Common and rare variants associated with kidney stones and biochemical traits.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.Kidney stone disease is a complex disorder with a strong genetic component. We conducted a genome-wide association study of 28.3 million sequence variants detected through whole-genome sequencing of 2,636 Icelanders that were imputed into 5,419 kidney stone cases, including 2,172 cases with a history of recurrent kidney stones, and 279,870 controls. We identify sequence variants associating with kidney stones at ALPL (rs1256328[T], odds ratio (OR)=1.21, P=5.8 × 10(-10)) and a suggestive association at CASR (rs7627468[A], OR=1.16, P=2.0 × 10(-8)). Focusing our analysis on coding sequence variants in 63 genes with preferential kidney expression we identify two rare missense variants SLC34A1 p.Tyr489Cys (OR=2.38, P=2.8 × 10(-5)) and TRPV5 p.Leu530Arg (OR=3.62, P=4.1 × 10(-5)) associating with recurrent kidney stones. We also observe associations of the identified kidney stone variants with biochemical traits in a large population set, indicating potential biological mechanism.Rare Kidney Stone Consortium 5U54DK083908-07 National Center for Advancing Translational Sciences (NCATS) Rare Diseases Clinical Research Network (RDCRN) Rare Kidney Stone Consortiu

PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels

I ndividual genomes contain millions of genetic variants. When considering which variants may be causative for a given rare genetic disease, applying filtering criteria (such as allele frequency, predicted variant consequence, familial segregation and mode of inheritance) decreases this number to hundreds of variants. However, such a number remains labor intensive for a diagnostic genetic testing laboratory to interpret as part of routine service for each patient or family. A list of genes with evidence of disease causation in the condition being assessed aids in prioritizing and ranking the variants. This prioritization decreases the number of candidates that laboratories or clinical geneticists must assess to identify the likely causative variants for clinical reporting. Established lists of genes with clear evidence of disease causation (referred to herein as virtual gene panels) are therefore a highly effective tool in variant prioritization.M. Caulfield was funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Center at Barts and The London School of Medicine and Dentistry. He is supported as an NIHR senior investigator, and this work was funded by the MRC eMedLab award. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health). The 100,000 Genomes Project is funded by the NIHR and NHSE. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructur