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Functional Assessment of Coding and Regulatory Variants From the DKK1 Locus

ABSTRACT: ABSTRACT The DKK1 gene encodes an extracellular inhibitor of the Wnt pathway with an important role in bone tissue development, bone homeostasis, and different critical aspects of bone biology. Several BMD genome?wide association studies (GWASs) have consistently found association with SNPs in the DKK1 genomic region. For these reasons, it is important to assess the functionality of coding and regulatory variants in the gene. Here, we have studied the functionality of putative regulatory variants, previously found associated with BMD in different studies by others and ourselves, and also six missense variants present in the general population. Using a Wnt?pathway?specific luciferase reporter assay, we have determined that the variants p.Ala41Thr, p.Tyr74Phe, p.Arg120Leu, and p.Ser157Ile display a reduced DKK1 inhibitory capacity as compared with WT. This result agrees with the high?bone?mass (HBM) phenotype of two women from our cohort who carried mutations p.Tyr74Phe or p.Arg120Leu. On the other hand, by means of a circularized chromosome conformation capture? (4C?) sequencing experiment, we have detected that the region containing 24 BMD?GWA variants, located 350?kb downstream of DKK1, interacts both with DKK1 and the LNCAROD (LncRNA?activating regulator of DKK1, AKA LINC0148) in osteoblastic cells. In conclusion, we have shown that some rare coding variants are partial loss?of?function mutations that may lead to a HBM phenotype, whereas the common SNPs associated with BMD in GWASs belong to a putative long?range regulatory region, through a yet unknown mechanism involving LNCAROD. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

SUBMITTER: Martinez?Gil N

PROVIDER: S-EPMC7745885 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Project description:SummaryInterpreting genetic variants of unknown significance (VUS) is essential in clinical applications of genome sequencing for diagnosis and personalized care. Non-coding variants remain particularly difficult to interpret, despite making up a large majority of trait associations identified in genome-wide association studies (GWAS) analyses. Predicting the regulatory effects of non-coding variants on candidate genes is a key step in evaluating their clinical significance. Here, we develop a machine-learning algorithm, Inference of Connected expression quantitative trait loci (eQTLs) (IRT), to predict the regulatory targets of non-coding variants identified in studies of eQTLs. We assemble datasets using eQTL results from the Genotype-Tissue Expression (GTEx) project and learn to separate positive and negative pairs based on annotations characterizing the variant, gene and the intermediate sequence. IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.799 using random cross-validation, and 0.700 for a more stringent position-based cross-validation. Further evaluation on rare variants and experimentally validated regulatory variants shows a significant enrichment in IRT identifying the true target genes versus negative controls. In gene-ranking experiments, IRT achieves a top-1 accuracy of 50% and top-3 accuracy of 90%. Salient features, including GC-content, histone modifications and Hi-C interactions are further analyzed and visualized to illustrate their influences on predictions. IRT can be applied to any VUS of interest and each candidate nearby gene to output a score reflecting the likelihood of regulatory effect on the expression level. These scores can be used to prioritize variants and genes to assist in patient diagnosis and GWAS follow-up studies.Availability and implementationCodes and data used in this work are available at https://github.com/miaecle/eQTL_Trees.Supplementary informationSupplementary data are available at Bioinformatics online.

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Functional Assessment of Coding and Regulatory Variants From the DKK1 Locus

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