Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

Using multiplexed functional data to reduce variant classification inequities in underrepresented populations

BACKGROUND

Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).

METHODS

We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN.

RESULTS

Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e - 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e - 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e - 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e - 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e - 06) and computational predictor (p = 6.92e - 05) evidence codes for individuals of non-European-like genetic ancestry.

CONCLUSIONS

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Engineering structural variants to interrogate genome function

Structural variation, such as deletions, duplications, inversions and complex rearrangements, can have profound effects on gene expression, genome stability, phenotypic diversity and disease susceptibility. Structural variants can encompass up to millions of bases and have the potential to rearrange substantial segments of the genome. They contribute considerably more to genetic diversity in human populations and have larger effects on phenotypic traits than point mutations. Until recently, our understanding of the effects of structural variants was driven mainly by studying naturally occurring variation. New genome-engineering tools capable of generating deletions, insertions, inversions and translocations, together with the discovery of new recombinases and advances in creating synthetic DNA constructs, now enable the design and generation of an extended range of structural variation. Here, we discuss these tools and examples of their application and highlight existing challenges that will need to be overcome to fully harness their potential.

Saturation profiling of drug-resistant genetic variants using prime editing

Methods to characterize the functional effects of genetic variants of uncertain significance (VUSs) have been limited by incomplete coverage of the mutational space. In clinical oncology, drug resistance arising from VUSs can prevent optimal treatment. Here we introduce PEER-seq, a high-throughput method based on prime editing that can evaluate the functional effects of single-nucleotide variants (SNVs). PEER-seq introduces both intended SNVs and synonymous marker mutations using prime editing and deep sequences the endogenous target regions to identify the introduced SNVs. We generate and functionally evaluate 2,476 SNVs in the epidermal growth factor receptor gene (EGFR), including 99% of all possible variants in the canonical tyrosine kinase domain. We determined resistance profiles of 95% of all possible EGFR protein variants encoded in the whole tyrosine kinase domain against the common tyrosine kinase inhibitors afatinib, osimertinib and osimertinib in the presence of the co-occurring substitution T790M, in PC-9 cells. Our study has the potential to substantially improve the precision of therapeutic choices in clinical settings.

High-resolution functional mapping of RAD51C by saturation genome editing

Pathogenic variants in RAD51C confer an elevated risk of breast and ovarian cancer, while individuals homozygous for specific RAD51C alleles may develop Fanconi anemia. Using saturation genome editing (SGE), we functionally assess 9,188 unique variants, including >99.5% of all possible coding sequence single-nucleotide alterations. By computing changes in variant abundance and Gaussian mixture modeling (GMM), we functionally classify 3,094 variants to be disruptive and use clinical truth sets to reveal an accuracy/concordance of variant classification >99.9%. Cell fitness was the primary assay readout allowing us to observe a phenomenon where specific missense variants exhibit distinct depletion kinetics potentially suggesting that they represent hypomorphic alleles. We further explored our exhaustive functional map, revealing critical residues on the RAD51C structure and resolving variants found in cancer-segregating kindred. Furthermore, through interrogation of UK Biobank and a large multi-center ovarian cancer cohort, we find significant associations between SGE-depleted variants and cancer diagnoses.

Federated analysis of autosomal recessive coding variants in 29,745 developmental disorder patients from diverse populations

Autosomal recessive coding variants are well-known causes of rare disorders. We quantified the contribution of these variants to developmental disorders in a large, ancestrally diverse cohort comprising 29,745 trios, of whom 20.4% had genetically inferred non-European ancestries. The estimated fraction of patients attributable to exome-wide autosomal recessive coding variants ranged from ~2-19% across genetically inferred ancestry groups and was significantly correlated with average autozygosity. Established autosomal recessive developmental disorder-associated (ARDD) genes explained 84.0% of the total autosomal recessive coding burden, and 34.4% of the burden in these established genes was explained by variants not already reported as pathogenic in ClinVar. Statistical analyses identified two novel ARDD genes: KBTBD2 and ZDHHC16. This study expands our understanding of the genetic architecture of developmental disorders across diverse genetically inferred ancestry groups and suggests that improving strategies for interpreting missense variants in known ARDD genes may help diagnose more patients than discovering the remaining genes.

Complex trait associations in rare diseases and impacts on Mendelian variant interpretation

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - in the onset and phenotypic presentation of rare diseases. Here, we comprehensively map individual polygenic liability for 1102 open-source PGS in a cohort of 3059 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. Using this resource, we demonstrate increased polygenic liability in probands with an inherited candidate disease variant (VUS) compared to unaffected carrier parents. Further, we show an enrichment for large-effect rare variants in putative core PGS genes for associated complex traits. Overall, our study supports and expands on previous findings of complex trait associations in rare diseases, implicates polygenic liability as a potential mechanism underlying variable penetrance of candidate causal variants, and provides a framework for identifying novel candidate rare disease genes.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Saturation genome editing maps the functional spectrum of pathogenic VHL alleles

To maximize the impact of precision medicine approaches, it is critical to identify genetic variants underlying disease and to accurately quantify their functional effects. A gene exemplifying the challenge of variant interpretation is the von Hippel-Lindautumor suppressor (VHL). VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here we optimize and apply saturation genome editing to assay nearly all possible single-nucleotide variants (SNVs) across VHL's coding sequence. To delineate mechanisms, we quantify mRNA dosage effects and compare functional effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types and reveal new mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered clinically and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.

Saturation genome editing of BAP1 functionally classifies somatic and germline variants

Many variants that we inherit from our parents or acquire de novo or somatically are rare, limiting the precision with which we can associate them with disease. We performed exhaustive saturation genome editing (SGE) of BAP1, the disruption of which is linked to tumorigenesis and altered neurodevelopment. We experimentally characterized 18,108 unique variants, of which 6,196 were found to have abnormal functions, and then used these data to evaluate phenotypic associations in the UK Biobank. We also characterized variants in a large population-ascertained tumor collection, in cancer pedigrees and ClinVar, and explored the behavior of cancer-associated variants compared to that of variants linked to neurodevelopmental phenotypes. Our analyses demonstrated that disruptive germline BAP1 variants were significantly associated with higher circulating levels of the mitogen IGF-1, suggesting a possible pathological mechanism and therapeutic target. Furthermore, we built a variant classifier with >98% sensitivity and specificity and quantify evidence strengths to aid precision variant interpretation.

Analyzing the functional effects of DNA variants with gene editing

Continual advancements in genomics have led to an ever-widening disparity between the rate of discovery of genetic variants and our current understanding of their functions and potential roles in disease. Systematic methods for phenotyping DNA variants are required to effectively translate genomics data into improved outcomes for patients with genetic diseases. To make the biggest impact, these approaches must be scalable and accurate, faithfully reflect disease biology, and define complex disease mechanisms. We compare current methods to analyze the function of variants in their endogenous DNA context using genome editing strategies, such as saturation genome editing, base editing and prime editing. We discuss how these technologies can be linked to high-content readouts to gain deep mechanistic insights into variant effects. Finally, we highlight key challenges that need to be addressed to bridge the genotype to phenotype gap, and ultimately improve the diagnosis and treatment of genetic diseases.

Defining and Reducing Variant Classification Disparities

Background

Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS).

Methods

We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN .

Results

Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry.

Conclusions

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Recent advances in CRISPR-based functional genomics for the study of disease-associated genetic variants

Advances in sequencing technology have greatly increased our ability to gather genomic data, yet understanding the impact of genetic mutations, particularly variants of uncertain significance (VUSs), remains a challenge in precision medicine. The CRISPR‒Cas system has emerged as a pivotal tool for genome engineering, enabling the precise incorporation of specific genetic variations, including VUSs, into DNA to facilitate their functional characterization. Additionally, the integration of CRISPR‒Cas technology with sequencing tools allows the high-throughput evaluation of mutations, transforming uncertain genetic data into actionable insights. This allows researchers to comprehensively study the functional consequences of point mutations, paving the way for enhanced understanding and increasing application to precision medicine. This review summarizes the current genome editing tools utilizing CRISPR‒Cas systems and their combination with sequencing tools for functional genomics, with a focus on point mutations.

Complex trait associations in rare diseases and impacts on Mendelian variant interpretation

Emerging evidence implicates common genetic variation - aggregated into polygenic scores (PGS) - impacting the onset and phenotypic presentation of rare diseases. In this study, we quantified individual polygenic liability for 1,151 previously published PGS in a cohort of 2,374 probands enrolled in the Genomic Answers for Kids (GA4K) rare disease study, revealing widespread associations between rare disease phenotypes and PGSs for common complex diseases and traits, blood protein levels, and brain and other organ morphological measurements. We observed increased polygenic burden in probands with variants of unknown significance (VUS) compared to unaffected carrier parents. We further observed an enrichment in overlap between diagnostic and candidate rare disease genes and large-effect PGS genes. Overall, our study supports and expands on previous findings of complex trait associations in rare disease phenotypes and provides a framework for identifying novel candidate rare disease genes and in understanding variable penetrance of candidate Mendelian disease variants.