Phenotype‐driven approaches to enhance variant prioritization and diagnosis of rare disease

Abstract

Rare disease diagnostics and disease gene discovery have been revolutionized by whole‐exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype−phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state‐of‐the‐art genomic interpretation.

Evaluation of a whole‐exome sequencing pipeline and benchmarking of causal germline variant prioritizers

“…However, the use of publicly available tools for assisting variant interpretation and prioritization remains underdeveloped and underutilized. Some studies have recently benchmarked phenotype-driven variant prioritization tools (Jacobsen, Kelly, Cipriani, Research Consortium, et al, 2022;Kelly et al, 2022; and the topic is gradually gaining more relevance. To our We have found that Exomiser performed best in the prioritization of causal variants.…”

Disease-causing variant recommendation system for clinical genome interpretation with adjusted scores for artefactual variants

“…Rare disorders (RD), of which 80% have genetic causes, are estimated to affect approximately 6% of the global population [1,2]. The advent of next-generation sequencing (NGS) has made a profound impact in the human genetics/genomics and medical genetics fields by revolutionizing the way rare disease diagnostics and disease gene discovery are performed [3]. The length of diagnostic odysseys of RD patients can be greatly shortened as genetic variants in all possible disease genes can be assessed simultaneously in an unbiased manner [4,5].…”

“…Exomiser filtered variants based on criteria such as the type of variants, allele frequencies, qualities of variants from the VCF file, a predefined set of genes, and inheritance patterns [14]. Although the variant filtering process helps improve variant prioritization accuracy, some diagnoses are filtered out because of low quality in the VCF, high allele frequencies, and incomplete penetrance [3]. Despite many attempts to address the problem of false calls affecting the diagnosis of RD patients, there is a fundamental limitation to previous approaches.…”

“…Variant quality score recalibration (VQSR) of GATK is a classification method for QC training using a Gaussian mixture model [11]. However, it should be noted that many false variant calls remain even after QC and affect finding the causative variants [3]. Such false-positives can be assessed as top-ranked causative variants by variant prioritization, which can lead to an incorrect diagnosis based on artefactual variants [13].…”

“…Variant prioritization based on genotype™phenotype knowledge alongside variant data is typically used to find the causative variant(s) [3]. There are some tools, such as Exomiser [14], AMELIE [15], and LIRICAL [16], which use clinical phenotype data of patients in terms of Human Phenotype Ontology (HPO) to prioritize each candidate variant based on reference phenotypic knowledge.…”