Linear Mixed Model Framework Research Articles

A graph model for genomic prediction in the context of a linear mixed model framework.

Genomic selection is revolutionizing both plant and animal breeding, with its practical application depending critically on high prediction accuracy. In this study, we aimed to enhance prediction accuracy by exploring the use of graph models within a linear mixed model framework. Our investigation revealed that incorporating the graph constructed with line connections alone resulted in decreased prediction accuracy compared to conventional methods that consider only genotype effects. However, integrating both genotype effects and the graph structure led to slightly improved results over considering genotype effects alone. These findings were validated across 14 datasets commonly used in plant breeding research.

Quantifying the resistance of Australian wheat genotypes to Pratylenchus thornei based on a continuous metric from a factor analytic linear mixed model

Genetic resistance to the parasitic root-lesion nematode, Pratylenchus thornei, is one of the main management strategies cereal growers can use to minimise the impact of nematodes on winter cereal cropping. Screening of genotypes in the presence of P. thornei populations must provide reliable resistance measures that are realised under field conditions. Adoption of the latest statistical methodologies can help to better differentiate between resistant and susceptible genotypes. In this study, post-harvest P. thornei population densities were measured from a collection of 17 field experiments, with varying starting P. thornei population densities, conducted between 2011 and 2018 in locations across the northern grain growing region of eastern Australia. The experiments primarily consisted of wheat genotypes. The post-harvest P. thornei population densities were analysed across multiple environments in a linear mixed model framework, with a factor analytic structure used to model genotype by environment (G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E) interaction effects exclusively for wheat genotypes. In general, genetic correlations between environments were found to be high, indicating limited G ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} E interaction for resistance to P. thornei. Post-processing of results using the factor analytic selection tools (FAST) method provided a measure of the overall performance for each wheat genotype, as well as a stability measure reflecting the consistency of the resistance status across environments. The FAST method quantified genotype resistance on a continuous scale, better reflecting the nature of genetic resistance based on a quantitative variable such as nematode population density, and provided a statistically robust and informative means of aiding selection decisions for resistance to P. thornei.

Body mass, diet, foraging strata and foraging behaviour predict variation in the propensity to join mixed‐species flocks across birds

Mixed‐species flocking is common in birds and is associated with increased foraging efficiency and reduced predation pressure. The propensity to join mixed‐species flocks within a community varies across species, possibly reflecting species‐specific needs for increased protection from predators, opportunities to increase foraging efficiency or both. Previous studies on predictors of mixed‐species flocking propensity across species have typically focused on local communities, with or without accounting for phylogenetic relatedness. Here, we examined predictors of mixed‐species flocking propensity across the published literature using a phylogenetic linear mixed model framework. We obtained 724 mixed‐species flocking propensity estimates from 31 different study sites located primarily in South America and East Asia. Flocking propensity was associated with diet, foraging strata and foraging behaviour, and was inversely related to body mass. Controlling for phylogeny and body mass, flocking propensity was significantly lower for nectarivores than for species with other diets, lower for species foraging on the ground than in higher strata, and lower for sallying species than for bark and near‐perch foragers. These results suggest that variation across species in the tendency to join mixed‐species flocks can be predicted from knowledge about various species‐specific ecological traits in birds.

Assessment of Repetitive Controlled Ovarian Stimulation (COS) Cycles on Oocyte Donors: Impact on Oocyte Quality and Viable Embryo Yield

The utilization of donor eggs has broadened the options for Assisted Reproductive Technology (ART) among women facing challenges with egg quantity or quality. Given that donors are typically selected from young and fertile individuals, In Vitro Fertilization with egg donation (IVF-ED) tends to exhibit higher rates of implantation, pregnancy, and live births compared to IVF with the woman's own eggs, especially for females over 35 years old. This has led to a projected increase in the demand for IVF-ED, surpassing the number of available donors. Consequently, many centers opt to use oocyte donors for multiple cycles. However, the correlation between repeated Controlled Ovarian Stimulation (COS) cycles and the performance of donors in terms of viable blastocyst stage embryo (VEC) or blastocyst embryo rate is not definitively established and remains of interest. This study aims to explore the preimplantation characteristics of embryo development and oocyte maturation status based on the number of donor COS cycles, employing a Generalized Linear Mixed Model (GLMM) framework. The study encompasses 1965 embryo transfer (ET) cycles involving 399 donors who underwent a minimum of two and a maximum of nine controlled ovarian hyperstimulation (COS) cycles. The findings indicate that, with the patient undergoing six or more cycles of ovarian stimulation, despite a 3.9% increase in both maturation and fertilization rates, there is a corresponding decrease of 4.5% in VEC rate and 4.7% in blastulation rates. In essence, an escalating number of donor COS cycles appears to be associated with a disadvantageous reduction in embryo quality.

Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data

Studies/trials assessing status and progression of periodontal disease (PD) usually focus on quantifying the relationship between the clustered (tooth within subjects) bivariate endpoints, such as probed pocket depth (PPD), and clinical attachment level (CAL) with the covariates. Although assumptions of multivariate normality can be invoked for the random terms (random effects and errors) under a linear mixed model (LMM) framework, violations of those assumptions may lead to imprecise inference. Furthermore, the response-covariate relationship may not be linear, as assumed under a LMM fit, and the regression estimates obtained therein do not provide an overall summary of the risk of PD, as obtained from the covariates. Motivated by a PD study on Gullah-speaking African-American Type-2 diabetics, we cast the asymmetric clustered bivariate (PPD and CAL) responses into a non-linear mixed model framework, where both random terms follow the multivariate asymmetric Laplace distribution (ALD). In order to provide a one-number risk summary, the possible non-linearity in the relationship is modeled via a single-index model, powered by polynomial spline approximations for index functions, and the normal mixture expression for ALD. To proceed with a maximum-likelihood inferential setup, we devise an elegant EM-type algorithm. Moreover, the large sample theoretical properties are established under some mild conditions. Simulation studies using synthetic data generated under a variety of scenarios were used to study the finite-sample properties of our estimators, and demonstrate that our proposed model and estimation algorithm can efficiently handle asymmetric, heavy-tailed data, with outliers. Finally, we illustrate our proposed methodology via application to the motivating PD study.

A Tutorial for Deception Detection Analysis or: How I Learned to Stop Aggregating Veracity Judgments and Embraced Signal Detection Theory Mixed Models

Historically, deception detection research has relied on factorial analyses of response accuracy to make inferences. However, this practice overlooks important sources of variability resulting in potentially misleading estimates and may conflate response bias with participants’ underlying sensitivity to detect lies from truths. We showcase an alternative approach using a signal detection theory (SDT) with generalized linear mixed models framework to address these limitations. This SDT approach incorporates individual differences from both judges and senders, which are a principal source of spurious findings in deception research. By avoiding data transformations and aggregations, this methodology outperforms traditional methods and provides more informative and reliable effect estimates. This well-established framework offers researchers a powerful tool for analyzing deception data and advances our understanding of veracity judgments. All code and data are openly available.

Multivariate Generalized Linear Mixed Models for Count Data

Univariate regression models have rich literature for counting data. However, this is not the case for multivariate count data. Therefore, we present the Multivariate Generalized Linear Mixed Models framework that deals with a multivariate set of responses, measuring the correlation between them through random effects that follows a multivariate normal distribution. This model is based on a GLMM with a random intercept and the estimation process remains the same as a standard GLMM with random effects integrated out via Laplace approximation. We efficiently implemented this model through the TMB package available in R. We used Poisson, negative binomial (NB), and COM-Poisson distributions. To assess the estimator properties, we conducted a simulation study considering four different sample sizes and three different correlation values for each distribution. We achieved unbiased and consistent estimators for Poisson and NB distributions; for COM-Poisson estimators were consistent, but biased, especially for dispersion, variance, and correlation parameter estimators. These models were applied to two datasets. The first concerns a sample from 30 different sites collected in Australia where the number of times each one of the 41 different ant species was registered; which results in an impressive 820 variance-covariance and 41 dispersion parameters are estimated simultaneously, let alone the regression parameters. The second is from the Australia Health Survey with 5 response variables and 5190 respondents. These datasets can be considered overdispersed by the generalized dispersion index. The COM-Poisson model overcame the other two competitors considering three goodness-of-fit indexes, AIC, BIC, and maximized log-likelihood values. As a result, it estimated parameters with smaller standard errors and a greater number of significant correlation coefficients. Therefore, the proposed model is capable of dealing with multivariate count data, either under- equi- or overdispersed responses, and measuring any kind of correlation between them taking into account the effects of the covariates.

Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions

Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.

A multi-dimensional incomplete stepped-wedge trial design to estimate the impact of standards-based audit.

A clinical audit is a low-cost process used for quality improvement in healthcare. Such audits are however infrequently used in resource poor countries, where the need for and potential impact of quality improvement is higher. Sets of standards for use in maternal and newborn care have been established based on internal guidelines and evidence. The before-after design of a clinical audit is prone to bias in the estimation of the impact of conducting a clinical audit. A trial design that would provide an unbiased estimate of the impact of implementing a clinical audit process on the attainment of standards selected (a standards-based audit) was needed. The aim of this paper is to introduce and describe the design of trials we developed to meet this need. A novel randomised stepped-wedge trial design to estimate the impact of conducting standards-based audits is presented. A multi-dimensional incomplete stepped-wedge cluster randomised trial design suitable for estimation of the impact of Standards-based audits on compliance with standard is proposed; two variants are described in detail. A method for sample size estimation is described. Analyses can be performed for the binary outcome using a generalised linear mixed model framework to estimate the impact of the approach on compliance with standards subjected to a standards-based audit; additional terms to consider including in sensitivity analyses are considered. The design presented has the potential to estimate the impact of introducing the standards-based audit process on compliance with standard, while providing participating healthcare providers opportunity to gain experience of implementing the standards-based audit process. The design may be applicable in other areas in which multiple processes are to be studied.

Missing Data Imputation with High-Dimensional Data

Imputation of missing data in high-dimensional datasets with more variables P than samples N, P ≫ N , is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables. Thus, the high dimension requires special imputation approaches. In this article, we provide an overview and realistic comparisons of imputation approaches for high-dimensional data when applied to a linear mixed modeling (LMM) framework. We examine approaches from three different classes using simulation studies: multiple imputation with penalized regression, multiple imputation with recursive partitioning and predictive mean matching; and multiple imputation with Principal Component Analysis (PCA). We illustrate the methods on a real case study where a multivariate outcome (i.e., an extracted set of correlated biomarkers from human urine samples) was collected and monitored over time and we discuss the proposed methods with more standard imputation techniques that could be applied by ignoring either the multivariate or the longitudinal dimension. Our simulations demonstrate the superiority of the recursive partitioning and predictive mean matching algorithm over the other methods in terms of bias, mean squared error and coverage of the LMM parameter estimates when compared to those obtained from a data analysis without missingness, although it comes at the expense of high computational costs. It is worthwhile reconsidering much faster methodologies like the one relying on PCA.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

A novel rare variants association test for binary traits in family-based designs via copulas.

With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.

Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units

The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.

Benefits and harms of perioperative high fraction inspired oxygen for surgical site infection prevention: a protocol for a systematic review and meta-analysis of individual patient data of randomised controlled trials

IntroductionThe use of high fraction of inspired oxygen (FiO2) intraoperatively for the prevention of surgical site infection (SSI) remains controversial. Promising results of early randomised controlled trials (RCT) have been...

Designing multicenter individually randomized group treatment trials.

In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between arms within the same center. While the design of a single-center IRGT trial has been studied, little is known about the planning of a multicenter IRGT trial. To address this gap, this paper provides analytical sample size formulas for designing multicenter IRGT trials with a continuous endpoint under the linear mixed model framework. We found that accounting for the additional center-level correlation at the design stage can lead to sample size reduction, and the magnitude of reduction depends on the amount of between-therapist correlation. However, if the variance components of therapist-level random effects are considered as input parameters in the design stage, accounting for the additional center-level variance component has no impact on the sample size estimation. We presented our findings through numeric illustrations and performed simulation studies to validate our sample size procedures under different scenarios. Optimal design configurations under the multicenter IRGT trials have also been discussed, and two real-world trial examples are drawn to illustrate the use of ourmethod.

Response of Teff ((Eragrostis tef (zucc.) Trotter) to nitrogen and phosphorus applications on different landscapes in eastern Amhara

The dynamic nature of soil fertility status across different landscapes attracted research attention in Ethiopia and the globe. Teff [Eragrotis tef] is a major staple cereal crop in Ethiopia but yields are low due to inadequate nutrient supply and other constraints. A field study was conducted in 2020 and 2021 in the Habru district of Amhara Region to determine teff yield response to fertilizer-N and -P at hillslope, midslope, and footslope positions with slopes of >15%, 5–15%, and 0–5%, respectively. N and P fertilizer rates were factorially combined in randomized complete block design with three replications in each farmer's field. A linear mixed modeling framework was used to determine effects on grain yield due to N rate, P rate, slope, study sites, and years. Model fit was examined using Akaike's Information Criterion and Bayesian Information Criterion. Economic analysis was done with a quadratic response function to determine the economics of fertilizer. Yield response to fertilizer-P was affected by slope but the response to fertilizer-N was not affected. Teff yield increase with fertilizer-N application up to 92 kg ha−1 the economic optimum rate based on the yield response function for nitrogen fertilizer was 85.4 kg ha−1 to obtain maximum profit (86878.8 birr ha-1). Similarly, the optimum phosphorus fertilizer rate at the hill slope was 39.7 kg ha−1 to obtain a maximum profit of (96847.8 birr ha−1). But there was not a profitable response at the midslope and foot slope positions. Therefore, for Habru district and similar agroecologies85.4 kg ha−1 N and 39.7 kg ha−1 P in hillslopes and only 85.4 N kg ha−1 for midslopes and foot slopes are expected to give the most profitable returns to fertilizer applied for tef production.

Maternal glycemia in pregnancy is longitudinally associated with blood DNAm variation at the FSD1L gene from birth to 5 years of age

BackgroundIn utero exposure to maternal hyperglycemia has been associated with an increased risk for the development of chronic diseases in later life. These predispositions may be programmed by fetal DNA methylation (DNAm) changes that persist postnatally. However, although some studies have associated fetal exposure to gestational hyperglycemia with DNAm variations at birth, and metabolic phenotypes in childhood, no study has yet examined how maternal hyperglycemia during pregnancy may be associated with offspring DNAm from birth to five years of age.HypothesisMaternal hyperglycemia is associated with variation in offspring DNAm from birth to 5 years of age.MethodsWe estimated maternal hyperglycemia using the area under the curve for glucose (AUCglu) following an oral glucose tolerance test conducted at 24–30 weeks of pregnancy. We quantified DNAm levels in cord blood (n = 440) and peripheral blood at five years of age (n = 293) using the Infinium MethylationEPIC BeadChip (Illumina). Our total sample included 539 unique dyads (mother–child) with 194 dyads having DNAm at both time-points. We first regressed DNAm M-values against the cell types and child age for each time-point separately to account for the difference by time of measurement for these variables. We then used a random intercept model from the linear mixed model (LMM) framework to assess the longitudinal association between maternal AUCglu and the repeated measures of residuals of DNAm. We adjusted for the following covariates as fixed effects in the random intercept model: maternal age, gravidity, smoking status, child sex, maternal body mass index (BMI) (measured at first trimester of pregnancy), and a binary variable for time-point.ResultsIn utero exposure to higher maternal AUCglu was associated with lower offspring blood DNAm levels at cg00967989 located in FSD1L gene (β = − 0.0267, P = 2.13 × 10–8) in adjusted linear regression mixed models. Our study also reports other CpG sites for which DNAm levels were suggestively associated (P < 1.0 × 10–5) with in utero exposure to gestational hyperglycemia. Two of these (cg12140144 and cg07946633) were found in the promotor region of PRDM16 gene (β: − 0.0251, P = 4.37 × 10–07 and β: − 0.0206, P = 2.24 × 10–06, respectively).ConclusionMaternal hyperglycemia is associated with offspring DNAm longitudinally assessed from birth to 5 years of age.

Heterospecific pollen deposition in understory plants of a forest in central Amazon

In co-flowering plant communities, pollinator movement can lead to the transfer of heterospecific pollen among species. This can affect the reproductive success of individuals, either by preventing conspecific pollen from reaching the stigma or by allelopathy, but little is known about this process in tropical areas. To analyze heterospecific pollen transfer and deposition, we investigated attributes related to pollinator attraction, such as corolla size, number of flowers per individual, floral arrangement, floral asymmetry, and distance from one flowering individual to another in a Central Amazon forest. The study was carried out in riparian plots of the Adolpho Ducke Forest Reserve during the rainy season. Stigmas and anthers were removed from all individual understory plants found in bloom in permanent plots. Slides were mounted with glycerinated gelatin, and all conspecific and heterospecific pollen grains were identified and counted. We then used a generalized linear mixed model framework to determine how the measured variables were related to the proportion of heterospecific pollen deposition (HPD) on stigmas. We found that the mean proportion of HPD in individuals was 4.7%, and 60% of individuals had no heterospecific pollen; from the 14 species studied, two had no heterospecific pollen. Species with wider flowers received less HPD, while species with more flowers per individual and with solitary flowers received more heterospecific pollen. Despite the significant variation in floral types in the Adolpho Ducke Reserve, there was a low percentage of heterospecific pollen deposition, probably due to specialized plant-pollinator interactions.

Multivariate generalized linear mixed models for underdispersed count data

Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to directly calculate from the regression model a correlation measure between the response variables. In this article, we employed the Multivariate Generalized Linear Mixed Models framework, which allows the specification of a set of response variables and calculates the correlation between them through a random effect structure that follows a multivariate normal distribution. We used the maximum likelihood estimation framework to estimate all model parameters using Laplace approximation to integrate out the random effects. The derivatives are provided by automatic differentiation. The outer maximization was made using a general-purpose algorithm such as PORT and Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS). We delimited this problem by studying count response variables with the following distributions: Poisson, negative binomial, Conway-Maxwell-Poisson (COM-Poisson), and double Poisson. While the first distribution can model only equidispersed data, the second models equi and overdispersed, and the third and fourth models all types of dispersion (i.e. including underdispersion). The models were implemented on software R with package TMB, based on C++ templates. Besides the full specification, models with simpler structures in the covariance matrix were considered (fixed and common variance, and ρ set to 0) and fixed dispersion. These models were applied to a dataset from the National Health and Nutrition Examination Survey, where two response variables are underdispersed and one can be considered equidispersed that were measured at 1281 subjects. The double Poisson full model specification overcame the other three competitors considering three goodness-of-fit measures: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and maximized log-likelihood. Consequently, it estimated parameters with smaller standard error and a greater number of significant correlation coefficients. Therefore, the proposed model can deal with multivariate count responses and measures the correlation between them taking into account the effects of the covariates.

Overview of Admixture Mapping.

Admixture mapping is a powerful method of gene mapping for diseases or traits that show differential risk by ancestry. Admixture mapping has been applied most often to Americans who trace ancestry to various combinations of Native Americans, Europeans, and West Africans. Recent developments in admixture mapping include improvements in the methods and reference data needed to make inferences about ancestry as well as extensions of the mapping approach in the framework of linear mixed models. This overview outlines the key concepts of admixture mapping. It describes approaches for inferring local ancestry and provides strategies for performing admixture mapping depending on the study design. Finally, linkage analysis, association analysis, and admixture mapping are compared, with an emphasis on integrating admixture mapping and association testing. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.