syndrome and identify a critical role for STXBP3 in VEOIBD, sensorineural hearing loss and immune dysregulation., (© The Author(s) 2021. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy., Competing Interests: The authors have declared that no competing interests exist.

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

The adenosine analogue remdesivir has emerged as a front-line antiviral treatment for SARS-CoV-2, with preliminary evidence that it reduces the duration and severity of illness 1 .Prior clinical studies have identified adverse events 1,2 , and remdesivir has been shown to inhibit mitochondrial RNA polymerase in biochemical experiments 7 , yet little is known about the specific genetic pathways involved in cellular remdesivir metabolism and cytotoxicity. Through genome-wide CRISPR-Cas9 screening and RNA sequencing, we show that remdesivir treatment leads to a repression of mitochondrial respiratory activity, and we identify five genes whose loss significantly reduces remdesivir cytotoxicity. In particular, we show that loss of the mitochondrial nucleoside transporter SLC29A3 mitigates remdesivir toxicity without a commensurate decrease in SARS-CoV-2 antiviral potency and that the mitochondrial adenylate kinase AK2 is a remdesivir kinase required for remdesivir efficacy and toxicity. This work elucidates the cellular mechanisms of remdesivir metabolism and provides a candidate gene target to reduce remdesivir cytotoxicity., Competing Interests: Competing interests The authors declare competing interests: a patent application has been filed on this work.

Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.

Although base editors are widely used to install targeted point mutations, the factors that determine base editing outcomes are not well understood. We characterized sequence-activity relationships of 11 cytosine and adenine base editors (CBEs and ABEs) on 38,538 genomically integrated targets in mammalian cells and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes (R ≈ 0.9) and efficiency (R ≈ 0.7). We corrected 3,388 disease-associated SNVs with ≥90% precision, including 675 alleles with bystander nucleotides that BE-Hive correctly predicted would not be edited. We discovered determinants of previously unpredictable C-to-G, or C-to-A editing and used these discoveries to correct coding sequences of 174 pathogenic transversion SNVs with ≥90% precision. Finally, we used insights from BE-Hive to engineer novel CBE variants that modulate editing outcomes. These discoveries illuminate base editing, enable editing at previously intractable targets, and provide new base editors with improved editing capabilities., Competing Interests: Declaration of Interests D.R.L. is a consultant and co-founder of Beam Therapeutics, Prime Medicine, Editas Medicine, and Pairwise Plants, companies that use genome editing technologies. The authors have filed a patent application on aspects of this work., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Importance: Pathogenic DNA variants associated with familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome are widely recognized as clinically important and actionable when identified, leading some clinicians to recommend population-wide genomic screening., Objectives: To assess the prevalence and clinical importance of pathogenic or likely pathogenic variants associated with each of 3 genomic conditions (familial hypercholesterolemia, hereditary breast and ovarian cancer syndrome, and Lynch syndrome) within the context of contemporary clinical care., Design, Setting, and Participants: This cohort study used gene-sequencing data from 49 738 participants in the UK Biobank who were recruited from 22 sites across the UK between March 21, 2006, and October 1, 2010. Inpatient hospital data date back to 1977; cancer registry data, to 1957; and death registry data, to 2006. Statistical analysis was performed from July 22, 2019, to November 15, 2019., Exposures: Pathogenic or likely pathogenic DNA variants classified by a clinical laboratory geneticist., Main Outcomes and Measures: Composite end point specific to each genomic condition based on atherosclerotic cardiovascular disease events for familial hypercholesterolemia, breast or ovarian cancer for hereditary breast and ovarian cancer syndrome, and colorectal or uterine cancer for Lynch syndrome., Results: Among 49 738 participants (mean [SD] age, 57 [8] years; 27 144 female [55%]), 441 (0.9%) harbored a pathogenic or likely pathogenic variant associated with any of 3 genomic conditions, including 131 (0.3%) for familial hypercholesterolemia, 235 (0.5%) for hereditary breast and ovarian cancer syndrome, and 76 (0.2%) for Lynch syndrome. Presence of these variants was associated with increased risk of disease: for familial hypercholesterolemia, 28 of 131 carriers (21.4%) vs 4663 of 49 607 noncarriers (9.4%) developed atherosclerotic cardiovascular disease; for hereditary breast and ovarian cancer syndrome, 32 of 116 female carriers (27.6%) vs 2080 of 27 028 female noncarriers (7.7%) developed associated cancers; and for Lynch syndrome, 17 of 76 carriers (22.4%) vs 929 of 49 662 noncarriers (1.9%) developed colorectal or uterine cancer. The predicted probability of disease at age 75 years despite contemporary clinical care was 45.3% for carriers of familial hypercholesterolemia, 41.1% for hereditary breast and ovarian cancer syndrome, and 38.3% for Lynch syndrome. Across the 3 conditions, 39.7% (175 of 441) of the carriers reported a family history of disease vs 23.2% (34 517 of 148 772) of noncarriers., Conclusions and Relevance: The findings suggest that approximately 1% of the middle-aged adult population in the UK Biobank harbored a pathogenic variant associated with any of 3 genomic conditions. These variants were associated with an increased risk of disease despite contemporary clinical care and were not reliably detected by family history.

Pancreatic ductal adenocarcinoma is an aggressive cancer with limited treatment options 1 . Approximately 10% of cases exhibit familial predisposition, but causative genes are not known in most families 2 . We perform whole-genome sequence analysis in a family with multiple cases of pancreatic ductal adenocarcinoma and identify a germline truncating mutation in the member of the RAS oncogene family-like 3 (RABL3) gene. Heterozygous rabl3 mutant zebrafish show increased susceptibility to cancer formation. Transcriptomic and mass spectrometry approaches implicate RABL3 in RAS pathway regulation and identify an interaction with RAP1GDS1 (SmgGDS), a chaperone regulating prenylation of RAS GTPases 3 . Indeed, the truncated mutant RABL3 protein accelerates KRAS prenylation and requires RAS proteins to promote cell proliferation. Finally, evidence in patient cohorts with developmental disorders implicates germline RABL3 mutations in RASopathy syndromes. Our studies identify RABL3 mutations as a target for genetic testing in cancer families and uncover a mechanism for dysregulated RAS activity in development and cancer.

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

Following Cas9 cleavage, DNA repair without a donor template is generally considered stochastic, heterogeneous and impractical beyond gene disruption. Here, we show that template-free Cas9 editing is predictable and capable of precise repair to a predicted genotype, enabling correction of disease-associated mutations in humans. We constructed a library of 2,000 Cas9 guide RNAs paired with DNA target sites and trained inDelphi, a machine learning model that predicts genotypes and frequencies of 1- to 60-base-pair deletions and 1-base-pair insertions with high accuracy (r = 0.87) in five human and mouse cell lines. inDelphi predicts that 5-11% of Cas9 guide RNAs targeting the human genome are 'precise-50', yielding a single genotype comprising greater than or equal to 50% of all major editing products. We experimentally confirmed precise-50 insertions and deletions in 195 human disease-relevant alleles, including correction in primary patient-derived fibroblasts of pathogenic alleles to wild-type genotype for Hermansky-Pudlak syndrome and Menkes disease. This study establishes an approach for precise, template-free genome editing.

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.

Despite major progress in defining the genetic basis of Mendelian disorders, the molecular etiology of many cases remains unknown. Patients with these undiagnosed disorders often have complex presentations and require treatment by multiple health care specialists. Here, we describe an integrated clinical diagnostic and research program using whole-exome and whole-genome sequencing (WES/WGS) for Mendelian disease gene discovery. This program employs specific case ascertainment parameters, a WES/WGS computational analysis pipeline that is optimized for Mendelian disease gene discovery with variant callers tuned to specific inheritance modes, an interdisciplinary crowdsourcing strategy for genomic sequence analysis, matchmaking for additional cases, and integration of the findings regarding gene causality with the clinical management plan. The interdisciplinary gene discovery team includes clinical, computational, and experimental biomedical specialists who interact to identify the genetic etiology of the disease, and when so warranted, to devise improved or novel treatments for affected patients. This program effectively integrates the clinical and research missions of an academic medical center and affords both diagnostic and therapeutic options for patients suffering from genetic disease. It may therefore be germane to other academic medical institutions engaged in implementing genomic medicine programs., Competing Interests: R.C.G. receives compensation for speaking or advisory services from AIA, Genome Medical, Helix, Invitae, Illumina, Prudential, and Roche. R.C.G., N.F., and S.R.S. are on the board of Veritas Genetics. C.A.M. receives compensation for advisory services from Personome and Recombine, and royalties for genetic testing from Partners HealthCare. The other authors declare no competing interests.

The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines. In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene's causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.

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.

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., (© 2015 WILEY PERIODICALS, INC.)

Glycosaminoglycans (GAGs) such as chondroitin are ubiquitous disaccharide carbohydrate chains that contribute to the formation and function of proteoglycans at the cell membrane and in the extracellular matrix. Although GAG-modifying enzymes are required for diverse cellular functions, the role of these proteins in human development and disease is less well understood. Here, we describe two sisters out of seven siblings affected by congenital limb malformation and malignant lymphoproliferative disease. Using Whole-Genome Sequencing (WGS), we identified in the proband deletion of a 55 kb region within chromosome 12q23 that encompasses part of CHST11 (encoding chondroitin-4-sulfotransferase 1) and an embedded microRNA (MIR3922). The deletion was homozygous in the proband but not in each of three unaffected siblings. Genotyping data from the 1000 Genomes Project suggest that deletions inclusive of both CHST11 and MIR3922 are rare events. Given that CHST11 deficiency causes severe chondrodysplasia in mice that is similar to human limb malformation, these results underscore the importance of chondroitin modification in normal skeletal development. Our findings also potentially reveal an unexpected role for CHST11 and/or MIR3922 as tumor suppressors whose disruption may contribute to malignant lymphoproliferative disease.

Population bottlenecks followed by re-expansions have been common throughout history of many populations. The response of alleles under selection to such demographic perturbations has been a subject of great interest in population genetics. On the basis of theoretical analysis and computer simulations, we suggest that this response qualitatively depends on dominance. The number of dominant or additive deleterious alleles per haploid genome is expected to be slightly increased following the bottleneck and re-expansion. In contrast, the number of completely or partially recessive alleles should be sharply reduced. Changes of population size expose differences between recessive and additive selection, potentially providing insight into the prevalence of dominance in natural populations. Specifically, we use a simple statistic, [Formula: see text], where xi represents the derived allele frequency, to compare the number of mutations in different populations, and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. We also provide empirical evidence showing that gene sets associated with autosomal recessive disease in humans may have a BR indicative of recessive selection. Together, these theoretical predictions and empirical observations show that complex demographic history may facilitate rather than impede inference of parameters of natural selection.

Patterns of amino acid conservation have served as a tool for understanding protein evolution. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity.

Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance., Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization., Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

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., (© 2013 WILEY PERIODICALS, INC.)

Immediately following the Boston Marathon attacks, individuals near the scene posted a deluge of data to social media sites. Previous work has shown that these data can be leveraged to provide rapid insight during natural disasters, disease outbreaks and ongoing conflicts that can assist in the public health and medical response. Here, we examine and discuss the social media messages posted immediately after and around the Boston Marathon bombings, and find that specific keywords appear frequently prior to official public safety and news media reports. Individuals immediately adjacent to the explosions posted messages within minutes via Twitter which identify the location and specifics of events, demonstrating a role for social media in the early recognition and characterization of emergency events. *Christopher Cassa and Rumi Chunara contributed equally to this work.

Objective: DNA samples are often processed and sequenced in facilities external to the point of collection. These samples are routinely labeled with patient identifiers or pseudonyms, allowing for potential linkage to identity and private clinical information if intercepted during transmission. We present a cryptographic scheme to securely transmit externally generated sequence data which does not require any patient identifiers, public key infrastructure, or the transmission of passwords., Materials and Methods: This novel encryption scheme cryptographically protects participant sequence data using a shared secret key that is derived from a unique subset of an individual's genetic sequence. This scheme requires access to a subset of an individual's genetic sequence to acquire full access to the transmitted sequence data, which helps to prevent sample mismatch., Results: We validate that the proposed encryption scheme is robust to sequencing errors, population uniqueness, and sibling disambiguation, and provides sufficient cryptographic key space., Discussion: Access to a set of an individual's genotypes and a mutually agreed cryptographic seed is needed to unlock the full sequence, which provides additional sample authentication and authorization security. We present modest fixed and marginal costs to implement this transmission architecture., Conclusions: It is possible for genomics researchers who sequence participant samples externally to protect the transmission of sequence data using unique features of an individual's genetic sequence.

There is an emerging consensus that when investigators obtain genomic data from research participants, they may incur an ethical responsibility to inform at-risk individuals about clinically significant variants discovered during the course of their research. With whole-exome sequencing becoming commonplace and the falling costs of full-genome sequencing, there will be an increasingly large number of variants identified in research participants that may be of sufficient clinical relevance to share. An explicit approach to triaging and communicating these results has yet to be developed, and even the magnitude of the task is uncertain. To develop an estimate of the number of variants that might qualify for disclosure, we apply recently published recommendations for the return of results to a defined and representative set of variants and then extrapolate these estimates to genome scale. We find that the total number of variants meeting the threshold for recommended disclosure ranges from 3955-12,579 (3.79%-12.06%, 95% CI) in the most conservative estimate to 6998-17,189 (6.69%-16.48%, 95% CI) in an estimate including variants with variable disease expressivity. Additionally, if the growth rate from the previous 4 yr continues, we estimate that the total number of disease-associated variants will grow 37% over the next 4 yr.

Summary: Accurate annotations of genomic variants are necessary to achieve full-genome clinical interpretations that are scientifically sound and medically relevant. Many disease associations, especially those reported before the completion of the HGP, are limited in applicability because of potential inconsistencies with our current standards for genomic coordinates, nomenclature and gene structure. In an effort to validate and link variants from the medical genetics literature to an unambiguous reference for each variant, we developed a software pipeline and reviewed 68 641 single amino acid mutations from Online Mendelian Inheritance in Man (OMIM), Human Gene Mutation Database (HGMD) and dbSNP. The frequency of unresolved mutation annotations varied widely among the databases, ranging from 4 to 23%. A taxonomy of primary causes for unresolved mutations was produced., Availability: This program is freely available from the web site (http://safegene.hms.harvard.edu/aa2nt/).

Datasets describing the health status of individuals are important for medical research but must be used cautiously to protect patient privacy. For patient data containing geographical identifiers, the conventional solution is to aggregate the data by large areas. This method often preserves privacy but suffers from substantial information loss, which degrades the quality of subsequent disease mapping or cluster detection studies. Other heuristic methods for de-identifying spatial patient information do not quantify the risk to individual privacy. We develop an optimal method based on linear programming to add noise to individual locations that preserves the distribution of a disease. The method ensures a small, quantitative risk of individual re-identification. Because the amount of noise added is minimal for the desired degree of privacy protection, the de-identified set is ideal for spatial epidemiological studies. We apply the method to patients in New York County, New York, showing that privacy is guaranteed while moving patients 25-150 times less than aggregation by zip code.

The Massachusetts Health and Homeland Alert Network (HHAN) is a secure, open-source web-based knowledge management portal for sensitive public health information. The HHAN rapidly disseminates notifications to user groups via email, the web, and telephonic alerts, and tracks whether alerts have been received and confirmed by notified parties.

Background: Knowledge of the geographical locations of individuals is fundamental to the practice of spatial epidemiology. One approach to preserving the privacy of individual-level addresses in a data set is to de-identify the data using a non-deterministic blurring algorithm that shifts the geocoded values. We investigate a vulnerability in this approach which enables an adversary to re-identify individuals using multiple anonymized versions of the original data set. If several such versions are available, each can be used to incrementally refine estimates of the original geocoded location., Results: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy). With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address. With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km., Conclusion: We demonstrate that multiple versions of the same data, each anonymized by non-deterministic Gaussian skew, can be used to ascertain original geographic locations. We explore solutions to this problem that include infrastructure to support the safe disclosure of anonymized medical data to prevent inference or re-identification of original address data, and the use of a Markov-process based algorithm to mitigate this risk.

Background: Genomic sequencing of SNPs is increasingly prevalent, though the amount of familial information these data contain has not been quantified., Methods: We provide a framework for measuring the risk to siblings of a patient's SNP genotype disclosure, and demonstrate that sibling SNP genotypes can be inferred with substantial accuracy., Results: Extending this inference technique, we determine that a very low number of matches at commonly varying SNPs is sufficient to confirm sib-ship, demonstrating that published sequence data can reliably be used to derive sibling identities. Using HapMap trio data, at SNPs where one child is homozygotic major, with a minor allele frequency

The development of integrated systems to provide patient-centric health records requires granting access to medical records that have been created at a set of disparate institutions. As many of these institutions no longer have an operational relationship with the patient, this poses a complex authentication problem. We propose a multi-factor authentication framework that allows a trusted intermediate authority to use the contents of potentially matching medical records to generate secondary authentication questions and to manage authorization of appropriate access. This helps to disambiguate between similar records from different patients, as well as ensure that the patient is who she purports to be.

Background: Widespread availability of geographic information systems software has facilitated the use of disease mapping in academia, government and private sector. Maps that display the address of affected patients are often exchanged in public forums, and published in peer-reviewed journal articles. As previously reported, a search of figure legends in five major medical journals found 19 articles from 1994-2004 that identify over 19,000 patient addresses. In this report, a method is presented to evaluate whether patient privacy is being breached in the publication of low-resolution disease maps., Results: To demonstrate the effect, a hypothetical low-resolution map of geocoded patient addresses was created and the accuracy with which patient addresses can be resolved is described. Through georeferencing and unsupervised classification of the original image, the method precisely re-identified 26% (144/550) of the patient addresses from a presentation quality map and 79% (432/550) from a publication quality map. For the presentation quality map, 99.8% of the addresses were within 70 meters (approximately one city block length) of the predicted patient location, 51.6% of addresses were identified within five buildings, 70.7% within ten buildings and 93% within twenty buildings. For the publication quality map, all addresses were within 14 meters and 11 buildings of the predicted patient location., Conclusion: This study demonstrates that lowering the resolution of a map displaying geocoded patient addresses does not sufficiently protect patient addresses from re-identification. Guidelines to protect patient privacy, including those of medical journals, should reflect policies that ensure privacy protection when spatial data are displayed or published.

Background: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal., Methods: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered., Results: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly., Conclusion: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms.

Widespread availability geographic information systems (GIS) software has facilitated the use health mapping in both academia and government. Maps that display patients as points are often exchanged in public forums (journals, meetings, web). However,even these low resolution maps may reveal confidential patient location information. In this report, we describe a method to test whether privacy is being breached. We reverse geocode from maps with cases and describe the accuracy with which patient addresses can be extracted.