Laure Frésard

Summary: Variants of uncertain significance (VUS) pose a significant challenge for those undergoing genetic testing, leading to prolonged uncertainty and inappropriate medical care. VUS rate reduction is critical to fully realize the utility of genetic testing for all populations. With the growth of large-scale biological data sources and modern Machine Learning (ML) techniques, predictive modeling has enormous potential for VUS reduction. For this purpose, we developed the Invitae Evidence Modeling™ Platform (EMP), with key features designed to maximize the utility and confidence of predictive algorithms for variant classification. First, input data for a new model is curated to correspond to a single major evidence category within a variant classification framework. Second, gene-specific training and/or validation is performed for each model type. Third, accuracy thresholds are set to filter out gene-specific models that do not meet stringent accuracy metrics. Finally, prediction scores for variant pathogenicity are calibrated to ensure internally consistent evidence weighting within the classification framework. The EMP has accelerated the development of ML algorithms and greatly expanded the amount of evidence available for variant classification. EMP evidence has been applied to more than 800,000 variants across 1 million individuals, 42% of which would have been VUS without this evidence. Importantly, definitive classifications (P, LP, LB, B) made with EMP evidence have high prospective concordance (>99%) with ClinVar submissions. Finally, we demonstrate that further use and development of EMP evidence for variant classification has the potential to reduce the VUS disparity across race/ethnicity/ancestry (REA) groups.

The basis for acquired resistance to JAK inhibition in patients with JAK2-driven hematologic malignancies is not well understood. We report a patient with a myeloproliferative neoplasm (MPN) with a BCR activator of RhoGEF and GTPase (BCR)–JAK2 fusion with initial hematologic response to ruxolitinib who rapidly developed B-lymphoid blast transformation. We analyzed pre-ruxolitinib and blast transformation samples using genome sequencing, DNA mate-pair sequencing (MPseq), RNA sequencing (RNA-seq), and chromosomal microarray to characterize possible mechanisms of resistance. No resistance mutations in the BCR-JAK2 fusion gene or transcript were identified, and fusion transcript expression levels remained stable. However, at the time of blast transformation, MPseq detected a new IKZF1 copy-number loss, which is predicted to result in loss of normal IKZF1 protein translation. RNA-seq revealed significant upregulation of genes negatively regulated by IKZF1, including IL7R and CRLF2. Disease progression was also characterized by adaptation to an activated B-cell receptor (BCR)–like signaling phenotype, with marked upregulation of genes such as CD79A, CD79B, IGLL1, VPREB1, BLNK, ZAP70, RAG1, and RAG2. In summary, IKZF1 deletion and a switch from cytokine dependence to activated BCR-like signaling phenotype represent putative mechanisms of ruxolitinib resistance in this case, recapitulating preclinical data on resistance to JAK inhibition in CRLF2-rearranged Philadelphia chromosome-like acute lymphoblastic leukemia.

Genome-wide association studies (GWAS) have identified thousands of variants associated with disease phenotypes. However, the majority of these variants do not alter coding sequences, making it difficult to assign their function. To this end, we present a multi-omic epigenetic atlas of the adult human brain through profiling of the chromatin accessibility landscapes and three-dimensional chromatin interactions of seven brain regions across a cohort of 39 cognitively healthy individuals. Single-cell chromatin accessibility profiling of 70,631 cells from six of these brain regions identifies 24 distinct cell clusters and 359,022 cell type-specific regulatory elements, capturing the regulatory diversity of the adult brain. We develop a machine learning classifier to integrate this multi-omic framework and predict dozens of functional single nucleotide polymorphisms (SNPs), nominating gene and cellular targets for previously orphaned GWAS loci. These predictions both inform well-studied disease-relevant genes, such asBIN1in microglia for Alzheimer’s disease (AD) and reveal novel gene-disease associations, such asSTAB1in microglia andMALin oligodendrocytes for Parkinson’s disease (PD). Moreover, we dissect the complex inverted haplotype of theMAPT(encoding tau) PD risk locus, identifying ectopic enhancer-gene contacts in neurons that increaseMAPTexpression and may mediate this disease association. This work greatly expands our understanding of inherited variation in AD and PD and provides a roadmap for the epigenomic dissection of noncoding regulatory variation in disease.

RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects for individuals affected with a genetically undiagnosed rare disorder. Pioneer studies have shown that RNA-seq could increase diagnostic rates over DNA sequencing alone by 8% to 36 % depending on disease entities and probed tissues. To accelerate the adoption of RNA-seq among human genetic centers, detailed analysis protocols are now needed. Here, we present a step-by-step protocol that instructs how to robustly detect aberrant expression, aberrant splicing, and mono-allelic expression of a rare allele in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on DROP (Detection of RNA Outliers Pipeline), a modular computational workflow that integrates all analysis steps, can leverage parallel computing infrastructures, and generates browsable web page reports.

Identifying regulatory genetic effects in pluripotent cells provides important insights into disease variants with potentially transient or developmental origins. Combining existing and newly-generated data, we characterized 1,367 iPSC lines from 948 unique donors, collectively analyzed within the “Integrated iPSC QTL” (i2QTL) Consortium. The sample size of our study allowed us to derive the most comprehensive map of quantitative trait loci (QTL) in pluripotent human cells to date. We mapped the effects of nearby common genetic variants on five expression phenotypes, identifying cis -QTL at gene-, exon-level and transcript-, splicing-, alternative polyadenylation-ratio (APA) for a total of 18,556 genes. For gene-level, we further quantified the effects of rare and singleton variants, and the effect of distal variants that act in trans ( trans -eQTL), which we replicated in independent samples. Our data are a valuable community resource, uncovering novel regulatory effects that have not previously been described in differentiated cells and tissues. Building on this regulatory map, we functionally explore GWAS signals for over 4,336 trait loci, finding evidence for colocalization with common and rare iPSC QTL for traits such as height and BMI, and diseases, such as cancer and coronary artery disease.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene¹. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches^2,3,4,5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases^6,7,8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders^6,9, and cultured fibroblasts from patients with mitochondrial disorders⁷. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.

High-throughput sequencing has ushered in a diversity of approaches for identifying genetic variants and understanding genome structure and function. When applied to individuals with rare genetic diseases, these approaches have greatly accelerated gene discovery and patient diagnosis. Over the past decade, exome sequencing has emerged as a comprehensive and cost-effective approach to identify pathogenic variants in the protein-coding regions of the genome. However, for individuals in whom exome-sequencing fails to identify a pathogenic variant, we discuss recent advances that are helping to reduce the diagnostic gap.

RNA sequencing (RNA-seq) is a complementary approach for Mendelian disease diagnosis for patients in whom exome-sequencing is not informative. For both rare neuromuscular and mitochondrial disorders, its application has improved diagnostic rates. However, the generalizability of this approach to diverse Mendelian diseases has yet to be evaluated. We sequenced whole blood RNA from 56 cases with undiagnosed rare diseases spanning 11 diverse disease categories to evaluate the general application of RNA-seq to Mendelian disease diagnosis. We developed a robust approach to compare rare disease cases to existing large sets of RNA-seq controls (N=1,594 external and N=31 family-based controls) and demonstrated the substantial impacts of gene and variant filtering strategies on disease gene identification when combined with RNA-seq. Across our cohort, we observed that RNA-seq yields a 8.5% diagnostic rate. These diagnoses included diseases where blood would not intuitively reflect evidence of disease. We identified RARS2 as an under-expression outlier containing compound heterozygous pathogenic variants for an individual exhibiting profound global developmental delay, seizures, microcephaly, hypotonia, and progressive scoliosis. We also identified a new splicing junction in KCTD7 for an individual with global developmental delay, loss of milestones, tremors and seizures. Our study provides a broad evaluation of blood RNA-seq for the diagnosis of rare disease.