Project description:<p>The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD).</p> <ol> <li>OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession <a href="./study.cgi?study_id=phs000762">phs000762</a>). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (<a href="javascript:getPage(this, 'variable.cgi', 222740);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222740);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222740);return true;">PHASE1CASECONTROL</a>) identifying participants in this sub-study. Data users should use the relevant case-classification variable (<a href="javascript:getPage(this, 'variable.cgi', 222742);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222742);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222742);return true;">CHRNONICTMDCASE</a>) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study.</li> <li>OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession <a href="./study.cgi?study_id=phs000850">phs000850</a>). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (<a href="javascript:getPage(this, 'variable.cgi', 222743);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222743);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222743);return true;">PROSPECTIVECOHORT</a>) identifying participants in this sub-study. Data users should use the relevant case-classification variable (<a href="javascript:getPage(this, 'variable.cgi', 222746);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222746);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222746);return true;">INCIDENTCASE</a>) and the relevant time-in-study variable (<a href="javascript:getPage(this, 'variable.cgi', 222745);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222745);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222745);return true;">FOLLOWUPYEARS</a>) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where <a href="javascript:getPage(this, 'variable.cgi', 222746);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222746);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222746);return true;">INCIDENTCASE</a> is the censoring variable and <a href="javascript:getPage(this, 'variable.cgi', 222745);return true;" onclick="javascript:getPage(this, 'variable.cgi', 222745);return true;" oncontextmenu="javascript:getPage(this, 'variable.cgi', 222745);return true;">FOLLOWUPYEARS</a> is time in study). </li> </ol>

Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.

			Action	DRS
	README.txt	Txt
	phs001516.eMERGE_EMRPhenome.analysis-PI.MULTI.tar.gz	Other
	phs001516.pha004655.txt	Txt
	GapExchange_phs001516.v1.p1.xml	Xml
	dbGaPEx2.1.5.xsd	Other

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Phenotype Risk Score Study of Rare Variants

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