%0 Journal Article %J Nat Med %D 2020 %T Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies. %A Unlu, Gokhan %A Qi, Xinzi %A Gamazon, Eric R %A Melville, David B %A Patel, Nisha %A Rushing, Amy R %A Hashem, Mais %A Al-Faifi, Abdullah %A Chen, Rui %A Li, Bingshan %A Cox, Nancy J %A Alkuraya, Fowzan S %A Knapik, Ela W %K Abnormalities, Multiple %K Animals %K Behavior, Animal %K Biological Specimen Banks %K Chondrocytes %K Disease Models, Animal %K Extracellular Matrix %K Fibroblasts %K Guanine Nucleotide Exchange Factors %K Humans %K Models, Biological %K Musculoskeletal System %K Osteogenesis %K Phenomics %K Phenotype %K Procollagen %K Protein Transport %K Secretory Pathway %K Syndrome %K Zebrafish %K Zebrafish Proteins %X

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.

%B Nat Med %V 26 %P 98-109 %8 2020 01 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/31932796?dopt=Abstract %R 10.1038/s41591-019-0705-y %0 Journal Article %J Nat Neurosci %D 2019 %T A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. %A Wang, Quan %A Chen, Rui %A Cheng, Feixiong %A Wei, Qiang %A Ji, Ying %A Yang, Hai %A Zhong, Xue %A Tao, Ran %A Wen, Zhexing %A Sutcliffe, James S %A Liu, Chunyu %A Cook, Edwin H %A Cox, Nancy J %A Li, Bingshan %K Animals %K Bayes Theorem %K Disease Models, Animal %K Gene Regulatory Networks %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Genomics %K Humans %K Mice %K Risk Factors %K Schizophrenia %X

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

%B Nat Neurosci %V 22 %P 691-699 %8 2019 05 %G eng %N 5 %1 https://www.ncbi.nlm.nih.gov/pubmed/30988527?dopt=Abstract %R 10.1038/s41593-019-0382-7 %0 Journal Article %J Bioinformatics %D 2019 %T De novo pattern discovery enables robust assessment of functional consequences of non-coding variants. %A Yang, Hai %A Chen, Rui %A Wang, Quan %A Wei, Qiang %A Ji, Ying %A Zheng, Guangze %A Zhong, Xue %A Cox, Nancy J %A Li, Bingshan %X

MOTIVATION: Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used 'black boxes' methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.

RESULTS: DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.

AVAILABILITY AND IMPLEMENTATION: The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

%B Bioinformatics %V 35 %P 1453-1460 %8 2019 May 01 %G eng %N 9 %1 https://www.ncbi.nlm.nih.gov/pubmed/30256891?dopt=Abstract %R 10.1093/bioinformatics/bty826