%0 Journal Article %J Science %D 2018 %T Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. %A Bastarache, Lisa %A Hughey, Jacob J %A Hebbring, Scott %A Marlo, Joy %A Zhao, Wanke %A Ho, Wanting T %A Van Driest, Sara L %A McGregor, Tracy L %A Mosley, Jonathan D %A Wells, Quinn S %A Temple, Michael %A Ramirez, Andrea H %A Carroll, Robert %A Osterman, Travis %A Edwards, Todd %A Ruderfer, Douglas %A Velez Edwards, Digna R %A Hamid, Rizwan %A Cogan, Joy %A Glazer, Andrew %A Wei, Wei-Qi %A Feng, QiPing %A Brilliant, Murray %A Zhao, Zhizhuang J %A Cox, Nancy J %A Roden, Dan M %A Denny, Joshua C %K Databases, Genetic %K DNA Mutational Analysis %K Electronic Health Records %K Exome %K Genetic Association Studies %K Genetic Diseases, Inborn %K Genetic Predisposition to Disease %K Genetic Variation %K Humans %K Phenotype %K Risk Factors %X

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

%B Science %V 359 %P 1233-1239 %8 2018 03 16 %G eng %N 6381 %1 https://www.ncbi.nlm.nih.gov/pubmed/29590070?dopt=Abstract %R 10.1126/science.aal4043 %0 Journal Article %J Elife %D 2017 %T Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system. %A Belbin, Gillian Morven %A Odgis, Jacqueline %A Sorokin, Elena P %A Yee, Muh-Ching %A Kohli, Sumita %A Glicksberg, Benjamin S %A Gignoux, Christopher R %A Wojcik, Genevieve L %A Van Vleck, Tielman %A Jeff, Janina M %A Linderman, Michael %A Schurmann, Claudia %A Ruderfer, Douglas %A Cai, Xiaoqiang %A Merkelson, Amanda %A Justice, Anne E %A Young, Kristin L %A Graff, Misa %A North, Kari E %A Peters, Ulrike %A James, Regina %A Hindorff, Lucia %A Kornreich, Ruth %A Edelmann, Lisa %A Gottesman, Omri %A Stahl, Eli Ea %A Cho, Judy H %A Loos, Ruth Jf %A Bottinger, Erwin P %A Nadkarni, Girish N %A Abul-Husn, Noura S %A Kenny, Eimear E %K Adolescent %K Adult %K Aged %K Child %K Collagen Diseases %K Female %K Fibrillar Collagens %K Genotype %K Heterozygote %K Hispanic Americans %K Homozygote %K Humans %K Male %K Middle Aged %K Molecular Epidemiology %K Multigene Family %K Musculoskeletal Diseases %K New York City %K Pedigree %K Whole Genome Sequencing %K Young Adult %X

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

%B Elife %V 6 %8 2017 09 12 %G eng %1 https://www.ncbi.nlm.nih.gov/pubmed/28895531?dopt=Abstract %R 10.7554/eLife.25060