Homozygosity mapping provides supporting evidence of pathogenicity in recessive Mendelian disease.

PURPOSE: One of the greatest challenges currently facing those studying Mendelian disease is identifying the pathogenic variant from the long list produced by a next-generation sequencing test. We investigate the predictive ability of homozygosity mapping for identifying the regions likely to contain the causative variant. METHODS: We use 179 homozygous pathogenic variants from three independent cohorts to investigate the predictive power of homozygosity mapping. RESULTS: We demonstrate that homozygous pathogenic variants in our cohorts are disproportionately likely to be found within one of the largest regions of homozygosity: 80% of pathogenic variants are found in a homozygous region that is in the ten largest regions in a sample. The maximal predictive power is achieved in patients with <8% homozygosity and variants >3 Mb from a telomere; this gives an area under the curve (AUC) of 0.735 and results in 92% of the causative variants being in one of the ten largest homozygous regions. CONCLUSION: This predictive power can be used to prioritize the list of candidate variants in gene discovery studies. When classifying a homozygous variant the size and rank of the region of homozygosity in which the candidate variant is located can also be considered as supporting evidence for pathogenicity.

Correction: Homozygosity mapping provides supporting evidence of pathogenicity in recessive Mendelian disease.

The original version of this Article contained an error in the top left of Figure 2: the number 1 on the y-axis had been changed to 0 during the typesetting process. This has now been corrected in both the PDF and HTML versions of the Article.

Evaluating the use of paralogous protein domains to increase data availability for missense variant classification.

BACKGROUND: Classification of rare missense variants remains an ongoing challenge in genomic medicine. Evidence of pathogenicity is often sparse, and decisions about how to weigh different evidence classes may be subjective. We used a Bayesian variant classification framework to investigate the performance of variant co-localisation, missense constraint, and aggregating data across paralogous protein domains ("meta-domains"). METHODS: We constructed a database of all possible coding single nucleotide variants in the human genome and used PFam predictions to annotate structurally-equivalent positions across protein domains. We counted the number of pathogenic and benign missense variants at these equivalent positions in the ClinVar database, calculated a regional constraint score for each meta-domain, and assessed this approach versus existing missense constraint metrics for classifying variant pathogenicity and benignity. RESULTS: Alternative pathogenic missense variants at the same amino acid position in the same protein provide strong evidence of pathogenicity (positive likelihood ratio, LR+ = 85). Additionally, clinically annotated pathogenic or benign missense variants at equivalent positions in different proteins can provide moderate evidence of pathogenicity (LR+ = 7) or benignity (LR+ = 5), respectively. Applying these approaches sequentially (through PM5) increases sensitivity for classifying pathogenic missense variants from 27 to 41%. Missense constraint can also provide strong evidence of pathogenicity for some variants, but its absence provides no evidence of benignity. CONCLUSIONS: We propose using structurally equivalent positions across related protein domains from different genes to augment evidence for variant co-localisation when classifying novel missense variants. Additionally, we advocate adopting a numerical evidence-based approach to integrating diverse data in variant interpretation.

Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how?

Recent research into the human genome has generated a wealth of scientific knowledge and increased both public and professional interest in the concept of personalised medicine. Somewhat unexpectedly, in addition to increasing our understanding about the genetic basis for numerous diseases, these new discoveries have also spawned a burgeoning new industry of 'consumer genetic testing'. In this paper, we present the principles learnt though the evaluation of tests for single gene disorders and suggest a comparable framework for the evaluation of genetic tests for susceptibility to common complex diseases. Both physicians and the general public will need to be able to assess the claims made by providers of genetic testing services, and ultimately policy-makers will need to decide if and when such tests should be offered through state funded healthcare systems.