Stephanie A. Santorico, Ph.D.
University of Colorado Denver
Department of Mathematical & Statistical Science
Human Medical Genetics and Genomics Program

USPS Address:
PO Box 173364
Campus Box 170
Denver, CO 80217-3364

Physical Address:
Student Commons Building
1201 Larimer St
Room 4215
Denver, CO 80204
Phone: (303) 315-1714
Fax: (303) 315-1704

Yahoo Messenger: Stephanie_Santorico
Skype: sasantorico


My research interests are in the area of statistical genetics and cover many subtopics within the area. I am most fascinated by the use of inheritance in humans to understand human disease, health and associated quantitative traits. This is a running theme and motivation throughout my work. I love the interface between science and statistics and find that the best statistical methods come from collaborations. My past work has included methods development in family-based association testing and genetics of gene expression studies. I have also worked over many other areas of statistical genetics from classical segregation studies to linkage and association studies. I am currently looking for a new area of methods work and am particularly interested in aspects of the human microbiome.

More details on my work, including teaching interests and experience can be found in my Curriculum Vitae.


I earned a Master of Statistics and Ph.D at North Carolina State University (NCSU) in the Department of Statistics. The latter degree included a concentration in statistical genetics and was under the guidance of Dr. Bruce S. Weir at NCSU and Dr. Normal L. Kaplan at the National Institutes of Environmental Health. My dissertation concerned methodological work initially concentrated on the use of linkage disequilibrium between alleles at two loci in order to localize disease susceptibility genes.  Methods were developed for samples of diseased individuals and their non-diseased siblings, a practical study design for diseases of late-onset (1, 2, 3). In addition, I developed methods that use linkage and linkage disequilibrium to locate quantitative trait loci (4).  These methods use families with parent and child information as well as families with only sibling information. This work was later extended to the use of general pedigrees (5). Papers relevant to this work follow:
  1. Monks SA, Kaplan NL, Weir BS (1998) A comparative study of sibship tests of linkage and/or association. Am J Hum Genet: 63:1507-1516. PMID: 9792878
  2. Monks SA, Martin ER, Umbach DM, Kaplan NL (1999) Two tests of association for a susceptibility locus for families of variable size: an example using two sampling strategies. Genetic Epidemiology 17: S655-S660. PMID: 10597509
  3. Anderson JL, Hauser ER, Martin ER, Scott WK, Ashley-Koch A, Kim KJ, Monks SA, Haynes CS, Speer MC, Pericak-Vance MA (1999) Complete genomic screen for disease susceptibility loci in nuclear families. Genetic Epidemiology 17: S473-S478. PMID: 10597478
  4. Monks SA, Kaplan NL (2000) Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus. Am J Hum Genet 66:576-592. PMID: 10677318
  5. Martin ER, Monks SA, Warren LL, Kaplan NL (2000) A test for linkage and association in general pedigrees: the pedigree disequilibrium test (PDT). Am J Hum Genet 67: 146-154. PMID: 10825280
My next area of research was inspired by collaborations with scientists at Rosetta Inpharmatics and Merck & Co. Eric Schadt was the major catalyst for these collaborations. Through the use of complementary information gained by applying statistical genetics principles to gene expression studies, our work aimed to enhance the ability to dissect complex interacting pathways that lead to disease susceptibility (6, 9). I developed algorithms and software for a variety of experimental designs that incorporated genetic variation, patterns of genetic inheritance, measures of relevant environmental influences and gene expression components, all facilitated by the latest in microarray technology. Work spanned over many organisms (7), including corn, mice and humans with my primary focus being on the latter (8). This work resulted in much success with publications in high profile journals and instigated my interest in multi-dimensional methods in statistics. Important publications follow:
  1. Schadt EE, Monks SA, Friend SH (2003) A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets. Biochem Soc Trans. 31(2):437-43. PMID: 12653656
  2. Schadt EE*, Monks SA * et al (2003) The genetics of gene expression: a survey across man, mouse and maize. Nature. 422:297-302 *Equal contributions by marked authors. PMID: 12646919
  3. Monks SA, Leonardson A, Zhu H, Cundiff P, Pietrusiak P, Edwards S, Phillips J, Sachs A, Schadt EE (2004) Genetic Inheritance of Gene Expression in Human Cell Lines. Am J Hum Genet 75(6):1094-105. PMID: 15514893
  4. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet 37(7):710-7. PMID: 15965475
I regularly work on collaborative projects with a preference to genetic studies, especially those involving family data. I think the best methodological work stems from real science and the associated experimental questions, designs and data. Collaborative publications follow and are separated into family based genetic studies, case-control genetic studies and other studies:

Family based genetic studies:

  1. Austin MA, Edwards KL, Monks SA, Koprowicz KM, Brunzell JD, Motulsky AG, Mahaney MC, Hixson JE (2003) Genomewide scan for quantitative trait loci influencing low-density lipoprotein size and plasma triglyceride in familial hypertriglyceridemia. J Lipid Res. 44(11):2161-8. PMID: 12923221
  2. Kim H, Hutter CM, Monks SA, Edwards KL (2005) Comparison of SNPs and microsatellites in detecting quantitative trait loci for alcoholism: The Collaborative Study on the Genetics of Alcoholism. BMC Genetics 6 (Suppl 1):S5. PMID: 16451661
  3. Monks SA (2006) Statistical Issues in Ecogenetic Studies. In Fundamentals of Ecogenetics, edited by LG Costa and DL Eaton. Wiley. DOI: 10.1002/0471758043.ch5
  4. Hing AV, Leblond C, Sze RW, Starr JR, Monks S, Parisi MA (2006) A novel oculo-oto-facial dysplasia in a Native Alaskan community with autosomal recessive inheritance. Am J Med Genet A. 140(8):804-12. PMID: 16523509
  5. Edwards KL, Hutter CM, Wan JY, Kim H, Monks SA (2008) Genome-wide linkage scan for the metabolic syndrome: the GENNID study. Obesity 16(7):1596-601. PMID: 18421265
  6.  Kippola TA, Santorico SA (2010) Methods for combining multiple genome-wide linkage studies. Methods Mol Biol. 2010;620:541-60. PMID: 20652521
  7. Edwards KL, Wan JY, Hutter CM, Fong PY, Santorico SA (2010) Multivariate Linkage Scan for Metabolic Syndrome Traits in Families With Type 2 Diabetes. Obesity. 23 Dec 2010. PMID: 21183932
Case-control genetic studies:
  1. Simon JS, Karnoub MC, Devlin DJ, Arreaza MG, Qiu P, Monks SA, Severino ME, Deutsch P, Palmisano J, Sachs AB, Bayne ML, Plump AS, Schadt EE (2005) Sequence variation in NPC1L1 and association with improved LDL-Cholesterol lowering in response to ezetimibe treatment. Genomics 86(6):648-56. PMID: 16297596
  2. French B, Lumley T, Monks SA, Rice KM, Hindorff LA, Reiner AP, Psaty BM (2006) Simple estimates of haplotype relative risks in case-control data. Genet Epidemiol 30(6):485-494. PMID: 16755519
  3. Sieh W, Edwards KL, Fitzpatrick AL, Srinouanprachanh SL, Farin FM, Monks SA, Kronmal RA, Eaton DL (2006) Genetic susceptibility to prostate cancer: prostate-specific antigen and its interaction with the androgen receptor. Cancer Causes and Control. 17:187-197. PMID: 16425097
  4. Marciante KD, Bis JC, Rieder MJ, Reiner AP, Lumley T, Monks SA, Kooperberg C, Carlson C, Nickerson DA, Heckbert SR, Psaty BM (2007) Renin-angiotensin system haplotypes and the risk of myocardial infarction and stroke in pharmacologically treated hypertensive patients. Am J Epidemiol. 166(1):19-27. PMID: 17522061
  5. Bis JC, Heckbert SR, Smith NL, Reiner AP, Rice K, Lumley T, Hindorff LA, Marciante KD, Enquobahrie DA, Monks SA, Psaty BM (2008) Variation in inflammation-related genes and risk of incident nonfatal myocardial infarction or ischemic stroke. Atherosclerosis 198(1):166-73. PMID: 17981284

Other collaborative studies:

  1. Nzila AM, Nduati E, Mberu EK, Sibley CH, Monks SA, Winstanley PA, Watkins WM (2000) Molecular evidence of greater selective pressure for drug resistance exerted by the long acting antifolate pyrimethamine/sulfadoxine compared with the shorter acting chlorproguanil/dapsone on Kenyan Plasmodium falciparum. J Infect Dis 181:2023-2028. PMID: 10837185
  2. LaGasse JM, Brantley MS, Leech NJ, Rowe RE, Monks SA, Palmer JP, Nepom GT, McCulloch DK, Hagopian WA (2002). Successful prospective prediction of Type 1 diabetes in school children through multiple defined autoantibodies: An eight year follow-up of the Washington State Diabetes Prediction Study. Diabetes Care. 25(3):505-511. PMID: 11874938
  3. Mberu EK, Nzila AM, Nduati E, Kokwaro GO, Watkins WM, Monks SA, Sibley CH (2002) Plasmodium falciparum: In vitro activity of sulfadoxine and dapsone in field isolates from Kenya: point mutations in dihydropteroate synthase are not the whole story in sulfa resistance. Exp Parasitol. 101(2-3): 90-96.PMID: 12427462
  4. Hastings MD, Bates SJ, Blackstone EA, Monks SA, Mutabingwa TK, Sibley CH (2002) Highly pyrimethamine-resistant alleles of dihydrofolate reductase in isolates of Plasmodium falciparum from Tanzania. Trans R Soc Trop Med Hyg. 96(6):674-6. PMID: 12630380