Laura Scott is a Research Professor in the Department of Biostatistics. She received
her Ph.D. in Cell Biology from Johns Hopkins School of Medicine in 1993 and an M.P.H.
in Epidemiology from the University of Michigan School of Public Health in 1995. Following
training in the epidemiology of breast cancer and hypertension at Michigan State University
and in the epidemiology and genetic epidemiology of complications of type 1 diabetes
at the Joslin Diabetes Center, she moved to the University of Michigan in 2000 to
work in statistical genetics. She joined the Department of Biostatistics faculty in
Dr. Scott's research focuses on identification of genetic variants that increase the risk of common diseases and on methods to identify associated variants. Her work also focuses on identification of eQTLs and differential expression in RNA-Seq data in muscle, adipose and pancreatic islet data, and on DNA methylation in blood. She has also developed methods for gene-set enrichment testing in ChIP-Seq data.
- BIOSTAT646: High Throughput Molecular Genetic and Epigenetic Data Analysis Syllabus (PDF)
- PUBHLTH610: Introduction to Public Health
- M.P.H., Epidemiology, University of Michigan, School of Public Health, 1995
- Ph.D., Biochemistry, Cell and Molecular Biology, Johns Hopkins School of Medicine, 1993
- B.A., Chemistry with concentration in French, Albion College, 1985
Research Interests & Projects
- Identification of genetic variants that increase the risk of many common diseases:
- Type II diabetes:GWAS-based meta-analysis, exome sequencing and whole genome sequencing (FUSION study (Finland United States Investigation of NIDDM Genetics))
- Bipolar disorder: Whole genome sequencing data on 3,700 European ancestry individuals (BRIDGES project)
- Bipolar disorder and schizophrenia: Whole genome sequencing data on 10,000 (projected) African American individuals (InPSYght project)
- Depression: GWAS data on 7,000 (projected) medical interns
- Pain: GWAS data on >20,000 individuals from the rapidly growing MGI cohort
- Pneumonia: GWAS data on 1000 children from a multi-ethnic cohort
- Analysis of RNA-Seq and methylation data
- eQTL, ASE(allele specific epression) and differential expression in muscle, adipose and pancreatic islet tissues (FUSION study)
- Chip and Sequence-based methylation data (Depression in interns and FUSION study)
- Methods research
- Increasing power while controlling type 1 error rate in meta-analysis of quantitative and qualitative traits
- Gene-set enrichment testing in ChIP-Seq data
- Taylor DL, Jackson AU, Narisu N, Hemani G, Erdos MR, Chines PS, Swift A, Idol J, Didion JP, Welch RP, Kinnunen L, Saramies J, Lakka TA, Laakso M, Tuomiulehto J, Parker SCJ, Koistinen HA, Smith GD, Boehnke M, Scott LJ*, Birney W*, and Collins FS*. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. Proceedings of the National Academy of Sciences, accepted for publication.
- Stahl EA, Breen G, Forstner AJ, McQuillin, A, Ripke S, …, Edenberg HJ, Cichon S, Ophoff RA, Scott LJ, Andreassen OA, Kelsoe J, Sklar P. Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. Nature Genetics, accepted for publication.
- Varshney A*, *Scott L*, Erdos M*, Welch R*, Chines P, Narisu N, Wolford B, Albanus RD, Orchard P, Kursawe R, Vadlamudi S, Cannon M, Didion J, Hensley J, Kirilusha A, Bonnycstle L, Taylor L, Watanabe R, Mohlke K. Boehnke M, Collins FS, Parker SCJ, Stitzel ML (2017) Islet RFX regulatory motifs enriched in Type 2 diabetes susceptibility loci, in preparation. Proceedings of the National Academy of Sciences, 1114:2301-2306. PMCID: PMC5338551.
- Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, Ma C, Fontanillas P, ..., Scott LJ, Morris AP, Kang HM, Boehnke M, Altshuer D, and McCarthy MI (2017) The genetic architecture of type 2 diabetes. Nature, 539:41-47. PMCID: PMC5034897.
- Scott LJ*, Erdos MR*, Huyghe JR*, Welch RP*, Beck AT, Wolford BN, Chines PS, Didion JP, Narisu N, Stringham HM, Taylor DL, Jackson AU, Vadlamudi S, Bonnycastle LL, Kinnunen L, Saramies J, Sundvall J, Albanus RD, Kiseleva A, Hensley J, Crawford GE, Jiang H, Wen X, Watanabe RM, Lakka TA, Mohlke KL, Laakso M, Tuomilehto J, Koistinen HA, Boehnke M, Collins FS, and Parker SCJ (2017) The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nature Communications, 7:11764. PMCID: PMC4931250.
- Lee S, Fuchsberger C, Kim S, Scott, LJ (2016) An efficient resampling method for calibrating for rare variant association analysis in case-control studies. Biostatistics, 17:1-15.
- Sklar P, Ripke S, Scott LJ, Andreassen OA, Cichon S, …, Hautzinger M, Reif A, Kelsoe JR, Purcell SM; Psychiatric GWAS Consortium Bipolar Disorder Working Group. (2011) Large-scale genome-wide association analysis of bipolar disorder identified a new susceptibility locus near ODZ4. Nature Genetics, 43:977-983. PMCID: PMC3637176.
- Welch R, Lee C, Imbriano PM, Patil S, Weymouth TE, Smith R A, Scott LJ, Sartor MA. (2014) ChIP-Enrich: Gene set enrichment testing for ChIP-seq data. Nucleic Acids Research, 42:e105. PMCID: PMC4117744.
- Ma C, Blackwell T, Boehnke M, Scott LJ (2013) Recommended Joint and Meta‐Analysis Strategies for Case‐Control Association Testing of Single Low‐Count Variants. Genetic Epidemiology, 37:539-550, 2013. PMCID: PMC4049324
- Xiao R, Scott LJ. (2011) Detection of cis-acting regulatory SNPs using allelic expression data. Genetic Epidemiology, 6:515-25. PMCID: PMC4372992.
- Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li X-Y, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, and Boehnke M (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science, 316:1341-1345.
- Skol AD, Scott LJ, Abecasis GR, and Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nature Genetics 38:209-213.
- American Society of Human Genetics