Abstract

Variations in disease onset and/or severity have often been observed in siblings with cystic fibrosis (CF), despite the same CFTR genotype and environment. We postulated that genomic variation (modifier and/or pharmacogenomic variants) might explain these clinical discordances. From a cohort of patients included in the Wisconsin randomized clinical trial (RCT) of newborn screening (NBS) for CF, we identified two brothers who showed discordant lung disease courses as children, with one milder and the other more severe than average, and a third, eldest brother, who also has severe lung disease. Leukocytes were harvested as the source of DNA, and whole-genome sequencing (WGS) was performed. Variants were identified and analyzed using in-house-developed informatics tools. Lung disease onset and severity were quantitatively different between brothers during childhood. The youngest, less severely affected brother is homozygous for HFE p.H63D. He also has a very rare PLG p.D238N variant that may influence host–pathogen interaction during chronic lung infection. Other variants of interest were found differentially between the siblings. Pharmacogenomics findings were consistent with the middle, most severely affected brother having poor outcomes to common CF treatments. We conclude that genomic variation between siblings with CF is expected. Variable lung disease severity may be associated with differences acting as genetic modifiers and/or pharmacogenomic factors, but large cohort studies are needed to assess this hypothesis.

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We assessed the results of genome sequencing for early-onset dementia. Participants were selected from a memory disorders clinic. Genome sequencing was performed along with C9orf72 repeat expansion testing. All returned sequencing results were Sanger-validated. Prior clinical diagnoses included Alzheimer’s disease, frontotemporal dementia, and unspecified dementia. The mean age of onset was 54 (41-76). Fifty percent of patients had a strong family history, 37.5% had some, and 12.5% had no known family history. Nine of 32 patients (28%) had a variant defined as pathogenic or likely pathogenic (P/LP) by American College of Medical Genetics and Genomics standards, including variants in APP, C9orf72, CSF1R, and MAPT Nine patients (including three with P/LP variants) harbored established risk alleles with moderate penetrance (odds ratios of ∼2-5) in ABCA7, AKAP9, GBA, PLD3, SORL1, and TREM2 All six patients harboring these moderate penetrance variants but not P/LP variants also had one or two APOE ε4 alleles. One patient had two APOE ε4 alleles with no other established contributors. In total, 16 patients (50%) harbored one or more genetic variants likely to explain symptoms. We identified variants of uncertain significance (VUSs) in ABI3, ADAM10, ARSA, GRID2IP, MME, NOTCH3, PLCD1, PSEN1, TM2D3, TNK1, TTC3, and VPS13C, also often along with other variants. In summary, genome sequencing for early-onset dementia frequently identified multiple established or possible contributory alleles. These observations add support for an oligogenic model for early-onset dementia.

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Abstract

Background

When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient’s phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance.

Methods

We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network.

Results

We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20.

Conclusions

We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

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