How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples Journal Article


Authors: Middha, S.; Lindor, N. M.; McDonnell, S. K.; Olson, J. E.; Johnson, K. J.; Wieben, E. D.; Farrugia, G.; Cerhan, J. R.; Thibodeau, S. N.
Article Title: How well do whole exome sequencing results correlate with medical findings? A study of 89 Mayo Clinic Biobank samples
Abstract: Whole exome sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic medical record via chart review. Variants [Single Nucleotide Variant (SNV) and insertion/deletion] were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency <0.1) and available HGMD/OMIM phenotype information. Variants were defined as Tier-1 (nonsense, splice or frame-shifting) and Tier-2 (missense, predicted-damaging) and evaluated in 56 ACMG-reportable genes, 57 cancer-predisposition genes, along with examining overall genotype-phenotype correlations. Following variant filtering, 7046 total variants were identified (~79/person, 644 Tier-1, 6402 Tier-2), 161 among 56 ACMG-reportable genes (~1.8/person, 13 Tier-1, 148 Tier-2), and 115 among 57 cancer-predisposition genes (~1.3/person, 3 Tier-1, 112 Tier-2). The number of variants across 57 cancer-predisposition genes did not differentiate individuals with/without invasive cancer history (P > 0.19). Evaluating genotype-phenotype correlations across the exome, 202(3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical records, while 3710(53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES. © 2015 Middha, Lindor, McDonnell, Olson, Johnson, Wieben, Farrugia, Cerhan and Thibodeau.
Keywords: gene mutation; gene sequence; cancer diagnosis; phenotype; allele; smad3 protein; genotype; brca2 protein; electronic medical record; bioinformatics; tumor gene; genetic predisposition; genetic polymorphism; sequencing; inheritance; apolipoprotein b; emr; exome; mendelian randomization analysis; human; article; exome sequencing; low density lipoprotein receptor; hgmd; omim
Journal Title: Frontiers in Genetics
Volume: 6
ISSN: 1664-8021
Publisher: Frontiers Media S.A.  
Date Published: 2015-07-24
Start Page: 244
Language: English
DOI: 10.3389/fgene.2015.00244
PROVIDER: scopus
PMCID: PMC4513238
PUBMED: 26257771
DOI/URL:
Notes: Export Date: 2 October 2015 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Sumit   Middha
    83 Middha