Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging

Abstract

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

Keywords: advanced imaging; deep phenotyping; genomics; metabolomics; precision medicine.

Fig. 1.

Summary of all tests and results. (A) Diagram of study process. Organs screened by whole-body MRI: brain: neck, orbits, paranasal sinuses, skull base, nasopharynx, suprahyoid/infrahyoid neck, thyroid, thoracic inlet, lymph nodes, vascular structures, and marrow; chest: lungs and large airways, pleura, heart, mediastinum and hila, chest wall, vessels, and marrow; abdomen: liver, bile ducts, gallbladder, pancreas, spleen, adrenals, kidneys, bowel, mesenteric lymph nodes, peritoneum, retroperitoneum, abdominal wall, and marrow; pelvis: reproductive organs, bladder, bowel, peritoneum, abdominal wall, and marrow. The asterisk indicates a list of 13 variants with allele frequency higher than 1% provided in SI Appendix. (B) Heatmap of all test results listed chronologically. Cancer Dx, cancer cases diagnosed in this study; CT, computed tomography coronary artery calcium scoring; DM, damaging variant defined by HGMD; ECHO/MRI, union of the dataset of echocardiography and cardiac MRI; IGT, impaired glucose tolerance test; IR, insulin resistance test; LoF, loss-of-function variant.

Fig. 2.

Associations between medically significant genetic findings and phenotype tests by disease group and test. (A) Numbers of cases (gene [cases]) per disease categories are listed. (B) Clinical synopses in OMIM and clinical phenotypes listed in the literature are used to establish associations between MSFs and phenotype tests. The heatmap shows the fraction of cases (percentage listed) where phenotype tests detected expected clinical features for the MSF. For example, for a participant with a pathogenic variant observed in the HNF1B gene, the team would review his/her results in clinical laboratory tests and metabolome for profiles that are indicators of impaired glucose tolerance or impaired insulin sensitivity and an MRI test for renal cysts. Thus, he/she may have reportable findings for MRI, family history, metabolome, and clinical laboratory tests; however, the phenotype and genotype association still counts as 1 event for the bar plot. Among the 206 individuals, 21 individuals have more than 1 MSF in different disease groups. The percentages of “not tested” for the participants with MSFs are 41.7% for CT, 38.8% for metabolome, 24.2% for ECG/CCM, 12.6% for clinical laboratory test, and 0% for MRI and ECHO. FHx, family history; PHx, past and current personal medical history.

Fig. 3.

Examples of integrated diagnoses. (A) A female (40s) heterozygous for an HNF1B likely pathogenic variant with renal cysts and diabetes syndrome. Yellow arrows point to the bilateral renal cysts. (B) A male (40s) heterozygous for a likely pathogenic PKD1 variant with polycystic kidney disease. (C) A male (50s) heterozygous for a pathogenic variant in the PCSK9 gene. The yellow arrow points to the calcified left main/anterior descending coronary artery. (D) A male (60s) heterozygous for an MYH7 pathogenic variant hypertrophic cardiomyopathy. (E and F) Clinical diagnosis of neurofibromatosis type 1. (E) Optic nerve glioma and stenosis in the right middle cerebral artery (MCA). (F) Decreased conspicuity of tertiary MCA branches compatible with Moyamoya syndrome. (G) A male (60s) compound heterozygous for CFTR pathogenic variants with digestive issues/bloating and chronic sinus infections. The green circle shows a tree-in-bud nodularity. (H) A female (60s) heterozygous for a pathogenic PKDH1 variant with a defined liver cyst (yellow arrow) and numerous scattered subcentimeter liver cysts/hemangiomas (white arrows). (I) Summary of clinical findings in represented cases. Dx, diagnosis; eGFR, estimated glomerular filtration rate; F, female; LV, left ventricular; M, male; RV, right ventricular.

Fig. 4.

Metabolite levels in heterozygous carriers of rare coding variants in recessive inborn errors of metabolism genes. Shown is the normalized Z score of the metabolite levels of each individual who carries a coding variant in each gene (y axis), with the amino acid position shown on the x axis. All variants have minor allele frequency (MAF) <0.5%, and all have Combined Annotation Dependent Depletion (CADD) scores >15. Red lines indicate cutoffs for normalized Z scores above 0, indicating increased levels of phenylalanine relative to the mean, and 2, indicating outliers of interest. Known causal variants are shown in black according to http://www.biopku.org/home/home.asp for PAH and ClinVar or HGMD for ETFDH and DMGDH (none of the DMGDH variants shown here were found in ClinVar or HGMD). Gene annotations were generated from pfam.xfam.org. (A) Phenylalanine levels of PAH variant carriers. Green, ACT domain; red, biopterin-dependent aromatic amino acid hydroxylase. (B) Dimethylglycine levels of DMGDH variant carriers. Green, FAD-dependent oxidoreductase; red, Flavin adenine dinucleotide (FAD)-dependent oxidoreductase central domain; blue, aminomethyltransferase folate-binding domain; yellow, glycine cleavage T-protein C-terminal barrel domain. (C and D) Octanoylcarnitine (C) and decanoylcarnitine (D) levels of ETFDH variant carriers. Green, Thi4 family; red, electron transfer flavoprotein-ubiquinone oxidoreductase, 4Fe-4S.