MEMCARE-SRC
The Metals and Metal Mixtures, Cognitive Aging, Remediation and Exposure Sources (MEMCARE) Superfund Research Center aims to understand how metals and metal mixtures contribute to cognitive decline in older age and the biological mechanisms underlying these effects; and to develop new ways to detect and remove metal contaminants in drinking water sources.Â
677 Huntington Avenue
Boston, MA 02115
Data Management and Analysis Core (DMAC)
The MEMCARE SRC encompasses research from many different disciplines, all requiring specialized statistical methods. In some cases, we need entirely new methods in order to better understand and model complex relationships. We also need a data repository so that others may easily access the tools and databases that we are developing. The DMAC provides this kind of support across all projects.
Our Goals
The goal of the DMAC is to provide support for all aspects of the project, including:
- statistical support
- bioinformatics support
- data management support
Our Approach
Our team is known for developing innovative statistical methods to help solve questions around environmental exposures and to integrate high dimensional exposure, molecular, and phenotypic data. We also provide support in geographic information systems (GIS) and statistics education and training for trainees and researchers connected with the MEMCARE SRC and engage pre- and post-doctoral trainees from the Biostatistics Department in MEMCARE SRC projects.
DMAC Team
Recent Publications
2024
Mork D, Kioumourtzoglou MA, Weisskopf M, Coull BA, Wilson A. Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution J Am Stat Assoc. 2024;119(545):14-26.
Joubert BR, Palmer G, Dunson D, Kioumourtzoglou MA, Coull BA. Environmental Mixtures Analysis (E-MIX) Workflow and Methods Repository medRxiv [Preprint]. 2024 Dec 22:2024.12.20.24318087.
Van Buren E, Azzara D, Rangel-Moreno J, Garcia-Hernandez ML, Murphy SP, Cohen ED, Lewis E, Lin X, Park HR. Single-cell RNA sequencing reveals placental response under environmental stress Nat Commun. 2024 Aug 2;15(1):6549.
2023
Antonelli J, Wilson A, Coull BA. Multiple exposure distributed lag models with variable selection Biostatistics. 2023 Dec 15;25(1):1-19.
Laha N, Huey N, Coull B, Mukherjee R. On statistical inference with high-dimensional sparse CCA Inf inference. 2023 Nov 17;12(4):iaad040.
Laha N, Mukherjee R. On Support Recovery with Sparse CCA: Information Theoretic and Computational Limits IEEE Trans Inf Theory. 2023 Mar;69(3):1695-1738.
McCaw ZR, Gaynor SM, Sun R, Lin X. Leveraging a surrogate outcome to improve inference on a partially missing target outcome Biometrics. 2023 Jun;79(2):1472-1484.
McGee G, Wilson A, Webster TF, Coull BA. Bayesian multiple index models for environmental mixtures Biometrics. 2023 Mar;79(1):462-474.
Schildroth S, Friedman A, White RF, Kordas K, Placidi D, Bauer JA, Webster TF, Coull BA, Cagna G, Wright RO, Smith D, Lucchini RG, Horton M, Claus Henn B. Associations of an industry-relevant metal mixture with verbal learning and memory in Italian adolescents: The modifying role of iron status Environ Res. 2023 May 1;224:115457.
Sun R, Shi A, Lin X. Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies Biostatistics. 2023 Dec 15;25(1):171-187.
Zhou H, Arapoglou T, Li X, Li Z, Zheng X, Moore J, Asok A, Kumar S, Blue EE, Buyske S, Cox N, Felsenfeld A, Gerstein M, Kenny E, Li B, Matise T, Philippakis A, Rehm HL, Sofia HJ, Snyder G; NHGRI Genome Sequencing Program Variant Functional Annotation Working Group; Weng Z, Neale B, Sunyaev SR, Lin X. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome Nucleic Acids Res. 2023 Jan 6;51(D1):D1300-D1311.
2022
Li X, Yung G, Zhou H, Sun R, Li Z, Hou K, Zhang MJ, Liu Y, Arapoglou T, Wang C, Ionita-Laza I, Lin X. A multi-dimensional integrative scoring framework for predicting functional variants in the human genome Am J Hum Genet. 2022 Mar 3;109(3):446-456.
Liu Z, Shen J, Barfield R, Schwartz J, Baccarelli AA, Lin X. Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies J Am Stat Assoc. 2022;117(537):67-81.