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Your Environment. Your Health.

Min Shi, M.D., Ph.D.

Biostatistics & Computational Biology Branch

Min Shi, M.D., Ph.D.
Min Shi, M.D., Ph.D.
Staff Scientist
Tel 984-287-3720
shi2@niehs.nih.gov
P.O. Box 12233
Mail Drop A3-03
Durham, N.C. 27709

Research Summary

Min Shi, M.D., Ph.D., is a staff scientist in the Biostatistics and Computational Biology Branch. She develops and applies statistical methods for studies of complex traits. Her research areas include both methodological and applied work, and the interaction between these two aspects has inspired many of her research interests. Shi develops statistical methods for studying complex traits using genetic/genomic, clinical, laboratory, and epidemiologic data.

Shi collaborates extensively with researchers from multiple disciplines on various research projects. These collaborations cover a wide range of research areas: statistical method development, genetic/epidemiologic studies of complex diseases, and biological/experimental studies.

Statistics is crucial in every phase of research from study design to data collection, data analysis, and result interpretation and presentation. In addition to collaboration, Shi provides statistical consultation to researchers within the entire intramural community. This service includes explaining statistical concepts, helping researchers design experiments and identifying the appropriate statistical methods for data analysis, advising on data presentation methods for manuscripts, assisting in responses to the journal reviewer’s statistical questions, reviewing the statistical sections of manuscripts, and providing hands-on data analysis.

Software

  • Case Control
    This archive provides information for fitting log-linear models and carrying out statistical tests for a design that includes two samples from the same population: one sample of affected individuals and their mothers and a second sample of unaffected individuals and their mothers.
  • Ctrl-mom-hybrid
    This archive provides information for fitting log-linear models and carrying out statistical tests for a hybrid design that includes a sample of affected individuals and their parents and a sample of unaffected individuals and their mothers (case-parent triad/control-mother dyad design).
  • GEI-TRIMM
    This package includes the program GEI-TRIMM, which implements the method described in the manuscript Shi M, Umbach DM, Weinberg CR 2010 "Testing Haplotype-Environment Interactions Using Case-parent Triads."
  • LEM scripts
    This archive provides information for fitting log-linear models and carrying out statistical tests for preterm birth study.
  • LEM Scripts Case-Sibling
    This package contains R scripts for analyzing case-sibling data using missing-parents approach as described in the manuscript Shi M, Umbach DM, Weinberg CR 2012 "Case-sibling studies that acknowledge unstudied parents and enroll unmatched individuals".
  • PIXLRT
    PIXLRT is a package can be used for the analysis of(non pseudo-autosomal) SNPs on the X chromosome in case-parent triads.
  • R-SCRIPTS
    This package contains two R scripts for generating scenarios 1-4 described in the manuscript Shi M, Weinberg CR 2011 "How much are we missing in SNP-by-SNP analyses of GWAS?"
  • TRIad Multi-Marker
    Performs association tests for a child's or mother's genetic effects using multiple markers from triad families.
  • TriadSim
    TriadSim is a R package for simulating genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage disequilibrium structure and allele frequency distribution. TriadSim generates genotype data by resampling triad genotypes from existing data.
  • TRIMMEST
    This package contains programs that fit log-linear models to estimate the relative risk associated with a candidate risk haplotype in a triad-based association study.

Selected Publications

  1. Mamyrova G, Kishi K, Shi M, Targoff IN, Huber AM, Curiel RV, Miller FW, Rider LG, the Childhood Myositis Heterogeneity Study Group (accepted). 2020. Anti-MDA5 autoantibodies associated with juvenile dermatomyositis constitute a distinct phenotype. Rheumatology. Nov 3;keaa429. [Abstract Mamyrova G, Kishi K, Shi M, Targoff IN, Huber AM, Curiel RV, Miller FW, Rider LG, the Childhood Myositis Heterogeneity Study Group (accepted). 2020. Anti-MDA5 autoantibodies associated with juvenile dermatomyositis constitute a distinct phenotype. Rheumatology. Nov 3;keaa429.]
  2. Vellers HL, Verhein KC, Burkholder AB, Lee J, Kim Y, Lightfoot JT, Shi M, Weinberg CR, Sarzynski MA, Bouchard C, Kleeberger SR. 2020. Association between mitochondrial DNA sequence variants and VO2 max trainability. Medicine & Science in Sports & Exercise. Nov;52(11):2303-2309. [Abstract Vellers HL, Verhein KC, Burkholder AB, Lee J, Kim Y, Lightfoot JT, Shi M, Weinberg CR, Sarzynski MA, Bouchard C, Kleeberger SR. 2020. Association between mitochondrial DNA sequence variants and VO2 max trainability. Medicine & Science in Sports & Exercise. Nov;52(11):2303-2309.]
  3. Resnik D, Smith E, Master Z, Shi M. 2020. Survey of equal contributions in biomedical research publications. Accountability in Research: Policies and Quality Assurance. doi:10.1080/ 08989621.2020. 1722947. [Abstract Resnik D, Smith E, Master Z, Shi M. 2020. Survey of equal contributions in biomedical research publications. Accountability in Research: Policies and Quality Assurance. doi:10.1080/ 08989621.2020. 1722947.]
  4. Vsevolozhskaya OA, Shi M, Hu F, Zaykin DV. 2020. DOT: Gene-set analysis by combining decorrelated association statistics. PLOS Comp Biol. doi: 10.1371/journal.pcbi.1007819. [Abstract Vsevolozhskaya OA, Shi M, Hu F, Zaykin DV. 2020. DOT: Gene-set analysis by combining decorrelated association statistics. PLOS Comp Biol. doi: 10.1371/journal.pcbi.1007819.]
  5. Shi M, O'Brien KM, Weinberg CR. 2020. Interactions between a polygenic risk score and non-genetic risk factors in young-onset breast cancer. Scientific Reports. Feb 24;10(1):3242 doi: 10.1038/s41598-020-60032-3. [Abstract Shi M, O'Brien KM, Weinberg CR. 2020. Interactions between a polygenic risk score and non-genetic risk factors in young-onset breast cancer. Scientific Reports. Feb 24;10(1):3242 doi: 10.1038/s41598-020-60032-3.]
  6. Karmaus PW, Shi M, Perl S, Cheung F, Biancotto A, Candia J, Cheung F, Kotliarov Y, Young N, Fessler MB, CHI Consortium. 2019. Effects of rosuvastatin on the immune system in healthy volunteers with normal serum cholesterol. JCI Insight.  Nov 1;4(21). pii: 131530. doi: 10.1172/jci.insight.131530. [Abstract Karmaus PW, Shi M, Perl S, Cheung F, Biancotto A, Candia J, Cheung F, Kotliarov Y, Young N, Fessler MB, CHI Consortium. 2019. Effects of rosuvastatin on the immune system in healthy volunteers with normal serum cholesterol. JCI Insight.  Nov 1;4(21). pii: 131530. doi: 10.1172/jci.insight.131530.]
  7. Weinberg CR, Shi M, O’Brien KM, Umbach DM. 2019. Adjustment for urinary creatinine or serum lipids for analytes assayed in pooled specimens. Epidemiology. Sep;30(5):768-779 doi: 10.1097/EDE.0000000000001053. [Abstract Weinberg CR, Shi M, O’Brien KM, Umbach DM. 2019. Adjustment for urinary creatinine or serum lipids for analytes assayed in pooled specimens. Epidemiology. Sep;30(5):768-779 doi: 10.1097/EDE.0000000000001053.]
  8. Shi M, Wise AS, Umbach DM, Weinberg CR. 2018. Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect. BMC Bioinformatics. 19(1):2 [Abstract Shi M, Wise AS, Umbach DM, Weinberg CR. 2018. Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect. BMC Bioinformatics. 19(1):2]
  9. Weinberg CR, Shi M, Basso O, DeRoo LA, Harmon Q, Wilcox AJ, Skjaerven R. 2017. Season of conception, smoking, and preeclampsia in Norway. Env Health Persp. Jun 29;125(6):067022. doi: 10.1289/EHP963. [Abstract Weinberg CR, Shi M, Basso O, DeRoo LA, Harmon Q, Wilcox AJ, Skjaerven R. 2017. Season of conception, smoking, and preeclampsia in Norway. Env Health Persp. Jun 29;125(6):067022. doi: 10.1289/EHP963.]
  10. Shi M*, O’Brien KM*, Sandler DP, Taylor JA, Zaykin DV, Weinberg CW. 2016. Previous GWAS hits in relation to young-onset breast cancer. Breast Cancer Research and Treatment. 161 (2) 333-344 doi: 10.1007/s10549-016-4053-z [Abstract Shi M*, O’Brien KM*, Sandler DP, Taylor JA, Zaykin DV, Weinberg CW. 2016. Previous GWAS hits in relation to young-onset breast cancer. Breast Cancer Research and Treatment. 161 (2) 333-344 doi: 10.1007/s10549-016-4053-z] 
  11. Wise AS*, Shi M*, Weinberg CR. 2016. Family-based multi-SNP X chromosome analysis using parent information. Frontiers in genetics. Feb 22;7:20. doi: 10.3389/fgene.2016.00020. [Abstract Wise AS*, Shi M*, Weinberg CR. 2016. Family-based multi-SNP X chromosome analysis using parent information. Frontiers in genetics. Feb 22;7:20. doi: 10.3389/fgene.2016.00020.]
  12. O’Brien K*, Shi M*, Taylor JA, Wise AS, Zaykin DV, Sandler DP, Weinberg CW. 2016. A family-based, genome-wide association study of young-onset breast cancer: Inherited variants and maternally mediated effects. Euro J Hum Genet. 2016 Feb 17. dot: 10.1038/ejhg.2016.11. [Abstract O’Brien K*, Shi M*, Taylor JA, Wise AS, Zaykin DV, Sandler DP, Weinberg CW. 2016. A family-based, genome-wide association study of young-onset breast cancer: Inherited variants and maternally mediated effects. Euro J Hum Genet. 2016 Feb 17. dot: 10.1038/ejhg.2016.11.]
  13. Shi M, DeRoo L, Sandler D, Weinberg CR. 2015. Migraine and possible etiologic heterogeneity for hormone-receptor-negative breast cancer. Scientific Reports. Oct 12;5:14943. doi: 10.1038/srep14943. [Abstract Shi M, DeRoo L, Sandler D, Weinberg CR. 2015. Migraine and possible etiologic heterogeneity for hormone-receptor-negative breast cancer. Scientific Reports. Oct 12;5:14943. doi: 10.1038/srep14943.]
  14. Shi M, Umbach DM, Weinberg CR. 2015. Using parental phenotypes in case-parent studies. Frontiers in genetics. 6:15, doi:10.3389/fgene.2015.00221. [Abstract Shi M, Umbach DM, Weinberg CR. 2015. Using parental phenotypes in case-parent studies. Frontiers in genetics. 6:15, doi:10.3389/fgene.2015.00221.]
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