Carlos Cruchaga, Ph.D.

Associate Professor

Human and Statistical Genetics Program
Neurosciences Program
Biomedical Informatics and Data Science Program
Molecular Genetics and Genomics Program

  • 314-286-0546

  • 314-362-2244

  • BJC Institute of Health (9th floor). 425 S Euclid Ave.

  • CCruchaga@WUSTL.EDU



  • Neurodegeneration, Alzheimer, genetics, genomics, proteomics, system biology, molecular phenotyping, functional genomics

  • The goal of my lab is to understand the biology of neurodegeneration by using high-dimensional omic data and functional genomic approaches.

Research Abstract:

My research interests are focused on leveraging multi-omic data (genetic, genomics, proteomics, epigenomics, lipidomis and others) and deep clinical phenotypes from large and well characterized neurodegenerative diseases (i.e: Alzheimer, Parkinson, Frontotemporal dementia) cohorts in order to identify novel genes, pathways, molecular biomarkers and drug targets for these diseases.

Unprecedented advances in sequencing and high-throughput omics technologies have greatly increased the power and precision of analytical tools used in genomic research and have accelerated the drive toward personalized medicine. Human biospecimens that are analyzed using these new and developing technology platforms have emerged as a critical resource for basic and translational research in neurodegenerative diseases because they are a direct source of molecular data from which targets for therapy, detection, and prevention are identified. The reliability of the data derived from these new platforms is dependent on the quality and consistency of the biospecimens being analyzed. As a result of the increased requirement for biospecimen quality, standardization of biospecimen resources using state-of the-science approaches has become a pressing need across the research enterprise.

In my lab, we generate genetic and omic data for a unique cohorts of Alzheimer and Parkinson disease cohort and we use novel approaches to understand the biological process that lead to disease. Multi-level data integration is already facilitating precision medicine and advancing medicine at the individual patient level. The identification of novel disease- associated genes, linking gene variants to human phenotypes, and the use of Mendelian randomization (a method to estimate causal effects) to predict biomarker validity or drug response are some examples of the kind of data integration of deeply clinical and molecular phenotyped cohorts. These approaches will put us one step closer to not only precision medicine but also precision treatment timing, as we will be able to predict when is the best time for a specific treatment.

In summary, the mission of our lab is to perform a world-class research where genetics, omics and functional genomics studies in neurodegeneration and diseases of the CNS are translated into improvements in human health through better understanding of the molecular underpinnings of disease.

Selected Publications:

Jiang S, Wen N, Li Z, Dube U, Del Aguila J, Budde J, Martinez R, Hsu S, Fernandez MV, Cairns NJ; Dominantly Inherited Alzheimer Network (DIAN); International FTD-Genomics Consortium (IFGC), Harari O, Cruchaga C, Karch CM. Integrative system biology analyses of CRISPR-edited iPSC-derived neurons and human brains reveal deficiencies of presynaptic signaling in FTLD and PSP. Transl Psychiatry. 2018; 8(1):265.

Deming Y, Dumitrescu L, Barnes LL, Thambisetty M, Kunkle B, Gifford KA, Bush WS, Chibnik LB, Mukherjee S, De Jager PL, Kukull W, Huentelman M, Crane PK, Resnick SM, Keene CD, Montine TJ, Schellenberg GD, Haines JL, Zetterberg H, Blennow K, Larson EB, Johnson SC, Albert M, Moghekar A, Del Aguila JL, Fernandez MV, Budde J, Hassenstab J, Fagan AM, Riemenschneider M, Petersen RC, Minthon L, Chao MJ, Van Deerlin VM, Lee VM, Shaw LM, Trojanowski JQ, Peskind ER, Li G, Davis LK, Sealock JM, Cox NJ; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Alzheimer Disease Genetics Consortium (ADGC), Goate AM, Bennett DA, Schneider JA, Jefferson AL, Cruchaga C, Hohman TJ. Sex-specific genetic predictors of Alzheimer`s disease biomarkers. Acta Neuropathol. 2018; 136(6) 857-872. PMCID: PMC6280657

Li Z, Del-Aguila JL, Dube U, Budde J, Martinez R, Black K, Xiao Q, Cairns NJ; Dominantly Inherited Alzheimer Network (DIAN), Dougherty JD, Lee JM, Morris JC, Bateman RJ, Karch CM, Cruchaga C, Harari O. Genetic variants associated with Alzheimer`s disease confer different cerebral cortex cell-type population structure. Genome Med. 2018; 10(1):43. PMCID: PMC5992755

Fernández MV, Budde J, Del-Aguila JL, Ibañez L, Deming Y, Harari O, Norton J, Morris JC, Goate AM; NIA-LOAD family study group; NCRAD, Cruchaga C. Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease. Front Neuroscience 2018; 12:209. PMCID: PMC5893779

Deming Y, Li Z, Kapoor M, Harari O, Del-Aguila JL, Black K, Carrell D, Cai Y, Fernandez MV, Budde J, Ma S, Saef B, Howells B, Huang KL, Bertelsen S, Fagan AM, Holtzman DM, Morris JC, Kim S, Saykin AJ, De Jager PL, Albert M, Moghekar A, O`Brien R, Riemenschneider M, Petersen RC, Blennow K, Zetterberg H, Minthon L, Van Deerlin VM, Lee VM, Shaw LM, Trojanowski JQ, Schellenberg G, Haines JL, Mayeux R, Pericak-Vance MA, Farrer LA, Peskind ER, Li G, Di Narzo AF; Alzheimer’s Disease Neuroimaging Initiative (ADNI).; Alzheimer Disease Genetic Consortium (ADGC)., Kauwe JS, Goate AM, Cruchaga C. Genome-wide association study identifies four novel loci associated with Alzheimer`s endophenotypes and disease modifiers. Acta Neuropathol. 2017; 133(5):839-856. PMCID: PMC5613285

Last Updated: 4/26/2019 2:18:25 PM

Back To Top

Follow us: