Front Genet. 2015 Jul 24;6:244. doi: 10.3389/fgene.2015.00244. eCollection 2015.

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

Middha S1, Lindor NM2, McDonnell SK3, Olson JE4, Johnson KJ5, Wieben ED6, Farrugia G7, Cerhan JR4, Thibodeau SN8.

Author information

• 1Department of Pathology, Memorial Sloan Kettering Cancer Center New York, NY, USA.
• 2Department of Health Sciences Research, Mayo Clinic Scottsdale, AZ, USA ; Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA.
• 3Department of Health Sciences Research, Mayo Clinic Rochester, MN, USA.
• 4Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Department of Health Sciences Research, Mayo Clinic Rochester, MN, USA.
• 5Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Informed DNA, St. Petersburg FL, USA.
• 6Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Department of Biochemistry and Molecular Biology, Mayo Clinic Rochester, MN, USA.
• 7Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Department of Gastroenterology and Hepatology, Mayo Clinic Jacksonville, FL, USA.
• 8Center for Individualized Medicine, Mayo Clinic Rochester, MN, USA ; Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA.

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.