Correction: Reproducible big data science: A case study in continuous FAIRness.

[This corrects the article DOI: 10.1371/journal.pone.0213013.].

Integration of genomics and transcriptomics predicts diabetic retinopathy susceptibility genes.

We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes with and without retinopathy. Those genes exhibiting the largest difference in glucose response were assessed for association with diabetic retinopathy in a genome-wide association study meta-analysis. Expression quantitative trait loci (eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified folliculin (FLCN) as a susceptibility gene for diabetic retinopathy. Expression of FLCN in response to glucose was greater in individuals with diabetic retinopathy. Independent cohorts of individuals with diabetes revealed an association of FLCN eQTLs with diabetic retinopathy. Mendelian randomization confirmed a direct positive effect of increased FLCN expression on retinopathy. Integrating genetic association with gene expression implicated FLCN as a disease gene for diabetic retinopathy.

One of the side effects of diabetes is loss of vision from diabetic retinopathy, which is caused by injury to the light sensing tissue in the eye, the retina. Almost all individuals with diabetes develop diabetic retinopathy to some extent, and it is the leading cause of irreversible vision loss in working-age adults in the United States. How long a person has been living with diabetes, the extent of increased blood sugars and genetics all contribute to the risk and severity of diabetic retinopathy. Unfortunately, virtually no genes associated with diabetic retinopathy have yet been identified. When a gene is activated, it produces messenger molecules known as mRNA that are used by cells as instructions to produce proteins. The analysis of mRNA molecules, as well as genes themselves, can reveal the role of certain genes in disease. The studies of all genes and their associated mRNAs are respectively called genomics and transcriptomics. Genomics reveals what genes are present, while transcriptomics shows how active genes are in different cells. Skol et al. developed methods to study genomics and transcriptomics together to help discover genes that cause diabetic retinopathy. Genes involved in how cells respond to high blood sugar were first identified using cells grown in the lab. By comparing the activity of these genes in people with and without retinopathy the study identified genes associated with an increased risk of retinopathy in diabetes. In people with retinopathy, the activity of the folliculin gene (FLCN) increased more in response to high blood sugar. This was further verified with independent groups of people and using computer models to estimate the effect of different versions of the folliculin gene. The methods used here could be applied to understand complex genetics in other diseases. The results provide new understanding of the effects of diabetes. They may also help in the development of new treatments for diabetic retinopathy, which are likely to improve on the current approach of using laser surgery or injections into the eye.

Reproducible big data science: A case study in continuous FAIRness.

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility-thus ensuring that big data are not hard-to-(re)use data. We evaluate our approach via a user study, and show that 91% of participants were able to replicate a complex analysis involving considerable data volumes.

A novel MERTK mutation causing retinitis pigmentosa.

PURPOSE: Retinitis pigmentosa (RP) is a genetically heterogeneous inherited retinal dystrophy. To date, over 80 genes have been implicated in RP. However, the disease demonstrates significant locus and allelic heterogeneity not entirely captured by current testing platforms. The purpose of the present study was to characterize the underlying mutation in a patient with RP without a molecular diagnosis after initial genetic testing. METHODS: Whole-exome sequencing of the affected proband was performed. Candidate gene mutations were selected based on adherence to expected genetic inheritance pattern and predicted pathogenicity. Sanger sequencing of MERTK was completed on the patient's unaffected mother, affected brother, and unaffected sister to determine genetic phase. RESULTS: Eight sequence variants were identified in the proband in known RP-associated genes. Sequence analysis revealed that the proband was a compound heterozygote with two independent mutations in MERTK, a novel nonsense mutation (c.2179C > T) and a previously reported missense variant (c.2530C > T). The proband's affected brother also had both mutations. Predicted phase was confirmed in unaffected family members. CONCLUSION: Our study identifies a novel nonsense mutation in MERTK in a family with RP and no prior molecular diagnosis. The present study also demonstrates the clinical value of exome sequencing in determining the genetic basis of Mendelian diseases when standard genetic testing is unsuccessful.