Stuart J Macdonald

Stuart Macdonald
  • Professor
He/him/his

Contact Info

785-864-5362
4043 Haworth Hall

Personal Links

Biography —

My principal research interest is in understanding the genetic basis of complex, polygenic trait variation. Over my career I have studied a range of traits in Drosophila, most recently focusing on responses to toxin exposure. By emphasizing a systems genetics approach, and integrating a diverse array of experimental and computational tools, my group uncovers the pathways, genes, and variants that contribute to trait variation.

I earned my undergraduate (1997) and graduate degrees (2000) at the University of Oxford in the UK. Following a brief period at University College London, in 2001 I moved to the University of California, Irvine to work with Dr. Tony Long, and in 2006 I started my faculty position at KU.

I am currently a Professor in the Department of Molecular Biosciences, am a member of the KU Center for Genomics, serve as Director of the K-INBRE Data Science Core at KU, and am one of the PIs for the IRACDA at KU Postdoctoral Training program.

Education —

Postdoc (Quantitative genetics), University of California, Irvine, 2006
Postdoc (Genetics of aging), University College London, 2001
D.Phil in Zoology, University of Oxford, 2000
B.A. in Biological Sciences, University of Oxford, 1997

Research —

Complex Trait Variation

Most traits of medical, agricultural, and ecological interest are influenced by a number of genetic and environmental factors. These complex traits include numerous common human diseases such as diabetes, psychiatric illnesses, cardiovascular disease, and many cancers. Despite their critical importance for understanding human health and organismal diversity, important questions about complex traits remain unanswered: are causative DNA sequence variants typically SNPs (Single Nucleotide Polymorphisms) or complex structural changes? are they at intermediate frequency or individually rare in populations? do genes segregate for multiple causative alleles? do variants show consistent effects on phenotype regardless of the environment, or exhibit genotype-by-environment interactions? These gaps in our understanding remain because identifying the causative variants contributing to complex trait variation is extremely challenging.

Genetic Mapping in the Drosophila Model System

Human geneticists have made dramatic progress in understanding disease risk variation in the last two decades by carrying out increasingly massive population-based, genomewide association studies (GWAS). But the GWAS approach lacks power to identify classes of variants that simulations demonstrate are key contributors to trait variation.

Genetic mapping in model systems, such as the fruit fly Drosophila melanogaster, offers a valuable, complementary avenue for obtaining insight into the factors that underlie complex trait variation. First, genetic dissection in flies can take advantage of their experimental flexibility; we can create specific mapping populations via complex breeding designs. Second, flies are amenable to high-throughput phenotyping, enabling increased power, and studies can be executed in a highly-controlled fashion to mitigate the effects of environmental noise. Third, downstream of mapping, flies have sophisticated tools for in vivo functional validation of candidate genes and mutations. As a result, we are often in a strong position to obtain mechanistic insights into the function of a gene/variant in the context of the whole organism.

The Drosophila Synthetic Population Resource (DSPR)

To enable high-powered, high-resolution genetic dissection of complex traits in flies we lead the development of the DSPR, a large set of 1,600 inbred fly strains. Our work has painted a detailed picture of the genomic variation segregating in the panel, and by sharing the strains with a worldwide set of investigators, a large community has provided significant insight into the genetic basis of complex traits. Our group has identified QTL (Quantitative Trait Loci) underlying a plethora of traits - including enzyme activity, response to chemotherapeutics, resistance to xenobiotic chemicals and heavy metals, behavior, lifespan - and demonstrated that rare structural variants are enriched at candidate genes, and that QTL often present as allelic series.

The Response to Metal Exposure

Exposure to many metals (e.g. lead, cadmium, mercury) poses significant environmental and health risks, even at remarkably low levels. Despite clear evidence of risks from human epidemiological studies, and evidence for inter-individual variation in adverse health outcomes following exposure to toxicants, the diversity of human responses to chemical hazards is not well understood. Given the obvious challenges conducting work with hazardous substances in human populations, genetically well-characterized populations of Drosophila can be a powerful tool. We are leveraging the DSPR to elucidate the physiological changes caused by metal exposure, and to discover conserved genetic susceptibility factors.

Genetics of Lifespan Variation

Understanding the genetic underpinnings of variation in lifespan and aging is critical, since advanced age is a major contributor to disease risk. Model systems have proven a valuable tool to understand the genetic and molecular pathways involved in aging, since many lab animals have relatively short lifespans, and experiments can be executed in controlled environmental conditions. Using our extreme, or X-QTL approach we have recently begun large-scale experiments to identify variants contributing to lifespan variation in flies. By raising tens of thousands of animals, and maintaining throughout their life, we can collect the most long-lived animals, sequence, and compare allele frequencies to control cohorts of flies, enabling the discovery of novel genetic contributors to lifespan variation.

Genomics and Computational Biology

We take an integrated approach to the genetic dissection of complex traits, and genomic analysis is fundamental to our work. For instance, we have generated high-quality genomes for multiple Drosophila strains to characterize the significant structural variation segregating in flies, and employ genomewide expression profiling to understand the response to metal stress. We continue to employ a range of genomic tools - whole genome sequencing, RNA-sequencing, and methods to characterize chromatin accessibility - to find and characterize sequence variants that give rise to trait variation.

Teaching —

  • BIOL 680 - Genomics (Spring)
  • BIOL 350 - Principles of Genetics (Fall)

My classroom teaching has encompassed a range of courses, focusing on introductory molecular biology (BIOL151) and genetics (BIOL350) for undergraduates, and research proposal development (BIOL925) for graduate students. Most recently I developed a class focused on modern genomics techniques and their applications in the biomedical sciences (BIOL680).

Outside of formal classroom instruction, one of my most important roles at KU is to advise, mentor and train junior scientists. I have worked with numerous creative and motivated undergraduates, graduate students, and postdoctoral researchers, helping them build their experimental, analytical, and critical-thinking skills as they execute research projects.

Selected Publications —

See all papers by Stuart J Macdonald on PubMed

  • Everman ER, Macdonald SJ. 2024. Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster. G3: Genes, Genomes, Genetics 14: jkae015. PMID: 38262701.
  • Huynh K, Smith BR, Macdonald SJ, Long AD. 2023. Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster. PLoS Genetics 19: e1010439. PMID: 37146087. PMCID: PMC10191298.
  • Everman ER, Macdonald SJ, Kelly JK. 2023. The genetic basis of adaptation to copper pollution in Drosophila melanogaster. Frontiers in Genetics 14: 1144221. PMID: 37082199. PMCID: PMC10110907.
  • Macdonald SJ, Long AD. 2022. Discovery of malathion resistance QTL in Drosophila melanogaster using a bulked phenotyping approach. G3: Genes, Genomes, Genetics 12: jkac279. PMID: 36250804. PMCID: PMC9713458.
  • Felmlee KR, Macdonald SJ, Everman ER. 2022. Pre-adult exposure to three heavy metals leads to changes in the head transcriptome of adult flies. microPublication Biology: 000591. PMID: 35856016. PMCID: PMC9287740
  • Macdonald SJ, Cloud-Richardson KM, Sims-West DJ, Long AD. 2022. Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing. Genetics 220: iyab238. PMID: 35100395. PMCID: PMC8893256.
  • Everman ER, KM Cloud-Richardson, and SJ Macdonald, 2021 Characterizing the genetic basis of copper toxicity in Drosophila reveals a complex pattern of allelic, regulatory, and behavioral variation. Genetics 217: 1–20. PMID: 33683361. PMCID: PMC8045719.
  • Macdonald SJ, and CA Highfill, 2020 A naturally-occurring 22-bp coding deletion in Ugt86Dd reduces nicotine resistance in Drosophila melanogaster. BMC Research Notes 13: 188. PMID: 32228671. PMCID: PMC7106894.
  • Smith BR, and SJ Macdonald, 2020 Dissecting the genetic basis of variation in Drosophila sleep using a multiparental QTL mapping resource. Genes 11: 294. PMID: 32168738. PMCID: PMC7140804.
  • Chakraborty M, JJ Emerson, SJ Macdonald, and AD Long, 2019 Structural variants exhibit widespread allelic heterogeneity and shape variation in complex traits. Nature Communications 10: 4872. PMID: 31653862. PMCID: PMC6814777.
  • Everman ER, CL McNeil, JL Hackett, CL Bain, and SJ Macdonald, 2019 Dissection of complex, fitness-related traits in multiple Drosophila mapping populations offers insight into the genetic control of stress resistance. Genetics 211: 1449–1467. PMID: 30760490. PMCID: PMC6456312.
  • Highfill CA, JH Tran, SKT Nguyen, TR Moldenhauer, X Wang, and SJ Macdonald, 2017 Naturally-segregating variation at Ugt86Dd contributes to nicotine resistance in Drosophila melanogaster. Genetics 207:311–325. PMID: 28743761. PMCID: PMC5586381.
  • Najarro MA, JL Hackett, and SJ Macdonald, 2017 Loci contributing to boric acid toxicity in two reference populations of Drosophila melanogaster. G3: Genes, Genomes, Genetics 7: 1631–1641. PMID:     28592646. PMCID: PMC5473745.
  • Hackett JL, X Wang, BR Smith, and SJ Macdonald, 2016 Mapping QTL contributing to variation in posterior lobe morphology between strains of Drosophila melanogaster. PLoS ONE 11: e0162573. PMID: 27606594. PMCID: PMC5015897.
  • Cloud-Richardson KM, BR Smith, and SJ Macdonald, 2016 Genetic dissection of intraspecific variation in a male-specific sexual trait in Drosophila melanogaster. Heredity 117: 417–426. PMID: 27530909. PMCID: PMC5117841.
  • Highfill CA, GA Reeves, and SJ Macdonald, 2016 Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population. BMC Genetics 17: 113. PMID: 27485207. PMCID: PMC4970266.
  • Najarro MA, JL Hackett, BR Smith, CA Highfill, EG King, AD Long, and SJ Macdonald, 2015 Identifying loci contributing to natural variation in xenobiotic resistance in Drosophila. PLoS Genetics 11: e1005663. PMID: 26619284. PMCID: PMC4664282.
  • Long AD, SJ Macdonald, and EG King, 2014 Dissecting complex traits using the Drosophila Synthetic Population Resource. Trends in Genetics 30: 488–495. PMID: 25175100. PMCID: PMC4253597.
  • Marriage TN, EG King, AD Long, and SJ Macdonald, 2014 Fine-mapping nicotine resistance loci in Drosophila using a multiparent advanced generation intercross population. Genetics 198: 45–57. PMID: 25236448. PMCID: PMC4174953.
  • King EG, BJ Sanderson, CL McNeil, AD Long, and SJ Macdonald, 2014 Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genetics 10: e1004322. PMID: 24810915. PMCID: PMC4014434.
  • Cridland JM, SJ Macdonald, AD Long, and KR Thornton, 2013 Abundance and distribution of transposable elements in two Drosophila QTL mapping resources. Molecular Biology and Evolution 30: 2311–2327. PMID: 23883524. PMCID: PMC3773372.
  • Kislukhin G, EG King, KN Walters, SJ Macdonald, and AD Long, 2013 The genetic architecture of methotrexate toxicity is similar in Drosophila melanogaster and humans. G3: Genes, Genomes, Genetics 3: 1301–1310. PMID: 23733889. PMCID: PMC3737169.
  • King EG, CM Merkes, CL McNeil, SR Hoofer, S Sen, KW Broman, AD Long, and SJ Macdonald, 2012 Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Research 22: 1558–1566. PMID: 22496517. PMCID: PMC3409269.
  • King EG, SJ Macdonald, and AD Long, 2012 Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits. Genetics 191: 935–949. PMID: 22505626. PMCID: PMC3389985.
  • McNeil CL, CL Bain, and SJ Macdonald, 2011 Multiple quantitative trait loci influence the shape of a male-specific genital structure in Drosophila melanogaster. G3: Genes, Genomes, Genetics 1: 343–351. PMID: 22384345. PMCID: PMC3276151.
  • Macdonald SJ, and AD Long, 2007 Joint estimates of Quantitative Trait Locus effect and frequency using synthetic recombinant populations of Drosophila melanogaster. Genetics 176: 1261–1281. PMID: 17435224. PMCID: PMC1894589.