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In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique\u2014including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model\u2014can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.<\/jats:p>","DOI":"10.1371\/journal.pbio.3002847","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T13:35:12Z","timestamp":1728480912000},"page":"e3002847","update-policy":"https:\/\/doi.org\/10.1371\/journal.pbio.corrections_policy","source":"Crossref","is-referenced-by-count":32,"title":["Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"],"prefix":"10.1371","volume":"22","author":[{"given":"Joshua G.","family":"Schraiber","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-2906","authenticated-orcid":true,"given":"Michael D.","family":"Edge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2886-3970","authenticated-orcid":true,"given":"Matt","family":"Pennell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"issue":"5181","key":"pbio.3002847.ref001","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1126\/science.8091226","article-title":"Genetic Dissection of Complex Traits.","volume":"265","author":"ES Lander","year":"1994","journal-title":"Science"},{"issue":"9357","key":"pbio.3002847.ref002","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/S0140-6736(03)12520-2","article-title":"Population stratification and spurious allelic association","volume":"361","author":"LR Cardon","year":"2003","journal-title":"The Lancet"},{"issue":"3","key":"pbio.3002847.ref003","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1534\/genetics.105.055335","article-title":"A General Population-Genetic Model for the Production by Population Structure of Spurious Genotype\u2013Phenotype Associations in Discrete, Admixed or Spatially Distributed Populations","volume":"173","author":"NA Rosenberg","year":"2006","journal-title":"Genetics"},{"issue":"1","key":"pbio.3002847.ref004","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1093\/emph\/eot021","article-title":"Windfalls and pitfalls: Applications of population genetics to the search for disease genes.","volume":"2013","author":"MD Edge","year":"2013","journal-title":"Evol Med Public Health"},{"issue":"6460","key":"pbio.3002847.ref005","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1126\/science.aax3710","article-title":"Deconstructing the sources of genotype-phenotype associations in humans","volume":"365","author":"AI Young","year":"2019","journal-title":"Science"},{"key":"pbio.3002847.ref006","first-page":"2023.02.26.530052","volume-title":"Interpreting population and family-based genome-wide association studies in the presence of confounding","author":"C Veller","year":"2023"},{"issue":"1","key":"pbio.3002847.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nrg3382","article-title":"The nature of confounding in genome-wide association studies","volume":"14","author":"BJ Vilhj\u00e1lmsson","year":"2013","journal-title":"Nat Rev Genet"},{"key":"pbio.3002847.ref008","first-page":"048181","article-title":"Subtle stratification confounds estimates of heritability from rare variants","author":"G Bhatia","year":"2016","journal-title":"bioRxiv"},{"issue":"6","key":"pbio.3002847.ref009","doi-asserted-by":"crossref","first-page":"e1008222","DOI":"10.1371\/journal.pgen.1008222","article-title":"Solving the missing heritability problem.","volume":"15","author":"AI Young","year":"2019","journal-title":"PLoS Genet."},{"issue":"3","key":"pbio.3002847.ref010","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/BF00276244","article-title":"Assortative mating and the genetic correlation","volume":"62","author":"D. 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