In cross-population comparison using United Kingdom Biobank data  we found shared but heterogeneous impacts of AM between populations.Assortative mating (AM) is a pattern characterized by phenotypic similarities between mating partners. Detecting the evidence of AM has been challenging due to the lack of large-scale datasets that include phenotypic data on both partners, especially in populations of non-European ancestries. Gametic phase disequilibrium between trait-associated alleles is a signature of parental AM on a polygenic trait, which can be detected even without partner data. Here, using polygenic scores for 81 traits in the Japanese population using BioBank Japan Project genome-wide association studies data ( Yamamoto et al. find genetic evidence of assortative mating based on dietary habits and disease phenotypes in the Japanese population, and show that this pattern of partner choice is markedly different from its European-ancestry counterpart. Partner similarities involve a wide range of factors: age, geographical factors, racial/ethnic background, religion, socioeconomic status and educational background, as well as physical, personality and psychological traits2, and AM for many of these traits has been demonstrated by comparing partner phenotypes8. In the field of population genetics, we know that AM increases the homozygosity of genotypes of the trait-associated variants, induces long-range correlations between alleles across the genome and increases genetically determined variance of the traits in a population scale9.Positive assortative mating (AM) is a commonly observed phenomenon in human mating, where individuals with similar phenotypes are more likely to form partnerships than expected by chance 12. An alternative approach is to study genetic similarities between partners. This approach revealed the existence of AM in Europeans on anthropometric traits (height and body mass index (BMI)), and social and behavioural phenotypes 17. Although partner genotype–phenotype data have been analysed in these studies, it has been relatively challenging to achieve biobank-scale sample sizes in populations of diverse ancestries.Quantitative impacts of AM in human genetics have been investigated by focusing on the deviation from the Hardy–Weinberg equilibrium (HWE) in trait-associated variants. However, this approach requires large sample sizes, especially when the effect sizes of the associated variants are small. Furthermore, ancestral endogamy  could confound these relationships18. The authors focused on the gametic phase disequilibrium (GPD) between trait-associated alleles19. Under AM, physically distant trait-associated alleles correlate with each other beyond local linkage disequilibrium (LD) in polygenic traits. Thus, the genetic effects of AM from parents are reflected as the correlation between two polygenic scores (PGS) from physically distant sets of chromosomes . The application of PGS to United Kingdom Biobank (UKB) GWAS data has provided evidence of AM for adult height and educational background, and the researchers further validated these results via AM estimation using spousal pairs. While this method has advantages in that it only requires GWAS data without partner information, its applications have so far been limited to European-ancestry populations. More generally, there have been very few investigations of the genetic effects of AM outside of the European-ancestry populations.Recently, Yengo et al. developed a new method to quantify the impact of AM using data from large-scale genome-wide association studies (GWAS) without partner data20. We estimate the AM-induced GPD across 81 human complex traits by calculating the correlation between PGSodd and PGSeven with robust adjustments for population stratification. We then compare our results with those derived from the UKB genotype–phenotype data. Our study provides evidence of AM in the previous generation of the current Japanese cohort, and highlights the importance of studying AM in populations representative of non-European ancestries.Here, we report a PGS-based analysis of AM in a Japanese cohort using BioBank Japan Project (BBJ) GWAS data, one of the largest non-European biobanks with deep phenotype information21  for phenotypic uniformity  approach to control for population stratification and relatedness24. Then, we calculated PGSodd and PGSeven for the individuals in the target subset using the posterior variant effect sizes inferred by PRS-CS25 from GWAS results generated with GCTA-fastGWA. We estimated GPD for each trait from correlations between PGSodd and PGSeven (θeven_to_odd and θodd_to_even) adjusted for 20 principal components (PCs) derived from odd-/even-numbered chromosomes (PCsodd/even) to correct for population stratification  are not publicly available, we adopted the tenfold leave-one-group-out (LOGO) meta-analysis method for BBJ to estimate AM-induced GPDn = 8,947 from the BBJ Ryukyu cluster, n = 1,275 from the Osaka University healthy cohort, two datasets from the Nagahama cohort study , n = 1,110 from the Japan Biological Informatics Consortium (JBIC) and n = 1,842 from UKB EAS. For each cohort, we derived PGSodd and PGSeven using the whole-sample MLM-GWAS in the BBJ mainland cluster, and estimated the GPD from correlations between PGSeven and PGSodd as described above. Finally, we meta-analysed the GPD estimates across all EAS. Regarding the UKB data, we applied the LOGO method and quantified GPD in the same way as described in the BBJ part .We replicated our findings in six independent cohorts of East Asian ancestry: θeven_to_odd and θodd_to_even values of each trait were similar but not completely identical due to the difference in the single nucleotide polymorphism (SNP) selected for PGS calculation, we conservatively adopted the value with larger variance as the GPD estimate of the trait (θ). We set a study-wide significance threshold at P = 6.2 × 10−4 (= 0.05/81) by applying Bonferroni’s correction based on the number of traits analysed.We estimated GPD across 81 complex traits measured in BBJ participants  = 0.0025, P = 5.2 × 10−14; Fig. θCAD = 0.015, s.e. = 0.0025, P = 2.2 × 10−9). Among dietary and behavioural traits, we detected significant GPD estimates for the frequency of light physical activity , natto  and yoghurt consumption . We did not detect significant evidence of AM on alcohol consumption and smoking status , which have been previously reported in other studies16.We detected significant GPD estimates in five traits. The most significant trait was type 2 diabetes . This observation can be explained by the genetic architecture of alcohol-related behaviours, which involves a subset of variants with strong effects in EAS populations. These variants are mainly located on even-numbered chromosomes 26, and they are related to natural selection and population stratification in EAS and the Japanese28. Thus, there is a stronger correlation between PGSeven and PCseven than between PGSodd and PCsodd  < 0.1 at each variant)26.Next, we compared ion Fig. . Althougθheight in UKB = 0.030 and 0.030 for the current and previous studies18, respectively). The GPD estimate of BMI in our work was slightly higher than in the previous study . It is noteworthy that the GPD estimates for adult height were relatively higher than those for BMI in both European-ancestry and Japanese populations . However, the GPD estimate for adult height was not as high in Japanese compared with the European-ancestry cohort. This result was consistent with previous epidemiological reports5, in which the correlation of height between spousal pairs in Western countries was higher than those in non-Western regions. We note that height was one of the traits with the strongest positive natural selection among Europeans29, whereas it was not in the Japanese28. The GPD estimates of T2D, CAD, light-PA and yoghurt consumption were higher in Japanese than in European-ancestry populations . This result suggests a population-specific effect of AM.We estimated GPD for six traits in the UKB GWAS data: T2D, CAD, light-PA and yoghurt consumption as traits with significant AM signatures in the Japanese, and adult height and obesity (BMI) as gold standard controls for AM Fig. . We robuθdummy = 0.0010, s.e. = 0.0024, P = 0.69; Fig. We performed sensitivity analyses to confirm the robustness of our findings. We investigated the potential effect of cryptic population stratification of the Japanese population in two ways. First, we simulated a heritable dummy phenotype as a negative control , equilibrium heritability (M) and sample size (n). Based on r and 35, M assumed between 10,000 and 100,000 and n from the reference GWAS size, we calculated the expected GPD in the dummy data, adult height, BMI, T2D and CAD  and detected strong regional differences in light-PA, natto consumption, yoghurt consumption, T2D and CAD. To correct for the influence of the regional differences in GPD estimates, we adopted the leave-one-region-out (LORO) approach  on traits associated with the liability to these diseases could be the cause of the observational similarity between partners.Previous studies have reported spousal concordance of T2D and CAD in populations of East Asian ancestries28. Population admixture and natural selection represent other potential causes of GPD, and they are entangled with AM. Although admixture and population stratification could create positive GPD, we empirically assessed the potential cause by robustly controlling for sample selection and population stratification. As natural stabilizing selection would induce a negative correlation between alleles and lead to negative GPD, it would result in an opposite effect to AM39.Several behavioural and dietary habits showed significant AM signatures: light-PA, natto, yoghurt and vegetable consumption. Natto consumption is a unique dietary habit of East Asian and Japanese populations. Our results suggest behavioural and dietary habits are a driving force of AM in Japan, and dietary habits are known to be involved in the natural selection pressure of Japanese40, in which partner PGS correlation would be influenced by background geographical factors not captured by PCs.Applying the LORO approach as an adjustment for geography-related effects not captured by PCs, we detected statistically significant GPD estimates in T2D, CAD and vegetable consumption. Given the difference in the geographical distribution in many traits and the decrease of the GPD estimates when using the LORO approach, our initial GPD estimates could reflect not only AM, but also include the effects of social homogamy related to geography. This genetic correlation induced by geography-related effect could be especially high in natto and yoghurt consumption. This insight corroborates the results provided by Okbay et al.18, but it is also associated with common diseases and cognitive traits40. Observational studies in the Japanese population have likewise reported both educational AM and associations between EA and cardiometabolic diseases and dietary habits43. On the other hand, other studies found that spousal similarities in cardiometabolic diseases were independent of EA38. As our results could reflect the potential involvement of EA, further work will be required in Japanese cohorts, including data on educational attainment.Our study has several limitations. One potential limitation is that we did not assess AM in educational attainment (EA), as such information was not collected in BBJ, a hospital-based biobank. In European-ancestry populations, EA is among the traits strongly influencing AMn = 7,645 in Hokkaido to n = 91,743 in Kanto-Koshinetsu) could also affect the power of GWAS and the estimation of local GPD using our LORO approach. While some social homogamy  and AM could not be completely independent45, these limitations could be mitigated through future enhancement of cohorts and collaborations.Furthermore, we could not conduct a confirmation analysis using spousal or partner data due to the lack of available biobank-scale data. We therefore have not examined correlations between our GPD estimates and partner genetic similarities. As BBJ is a hospital-based medical cohort, the distribution of phenotypes and genotypes may not fully reflect that of a healthy population. Furthermore, due to the lack of information on birth places or residences in BBJ, our results may not fully account for geographical differences. Large sample size differences between geographical regions  individuals with low genotyping call rates (<98%), (2) closely related individuals  and (3) outliers from the Japanese cluster based on principal component analysis (PCA) using PLINK2  with samples of the 1000 Genomes Projects. Further, we separated the BBJ individuals into two Japanese clusters27 the mainland cluster  and Ryukyu cluster , by visual inspection based on the PCA plot . JBIC consists of Epstein–Barr virus-transformed B lymphoblast cell lines of unrelated Japanese individuals47. Osaka University healthy cohort is a volunteer-based cohort study recruited from the Osaka University Graduate School of Medicine, the University of Tokyo and the University of Tsukuba48. For each cohort, we also excluded individuals with a low genotyping call rate, a high heterozygosity rate, closely related individuals (PI_HAT ≥ 0.125) and PCA outliers from EAS populations49. In addition, we extracted the EAS individuals from UKB. UKB is a population-based cohort that recruited approximately 500,000 individuals between 40 and 69 years of age from across the United Kingdom50. We obtained EAS individuals from unrelated UKB individuals based on PCA visualization combined with the 1000 Genomes Projects . This study was approved by the ethical committee of Osaka University Graduate School of Medicine.The Japanese subjects in replication cohorts were collected from three Japanese population-based cohorts . The Nagahama cohort study is a community-based cohort in Nagahama city, Shiga prefecture, Japan. The study recruited healthy individuals between the ages of 30 and 74 . For each PA, participants were also asked for the frequency and the length of time per week on a seven-point scale, and we quantified the activity by converting the responses to total minutes of activity time per week (min week–1), where ≥30 (15) min day–1 = 210 (105), <30 (15) min day–1 = 140 (70), three to four times a week for ≥30 (15) min = 105 (52.5), three to four times a week for <30 (15) min = 70 (35), one to two times a week for ≥30 (15) min = 45 (22.5), one to two times a week for <30 (15) min = 30 (15) and rarely = 0 (the number in parentheses indicates gymnastics time).BBJ collected baseline clinical information and dietary and activity habits information through interviews and reviews of medical records using a standardized questionnaire. We selected 81 traits , where the number of cases exceeded 10,000 individualsh2 = 0.5) from 10,000 causal variants randomly sampled from BBJ GWAS data using GCTA GWAS simulation56. The phenotype followed the model yj = gj + ej, where gj = Σi(Wijβi) and Wij = (xij – 2pi)[2pi(1 – pi)]−1/2, where xij is the genotype for the ith causal variant of the jth individual, pi is the allele frequency of the ith causal variant within a population and ej is the residual effect generated from a normal distribution with mean 0 and variance Var(gj)(1 − h2)/h2. βi is the effect size of the ith causal variant generated from a normal distribution with mean 0 and variance 1 (ref. 57). The values were normalized by applying a rank-based inverse normal transformation.In addition, we set a dummy phenotype as a negative control. We generated a phenotype with heritability  call rate <99%, (2) P value for HWE < 1.0 × 10−6, (3) number of heterozygotes <5 and (4) a concordance rate <99.5% or a non-reference concordance rate between the GWAS array and whole genome sequencing. The genotype data were phased by Eagle , and imputed with the 1000 Genomes Project Phase3 (v.5) and BBJ1K using Minimac3 software . After imputation, we excluded variants with an imputation quality of R-square (Rsq) < 0.7 and those with a minor allele frequency (MAF) < 1%.The BBJ GWAS data were genotyped using the Illumina HumanOmniExpressExome BeadChip or a combination of the Illumina HumanOmniExpress and HumanExome BeadChips. The quality control of the genotypes was described elsewhereP < 1.0 × 10−7 (ref. 47). Osaka University healthy cohort was genotyped using Illumina Infinium Asian Screening Array. We excluded the variants that satisfied (1) call rate < 0.99, (2) minor allele count < 5 and (3) HWE P < 1.0 × 10−5 (ref. 48). The Nagahama cohort study was genotyped using six genotype arrays. We then selected two platforms (Illumina Human610-Quad Beadchip and Illumina HumanOmni2.5-4v1 Beadchip) with a large number of samples. For each of the two datasets, we excluded variants with (1) call rate < 0.98, (2) MAF < 1% and (3) HWE P < 1.0 × 10−6 (ref. 28). Genotype data were phased by Shapeit  or Eagle, and imputed with the reference panel from the 1000 Genomes Project Phase3 (v.5) and BBJ1K using Mimimac3. After imputation, we excluded variants with an imputation quality of Rsq < 0.7 and MAF < 1%.As for the other Japanese datasets, JBIC was genotyped using Illumina HumanCoreExome Beadchip. As stringent quality control filters, we excluded the variants that satisfied (1) call rate < 0.99, (2) MAF < 1% and (3) HWE 50. We extracted EAS individuals and excluded variants with INFO score ≤0.8 and MAF ≤ 1%.The UKB project was genotyped using either Applied Biosystems UK BiLEVE Axiom Array or Applied Biosystems UKB Axiom Array. The genotypes were imputed using the Haplotype Reference Consortium, UK10K and the 1000 Genomes Phase 3 reference panel by IMPUTE4. The detailed characteristics of the cohort and genotype–phenotype data were described elsewhere21. We first randomly split the BBJ mainland samples into the 10 target subsets. GWAS was performed on 81 complex traits for samples excluding the target subset using GCTA-fastGWA  as a MLM approach with 7,401,847 autosomal variants24. For GCTA-fastGWA, we computed a sparse genetic relationship matrix (GRM) for BBJ participants  using slightly LD-pruning variants . Regarding the 57 anthropometric traits and biomarkers, the 11 dietary traits, the four PA traits and the two binary traits in the behavioural traits, we fitted age, age-squared, sex, the top 20 PCs and 47 disease status as covariates. For the six diseases, we also fitted age, age-squared, sex and the top 20 PCs as covariates. We also performed GWAS using GCTA-fastGWA for all individuals in the BBJ mainland cluster to apply to other Japanese or EAS datasets. LD score regression  was applied to the summary statistics of the whole-sample GWAS to estimate potential population stratification. We adopted the HapMap3 SNPs, excluding the human leukocyte antigen region, using precomputed LD scores from 1KG EAS downloaded from the LDSC software website 57. We created the GRM using all variants for BBJ mainland samples. We estimated LD scores using default parameters in GCTA, and stratified SNPs into LD score quartiles. Next, we divided the SNPs within each LD score quartile into six MAF groups  and generated 24 GRMs. We calculated the phenotypic variance for each GRM and summed them to derive the total phenotypic variance  to construct PGSs that included genome-wide HapMap3 variants. PRS-CS is one of the beta shrinkage methods, which applies a Bayesian regression framework to identify posterior variant effect sizes based on continuous shrinkage before using both GWAS summary data and the external LD reference panel25. When the training sample size was large enough and the case–control imbalance was small, the automated optimization model (PRS-CS-auto) had the same precision as the grid model60. Therefore, for each of the target folds, we estimated the posterior mean effects of SNPs from the MLM-GWAS summary data of all training samples using PRS-CS-auto with the precomputed HapMap3 SNP LD reference panel from 1KG EAS downloaded from the PRS-CS website. We calculated PGSodd and PGSeven of individuals within the target subset using the estimated posterior effect of SNPs by PLINK2 score function. We normalized the calculated PGSs for each trait in each target subset to compare the effect sizes across the phenotypes.To derive PGSs of individuals in each of the target subsets, we applied PRS-CS  implemented in R . We also estimated the GPD for the other Japanese and EAS datasets using the summary results of the whole BBJ sample GWASs by PRS-CS-auto. Finally, we performed a meta-analysis on the GPD estimates from the BBJ and other Japanese and EAS datasets by the fixed effect method using metafor. We estimated the P value of meta-analysed GPD using the Wald test.For GPD estimation, we performed PCA of even and odd number chromosomes for each of the target subsets. We then estimated GPD using a linear regression method following the formula based on the original studyTo assess the robustness of our analysis to the chosen grouping of chromosomes, we altered the combinations of chromosomes such that the number of SNPs was the same in the two groups: (1) first half and second half; chromosomes 1–8 versus chromosomes 9–22, and (2) pseudo-random chromosomes; chromosomes 1, 3, 5, 6, 9, 10, 13, 14, 17 and 18 versus chromosomes 2, 4, 7, 8, 11, 12, 15, 16, 19, 20, 21 and 22. Using the two alternative combinations, we estimated the GPD for each cohort and meta-analysed the results.18. The theoretical value (θ) followed the formula,r is a phenotypic correlation between spouses, M is the number of causal variants and n is the sample size of the GWAS.We also calculated the theoretical GPD derived in the original studyn = 337,139) from UKB by adopting the tenfold LOGO approach to the six available traits 50. When adult height and BMI were measured multiple times, we adopted the mean value to obtain a single value per participant and normalized the values using the rank-based inverse normal transformation method. Regarding T2D, the case was defined following the ICD-10 codes and ‘probable T2D’ and ‘possible T2D’ in a T2D inference algorithm based on Eastwood et al.61. We also defined individuals without any diabetes status as the T2D control based on ICD-10 and the inference algorithm. As for CAD, the case was extracted following ICD-10 codes, surgical procedure recodes, self-reported illness codes and self-reported operation codes based on Fall et al.62. Regarding the duration of light-PA (Data-Field 104920), we extracted the data from instance 0  and converted the coding to categorical values. Regarding the consumption of yoghurt, we extracted data from instance 0 within consumers of yoghurt/ice cream as binary traits . From the imputed GWAS data, we excluded the variants that satisfied MAF ≤ 1% and INFO score ≤0.8, and fastGWA conducted generalized MLM approaches for nine subset samples with adjustment for age, age-squared, sex, top 20 PCs, ascertainment centre information and batch information as covariates. For the six phenotypes, we estimated the PGSs for odd and even chromosomes by PRS-CS-auto using genome-wide HapMap SNPs and the 1KG EUR LD reference panel, and the GPD was estimated in the same way as described in the Japanese study. We further meta-analysed the GPD estimate from each of the ten subgroups by the fixed effect method using metafor.We analysed individuals of white British ancestry determined by PCA (Further information on research design is available in the Supplementary InformationSupplementary Note, Figs. 1–6 and Tables 1, 3, 4, 6 and 7.Reporting SummarySupplementary Table 2Overview of the studied traits in BBJ.Supplementary Table 5LD score regression results of whole-sample GWAS and variance explained by PGS in BBJ.Supplementary Table 8Phenotype QC in BBJ.


Their simulation study, however, was limited to a two-way admixed population. Realizing that their findings might not translate to other admixture scenarios, we simulated a three- and five-way admixed population to compare the LAAA model to other models commonly used in genome-wide association studies (GWAS). We found that, given our admixture scenarios, the LAAA model identifies the most causal variants in most of the phenotypes we tested across both the three-way and five-way admixed populations. The LAAA model also produced a high number of false positive hits which was potentially caused by the ancestry effect size that we assumed. Considering the extent to which the various models tested differed in their results and considering that the source of a given association is unknown, we recommend that researchers use multiple GWAS models when analysing populations with complex ancestry.Researchers would generally adjust for the possible confounding effect of population structure by considering global ancestry proportions or top principle components. Alternatively, researchers would conduct admixture mapping to increase the power to detect variants with an ancestry effect. This is sufficient in simple admixture scenarios, however, populations from southern Africa can be complex multi-way admixed populations. Duan  Differential assortment of ancestral allele frequencies across a genome results from selection, mutation or genetic drift, when previously isolated populations interbreed , 2. TakiA genome-wide association study (GWAS) is commonly used to discover associations between single nucleotide polymorphisms (SNPs) and a diverse spectrum of complex traits of interest . AttribuDuan et al.  expandedWe used the complex demographic history of the Nama and the South African Coloured (SAC) populations to simulate a three-way admixed population (as represented by the Nama) and a five-way admixed population (as represented by the SAC). The genotypes from these simulated populations were used to simulate three phenotypes, each with a different association signal. Therefore the aim of this study was to determine if the LAAA model will be able to robustly capture the true causal variants regardless of the source of the association or the complexity of the admixture compared to other frequently used GWAS models. We also compare other models commonly used in GWAS and we investigate the efficacy of each model when presented with true versus inferred ancestry.https://github.com/popsim-consortium/demes-python) that was then converted to a msprime demographic model using the demes python library. Demes Specifications are YAML files that contain information about populations, their properties (like effective population size changes) and the relationship of a population to other populations.msprime  was usedet al. Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: YesReviewer #3: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The authors set out to validate a recently published approach to use admixture LD to improve GWAS in more complex admixture scenarios (3-way and 5-way). The authors use realistic effect sizes for genetic effects maximizing the applicability of their results to real GWAS situations. However, because of the complex demographic history of the source populations for the admixed communities used as models in this study, it is hard to disentangle effects from varying levels of a particular ancestry and those from a difference in diversity from a single ancestry. The paper has the potential to be a broadly useful resource to the genetic epidemiology community, and could be improved if a simplified 3-way and 5-way admixture scenario were simulated and tested to avoid additional confounding effects on the GWAS methods tested. However, that may be beyond the scope of what the authors hope to present here and I would not require it for accepting this manuscript.I do find several issues with the paper that I think need to be addressed. While GWAS models are well specified (lines 225-253), the conditions of the simulated datasets (both phenotypes and genotypes) are not. It is difficult to evaluate the paper's claims without being able to evaluate how the data were produced.1. I would suggest the authors include links to the model files used and the custom script used for ancestry assignment (line 161) from the simulated data should be linked to or provided as supplementary data.2. It is unclear if the authors simulated all the admixture events (arrows) represented in figure 1.3. It is unclear to me how the genotypes/phenotypes are being linked for analysis? How were the 10 causal SNPs chosen?4. The three types of phenotypes simulated need to be explained in more detail. The discussion (lines 388-390) seems to suggest that a phenotype caused by a local ancestry-specific SNP was not simulated and tested with the various methods. Would distributing causal SNPs randomly across ancestries not result in biased results of all methods tested that are looking for an ancestry-specific effect? Could this explain the high number of false positives seen in the APA and LAAA models compared to the Standard and GA?5. The relationship alluded to in Lines 316-318 between the amount of ancestry and false positives should be more formalized, for example simulating datasets with 5-95% of a certain ancestry and plotting the change in false positives. This would be broadly informative beyond the study populations used here.6. On Line 271, how was LAI accuracy inferred? Were inaccurate segments identified and removed or were they misassigned?7. The results of this study showing that in many instances the Standard model performs as well or nearly as well as the APA or LAAA, while providing orders of magnitude fewer false positives. Identifying 2-3 fewer true hits but avoiding ~1000 false positives that would make interpreting GWAS results extremely difficult, so to support the use of only a Standard Model. This is quite counter-intuitive and requires more discussion.Reviewer #2: The author is not consistent with abbreviation and full name (under abstract GWAS is not describes) then later LAAA is used as abbreviation and as a full term.The aim and the rational of this study its not clearly stated.The Author is using a public data but it was not mentioned clearly under method section that, this is a public data and therefore there is no need for ethical clearance.I think there is a need for a paragraph of sample/data description.Figures have poor quality (they are blur).All Figure legends have incomplete description of what is happening in a figure.I think sub title will make it easier to follow.Reviewer #3: 1 This is an interesting and self-contained paper that will be very helpful to people analysing complex populations. The authors simulated 3-way and 5-way admixture populations that are broadly representative of two groups in South Africa and then with different phenotypes try to understand which models correctly detect SNPs associated with the phenotypes. There are several different modelling approaches and as seen in the paper how you do this has a significant timpact2 The paper is written clearly and overall the methods are sound.3 I have one major criticism4 There is no definition of true positive and false positive -- see detailed comments below. Without knowing this, I am not sure I agree with the conclusions (or at least I can't independently say I do). If their definition of a false positive would include a SNP that is _not_ a SNP in the "input" of the simulation as being causal but is for example 20 base pairs away and has been detected because of LD effects then I think that needs considerable motivation and explanation to justify calling that a false positive. Anyway -- please be clear and define this.5. The number of SNPs used is only 400k -- which is small -- does this impact the result? If we were using a dense array and/or imputed data would this help or make things more complicated6. The limitations expressed on page 15 are significant There are different results for 3-way and 5-way admixture (and as the authors point out this differs from results by others for 2-way admixture). But is that a function of the n in n-way or is it more complex than that? I suppose I want a more definitive answer, which is not possible with the evidence givern.7. As a point of principle I think the code that was developed should be made available via GitHub or the like.8. Bibliography -- a very annyoing feature of the paper is that references are numbered in the text but there is no number in the bibliography!!!!9 Other issues:l97-98: There is no reference for the claim that two-way admixed populations "generally" have simple demographic histories and originate from a single pulse admixture event. I suppose this depends what is meant by "simple" and "pulse", but I think either give some reference that supports this or weaken the claim.l117 : "From" -> fromComma after "Khoe-San" on l128 should deleted -- the subject of the sentence is complex and hence the temptation but the comma is wrong and it would be better to simplify the txt.l122 : See point above about availability -- shouldn't these specifications be made available publicly. Just from a selfish point of view you are more likely to get citations if you do and people use them.l 126-130 : I think the characterisation of Khoe-San as a group culturally related needs care as this is contested esp by members of the communities. This is not my area, but the term Khoe-san language is a term that is more convenient for people outside those communities rather than accurate (perhaps like speaking about the Danish-Japanese language gruop?) and I don't think you can say that pastoralism and hunter-gatherer lifestyles are close culturally. As a relatively recent phenomenon as a result of colonialism since the the late 18th C this may have become true in recent historical times but to extrapolate from one place may be risk over-generalising.l142: wouldn't South Asian be better than East Asian if you are using GIH as the proxy.l143: This modelling of SAC is as the authors recognise a simplification -- white settlement in the Cape only started ~370 years ago with ongoing admixture -- and probably at peak from 300-150 years ago. This is not intended as a criticism and I don't think this result weakens the results but I think the authors should address the consequences of this simplification.l167 Simulating phenotypes: A little more detail would be useful here -- I'd like to understand how this takes LD and both global and local ancestry into account. I didn't understand how the SNPs were chosen -- do you just choose one lead SNP in a region or do you simulate SNPs that though not causal are in LD with the "causal" SNP. This para ia bit opaque. Is different LD in different popualtions considered.l178 -- use dash not hyphen l 288-307 -- I don't think true positives etc should be hyphenatedl299 -- semi-colon not comma at endl378 -- principle -> principall383 -- amount -> numberl395 -- I am intrigued by this. When we do a GWAS we often get hits caused by SNPs being in LD wth the causal (or at least a lead SNP). We don't see these as false hits though because we understand that they tag the causal SNP. Of course this depends on how bug the admixture block is -- I wonder whether your framework of true positives/false positives is correct, and I think this needs clarificaition at least. If you define a false positive as a SNP that is found in the simulated data output then I think it is too strong -- I think this related to my comment on line 167 above -- see my general comment.l436 -- no comma -- the subject is complex but it just makes it worse to put a comma in. Similar issue on line 456l448 I don't think only more complex admixture scenarios. Also just understanding the variability you get that may depend on changes in admixture ratios, LD-structure, population difference and so on.l 462. This sentence needs a main verb.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: NoReviewer #3: Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. 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 While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  5 Aug 2022Thank you for your review and comments on our research manuscript. Please find our point-by-point response to the Reviewers comments below. Corrections to the conform to the journal requirements have been made as suggested. This include:- Figures were regenerated and submitted to (PACE) digital diagnostic tool to ensure that figures meet PLOS requirements. - Figure legends were updated. - Sentence reconstruction where more clarity was required. - Bibliography was updated to showcase numbers as requiredAmendment to ‘Funding Statement’: Please including the following as our funding statement. There is no specific grant number attached to this, but the phrasing has been approved the South African Medical Research Council.This research was funded  by the South African government through the South African Medical Research Council and the National Research Foundation. GvE was supported by the DST-NRF Innovation Doctoral Scholarship. YS was supported by a Stellenbosch University Postgraduate Bursary. There was no additional external funding received for this study. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.Amendment tot Data availability statement: http://www.internationalgenome.org). All scripts used to generate and conduct data analysis are deposited and publicly available on Github: https://github.com/YolandiSwart/GWAS_SAAll genetic data used in this manuscript was simulated or retrieved from public repositories  and the false positives (the number of times the source of the association was incorrectly identified as a true association) from each GWAS model applied to all of the simulated phenotypes to get an indication of model success rate in the simulated Nama and SAC populations.”- Consider a simplified 3-way and 5-way admixture scenarios to be simulated and tested to avoid additional confounding effects on the GWAS methods tested. o Thank you for this comment. We agree with the reviewer that this would be interesting and we indeed considered including more simplified 2-way, 3-way and 5-way admixed populations. However, just with the factors we considered we already simulated 24 distinct phenotypes with three different causal sets for each and tested 5 GWAS models on each of those iterations. Thus, 360 GWAS was run in total. This was computationally intensive and we were restricted with computational resources. Thus, we focused on the most realistic scenario one would encounter if you would conduct GWAS analysis on South African populations. - The model and scripts made readily available to the readers https://github.com/YolandiSwart/GWAS_SA)o All scripts used in the manuscript were made publicly available via a Github link. (Responses to each reviewers comments: Reviewer #1: 1. I would suggest the authors include links to the model files used and the custom script used for ancestry assignment (line 161) from the simulated data should be linked to or provided as supplementary data.https://github.com/YolandiSwart/GWAS_SA)Thank you for this comment. All scripts are now publicly available via a github link.  represented in figure 1.Thank you for this comment. No, not all admixture events were simulated that are represented in figure 1. To clear up any confusion we added two additional sentences in lines 133-136. “The two admixture events for the Nama are indicated with the dashed lines with arrowheads (purple and light pink). Four admixture events were simulated at one time point for the SAC. These are indicated by the four solid lines with open arrowheads .”3. It is unclear to me how the genotypes/phenotypes are being linked for analysis? How were the 10 causal SNPs chosen?Thank you for this comment. The genotypes are linked with the phenotypes when conducting regression model analysis for each simulated phenotype to see if there is a significant association between the simulated phenotype and causal variant (represented as the continuous outcome (Y)). The choice of 10 causal SNPs are explained in lines 169-172. The 10 causal SNPs are randomly simulated, since one would not know before conducting a GWAS where the causal SNP would be located. “Three replications, each with a different set of 10 causal SNPs of each kind of phenotype were simulated randomly and a replication was simulated with each ancestral population (three for the Nama and five for the SAC) as the source of the local ancestry effect.” This is also confirmed in the discussion in lines 411-413, “This effect could be due to chance, since causal SNPs were drawn randomly from the dataset without any regard for the local ancestry of the SNP.”4. The three types of phenotypes simulated need to be explained in more detail. Thank you for this comment. We have added a thorough explanation of the simulated three phenotypes in lines 167-173. Two tables (Table 1 for Nama and Table2 for the SAC) are included to showcase the breakdown of the effect sizes of the simulated phenotypes. The scripts on Github will also clarify the confusion. “Three phenotypes were simulated for the Nama (Table 1) and the SAC (Table 2) - phenotypes with an allelic only (AO) effect, phenotypes with an ancestry plus allelic (APA) effect and phenotypes with an APA and local ancestry adjusted allelic (LAAA) effect. Three replications, each with a different set of 10 causal SNPs of each kind of phenotype were simulated randomly and a replication was simulated with each ancestral population (three for the Nama and five for the SAC) as the source of the local ancestry effect. Therefore, in total 27 phenotypes were simulated for the Nama and 45 phenotypes were simulated for the SAC.”The discussion (lines 388-390) seems to suggest that a phenotype caused by a local ancestry-specific SNP was not simulated and tested with the various methods. Would distributing causal SNPs randomly across ancestries not result in biased results of all methods tested that are looking for an ancestry-specific effect? Could this explain the high number of false positives seen in the APA and LAAA models compared to the Standard and GA?Thank you for this comment. The goal of this manuscript was to test if the LAAA robustly capture the true causal variants regardless of the source of the association or the complexity of the admixture compared to other frequently used GWAS models. We intended to distribute causal SNPs across ancestries, because this is the most realistic scenario that we witness in admixed South African populations. Hence, the models used should be robust in any admixture scenario. We also included an explanation for the high number of false-positives in the discussion in lines 449-451, “Another unexpected observation in the results, is the large number of false positive hits for both the APA and LAAA models compared to the other three models . This could be due to the simulated local ancestry effect size being too large, causing associations of whole ancestral blocks with the trait of interest.”5. The relationship alluded to in Lines 316-318 between the amount of ancestry and false positives should be more formalized, for example simulating datasets with 5-95% of a certain ancestry and plotting the change in false positives. This would be broadly informative beyond the study populations used here.Thank you for this comment. We agree that it would be an interesting analysis to do, but the level of heterogeneity in the simulated data already encompasses a wide range of admixture which is more representative of modern populations. This will become even more applicable in the future. 6. On Line 271, how was LAI accuracy inferred? Were inaccurate segments identified and removed or were they misassigned?https://github.com/YolandiSwart/GWAS_SA/blob/main/Ancestry_inference/lai_accuracy_per_anc.R). Thank you for this comment. To test the LAI accuracy of a given run, the inferred ancestry was compared to the true ancestry per locus per individual. Segments were not removed as we wanted to assess the effects of misassigned tracks on the models . This is evident in the scripts added to the Github page  then later LAAA is used as abbreviation and as a full term.Thank for this comment. We apologise for this inconsistency. Abbreviations were corrected throughout the manuscript. The aim and the rational of this study it’s not clearly stated.Thank for this comment. The aim and rationale are now stated in line 92. “Therefore the aim of this study was to determine if the LAAA model will be able to robustly capture the true causal variants regardless of the source of the association or the complexity of the admixture. ”The Author is using a public data but it was not mentioned clearly under method section that, this is a public data and therefore there is no need for ethical clearance.http://www.internationalgenome.org). All scripts used to generate and conduct data analysis are deposited and publicly available on Github: https://github.com/YolandiSwart/GWAS_SAThank for this comment. All genetic data used in this manuscript was simulated or retrieved from public repositories (I think there is a need for a paragraph of sample/data description.https://github.com/YolandiSwart/GWAS_SAThank for this comment. In lines 158-194 we explain the sample/data simulated for this study with two descriptive tables (Table 1 and Table 2), as well as included the scripts to generate this datasets on Github. Figures have poor quality (they are blur).Thank for this comment. We re-uploaded figures to ensure good quality. All Figure legends have incomplete description of what is happening in a figure.Thank for this comment. We agree and expanded the descriptions of all figures throughout the manuscript. Fig 1. The demographic histories of the simulated populations (Nama and SAC). Thick solid lines with open arrowheads indicate an ancestor -> descendant relation, dashed lines indicate an admixture pulse and faint solid lines with closed arrowheads indicate continuous migration. The two admixture events for the Nama are indicated by the dashed lines with arrowheads (purple and light pink). Four admixture events were simulated at one time point for the SAC. These are indicated by the four solid lines with open arrowheads . Fig 2. Overview of the methods used to simulate genotypes using the software msprime and phenotypes using the software PhenotypeSimulator.Fig 3. Comparison of true positives (left) and false positives (right) for the Nama with inferred local ancestry used in GWAS models. The average hits for three runs with different causal SNPs are shown. The simulated phenotypes are denoted as “phenotype-ancestral source of association”, e.g.,LAAA-LWK means the LAAA phenotype with the LWK ancestral component as the ancestral source of association. The various GWAS models used are indicated in different colours. Grey represents the APA model, yellow represents the GA model, blue represents the LA model, green represents the LAAA model and the orange represents the Standard model. Fig 4. Comparison of true positive hits (left) and false positive hits (right) for the Nama with true local ancestry used in GWAS models. The average hits for three runs with different causal SNPs are shown. The simulated phenotypes are denoted as “phenotype-ancestral source of association”, e.g.,LAAA-LWK means the LAAA phenotype with the LWK ancestral component as the ancestral source of association. The various GWAS models used are indicated in different colours. Grey represents the APA model, yellow represents the GA model, blue represents the LA model, green represents the LAAA model and the orange represents the Standard model. Fig 5. Comparison of true positive hits (left) and false positive hits (right) for the SAC with inferred local ancestry used in GWAS models. The average hits for three runs with different causal SNPs are shown. The simulated phenotypes are denoted as “phenotype-ancestral source of association”, e.g.,LAAA-CHB means the LAAA phenotype with the CHB ancestral component as the ancestral source of association. The various GWAS models used are indicated in different colours. Grey represents the APA model, yellow represents the GA model, blue represents the LA model, green represents the LAAA model and the orange represents the Standard model. Fig 6. Comparison of true positive hits (left) and false positive hits (right) for the SAC with inferred local ancestry used in GWAS models. The average hits for three runs with different causal SNPs are shown. The simulated phenotypes are denoted as “phenotype-ancestral source of association”, e.g.,LAAA-CHB means the LAAA phenotype with the CHB ancestral components as the ancestral source of association. The various GWAS models used are indicated in different colours. Grey represents the APA model, yellow represents the GA model, blue represents the LA model, green represents the LAAA model and the orange represents the Standard model. Reviewer #3: 1 This is an interesting and self-contained paper that will be very helpful to people analysing complex populations. The authors simulated 3-way and 5-way admixture populations that are broadly representative of two groups in South Africa and then with different phenotypes try to understand which models correctly detect SNPs associated with the phenotypes. There are several different modelling approaches and as seen in the paper how you do this has a significant timpact2 The paper is written clearly and overall the methods are sound.3 I have one major criticism4 There is no definition of true positive and false positive -- see detailed comments below. Without knowing this, I am not sure I agree with the conclusions (or at least I can't independently say I do). If their definition of a false positive would include a SNP that is _not_ a SNP in the "input" of the simulation as being causal but is for example 20 base pairs away and has been detected because of LD effects then I think that needs considerable motivation and explanation to justify calling that a false positive. Anyway -- please be clear and define this.Thank you for this comment. We added defined true and false positives in lines 282-285, “We compared the true positives (the number of times the source of association could be correctly identified) and the false positives (the number of times the source of the association was incorrectly identified as a true association) from each GWAS model applied to all of the simulated phenotypes to get an indication of model success rate in the simulated Nama and SAC populations.”Indeed, we do not consider a SNP in LD with a true causative variant as a false positive, as pointed out in the discussion (line 399):, “Our simulations would have to be adjusted to ensure that all of the causal SNPs are found in or outside of the contributing ancestry’s ancestral LD blocks and many more iterations would have to be done to test this.”This is also further discussed in lines 438-442, “We assumed independence of ancestry-specific effects across chromosomes. Therefore, the effect of other ancestral LD blocks  on a given phenotype was not simulated. This is important to note when considering our results in terms of real data, since this omission can have an impact on elucidating the true causal SNPs.” 5. The number of SNPs used is only 400k -- which is small -- does this impact the result? If we were using a dense array and/or imputed data would this help or make things more complicatedThank you for this comment. Yes, increasing the number of SNPs would indeed make things more complicated and required more computational resources. We have previously investigated imputation accuracy and showed we that imputation in southern African populations are feasible and produces quality data . Due to the complexity and computational expense of the work presented in this manuscript, we chose not to use imputed data to introduce more statistical bias. Increasing the number of SNPs in our simulations would not have improved the accuracy, as we knew where the causal SNPs were.6. The limitations expressed on page 15 are significant There are different results for 3-way and 5-way admixture (and as the authors point out this differs from results by others for 2-way admixture). But is that a function of the n in n-way or is it more complex than that? I suppose I want a more definitive answer, which is not possible with the evidence givern.Thank you for this comment. Each admixture scenario will be different due to historical events. Most African populations had integration from various continental ancestral populations and the exact and location of ancestry proportions are mostly unknown (genetic heterogeneity). Hence this is more complex than the n in n-way, since ancestry patterns may be different between admixed populations formed by the same source populations.7. As a point of principle I think the code that was developed should be made available via GitHub or the like.https://github.com/YolandiSwart/GWAS_SAThank you for this comment. We agree with this statement and all scripts are now publicly available via a github link. 8. Bibliography -- a very annyoing feature of the paper is that references are numbered in the text but there is no number in the bibliography!!!!Thank you for comment. We apologise and corrected the numbers in the bibliography. 9 Other issues:l97-98: There is no reference for the claim that two-way admixed populations "generally" have simple demographic histories and originate from a single pulse admixture event. I suppose this depends what is meant by "simple" and "pulse", but I think either give some reference that supports this or weaken the claim.Thank you for this comment. We have removed this statement.l117 : "From" -> from – This was corrected Comma after "Khoe-San" on l128 should deleted -- the subject of the sentence is complex and hence the temptation but the comma is wrong and it would be better to simplify the txt.Thank you for this comment. This sentence was restructured in lines 132-133 for easier reading. “Uren et al. (13) found that the SAC has Khoe-San (32-43%), Bantu-speaking African (20-36%) , European (21-28%) and East and South-East Asian (9-11%) ancestral contributions.”l122 : See point above about availability -- shouldn't these specifications be made available publicly. Just from a selfish point of view you are more likely to get citations if you do and people use them.https://github.com/YolandiSwart/GWAS_SAThank you for this comment. All scripts are now publicly available via a github link. l 126-130 : I think the characterisation of Khoe-San as a group culturally related needs care as this is contested esp by members of the communities. This is not my area, but the term Khoe-san language is a term that is more convenient for people outside those communities rather than accurate (perhaps like speaking about the Danish-Japanese language gruop?) and I don't think you can say that pastoralism and hunter-gatherer lifestyles are close culturally. As a relatively recent phenomenon as a result of colonialism since the the late 18th C this may have become true in recent historical times but to extrapolate from one place may be risk over-generalising.Thank you for this comment. The term Khoe-San encompasses both Khoekhoe  and San (hunter-gatherer) populations. Khoe-San refers to the two population groups collectively, rather than referring to a language group. During the course of our research on these communities, we have seen that this term has largely been accepted by these communities but each have their own identity. We however agree that our previous statement was confusing and have rephrased the sentence for clarity in lines 110-114. “The demographic history of the Nama, an indigenous Khoe-San population of southern Africa (13), is complex and extends back to the emergence of modern humans. Similar to the SAC, the Nama also received ancestral contributions from a number of non-African populations. “ l142: wouldn't South Asian be better than East Asian if you are using GIH as the proxy.Thank you for this comment. We used GIH as proxy for South Asian in line 141. l143: This modelling of SAC is as the authors recognise a simplification -- white settlement in the Cape only started ~370 years ago with ongoing admixture -- and probably at peak from 300-150 years ago. This is not intended as a criticism and I don't think this result weakens the results but I think the authors should address the consequences of this simplification.Thank you for this comment. We agree with this statement and added an additional discussion in lines 455-459. “The simplification of the SAC as multiple admixture events simultaneously could have implications for the admixture-induced LD blocks, since colonisation and admixture that occurred in the Cape was complex, at different time points and still ongoing. Hence, the simulations should be expanded to multiple admixture events at different time points in the future.” l167 Simulating phenotypes: A little more detail would be useful here -- I'd like to understand how this takes LD and both global and local ancestry into account. I didn't understand how the SNPs were chosen -- do you just choose one lead SNP in a region or do you simulate SNPs that though not causal are in LD with the "causal" SNP. This para ia bit opaque. Is different LD in different popualtions considered.Thank you for this comment. LD was not simulated in the different populations. Line 288 specifies this: “positives and false positives  were averaged to”. The choice of 10 causal SNPs are explained in lines 167-173. The 10 causal SNPs are randomly simulated, since one would not know before conducting a GWAS where the causal SNP would be located. We have added a thorough explanation of the simulated three phenotypes in lines 167-173. Two tables (Table 1 for Nama and Table2 for the SAC) are included to showcase the breakdown of the effect sizes of the simulated phenotypes. The scripts on Github will also clarify the confusion. “Three phenotypes were simulated for the Nama (Table 1) and the SAC (Table 2) - phenotypes with an allelic only (AO) effect, phenotypes with an ancestry plus allelic (APA) effect and phenotypes with an APA and local ancestry adjusted allelic (LAAA) effect. Three replications, each with a different set of 10 causal SNPs of each kind of phenotype were simulated randomly and a replication was simulated with each ancestral population (three for the Nama and five for the SAC) as the source of the local ancestry effect. Therefore, in total 27 phenotypes were simulated for the Nama and 45 phenotypes were simulated for the SAC.”Both global and local ancestry are only inferred after simulation of genotypes. Although the ancestral populations contributing to the admixed population is simulated as stipulated in lines 138-151. “The demographic model for the Nama  was based on the above mentioned events and a conference poster by Ragsdale et al. [21] where they inferred detailed parameterized demographic models for five present day populations  using joint allele frequency and LD statistics. Khoe-San groups also contributed a significant ancestral component to many South African populations, including a highly admixed group from multiple ancestral populations (SAC) [22][23]. Many non-African and African populations moved into southern Africa over the last ~600 years and integrated with Khoe-San groups [22]. Uren et al. [13] found that the SAC has Khoe-San (32-43%), Bantu-speaking African (20-36%) , European (21-28%) and East and South Asian (9-11%) ancestral contributions. The demographic model for the SAC  was based on the above ancestral proportions with a single admixture event, for simplicity, between the ancestral populations ~400 years ago.We made the following corrections to the manuscript and apologise for these errors:l178 -- use dash not hyphen All dashes were changed to hyphens.l 288-307 -- I don't think true positives etc should be hyphenated We removed the hyphen in all instancesl299 -- semi-colon not comma at end We have corrected this.l378 -- principle -> principalCorrectedl383 -- amount -> number Correctedl436 -- no comma -- the subject is complex but it just makes it worse to put a comma in. Similar issue on line 456 Correctedl 462. This sentence needs a main verb. A main verb was addedl395 -- I am intrigued by this. When we do a GWAS we often get hits caused by SNPs being in LD wth the causal (or at least a lead SNP). We don't see these as false hits though because we understand that they tag the causal SNP. Of course this depends on how bug the admixture block is -- I wonder whether your framework of true positives/false positives is correct, and I think this needs clarificaition at least. If you define a false positive as a SNP that is found in the simulated data output then I think it is too strong -- I think this related to my comment on line 167 above -- see my general comment.Thank you for this comment, yes we do not see these as false positives. We stated that we excluded sites in LD with causal SNPs in line 283. “positive and false positive hits  were averaged to”. We also expanded on the definition of false positive hits and true positive hits in our study context in lines 282-284. “We compared the true positive (the number of times when the source of association could be correctly identified) and the false positive hits (the number of times when the source of the association was incorrectly identified as a true association) from each GWAS model applied”This is also further discussed in lines 438-442, “We assumed independence of ancestry-specific effects across chromosomes. Therefore, the effect of other ancestral LD blocks  on a given phenotype was not simulated. This is important to note when considering our results in terms of real data, since this omission can have an impact on elucidating the true causal SNPs.” l448 I don't think only more complex admixture scenarios. Also just understanding the variability you get that may depend on changes in admixture ratios, LD-structure, population difference and so on.Thank you for this comment. We agree with this statement. We removed this statement in lines 455-458. We thank all reviewers for this opportunity to revise and improve our manuscript.AttachmentResponse_to_reviewers (1).docxSubmitted filename: Click here for additional data file. 8 Aug 2022GWAS in the Southern African contextPONE-D-22-03783R1Dear Dr. Swart,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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Arabidopsis thaliana population (less than 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than the previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genetic heterogeneity and epistasis, would be expected to increase in severity with the geographical range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’. IlluminIn recent years, genome-wide association studies (GWAS) have emerged as a tool of choice for elucidating the genotype-to-phenotype links that shape plant metabolic diversity –5. GWAS Arabidopsis, rice and maize (N = 305–529 plant accessions per study) [A paradoxical pattern emerging from GWAS of plant metabolic features is that only a few loci are associated with variation in the abundance of a given metabolite . Indeed,r study) –9. Such r study) . One potr study) ,12, and r study) .Arabidopsis Regional Mapping Population (RegMap) and 1001 Genomes Project (1001G), which are composed predominantly of accessions collected across the European continent [Much attention has been paid to forces that reduce the efficacy of GWAS, and to both experimental designs and statistical approaches to mitigate them ,15. One ontinent ,17. ThisThe most popularized cause of confounding driven by geography concerns population structure ,19. FalsHowever, even with effective control for the effects of long-range linkage disequilibrium, additional confounding factors are strengthened in geographically structured populations. Three processes in particular can dilute the strength of association at a causal variant. First, many alleles have geographically restricted distributions, causing the genetic basis of a trait to vary across regions (genetic heterogeneity) ,24,25. BArabidopsis [N > 300) spanning Europe have consistently described associations at only three biosynthetic loci [Glucosinolates (GSLs), the primary class of secondary defensive metabolites in bidopsis , are a wbidopsis . Each stbidopsis . Yet thrtic loci ,13,32, etic loci .Arabidopsis [Intriguingly, GSLs across the European distribution of bidopsis  exhibit bidopsis ,32,34. Hbidopsis . Distribbidopsis ,32. If sbidopsis ,36.Arabidopsis, compared the genetic architecture revealed through GWAS in this local population and geographically broad mapping panels, and explored potential confounding factors underlying differences in the performance of the mapping populations. We focused on a population from Toulon-Sur-Arroux (TOU-A), France, which was collected along a fence line spanning only a few hundred metres [Here, we quantified variation in GSL profiles in a single local population of d metres . Previoud metres ,38. We rd metres . Decades. 2 (a)N = 1 replicate per accession per complete block). After 4 days stratification at 4°C, growth trays were moved to a chamber with white LED light (180–200 µmol s−1) at 20°C in 10 h : 14 h light : dark. Seedlings were thinned to one per cell 1 week after germination. Trays were rotated and bottom-watered every second day with fertilizer (15N-16P-17 K) solution at 100 ppm N until harvesting at 21 days.To minimize maternal effects, seeds were harvested from 294 TOU-A accessions grown at 22°C with a 16 : 8 h light : dark photoperiod, with 3 weeks vernalization at 4°C in 8 h : 16 h light : dark to synchronize flowering, in autumn 2017. For GSL profiling in mid-2019, seeds were sown on a 1 : 1 blend of nutrient retention (BM1) and seed germination (BM2) soil mixes  in a complete randomized block design with four replicates per accession (i.e.  (b)N = 4 biological replicates per accession. All liquid preparation and storage steps throughout the following protocol were conducted in polypropylene 96-well plates sealed with silicone cap mats. Entire rosettes were first clipped from the root, weighed and directly submerged into 1.2 ml 80% methanol, which inhibits endogenous myrosinase activity [Glucosinolates were extracted and quantified for each harvested rosette individually, yielding activity . After 2et al. [m/z 350–520 with daughter ions of m/z 97, which correspond to the sulfate moiety of the GSL analytes. External standards  interspersed throughout each run were monitored to ensure consistency. Individual GSLs were identified based on their fragmentation pattern and retention time [MSnbase v. 2.8.3 [xcms v. 3.4.4 [GSL content was quantified with an Agilent 1200 Series HPLC machine coupled to an Agilent 6410 triple quadrupole mass spectrometer with parameters described in Humphrey et al. . Samplesion time  et al. [et al. [Genotypes for the TOU-A population were obtained from Frachon et al. . Genotypet al.  and 1001et al.  datasets [et al. . For the (d)lme4 [N = 3.93 for TOU-A, see above; mean N = 3.69 for Brachi et al. [N = 2.00 for Katz et al. [We fitted linear mixed models for log-transformed ion counts per milligram of leaf tissue using lme4 , includii et al. ; mean N z et al. ). Varianz et al.  and 1001z et al.  accessio (e)lme4qtl v. 0.2.2 [R2m) using MuMIn v. 1.43.17 [To standardize comparisons across datasets, analyses were conducted identically for the TOU-A, 1001G and RegMap datasets. First, best unbiased linear predictors (BLUPs) were extracted from the linear mixed models above; for one dataset  that poov. 0.2.2  with the 1.43.17 .2-transformed ratios of the abundances of pairs of molecules with precursor:product relationships . For indolic GSLs in TOU-A, we also implemented a multi-trait GWAS approach  [p-values < 0.01).Traits that were modelled separately for GWAS included (i) abundances of each of the heritable GSL molecules, and (ii) logl, mvLMM ), which  > 1.04)  or an exTo search for significant associations harbouring GSL biosynthetic loci, we used a recently compiled catalog of functionally validated genes in the aliphatic and indolic GSL biosynthetic pathways Methods for all population genetic analyses are described in the electronic supplementary material, Methods.. 3 (a)a). Second, despite reduced overall diversity (1.9 M SNPs in TOU-A versus 11.5 M SNPs in 1001G), the TOU-A population (1.3 M) and 1001G panel (2.2 M) had a relatively comparable number of common variants . Indeed, a large fraction of common variants from the 1001G panel (2.2 M) were also common in TOU-A , indicating the reduced genetic diversity in TOU-A arises from a lessened contribution of rare variants. This was reflected in the allele frequency spectrum: after downsampling the 1001G to account for differences in sample size, the TOU-A population still displayed a less pronounced enrichment of rare relative to higher frequency variants (b), resulting in higher genome-wide values of Tajima's D (c). This strong reduction in both total and rare variants is expected to reduce confounding effects of genetic and allelic heterogeneity in TOU-A, while the presence of many common variants suggests this does not come at the expense of drastically culling the polymorphisms that can be interrogated through GWAS.A population genetic comparison between TOU-A and the European 1001G accessions revealed favourable conditions for GWAS relative to geographically broad mapping panels. First, for the particular example of glucosinolates, we found that epistatic variation increases rapidly with geographical distance a. Secondvariants b, resultjima's D c. This s (b)We quantified the relative concentrations of 13 major aliphatic and four indolic glucosinolates in 294 accessions from the TOU-A population under controlled growth chamber conditions. By contrast to broader geographical scales, where loss-of-function mutations within the glucosinolate biosynthetic pathway are pervasive, every TOU-A accession exhibited a fully functional GSL biosynthetic pathway. This was evidenced by abundant concentrations of the final products in the biosynthetic pathways for both short-chain aliphatic  and indolic GSLs .2H < 0.92 . In fact, analysis of GSL measurements from previous studies revealed systematically higher heritability estimates in TOU-A than the RegMap  and no significant difference between TOU-A and the 1001G   . Althoug (c)For 192 phenotyped accessions with whole-genome sequences, we conducted GWAS using mixed models that controlled for confounding due to population structure by including a matrix of kinship among accessions as a random effect. We first focused on the abundances and relationships between 13 aliphatic GSLs. (i)a). By contrast, only four cumulative associations (three per dataset) were recovered using the same approach in a re-analysis of three previous GWAS datasets, which consisted of mapping populations spanning the European continent .The identity of associated loci in TOU-A depended on how GSL phenotypes were represented. First, we performed separate GWAS for the abundance of each molecule. This approach cumulatively uncovered significant associations at five biosynthetic loci a. By conb). The same approach in geographically broad European panels recovered only three cumulative associations .When traits are correlated, as expected for pleiotropic traits such as metabolites from the same biosynthetic pathway, GWAS approaches that use information from multiple traits in a single analysis may increase power . Given tfigure 3c and electronic supplementary material, figure S3a). Many of these same associations were reported in the authors' original analyses [Overall, the significant associations in TOU-A included all three loci  that we also recovered in all broad European panels, and an additional locus (CYP79F1/F2) recovered in only one other panel p-value distribution revealed no systematic genomic inflation in any of the mapping populations . Thus, GWAS in TOU-A retains the ability to narrowly pinpoint candidate genes.Identifying candidate genes for functional validation is more efficient when GWAS associations narrowly peak at or near the causal mutations. Precise peaks of association arise when a causal variant recombines into different haplotypes. In populations with reduced genetic diversity, the presence of a given causal variant on fewer haplotypes could result in broader peaks of association, posing a drawback for GWAS in local populations. Relative to European panels, GWAS peaks in TOU-A were indeed broader, although patterns varied among loci . Nevertheless, the leading SNP for over half the associations fell within the transcribed or flanking intergenic regions of the biosynthetic gene (four of seven loci), and the cluster of significant SNPs overlapped with these regions in all but two cases . Further, an inspection of the genome-wide  (iii)d). We sought to use these relationships, supplemented with GSL profiles from gene knockout mutants in previous studies, to validate each locus by comparing them to the effects inferred in our GWAS. To do this, we identified the leading SNP  at each locus, extracted its GWAS model-fitted effect on the abundance of each GSL molecule, and visualized the effects on the map of GSL molecular variation in TOU-A (e). In addition to offering further evidence supporting the hypothesized causal genes at each locus, this approach illuminates how these loci generate different aspects of GSL profile variation in the TOU-A population.A model for how the putatively causal enzymes at the seven significant loci generate GSL profile variation in the TOU-A population emerges simply by overlaying the reaction catalyzed by each enzyme, from precursor to product molecules, onto a plot of the major aliphatic GSLs detected in TOU-A plants. This produces a visual map of the variable steps in the biosynthetic pathway d. We souin TOU-A e. In addArabidopsis, distinct from effects of the well-characterized variation at the MAM locus. The BCAT3 locus affected the abundances of GSLs with intermediate-length side chains, mirroring effects previously observed in a BCAT3 knockout mutant . By contrast, functional genetic and biochemical assays have shown that the MAM1 and MAM2 enzymes primarily affect the abundance of GSLs with short side chains [figure 3e and electronic supplementary material, figure S6).The effects of the BCAT3 locus in TOU-A suggest that this gene underlies a dimension of variation in GSL side-chain length previously undescribed in natural populations of e chains , similard), the novel association at the paralogous CYP79F1 and CYP79F2 genes [e), with especially large effects on molecules with the shortest observed side-chain length. This is consistent with the finding that among all biosynthetic enzymes, CYP79F2 exerts the strongest effect on pathway flux, with an outsized effect on propyl GSLs (i.e. GSLs with 3C side-chain lengths) [Brassica juncea [Boechera stricta [Arabidopsis dataset , strengthening the evidence that CYP79F is a broadly important determinant of GSL profile variation across populations and species.Of two previously unreported associations at cytochrome P450 monooxygenases functioning downstream of MAM and BCAT3 in the biosynthetic pathway d, the noF2 genes  is especlengths) . Functioa juncea , and sep stricta . The assS-oxygenation of methylthioalkyl to methylsulfinylalkyl GSLs with broad substrate specificity. While natural variation in the locus containing GS-OX2, GS-OX3 and GS-OX4 had been detected through QTL mapping with biparental RILs [a), GS-OX1 harboured the strongest genome-wide association for many molecules when slightly rarer variants were considered . Although biases in our GWAS model can yield inflated or deflated signals of association for alleles below this threshold, the strength of the association for this variant is exceptional even among alleles of similar frequency (0.05 > MAF > 0.03). Intriguingly, the strongest associations at GS-OX1 did not involve methylthioalkyl GSL abundances individually or as a ratio compared to their derived methylsulfinylalkyl GSLs , suggesting that linkage disequilibrium with other loci (or an unexpected effect of GS-OX1) may contribute to this association. Nevertheless, the effect on its direct precursor and/or product molecules is sufficient to drive a significant association: we further performed GWAS for a principal component capturing opposing shifts in the abundance of long-chain methylthioalkyl versus methylsulfinylalkyl GSLs, and GS-OX1 harboured the strongest, statistically significant genome-wide association .Two distinct loci harbour paralogous GS-OX genes that catalyze the tal RILs ,52, neite).Finally, effects of the two remaining polymorphisms in TOU-A, at the AOP  and GS-Ofigure 3It is important to note that the predicted effects do not include epistatic interactions and that more subtle effects may not be discovered through GWAS. Accordingly, the effects described above should be interpreted only as the strongest, additive effects of each locus. (d) (i)We implemented the same association mapping approach for four indolic GSL molecules and were most successful when mapping traits that captured the relationships among abundances of different molecules. Our initial approach mapping the abundance of single molecules only recovered one association in both the TOU-A population and in the geographically broad European panel with high-quality indolic GSL data . We recovered an additional association in TOU-A when mapping GSL precursor:product ratios .a). The model encountered algorithmic termination errors when applied to the geographically broad panel, preventing a comparison with TOU-A for this approach.Multi-trait mixed models, which jointly model the relationships among two or more traits together, may further increase power by using the relationships among traits as additional information. Importantly, these models can recover genetic variants affecting both individual traits and the relationships among traits, which may have distinct genetic bases . We emplfigure 4b). One of these loci was also discovered through QTL mapping, and CYP81F2 was functionally validated as the causal gene [Overall, of these three loci recovered in TOU-A, two (both CYP81F loci) have been previously identified in GWAS  figure b. One ofsal gene ,60. The  (ii)Similar to aliphatic GSLs, peaks of significantly associated SNPs varied from narrow to broad, depending on the locus . Notably, significance peaked directly within the tandem array of IGMT paralogs, again highlighting the ability of GWAS in TOU-A to pinpoint candidate genes. (iii)c). Using the effects of each locus extracted from our GWAS models (figure 4d), we looked for concordance with previous QTL mapping, functional genetic, and knockout mutant studies to inform how these loci shape GSL variation in TOU-A.Each putatively causal biosynthetic enzyme underlying the associations with indolic GSL variation in TOU-A has been functionally characterized through biochemical assays and gene knockout mutants. CYP81F paralogs collectively catalyze the first elaboration step at different sites of the indolic GSL ring structure ,60, and figure 4in planta, despite overlap in substrate specificities in vitro [The CYP81F subfamily of cytochrome P450 monooxygenases are responsible for hydroxylation of indolyl-3-ylmethyl (I3M) GSL ,60, whicin vitro ,60.Arabidopsis form a tandem array at the locus identified in our GWAS [d). Although IGMT1-4 enzymes cumulatively can methoxylate both 1- and 4-hydroxy-I3M-GSL in biochemical assays, our observation of effects restricted to 4-hydroxy-I3M-GSL methoxylation support a model previously inferred from the characterization of an IGMT5 knockout mutant, which retained functional copies of all four IGMT1-4 paralogs [in planta.Four of the five indole glucosinolate O-methyltransferases (IGMT1-4) in our GWAS . This loparalogs . The mutTaken together, our results more fully link the functional variation characterized in enzyme biochemical and gene knockout studies with the variation for indolic GSLs observed in natural populations, identifying loci acting at three of the four secondary modification steps that give rise to the major I3M-derived GSLs in the TOU-A population. (e)k = 5) contributing to population structure separately within the TOU-A and the 1001G accessions. Focusing on the 10 glucosinolate biosynthetic loci recovered by GWAS in TOU-A, we found that among-group variation in allele frequency was not elevated in the 1001G relative to TOU-A . This suggests that the efficacy of GWAS for GSLs in TOU-A is unlikely to be the product of weaker population structure at causal loci, and may instead arise from differences in other confounding factors that are exaggerated in geographically broad mapping panels.GSL profiles, and some of the large effect loci that underlie them, show strong geographical clines within and across Europe ,32. This. 4N = 192 accessions) led not only to the discovery of variants that were absent in geographically broad mapping panels with 1.5–4.0× more accessions but also to novel loci whose contribution to natural variation was unknown despite numerous QTL mapping studies  previously conducted for GSLs. These associations spanned each major portion of the pathway  relative to Arabidopsis [The simplest explanation for the effectiveness of GWAS in TOU-A may be the observed reduction in genetic diversity relative to the broader European population. Theory predicts that allelic heterogeneity, which poses a major obstacle for GWAS, will be more pervasive in more genetically diverse populations. Further, the fact that diversity was reduced in TOU-A primarily through a relative deficit of rare variants, as expected if rare variants are geographically restricted and therefore locally more common , likely bidopsis –65. Brasbidopsis ,65,66, wArabidopsis, and GS-OH itself segregates numerous loss-of-function alleles [While the general benefits of reduced geography-driven confounding in local populations should extend to GWAS for a variety of traits, our findings also illustrate properties of local populations likely to be especially beneficial when studying metabolite diversity specifically. In particular, the confounding effects of loss-of-function polymorphisms were absent from the major loci  that segregate such mutations over broad geographical scales. Loss-of-function mutations produce a particularly severe form of allelic heterogeneity. Many different mutations can produce analogous loss-of-function alleles at a gene, resulting in a high gene-wide mutation rate, such that many loss-of-function polymorphisms involve multiple haplotypes with parallel loss-of-function mutations . Further alleles . Of the  alleles ,13,32,34Although statistical approaches exist to mitigate geographically driven confounding factors, they cannot entirely control for them. For example, GWAS models can be extended to include epistatic interactions alongside, or instead of, additive effects . HoweverArabidopsis accessions sampled throughout Europe revealed how GSL profiles have been shaped by adaptation and demography across the species range [Despite their benefits, GWAS in local populations are certainly not ideal for every research question. GWAS of GSLs in different mapping populations illustrate this clearly: integrating population genomic analyses with GWAS using es range ,32,34, w