Professor of Hematology, Adjunct Professor of Human Genetics, Adjunct Professor of Biomedical Informatics, and Adjunct Professor of Family and Preventive Medicine
Statistical Genetics, Genetic Epidemiology
The identification of inherited genetic risk variants is critical in understanding disease mechanisms. However, such discoveries are challenging for complex diseases. Novel methods and study designs play essential roles in addressing these challenges. Certainly there is no guarantee that a new method will produce a leap of knowledge; however, it can be high-impact and cutting edge when it does. The hope is that a better understanding of inherited genetic risk will lead to improvements in prevention, detection, diagnosis, and treatment strategies.
The main focus of my research is the identification of germ-line genetic variants that increase susceptibility to disease, with specific interests in breast cancer, chronic lymphocytic leukemia (CLL) and multiple myeloma (MM). Importantly, my work includes both the development of novel statistical genetic approaches, in addition to the application of such methods in applied gene-finding projects. There is a natural and powerful synergy in the integration of the theoretical and the applied. Both can be ineffective in isolation: brilliant new methodological ideas can fail if they are too abstract, lack interpretability and ignore ‘real life’ data issues; and while standard methods may identify ‘low-hanging fruit’, these methods may not apply to all data structures and likely ignore other important dimensions of the problem. Arguably two of the major obstacles to identification of novel risk variants are genetic and disease heterogeneity. Hence, these sources of heterogeneity often drive the types of statistical genetic techniques and study designs pursued. For example, current interests include the incorporation of gene-expression (or other molecular-level phenotypic data) with germ-line genetic data (high-density SNP and/or sequencing data) in novel methods for risk variant identification. Due to the unique and powerful genealogical resources available in Utah (the Utah Population Database, or UPDB), these methods also often include an emphasis on high-risk pedigrees.
Current projects include: whole exome and whole genome massively parallel sequencing in a high-risk CLL pedigree; high-density genomewide SNP genotyping in CLL, MM and controls; and apoptosis candidate pathway genotyping and sequencing in high-risk breast cancer and controls. These projects often involve multi-disciplinary collaborations across campus, in addition to joint research within large, collaborative consortia.
- Berndt SI*, Skibola CF*, Joseph V*, Camp NJ*, Nieters A*, Wang Z, Cozen W, Monnereau A, Wang SS, Kelly RS, Lan Q, Teras LR, Chatterjee N, Chung CC, Yeager M, Brooks-Wilson AR, Hartge P, Purdue MP, Birmann BM, Armstrong BK, Cocco P, Zhang Y, Severi G, Zeleniuch-Jacquotte A, Lawrence C, Burdette L, Yuenger J, Hutchinson A, Jacobs KB, Call TG, Shanafelt TD, Novak AJ, Kay NE, Liebow M, Wang AH, Smedby KE, Adami HO, Melbye M, Glimelius B, Chang ET, Glenn M, Curtin K, Cannon-Albright LA, Jones B, Diver WR, Link BK, Weiner GJ, Conde L, Bracci PM, Riby J, Holly EA, Smith MT, Jackson RD, Tinker LF, Benavente Y, Becker N, Boffetta P, Brennan P, Foretova L, Maynadie M, McKay J, Staines A, Rabe KG, Achenbach SJ, Vachon CM, Goldin LR, Strom SS, Lanasa MC, Spector LG, Leis JF, Cunningham JM, Weinberg JB, Morrison VA, Caporaso NE, Norman AD, Linet MS, De Roos AJ, Morton LM, Severson RK, Riboli E, Vineis P, Kaaks R, Trichopoulos D, Masala G, Weiderpass E, Chirlaque MD, Vermeulen RC, Travis RC, Giles GG, Albanes D, Virtamo J, Weinstein S, Clavel J, Zheng T, Holford TR, Offit K, Zelenetz A, Klein RJ, Spinelli JJ, Bertrand KA, Laden F, Giovannucci E, Kraft P, Kricker A, Turner J, Vajdic CM, Ennas MG, Ferri GM, Miligi L, Liang L, Sampson J, Crouch S, Park JH, North KE, Cox A, Snowden JA, Wright J, Carracedo A, Lopez-Otin C, Bea S, Salaverria I, Martin-Garcia D, Campo E, Fraumeni JF Jr, de Sanjose S, Hjalgrim H, Cerhan JR, Chanock SJ, Rothman N, Slager SL (2013) Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia. Nat Genet. 45(8):868-76
- Camp NJ, Parry M, Knight S, Abo R, Elliott G, Rigas SH, Balasubramanian SP, Reed MWR, McBurney H, Latif A, Newman WG, Cannon-Albright LA, Evans DG, Cox A (2012) Fine-mapping CASP8 risk variants in Breast Cancer. Can Epidemiol Marker Prev 21(1):176-81
- Cai Z, Knight S, Thomas A, Camp NJ (2011) Pairwise Shared Genomic Segment Analysis in High Risk Pedigrees: an application to GAW17 exome-sequencing SNP data. BMC Proc. 5 Suppl 9:S9
- Abo R, Knight S, Thomas A, Camp NJ (2010) Automated Construction and Testing of Multi-locus Gene-Gene Associations. Bioinformatics 27(1):134-6
- Christensen GB, Knight S, Camp NJ (2009) The sumLINK statistic for genetic linkage analysis in the presence of heterogeneity. Genetic Epidemiol 33(7):628-36
- Shephard N, Abo R, Rigas S, Frank B, Lin WY, Brock I, Shippen A, Balasubramanian, Reed MW, Bartram CR, Meindl A, Schmutzler RK, Engel C, Burwinkel B, Cannon-Albright, Allen-Brady K, Camp NJ*, Cox A* (2009) A breast cancer risk haplotype in the caspase-8 gene. Cancer Res 69(7):2724-8
- Curtin K, Iles MM, Camp NJ (2009) Identifying Rarer Genetic Variants for Common Complex Diseases: Diseased versus Neutral Discovery Panels. Ann Hum Genet 73(1):54-60
- Abo R, Knight S, Wong J, Cox A, Camp NJ (2008) hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework. Bioinformatics 24(18):2105-7
- Thomas A, Camp NJ, Farnham JM, Allen-Brady K, Cannon-Albright LA (2008) Shared Genomic Segment Analysis. Mapping Disease Predisposition Genes in Extended Pedigrees Using SNP Genotype Assays. Ann Hum Genet 72(Pt 2):279-87
- Curtin K, Wong J, Allen-Brady K, Camp NJ (2007) PedGenie: meta genetic association testing in mixed family and case-control designs. BMC Bioinformatics 8(1):448
- Camp NJ, Farnham JM, Allen-Brady K, Cannon-Albright LA (2007) Statistical recombinant mapping in extended high-risk Utah pedigrees narrows the 8q24 prostate cancer locus to 2.0 Mb. Prostate 67(13):1456-64
- Camp NJ, Farnham JM, Cannon-Albright LA (2006) Localization of a prostate cancer predisposition gene to an 880-kb region on chromosome 22q12.3 in Utah high-risk pedigrees. Cancer Res 66(20):10205-12