Steven Schrodi

Department

Medical Genetics

Research Interests

Mapping genetic regions involved in complex diseases   

Research Fields

Human Genomics, Statistical Genetics, Theoretical Genetics

Research Description:

A complex interplay between genetics, epigenetics and environmental exposures causes common disease states and impacts disease severity, trajectory and treatment efficacy.  Although progress is accelerating, these causal factors and their interactions remain poorly understood.  Gaining insight into the molecular pathogenesis of these phenotypes requires interdisciplinary approaches involving human population genetics, powerful statistical methods, disease genetics models, pathway biochemistry, clinical knowledge, and new genomic techniques.  Successful human genetics studies not only reveal the underlying molecular mechanisms that drive disease susceptibility and suggest novel therapeutics, but, more broadly, also illuminate fundamental genotype-phenotype connections.

The Schrodi Lab is focused on two main areas of investigation: 1) the development of realistic disease genetics models that motivate novel statistical analyses to better discover regions of the genome and epigenome that contribute to disease risk, and 2) the application of these methods to study the inherited component of immunopathologies and metabolic diseases.  Primarily using molecular intermediate phenotypes coupled with these non-traditional genetic mapping methods, we aim to discover the genetics and epigenetics that drive autoimmunity, systemic inflammation, and metabolic dysfunction.  Additionally, we have an interest in developing molecular-based predictive models for these diseases.

Representative Publications:

Search PubMed for more publications by Steve Schrodi

Guo S, Jiang S, Epperla N, Ma Y, Maadooliat M, Ye Z, Olson B, Wang M, Kitchner T, Joyce J, An P, Wang F, Strenn R, Mazza JJ, Meece JK, Wu W, Jin L, Smith JA, Wang J*, Schrodi SJ*. A gene-based recessive diplotype exome scan discovers FGF6, a novel hepcidin-regulating iron-metabolism gene. (2019) Blood 133(17):1888.

Bansal NK, Maadooliat M, Schrodi SJ. (2018) Empirical Bayesian approach to testing multiple hypotheses with separate priors for left and right alternatives. Stat Appl Genet Mol Biol 17(4).

Liu Y, Ye Z, Li X, Anderson JL, Khan M, DaSilva D, Baron M, Wilson D, Bocoun V, Ivacic LC, Schrodi SJ, Smith JA. (2017) Genetic and functional associations with decreased anti-inflammatory tumor necrosis factor alpha induced protein 3 in macrophages from subjects with axial spondyloarthritis. Front Immunol 8:860.

Maadooliat M, Bansal NK, Upadhya J, Farazi MR, Li X, He MM, Hebbring SJ, Ye Z, Schrodi SJ. (2016) The decay of disease association with declining linkage disequilibrium: A fine mapping theorem. Front Genet 7:217.

Carter TC, Rein D, Padberg I, Peter E, Rennefahrt U, David DE, McManus V, Stefanski E, Martin S, Schatz P, Schrodi SJ. (2016) Validation of a metabolite panel for early diagnosis of type 2 diabetes. Metabolism 65(9):1399.

Schrodi SJ. (2016) The use of multiplicity corrections, order statistics and generalized family-wise statistics with application to genome-wide studies. PLoS One 11(4):e0154472.

Schrodi SJ, DeBarber A, He M, Ye Z, Peissig P, Van Wormer JJ, Haws R, Brilliant MH, Steiner RD. (2015) Prevalence estimation for monogenic autosomal recessive diseases using population-based genetic data. Hum Genet 134(6):659.

Hebbring SJ, Slager SL, Epperla N, Mazza JJ, Ye Z, Zhou Z, Achenbach SJ, Vasco DA, Call TG, Rabe KG, Kay NE, Caporaso NE, Lanasa MC, Camp NJ, Strom SS, Goldin LR, Cerhan JR, Brilliant MH, Schrodi SJ. (2012) Genetic evidence of PTPN22 effects on chronic lymphocytic leukemia. Blood 121(1):237.

Nair RP, et al. (2009) Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappaB pathways. Nat Genet 41(2):199.

Chang M, Li Y, Yan C, Callis-Duffin KP, Matsunami N, Garcia VE, Cargill M, Civello D, Bui N, Catanese JJ, Leppert MF, Krueger GG, Begovich AB, Schrodi SJ. (2008) Variants in the 5q31 cytokine gene cluster are associated with psoriasis. Genes Immun 9(2):176.

Cargill M*, Schrodi SJ*, Chang M, Garcia VE, Brandon R, Callis KP, Matsunami N, Ardlie KG, Civello D, Catanese JJ, Leong DU, Panko JM, McAllister LB, Hansen CB, Papenfuss J, Prescott SM, White TJ, Leppert MF, Krueger GG, Begovich AB. (2007) A large-scale genetic association study confirms IL12B and leads to the identification of IL23R as psoriasis-risk genes. Am J Hum Genet 80(2):273.

Schrodi SJ. (2005) A probabilistic approach to large-scale association scans: a semi-Bayesian method to detect disease-predisposing alleles. Stat Appl Genet Mol Biol 4:Article 31.