Laura Scott, PhD
- Research Professor, Biostatistics
Laura Scott is a Research Professor in the Department of Biostatistics. She received her PhD in Cell Biology from Johns Hopkins School of Medicine (1993) and an MPH in Epidemiology from the University of Michigan School of Public Health (1995). She then worked on breast cancer epidemiology at Michigan State University and the genetic epidemiology of complications of type 1 diabetes at the Joslin Diabetes Center. She began work in statistical genetics at the University of Michigan in 2000 as a postdoctoral fellow and joined the Department of Biostatistics faculty in 2003.
Dr. Scott is the Chair of the Biostatistics Diversity, Equity and Inclusion Committee and is active in other University and Community-based organizations that seek to increase a sense of belonging, racial equity and social justice.
American Society of Human Genetics
- MPH, Epidemiology, University of Michigan, School of Public Health, 1995
- PhD, Biochemistry, Cell and Molecular Biology, Johns Hopkins School of Medicine, 1993
- B.A., Chemistry with concentration in French, Albion College, 1985
The goal of Dr. Scott's work is to understand the factors that regulate gene expression levels and influence the regulatory environment in T2D-related tissues, including muscle, adipose and pancreatic islets. Dr. Scott's research focuses on variability in mRNA, miRNA, metabolites and methylation levels by sex, T2D-related phenotypes and genotypes (eQTLs, miQTL, meQTL) within bulk skeletal muscle and subcutaneous adipose tissue data. Within muscle single cell data, she works to understand what causes differences in cell-type expression levels (RNA-seq) and regulatory region accessibility (ATAC-Seq) by sex, T2D-related phenotypes and genotype. Dr. Scott leads studies to identify genetic variants that increase the risk of common diseases, including type 2 diabetes, bipolar disorder and schizophrenia, with emphasis on analysis in African American sample. She develops methods to identify disease-associated variants and analyze experimental data.
Integration of mRNA, miRNA-Seq, ATAC-Seq and methylation data with sex, phenotype and genotypes (FUSION study (n=300)
Differential expression and methylation by phenotype and genotype (QTL) in bulk muscle and adipose tissue
Differential expression and regulatory peak (ATAC-Seq) levels by phenotype and genotype (QTL) in
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 and schizophrenia: Whole genome sequencing data in >7,000 African American individuals (InPSYght project)
Depression in Medical Interns: GWAS data on > 7,000 (Intern Health Study)
Pneumonia: GWAS data on 500 African American children with pneumonia with control samples from the Michigan Genomics Initiative (MGI) study
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)
Efficient combination of association tests across studies
Imputation quality in samples of different ancestries
Gene-set enrichment testing
Increasing power while controlling type 1 error rate in meta-analysis of quantitative and qualitative traits
Quick C, Guan L, Li G, Li Z, Li X, Dey, R. Liu, Y, Scott LJ, Lin X. (2020) A versatile toolkit for molecular QTL mapping and anaylsis at scale. medRxiv doi: https://doi.org/10.1101/2020.12.18.423490
El-Sayed Moustafa JS, Jackson AU, Brotman SM, Guan L, Villicana S, Roberts AL, Zito A, Bonnycastle L, Erdos MR, Narisu N, Stringham HM, Welch R, Yan T, Lakka T, Parker S, Tuomilehto J, Collins FS, Pajukanta P, Boehnke M, Koistinen HA, Laakso M, Falchi M, Bell JT, Scott LJ, Mohlke KL, Small KS. (2020) ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition. medRxiv doi: https://doi.org/10.1101/2020.08.11.20171108.
Vinuela A, Varshney A, van de Bunt M, Prasad RB, Asplund O, Bennett A, Boehnke M, Brown AA, Erdos MR, Fadista J, Hansson O, Hatem G, Howald C, Iyengar AK, Johnson P, Krus U, MacDonald PE, Mahajan A, Manning Fox JE, Narisu N, Nylander V, Orchard P, Oskolkov N, Panousis NI, Payne A, Stitzel ML, Vadlamudi S, Welch R, Collins FS, Mohlke KL, Gloyn AL, Scott LJ, Dermitzakis ET, Groop L, Parker SCJ, McCarthy MI. (2020) Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D. Nature Communications 11:4912.
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 (2019) Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. Proceedings of the National Academy of Sciences, U S A 116:10883-10888
Stahl EA, Breen G, Forstner AJ, McQuillin, A, Ripke S, ..., Edenberg HJ, Cichon S, Ophoff RA, Scott LJ, Andreassen OA, Kelsoe J, Sklar P (2019) Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder. Nature Genetics, 51:793-803.
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.
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.
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.
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.
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.
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.
Xiao R, Scott LJ. (2011) Detection of cis-acting regulatory SNPs using allelic expression data. Genetic Epidemiology, 6:515-25.
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.
Areas of Expertise: Biostatistics