Reissuance of NIDCR Small Research Grants for Data Analysis and Statistical Methodology Applied to Genome-wide Data Program (R03)

Translational Genomics Research Branch, Division of Extramural Research


The goal for renewing the NIDCR Small Research Grants for Data Analysis and Statistical Methodology Applied to Genome-wide Data Program (R03) is to continue supporting projects that re-analyze existing genome-wide omics data to investigate questions about DOC conditions or traits.  Funded projects can use existing and/or develop new methods for data analyses and will likely combine data across studies to more powerfully address research questions.  


Since 2009, this program has published three iterations of funding opportunity announcement (FOA), resulting in 77 base project applications.  Interests in the program have continued to grow, with the second FOA responded with 39% more applications than the initial one, and the third one with 36% more than the second one.  A total of 20 applications have been funded so far, and the last round of funding is pending Council approval.  These projects have used data derived from NIDCR-funded studies or other sources publicly available from FaceBase1, dbGaP2, ENCODE2, and other resources.  From various angles, they investigated dental caries, orofacial clefts, head and neck cancer, facial morphology, Sjögren's Syndrome, periodontitis, and other DOC conditions or traits.  Analytical methods used were statistically and computationally oriented, including deep machine learning in more recently funded projects.  Over 90 peer reviewed articles have resulted from these projects so far, reporting 1) mechanistic insights on DOC related regulatory networks, genetic risk factors, or gene-environmental interactions; 2) disease risk prediction models; 3) demographic and behavioral factors that impact oral health; and 4) new analytical approaches and tools.  In addition to supporting research, the program has served additional purposes such as cost-saving (by reusing data) and strengthening the capability of large volume data analysis for DOC research.  

Today, significantly more types and larger volume of omics, clinical, phenotypic, environmental, and life style data are available than ten years ago and are with better quality, and the availability continues to increase owing to technology advancement and broad data sharing.  And, statistical, computational, and ontological tools4 continue to improve, making more sophisticated data analyses feasible.  At the same time, many successful genome-wide studies have reached a stage where larger volume and/or additional data types are necessary in order to validate discoveries made or obtain deeper and more global biological insight.  Consequently, the goal of the program is still highly relevant to NIDCR’s mission of improving DOC health, if not more.  Staff estimates investigators’ continuing interests in the program.  Staff expects that funded studies will lead to deeper and more global mechanistic insight about DOC biology, more knowledge of socioeconomic factors that impact oral health, and improved analytical methods.  

Areas of Interests (Scope)

Using statistic and computational approaches, funded projects can analyze existing genome-wide data including genotyping, DNA sequencing, RNA sequencing, and methylation sequencing data; oral microbiome data; metagenomic data; singe cell expression data; clinical and phenotypic data; environmental data; and data that reflect life styles.  A project can apply existing analytical methods and/or develop novel methods for the analyses.  It should be relevant to NIDCR’s mission of improve DOC health.  Data resources include FaceBase, dbGaP, ENCODE, Gabriella Miller Kids First Data Resource Center5, and other public databases.  


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  2. Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, Hilton JA, Jain K, Baymuradov UK, Narayanan AK, Onate KC, Graham K, Miyasato SR, Dreszer TR, Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 2018 Jan 4;46(D1):D794-D801. doi: 10.1093/nar/gkx1081.
  3. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, Hao L, Kiang A, Paschall J, Phan L, Popova N, Pretel S, Ziyabari L, Lee M, Shao Y, Wang ZY, Sirotkin K, Ward M, Kholodov M, Zbicz K, Beck J, Kimelman M, Shevelev S, Preuss D, Yaschenko E, Graeff A, Ostell J, Sherry ST. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007 Oct;39(10):1181-6.
  4. Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D, Cipriani V, Balhoff JP, Conlin T, Blau H, Baynam G, Palmer R, Gratian D, Dawkins H, Segal M, Jansen AC, Muaz A, Chang WH, Bergerson J, Laulederkind SJF, Yüksel Z, Beltran S, Freeman AF, Sergouniotis PI, Durkin D, Storm AL, Hanauer M, Brudno M, Bello SM, Sincan M, Rageth K, Wheeler MT, Oegema R, Lourghi H, Della Rocca MG, Thompson R, Castellanos F, Priest J, Cunningham-Rundles C, Hegde A, Lovering RC, Hajek C, Olry A, Notarangelo L, Similuk M, Zhang XA, Gómez-Andrés D, Lochmüller H, Dollfus H, Rosenzweig S, Marwaha S, Rath A, Sullivan K, Smith C, Milner JD, Leroux D, Boerkoel CF, Klion A, Carter MC, Groza T, Smedley D, Haendel MA, Mungall C, Robinson PN. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res. 2019 Jan 8;47(D1):D1018-D1027. doi: 10.1093/nar/gky1105.
  5. Gabriella Miller Kids First Data Resource Center:
Last Reviewed
May 2019