Paucity of data from African populations due to under-representation in human genetic studies has impeded detailed understanding of the heritable human genome variation. This is despite the fact that Africa has sizeable genetic, cultural and linguistic diversity. There are renewed efforts to understand health problems relevant to African populations using more comprehensive datasets, and by improving expertise in health-related genomics among African scientists. We emphasise that careful consideration of the sampled populations from national and within-continental cohorts in large multi-ethnic genetic research efforts is required to maximise the prospects of identifying and fine-mapping novel risk variants in indigenous populations. We caution that human demographic history should be taken into consideration in such prospective genetic-association studies. This catalogue of over 88 million high-quality variants from 26 populations has enhanced power to screen for common and rare variants that depict geographic and demographic differentiation2. This represents 80% (approximately 80 million) of all variants contributed or validated in the public dbSNP catalogue, with recent major enhancements for genetic variation within several South Asian and African populations (24% and 28% of novel variants respectively)2. Most of the low-frequency (< 0.5%) variants likely to be of functional significance are disproportionately present in individuals with substantial African ancestry, indicating bottlenecks in non-African populations3. The“Luhya in Webuye, Kenya” (LWK) population has the most accentuated number of these rare variants.The 1000 Genomes Project (1000GP) is an invaluable resource that has improved understanding of global human genetic variation and its contribution to disease biology across multiple populations of distinct ethnicity4. African populations are more genetically diverse, with considerable population substructure, and lower linkage disequilibrium (LD) compared to non-African populations5. Inclusion of more African populations will improve understanding of genetic variation attributed to complex population history, variations in climate, lifestyles, exposure to infectious diseases, and diets6. Diverse multi-ethnic imputation panels will undoubtedly improve fine-mapping of complex traits and provide detailed insights on disease susceptibility, drug responses, and improve therapeutic treatments. One such integrated panel, consisting of the phase 1 1000GP and African Genome Variation Project (AGVP) whole genome sequence panels, has shown marked improvement in detecting association signals in specific African populations poorly represented in the 1000GP7. AGVP also present a new genotype array design that captures genetic variation in African populations.Paucity of data from African populations has restricted understanding of the heritable human genome variation. Although under-represented in human genetic studies, Africa has sizeable genetic, cultural and linguistic diversity (> 2000 distinct ethno-linguistic groups)9. The H3Africa consortium consists of over 500 members, from more than 30 of the 55 African countries. H3Africa projects are focused on establishing genetic and environmental determinants associated with infectious  and non-communicable diseases 10. H3Africa is driven by African investigators, and is anticipated to close the gaps of ‘missing’ heritability by increasing the number of causal variants identified within genes, from a dataset of over 70,000 individuals collected using standardized protocols10. This presents a unique opportunity for the investigators to not only develop and direct their independent research agendas, but also enrich the datasets using their extensive knowledge of the continent’s history. However, careful consideration of the sampled populations in similar projects is required to maximise the prospects of identifying and fine-mapping novel risk variants in indigenous populations. In order to translate genomic research findings to useful resources for clinicians and drug development, substantial knowledge about reference populations that are relevant to the individuals being treated alongside the actionable variants is required10. This is in addition to harmonised and well curated phenotype data that will allow easy integration and direct comparison of data outputs across different cohorts and phenotypes. Attentiveness to the considerable genetic substructure in African populations may reveal uncaptured variation and distinct ancestry11. This extensive genetic diversity would benefit from strategies that explore genomics datasets that put local populations in context to provide more detail from disease mapping efforts in Africa. An example is the LWK in the 1000GP who do not represent all the“Luhya people”, a Bantu-speaking Niger-Congo population with a complex population history composed of 17 tribes, each with a distinct dialect  initiative is aimed at understanding health problems relevant to African populations, and tilting the scales of data deficit and lacking expertise in health-related genomics among African scientistsdialect 12,13. 2. We compared LWK to African populations from the 1000GP phase 3 (Yoruba (YRI) from Ibadan, Nigeria (n=108); Esan (ESN) from Nigeria (n=99); Mandika (GWD) from The Gambia (n=113); and the Mende (MSL) from Sierra Leone (n=85)) examining the same 193,634 variants, since these populations also speak the Niger-Kordofanian languages, and share recent genetic ancestry14. The 1000GP call set was already filtered down usingVCFtools (v 0.1.12b) andPLINK (v1.90b6.2), and only contained biallelic, non-singleton SNV sites that are a minimum of 2KB apart from each other and a minor allele frequency > 0.0516. We considered just the first three principal components (PCs) computed to resolve the population substructure. We then usedADMIXTURE (v1.3) to estimate ancestry for K values from 2 through 2017. Distruct plots of the output ancestry fractions were generated usingGenesis (v 0.2.6b)18.We used principal component analysis (PCA) to examine relationships within the Luhya (LWK) from Webuye, Kenya, population (n=99) using 193,634 variants from the 1000GP phase 3Our PCA analyses reveal that all individuals in the LWK population cluster closely except five individuals along PC2 (n=2) and PC3 (n=3), possibly suggesting that the outliers are individuals from different Luhya tribes . We sugg214. These languages are spoken by approximately one quarter of the SSA population (~200 million people)19. The Bantu languages fall into this category, and consist of separate groups that constitute part of the Niger-Congo language phylum20. The spread of Bantu-speaking populations in SSA is primarily due to historical migration of populations, approximately 3000–5000 years ago, and not solely due to diffusion of language14. This demographic history is associated with admixture and changes in population structure, resulting in complex patterns of genetic variation in present day populations22. An example is the identification of haplotypes among Nilo-Saharan language speakers of the Luo community that neighbours the Luhya of Western Kenya, which were previously thought to be private in Bantu populations, that are now associated with interactions between these distinct populations during the migration of the Bantu farmer populations22. Previous studies on Bantu expansion and migration suggest populations first moved south from their homeland, near the Nigeria-Cameroon border, through the rainforest and split into two groups: one branched south and west; while another moved east towards the Great Lakes23. The East Bantu languages, which also include the Luhya language, are distributed in East and Southern Africa23. In Kenya, these Eastern Bantu speaking populations are further categorised into two based on their migratory routes to present day Kenya: the Eastern Kenya Bantus  and Western Kenya Bantus 25.In sub-Saharan African (SSA), there are nearly 500 closely related but distinct languages distributed over a total area of approximately 500 000 km28. Previous studies on intricate languages in China, and Australia suggest consistency of genetic and linguistic evolution, with striking evidence of compatible phylogenetic signal and phonological evolution30. In SSA such studies are hindered by paucity of data with only a limited number of reasonably close populations available, impeding more detailed analysis31. A recent study highlights population differentiation between two South Eastern Bantu groups in South Africa, which were assumed to be genetically homogenous, further emphasising the importance of having a clear perspective of population structure in disease-association studies18. This result was arrived at by understanding ethnolinguistic divisions within the present-day population, and purposely recruiting from rural areas or regions with little ethnolinguistic diversity18.Multi-disciplinary approaches that enlist the knowledge of anthropologists, linguists, geneticists, and historians would significantly improve understanding on human history and migration of populations, genetics of complex traits and adaptive variations to modern environments, and language and cultural changesLuhya people”, adequate sampling of underrepresented tribes would provide a high-resolution view of their ancestral history. Haphazard sampling would significantly reduce power to detect signal due to population substructure, even within this single community. We speculate that this was largely circumvented at recruitment when sampling LWK in the 1000GP by asking the participants whether all four of their grandparents were of the Bukusu tribe. Whereas projects covering relatively small geographical areas are able to overcome such challenges, national and within-continental cohorts in large multi-ethnic genetic research efforts must have well thought out documented protocols that carefully consider human demographic history.The multi-ethnic genetic-association studies, like those in the H3Africa initiative, now offer a unique opportunity to resolve this challenge using multiple large scale GWAS analyses of important genetic traits from diverse populations across Africa. GWAS studies largely rely on self-reported data on ethnic background. Genetic information is then used to confirm ancestral backgrounds and exclude outliers. However, this may lead to insufficient representation of some populations, and disease-association studies of low prevalence or late onset conditions, such as Alzheimer’s disease, would be underpowered. Thus, in order to understand complex traits in say the entire “http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/admixture_files/The LWK, ESN, GWD, MSL, and YRI datasets were obtained from the European Bioinformatics Institute 1000 Genomes Project website I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I am still concerned that the content is a bit thin for a research paper, but as a short research note perhaps it is sufficient. I suggest the abstract is updated to not just mention cautions but to add a couple of sentences about what the paper aimed to do and the key findings before the cautionary comments.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The new title is appropriate and the paper is much improved. This is a single author paper so the attribution to “we” should be “I”. Please check throughout or revise sentences. Introduction: First paragraph: It is unclear what “80% (80 million) of all variants” in dbSNP is based on. The previous sentence refers to 88 million variants. Reference 2 was published in 2015 and therefore is no longer current as dbSNP has increased considerably. Some minor editing would be beneficial Introduction second paragraph: Second last sentence requires revision. Third paragraph: line 11 from the bottom of the page – “population” should be “populations” may reveal… The multi-ethnic genetic-association studies, like those in the H3Africa initiative, now offers (should be “offer”) a unique opportunity….. Data availability: Also include the other populations from 1000GP that were used in the study.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This paper uses the LWK population from the 1000 Genomes dataset to demonstrate the need for considering demographic data when sampling. The paper is very thin on results for a research paper as it only really includes a PCA on a small sample set. In order for it to be a reasonable research paper it would need to include other populations and data to verify the findings. It is also not clear to me whether there is support for the ethnic origin of the samples, is this information available?The title refers to H3Africa but the paper is about the 1000 Genomes data so I recommend that the title changes, the results are relevant to all studies, not just H3Africa.In the abstract and later there is mention of "titling" was this supposed to "tilting"?There are too many figures to demonstrate a simple point. Additional comments: Though the message of the paper is important, I think it needs more work. The PCAs and the paper in general need to include more populations to verify the point.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We have made the following changes to this research note paper: We have revised the analysis to include more populations in the analysis to verify our findings. We now also include anthropology and population genetics references on the history of the Bantu migration, and the Luhya population of Western Kenya. We have made revisions to the analysis, and now include other populations that share recent genetic ancestry from the 1000GP that belong to the Niger-Kordofanian Bantoid language group for comparison. We have made revisions to the title to encompass all studies, not just H3Africa. We have amended the word "titling" to "tilting". We have also revised the manuscript and reduced the number of figures. We have made revisions to the analysis, and include other populations that share recent genetic ancestry from the 1000GP that belong to the Niger-Kordofanian language group for comparison. This study uses genotype data from the 1000 Genomes Project to examine population structure among the 99 individuals of the Luhya people sampled from Webuye in Kenya. It emphasizes the need for careful participant selection in disease-related genome-wide studies in African populations. Principal component analysis (PCA) and ADMIXTURE are used. I fully agree that we need to consider demographic histories when analyzing genomic data, but found the title misleading in two ways: Why focus on H3Africa as the target audience and not more generally research among Africans (and other populations) and, secondly, how does selection of this group of Luhya people demonstrate that the sampling was less than optimal. The only suggestion in the paper, related to the title, is to request data on the ethnolinguistic origins of the grandparents during the participant selection phase – I was expecting more insight into how to include knowledge of demographic history into a sampling framework . Sampling frameworks will of course be influenced by the aims and objectives of the research.    1. The study focused on a single population and no additional or neighboring populations were included in the PCA and ADMIXTURE analyses.  It is therefore unclear what the three ancestral components (K=3) are likely to be and what the origins of the 5 outliers may be. The sampling appears to have been good in identifying a closely related group of individuals, which is not unexpected as they were from a single ethnolinguistic origin and geographic location. It would be very interesting to sample all 17 Luhya tribes to determine whether they are genetically distinguishable on PCA, but I appreciate that these data are not available. An understanding of their demographic history could provide some clues as to what one may expect. Perhaps refer to the recent paper on South African genomes by Choudhury et al. Currently there are good analysis programs that either rely on adjusting for ancestral component diversity or using meta-analysis when participants are from significantly different populations . Since there is a vast amount of data on every participant in GWAS studies, PCA can effectively be used to identify outliers and to exclude them, if appropriate, or adjust for population sub-structure.www.bioinf.wits.ac.za/software/genesis) to ensure that the individuals are shown in the same order and that the same color is used throughout to represent the same ancestral component for the different K values. The paper would be strengthened by providing other examples and illustrating how some studies/populations have been successful at using demographic history in their sampling strategy whereas others fall short, as they have ignored this. The figures are numerous and do not all add additional value, no need to show structure plots at high K values. The structure plots would be enhanced by using a tool such as Genesis ( There are several sentences that could be rewritten to improve meaning and direct repetition of whole sentences should be avoided between the abstract and paper content. Presumably the author meant “tilting the scales” and not “titling the scales”?I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We have revised the title to make it more general to encompass all studies, not just H3Africa. We have made revisions to illustrate that these considerations should be made to all multi-ethnic GWAS studies. We have amended the manuscript and discuss the importance of multi-disciplinary approaches that enlist the knowledge of anthropologists, linguists, geneticists, and historians to improve understanding on human history and migration of populations, genetics of complex traits and adaptive variations to modern environments, and language and cultural changes.      We have made revisions to the analysis, and include other populations that share recent genetic ancestry from the 1000GP that belong to the Niger-Kordofanian language group for comparison. We agree that current analysis program that implement standard linear regression can use principal components from PCA analysis to avoid errors due to population stratification, as well as speed-up computation. Essentially, our manuscript is aimed at highlighting population stratification; and the importance of multi-disciplinary approaches when sampling underrepresented populations of interest to put local populations in the right context and provide more detailed and accurate information for disease mapping efforts in Africa. This will provide a more granular understanding on the genetic traits associated with these populations. Although GWAS studies largely rely on self-reported data on ethnic background, which is the verified using genetic information to confirm ancestral backgrounds and exclude outliers. The exclusion of individuals may lead to insufficient representation of some populations, and disease-association studies of low prevalence or late onset conditions, such as Alzheimer’s disease, would be underpowered. Taking population history into account during study design my help to alleviate these challenges. We have revised the manuscript to provide examples of how multi-disciplinary approaches could improve research findings. We have revised the manuscript to scale down the number of figures. We have also redrawn the structure plot. We have made revisions to the manuscript to remove repetitions and improve meaning. We have amended the word "titling" to "tilting".


Aedesaegypti is the main mosquito vector of these arboviruses andits control is the only solution to reduce transmission. In recent years, South America has suffered the burden of continuous highimpact outbreaks of dengue, chikungunya and Zika.  In order to improve vector control it is essential to study mosquitopopulation genetics in order to better estimate the population structuresand the geneflow among them.kdr) mutations from a trans-border region in Amazoniabetween the state of Amapá (Brazil) and French Guiana (overseas territory ofFrance), to provide further knowledge on these issues. These two countrieshave followed distinct vector control policies since last century. Forpopulation genetic analyses we evaluated variability in 13 well-establishedmicrosatellites loci in Ae. aegypti from French Guiana(Saint Georges and Cayenne) and Brazil (Oiapoque and Macapá). The occurrenceand frequency of kdr mutations in these same populationswere accessed by TaqMan genotype assays for the sites 1016  and1534 (Phe/Cys). We have analysed microsatellites and knockdown resistance(kdrmutations revealed distinct patterns for Cayenne and Macapá associated withtheir different insecticide use history, and an admixture zone between thesetwo patterns in Saint Georges and Oiapoque, in accordance with populationgenetic results. We have detected high levels of gene flow between the closest cross-bordersamples of Saint-Georges and Oiapoque. These results suggest one commonorigin of re-colonisation for the populations of French Guiana and Oiapoquein Brazil, and a different source for Macapá, more similar to the othernorthern Brazilian populations. Genotyping of the  The present study highlights the need for regional-local vector surveillanceand transnational collaboration between neighboring countries to assess theimpact of implemented vector control strategies, promote timely actions anddevelop preparedness plans. Aedes aegypti is involved in recurrent dengue epidemics and in recentchikungunya and Zika outbreaks that have occurred in South America.,Ae. aegypti from French Guiana and Brazil. However, the mosquitoreturned a decade later resistant to this molecule. Organophosphates (OP) were thenapplied and replaced in the 90s by pyrethroids (PY) as adulticides and Bacillusthuringiensis israelensis (Bti) as larvicides.,,,kdr). Two main mutations are monitored and widespread inAe. aegypti populations across Latin America and the Caribbean.Some publications relate that V1016I is associated with deltamethrin resistance,kdrmutations V1016I and F1534C has been reported in Cayenne since 2011.kdr haplotypes differ according to geographicalregion, suggesting structured populations. The R1 haplotype (mutant only at position1534) was found in the whole country, whereas R2 (mutant at both 1016 and 1534positions) is more common in the Central and Southern region.Presently, vector control is implemented in both territories by breeding sites removal ortreatment all year round against larvae, spatial spraying of adulticides duringoutbreaks and by community engagement programs.,Ae.aegypti have been studied in Brazil.,,Ae. aegypti.,kdr alleles.,The success of vector control campaigns is influenced by aspects like the effective sizeof the mosquito population, their temporal genetic stability and the connectivitybetween adjacent populations.kdr mutations from fourAe. aegypti populations from Amapá state (Brazil) and French Guiana(France), in order to: (1) decipher if insecticide resistance is due to a localselective pressure; (2) if distance and the Oyapock river act as a physical barrier forthe spread of kdr genes and (3) if seasonal factors could influence thepattern of kdr frequencies.The lack of knowledge in trans-border regions such as that between Brazil and FrenchGuiana can be hazardous for control efforts implemented in both sides of the frontier.Here we have analysed microsatellites and Study sites - The cross-border region of Amapá state, Brazil andFrench Guiana, France, is situated in the North of South America ( America A. We hav America B. The diMosquito collection - Sampling was performed during the rainy (May)and dry (December) seasons from December 2013 to December 2014 in French Guiana inCayenne-CAY and Saint-Georges de l’Oyapock-SGO. In Brazil, collections took place inthe rainy (May) and dry (December) seasons of 2014 in two locations of the Amapástate (Macapá-MAC and Oiapoque-OIA). At each season, in ca. 1 km²area around the central point of each locality, sampling was performed by usingovitraps left for one week in the selected areas  for samplesOIA14B and MAC14B  and allele richness (R) based on a minimum samplesize of 36 individuals (72 genes) were obtained with HP-RARE.Ovitraps may collect several eggs from the same mosquito female oviposition, whichmay result in sampling of sibling individuals. To ensure that we did not violateassumptions of independent genotypes, we used the maximum-likelihood methodimplemented in ML-RELATEThe extent of genetic differentiation among temporal and geographical samples wasquantified by pairwise Rst based on the stepwise mutation model SMM.In order to compare temporal and spatial continuity across samples, the number ofmigrants (Nm) was estimated using the private allele method from GENEPOP; which usesrare alleles to estimate gene flow and assumes that they have reached aquasi-equilibrium state.Bayesian clustering analysis, as implemented in STRUCTURE 2.3.4,Factorial correspondence analysis over the initial populations was performed based onpairwise allelic differences using GENETIX v4.03.Ne estimates, alleles withfrequency below 0.05 were not considered.Single-sample estimates of current effective population size (Ne) were calculated bythe bias-corrected LD method described by Waples and Do, as implemented inNeEstimator v.2.Sequential Bonferroni corrections were used to adjust critical probability values formultiple tests to minimise type I errors.Genotyping of kdr mutations - We have genotyped the 1016 and 1534sites of the voltage gated sodium channel for the specific variations 1016Val+ or Ilekdr and 1534 Phe+ or Cyskdr using a TaqMan  allelicdiscrimination assay for samples from French Guiana,Microsatellite data analysisGenetic diversity - The analysis with Dfdist showed no evidence ofoutlier loci and all subsequent analyses were thus performed using the 13 loci. The13 microsatellite loci were polymorphic with the number of alleles ranging from two(AC4) to 19 (AG2). The mean allelic richness over loci varied between four and five.The summary statistics for the 13 loci and the eight populations is presented inSupplementarydata (Table I). A total of 12 out of 104 (11%)population by locus comparisons deviated significantly from HWE[Supplementarydata (Table I)]. All deviations were caused byheterozygote deficit. After MICROCHECKER analysis, null alleles were suspected infour loci . However, none of the loci consistently deviatedfrom HWE in all the population samples. Indeed, in populations of CAY14B and SGO14Bno loci showed evidence for null alleles. As microsatellites used in this study havebeen extensively validated in previous studies,,Supplementarydata (Table II)].Genetic differentiation - The mean pairwise genetic differentiationover temporal samples measured by Rst (Rst = 0.011) was four times lower than Rstover all populations (Rst = 0.047), and six times lower than the mean pairwise Rstover different spatial samples collected in the same season (May-June 2014: 2014A)(Rst = 0.066). None of the pairwise Rst estimates between temporal samples wassignificant  was eight (3-12) among the three temporal samplesfrom CAY and 10 (3-11) from SGO  MAC and CAYappear as the most differentiated populations, while the individuals from thedifferent temporal samples of SGO are mixed with the individuals from OIA.of K = 3 D, corresKdr mutations - Among all specimens, 321 individuals weresuccessfully genotyped for the 1534 codon, 325 for the 1016 codon of thevoltage-gated sodium channel and both loci were obtained for 311 of them. Theresistant mutation 1016I was absent from MAC but present at high proportions in OIA(67%)Supplementarydata (Table III)]. On the other hand, the mutantvariation 1534C was close to fixation in CAY (96-100%) and present at highfrequencies in SGO (77%-80%), OIA (84%) and MAC (90%).kdr genotypic frequencies are presented in Supplementarydata (Table IV)].The kdr markers. Our results suggest thepresence of two genetic groups. In relation with previous knowledge onre-colonisation,Ae. aegypti re-invasion inthat area may have occurred through two routes. They also suggest that differencesin vector control strategies may have influenced the distinct resistant alleledistribution of Ae. aegypti from French Guiana and Brazil.The present study in the Amazonian trans-border region between Brazil and FrenchGuiana revealed high levels of gene flow between the closest cross-border samples ofSaint-Georges in French Guiana and Oiapoque in Brazil, showing that the OyapockRiver does not act as a physical barrier to mosquito dispersal. The mosquitopopulations in French Guiana were temporally stable, so natural seasonal factors donot seem to be affecting gene flow and the insecticide resistance patterns in thisarea. Macapá, the most distant city, was also the most differentiated population forboth microsatellites and Anopheles mosquitoes such as in Saint Georges del’Oyapock.Ae. aegypti from Cayenne or SaintGeorges de l’Oyapock, where Nm and Ne estimates were consistent with relative stabletemporal samples over one year sampling. Populations at each collection siteremained in the same genetic group over time and none of the pairwise comparisonsamong temporal samples was significantly differentiated. Due to its urban natureassociated with artificial breeding sites, Ae. aegypti is probablyless affected by natural seasonal factors. Similar findings have been reported inBotucatu  where the Ne remained stable over the years,irrespective of the large variation in mosquito abundance.Genetic temporal changes in mosquito populations may occur due to fluctuations inpopulation size, followed by recolonisation or genetic drift events. Such populationchanges may be human-driven (ex: vector control)kdr mutations found in the territory. Inaddition, local elimination of mosquitoes may be rapidly followed by re-colonisationfrom neighboring areas that are genetically similar.The pyrethroid-based vector control undertaken in French Guiana during 2014 seems tohave had minimal impact in the vector’s effective population size. This agrees withthe elevated frequency of ,,Ae. aegypti between both sides can be driven by eggs or larvaeremaining in cargo and goods transported by boats,The levels of genetic diversity were similar among our samples and comparable withmost South American populations.Ae. aegypti in the middleof the 20th century had not been fully achieved in Venezuela, Suriname, Guyana,south of USA and a few Caribbean countries.It has been recognised that eradication of ,Ae. aegypti in 1959 of French Guiana had itsorigins on the west neighbor country Surinames,,,When we combined our results with other microsatellite data from South America, weconfirmed that our study samples grouped with the above mentioned northernpopulations,Kdr genotype distributions show a clear distinctionbetween Macapá and all the other populations. In Macapá there is an abundance of R1alleles (associated with 1534C kdr), while in the populations fromFrench Guiana and Oiapoque from Brazil there is higher genotypic diversity and ahigher proportion of R2 alleles .However, malaria control had occurred in parallel for which DDT was the maininsecticide until 1992, when PYs were introduced for both residual spraying andinsecticide-treated nets.Kdr mutations are associated with cross-resistance to bothinsecticides. Samples collected in Cayenne 2011 were characterised as highlyresistant to deltamethrin.kdr genotype proportions very closeto the ones we found in 2013-2014, with the predominance of the R2R2 and R1R2genotypes. Our samples from Saint Georges were more diversified than Cayenne showingbesides R2R2 other genotypes at lower frequency . Like for the microsatellite data, Oiapoque was similar to Saint Georges withR2R2, R1R2 and SR2/R1R3 genotypes, but with R1R1 genotype that was absent from theGuyanese samples. On the other hand, this was the dominant genotype in Macapá, whereSR1 and SS were also observed in lower frequencies. The kdr patternof Macapá was similar to the one reported for cities in the neighbor state of Paráin 2011.In French Guiana, the pattern of kdr patterns for Cayenne and Macapá likelyassociated with their different insecticide use history, and an admixture zonebetween these two patterns in Saint Georges and Oiapoque. This admixture zoneincludes a Guyanese city and a Brazilian city where insecticide use patterns werenot synchronised. This probably results from the above-mentioned gene flow detectedin neutral markers, and also to a possible impact of localAnopheles vector and pest mosquito control campaigns.Our results show distinct In conclusions - The patterns of cross-border gene flow betweenAe. aegypti populations and consequent dispersal of insecticideresistance genes observed in this study highlights the need for transnationalcooperation in order to monitor and prevent cross-country transportation of vectors(and pathogens). Such cooperation would provide the chance for concerted initiativesthat more broadly protect human health through development of preparedness plans. Inlight of the present results, it is recommended that French and Brazilianauthorities adopt common insecticide resistance management plans in thistrans-border region.


Puma concolor) and native camelids (Vicugna vicugna and Lama guanicoe) for the persistence of Andean condors (Vultur gryphus) in southern South America, and compared the demographic history of three vultures in different continents. We sequenced and compiled mtDNA to reconstruct past population dynamics. Our results suggest that Andean condors increased in population size >10 KYA, whereas vicuñas and pumas showed stable populations and guanacos a recent (<10 KYA) demographic expansion, suggesting independent trajectories between species. Further, vultures showed positive demographic trends: white-backed vultures (Gyps africanus) increased in population size, matching attenuated community changes in Africa, and California condors  exhibited a steep demographic expansion ~20 KYA largely concurrent with North American megafaunal extinctions. Our results suggest that dietary plasticity of extant vulture lineages allowed them to thrive despite historical environmental changes. This dietary flexibility, however, is now detrimental as it enhances risk to toxicological compounds harbored by modern carrion resources.The late Quaternary megafauna extinctions reshaped species assemblages, yet we know little about how extant obligate scavengers responded to this abrupt ecological change. To explore whether obligate scavengers persisted by depending on contemporary community linkages or via foraging flexibility, we tested the importance of the trophic interaction between pumas ( Pleistocene communities, in particular, sustained diverse vertebrate assemblages of mega-carnivores and -herbivores (average body size ≥44 kg)3 that provided plentiful carrion resources for scavenging4. These carrion resources precipitated a rapid radiation of avian obligate scavengers7 that varied in morphology and body size according to different feeding strategies8. Climatic changes and human impacts during the Pleistocene-Holocene transition triggered a massive loss of megafauna11: only ~5% of megaherbivores, 40% of megacarnivores12, and ~41% of obligate scavengers13 genera persisted. The near-complete disassembly of Pleistocene communities radically transformed ecological interactions for those species that went through the extinction epoch1. Yet, the mechanisms by which species survived ecological changes are poorly understood, especially for guilds tightly linked to mega-mammals.Historical species assemblages can provide insight into the contemporary structure and functioning of communities14. Across Africa and southeast Eurasia, where large-bodied species had a deep history of coevolution with hominids, loss of megafauna was relatively moderate and gradual15. For example, in Sub-Saharan Africa most of the Pleistocene megafauna persist today, including 31 genera of mega-herbivores and 5 mega-carnivores12. Unsurprisingly, then, Africa (n = 11) and southern Asia (n = 10) support the greatest diversity of the largest obligate scavengers16 which rely upon carrion from herds of ungulates that die primarily from non-predatory causes18. In contrast, the megafauna of the Americas experienced a punctuated wave of extinctions, losing 70–80% of Pleistocene megafauna genera19 from a combination of climate changes and human arrival20. In North America, this extinction wave drove the disappearance of at least seven genera of vultures22. The California condor , however, persisted into the Holocene by relying on marine-derived food resources24.The Pleistocene megafaunal extinctions were a globally heterogeneous phenomenon25, and at least 50% of vultures28. Notably, however, a tightly-linked community module of pumas (Puma concolor) preying largely on wild camelids, guanacos (Lama guanicoe) and vicuñas (Vicugna vicugna), emerged in Patagonia and the southern Andes29. Moreover, these species exhibited a demographic expansion (increased effective population size) in the mid-Holocene32, leading to a rapid reorganization of the ecological community to one that became dominated by mid-sized vertebrates33. The largest extant scavenger of South America, the Andean condor (Vultur gryphus), currently relies almost exclusively on terrestrial food resources35. In areas where native ungulates have been extirpated, Andean condors now forage on an array of exotic prey 37. Comparatively, in pristine landscapes Andean condors show a strong dependency on puma-killed camelids, indicating a tight ecological association among these species34. It is unknown, however, whether this trophic linkage to the puma-camelid predatory interaction occurred historically and played a role in sustaining Andean condors during the Pleistocene extinctions. Further, it is unknown how these two different contemporary foraging strategies affect the long-term viability and persistence of Andean condor populations.The megafauna extinctions in South America were more extensive than on any other continent with the complete loss of mega-herbivores, 50% of megacarnivores38. Apex predators can impact the abundance of scavenger populations by hunting large prey continuously through time, thus providing spatially and temporarily reliable access to carrion41. If necrophagous birds rely on predators provisioning the carrion of mammalian herbivores, their historic demographic trajectories should mirror that of the species they depended upon. Alternatively, if carrion availability is from other resources  or other sources of ungulate mortality , there should be independent population size changes between predators, scavengers and ungulates. To test these competing hypotheses, we first explored past trophic linkages in South American communities. We hypothesized that if the puma-camelid predator-prey interaction sustained Andean condor populations in southern South America from the early Holocene to historical times, the demographic trajectories for Andean condors, pumas, vicuñas and guanacos would be coupled and have expanded synchronously in the early Holocene. On the other hand, if Andean condors persisted through the Pleistocene extinctions by consuming other dietary resources, their paleo-demographic trajectories would not be synchronized with those of pumas, vicuñas and guanacos, indicating that Andean condors persisted into the Holocene thanks to a plastic foraging behavior. Secondly, we reconstructed the demographic trajectory of other vulture species that share similar life history strategies to Andean condors, but experienced different historical changes to their communities: white-backed vultures (Gyps africanus) in Africa and California condors in North America. Given that white-backed vultures are dependent on African ungulates, which experienced relatively attenuated community changes in the late Pleistocene18, we predicted that these vultures will exhibit a relatively stable population size over time. For California condors, which persisted through the late Quaternary extinctions by shifting from terrestrial to marine food resources24, we predicted that they would exhibit similar historical demographic dynamics to Andean condors given that both species had to rely upon alternative food resources after the collapse of megafauna. To test our predictions, we implemented three complementary analytical methods that infer past population dynamics from contemporary gene sequences. In particular, we sequenced mitochondrial (mtDNA) and nuclear loci (nDNA) of Andean condors and compiled available mtDNA sequences from GenBank to study changes in population size of pumas, Andean condors, vicuñas, guanacos, California condors and white-backed vultures via neutrality tests, mismatch distributions, and Extended Bayesian Skyline Plot coalescent models (EBSP).The demography of obligate scavengers are generally linked to the availability of carrion resourcesFor Andean condors, we amplified 522–538 base pairs of the c-myc gene, and 1502–1639 bp of mitochondrial control region and partial 12S Table . NeutralFor guanacos, significant neutrality tests Table , largelySkyline analysis revealed population expansion of California condors since ~20 KYA 43. In contrast to EBSP, neutrality tests are robust to small sample sizes and reflect either the effects of natural selection or recent demographic changes43. Given the long generation time, low genetic diversity, and lack of population structure in the Andean condor45, we are confident that our results are driven by a demographic change and not by natural selection. In contrast to Andean condors, vicuñas and pumas showed stable populations, and guanacos a steep and recent (<10 KYA) demographic expansion. These results corroborate previous work that have shown increasing guanaco populations during the early-Holocene31 and nominal change for vicuñas when restricted to southern South America32. It is notable that vicuña populations in northern Peru exhibited recent demographic expansion  may be overestimated. Regardless, our discordant neutrality tests , and largely caused by anthropogenic impacts53.While extant lineages of Andean condors appear to have expanded along the Andes, condor populations in their eastern range went extinct after the Pleistocene57. Notably, California condors appear to have undergone a steep demographic expansion ~20 KYA, which could explain their high mitochondrial DNA diversity58. California condors possibly benefited from relaxed competition due to the extinction of other avian scavengers during the particularly abrupt megafaunal extinctions that occurred in North America during the Pleistocene-Holocene transition60. Contrary to our expectation, our neutrality tests and mismatch analysis suggested that the most widespread and common African vulture, the white-backed vulture16, also experienced a demographic expansion ~47 KYA, which is supported by previous studies showing high genetic diversity in historically large populations62. As with Andean condors, though, EBSP 95% highest posterior density of population changes overlapped zero  of a tightly linked scavenger-predator-prey community module of southern South America. Additionally, we explored and compared how historical community changes affected the demographic trajectory of three large obligate scavengers inhabiting different continents that share life history strategies with long-generation times, and lifestyles involving social roosting and feeding habits, large individual home ranges and dependence on soaring flightn = 11; −29.07°S, −69.35°W), La Payunia Provincial Reserve  and Auca Mahuida Nature Reserve  44. We amplified the mitochondrial (mtDNA) complete Glu and partial control region with primers L16652-H621 (hereafter CR1), control region with L798-H1455 (hereafter CR2), and complete Phe and partial 12S with L798-H1795 (hereafter 12S)45. Additionally, we amplified exon 3 of the nuclear gene c-myc with the primers mycEX3D-RmycEX3D and mycEX3A-RmycEX3A73. Details on laboratory analyses are presented in the Supplementary material.We extracted DNA from molted feathers of individual Andean Condors collected in 2013 from active roosting sites in northwestern Argentina: San Guillermo National Park  from GenBank  to find the best fit evolutionary model with jModeltest 2.1.477. Skylines plots and analyses for estimation of molecular clock rates were implemented in BEAST v.2.4.778. Convergence to the stationary distribution and sufficient effective sampling sizes (>200) for each estimated parameter were checked using Tracer v1.579, and four independent runs were combined using Log Combiner v2.4.7, a software implemented in BEAST2.We obtained Andean condor haplotype statistics via DNAsp v6.10.180 using available mtDNA control region sequences from historical samples of California condors58 and Andean condor sequence generated in this study via CR1. The resulting molecular substitution rate for the Andean condor was used to estimate molecular evolution rates for CR2, 12S and c-myc via a coalescent constant population model process implemented in BEAST2. To estimate site and species-specific substitution rates for pumas, we also constructed multispecies coalescent analyses for loci ATP8 and NADH5 with Puma yaguaroundi as an outgroup47. We conducted a similar analysis to estimate cytochrome b oxidase I (cyt-b) substitution rate of Gyps africanus using Gyps ruepelli as an outgroup. For vicuñas and guanacos, mtDNA sequences from fossil samples and associated dates estimated by Metcalf et al.10 were used for calibration of fossilized birth-death models81 implemented in BEAST2 using the Sampled Ancestors add-on package82. For all analyses we compared the performance of a strict and uncorrelated relaxed lognormal clock model; we subsequently combined results of two independent runs of the best model .We estimated clock rates for Andean and California condors implementing a Bayesian multispecies coalescent tree in *BEAST2D83, Fu’s Fs84, Fu and Li D* statistics in DNAsp v6.10.176 with 10,000 coalescent simulations to calculate significance values. Second, we tested deviations from null models of constant populations via the distribution of pairwise sequence differences, or mismatch distribution, using the same software as above; observed versus expected results were plotted in R v3.4.2 (R Development Core Team 2017). For pumas and Andean condors, these two methods were implemented for concatenated mtDNA sequences. Finally, we estimated the timing and degree of population changes with Extended Bayesian Skyline Plot coalescent models (EBSP)42. These analyses depend on the estimation of evolutionary rates, which rely heavily on the statistical methods used to calibrate the clock52. To account for some of the uncertainty around the rates estimated, EBSP analyses were run using estimated molecular clock rates via log normal priors informed by median and 95% High Posterior Density (HPD) values from initial analyses , we obtained an approximated time since expansion based on mismatch analysis using the formula t = τ/2 µ, where µ = mµ is the mutation rate (as described previously) of the entire segment of m base pairs and τ is estimated based on the crest of the mismatch distribution86. Because we did not have a mutation rate for our entire mtDNA sequence of Andean Condors, we calculated the mean rate of the 3 fragments analyzed (µaver = 0.02), which was equal to the widely used substitution rate for mtDNA of birds87.We used three complimentary methods to infer changes in population size over time in our study species. We conducted neutrality tests, against null hypothesis of a constant population size, using Tajima’s Supplementary material


Medicago truncatula, using geographical locations as covariates for admixture analysis. The method’s prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, The online version of this article (10.1186/s13059-019-1697-0) contains supplementary material, which is available to authorized users. Living organisms adapt to the changing environment. Species respond to environmental changes by altering population structure via migration, by allele sorting due to random events (genetic drift), and by natural selection . SelectiMedicago truncatula, a wild legume species, is an attractive model with detailed genomic data available across a number of circum-Mediterranean populations [M. truncatula is expected to have a more differentiated population structure than outcrossing species [Unlike animals, plants feature complex mating systems including selfing and limited gene dispersal through seeds and pollen and a distinct immune system. Importantly, plants must survive under permanent selective pressure from local environmental conditions. These features make plants excellent subjects to test polygenic adaptation hypotheses and to evaluate the role of migration and drift in the genetic and quantitative phenotypic differentiation among populations . Medicagulations –13. As w species . However species –17.Many population genetic approaches assume a theoretical framework for the origin of the populations, such as the “stepping stone” , hierarcFST scans and other measures of genetic differentiation between population, such as the nucleotide diversity π and Tajima’s D, similarly allow identification of candidate genes for adaptation [FST tests [The determination of all the genomic variations underlying quantitative traits is challenging  and has aptation , 28. Howaptation  or via FST tests  because ST tests , 35. GenST tests  and may ST tests , 38. AlsST tests , as exemST tests .When the adaptive phenotype is known or easy to score, a number of experimental approaches aim to identify quantitative trait loci (QTLs) using linkage-based analysis in experimental crosses, or even quantitative trait nucleotides (QTNs) using genome-wide association studies (GWAS). Over recent years, this approach, fueled by increasingly affordable genome sequencing or genotyping, has led to an explosion of disease-related gene discoveries in humans . In planp values in GWAS [Both selection and population history have important influences on the amount and patterns of genetic variation . As a co in GWAS  and is c in GWAS , 59. Ana in GWAS , 61.M. truncatula individuals, an approach we named “WhoGEM.” The overall goal is to predict quantitative phenotypes rather than identify causative variations or infer the relative role of demography and selection in the evolution of quantitative phenotypes. Gene detection is not the purpose of this model. The WhoGEM prediction machine is developed around three key data inputs, data preprocessing (genotypes), the ProvenancePredictor algorithm , and phenotypic characterization . A multi-criteria approach is used to determine the optimal number of population subdivisions for the M. truncatula species around the Mediterranean Basin, to assign an admixture proportion vector to each accession, and then to characterize/predict quantitative phenotypes. The ProvenancePredictor algorithm was developed to use geographical covariates as an aid to define the optimum number of admixture components. Significantly, the phenotypic characterization results indicate that admixture proportions of populations explain a significant proportion of several key quantitative functional traits and quantitative disease resistances. The resulting admixture components are also significantly associated with major bio-climatic and geographic variables. This demonstration of the WhoGEM prediction machine indicates that it can outperform the current genomic prediction/genomic selection models typically used to infer quantitative phenotypes in plants and animals. We argue that the WhoGEM prediction machine may be extended to breeds of domesticated plant and animal species, or populations of dividing human cells, that all undergo selective pressure and potentially strong genetic drift.Here, we propose and test a novel method to explain variation in genetically complex traits using population admixture proportions of Inferring ancestral genomes that will encounter secondary contact/admixture zones is at the core of the WhoGEM working hypothesis. The most likely number of admixture components representing the putative ancestral genomes was determined using a multi-criteria approach. We conducted a three-step data analysis for the determination of admixture components that 1) defines an initial most likely range of admixture components by minimizing the cross-validation error of admixture  analysis defines M. truncatula accessions around the Mediterranean Basin  criterion. The BIC value was the lowest for K = 7 … 9 . The set of 843,171 LD-pruned SNPs was submitted to PCA, and 80 principal components were kept to reduce the dimensionality of the data. n-dimensional admixture component vector. This admixture-based distance is subsequently converted to geographical distance using the linear relationship observed between genetic and geographic distances .We used geographical covariates to resolve the number of putative admixture components, using our new ProvenancePredictor algorithm. The ProvenancePredictor algorithm determines the most probable geographical location of a test sample based on its genetic relationships with a geo-localized reference set, by comparing their admixture components. For that, ProvenancePredictor calculated the Euclidean distance between the sample’s admixture proportions and a reference dataset. The shortest distance measure represents the test sample’s genetic deviation from its nearest reference population based on its K, from K = 2 to K = 12. The rationale is that the optimal number of admixture components should minimize the distance between observed and predicted locations and maximizes the number of correct assignments of samples to their population of origin. ProvenancePredictor uses the “leave-one-out” cross-validation approach at the “accession” level to estimate the difference between predicted and reported location for each sample . A linear relationship between geographical and genetic distances is restricted to distances less than 950 km . Thus, a linear relationship between geographical and genetic distances was fitted for geographical distances less than 950 km. The regression equation is Geo = 0.204 + 4.973 × Gen + ϵ with adjusted R2 = 0.61 and model p < 2.2 × 10−16.For all subsequent analyses, the 840 K genotype dataset was converted into M. truncatula can be adequately explained using eight admixture components  indicated that they are strongly differentiated  are similar among the eight populations, suggesting no obvious intra-population heterogeneity , but less on the number of leaves . The results suggest that a latitudinal cline for leaf numbers may exist, with accessions south of the Mediterranean Basin harboring more leaves. For nodulation efficiencies, key adaptive traits for legume plants, we describe very significant relationships between genome admixture components and two nodulation parameters  and a validation data set (y2), given some putative constraints, such as population structure or spatial proximity. Model parameters are estimated in the training data set. Parameter estimates from y1 are then used to predict observations in the validation data set  in the data set to test the reliability of the models. The reliability is proportional to the phenotypic variation explained by the models [k-fold cross-validation based on 50 rounds of fivefold cross-validation. For each fold, proportional sampling of the training set in the eight M. truncatula populations is conducted to include the constraint due to the population structure and to be as close as possible of realized observations.In this work, repeated e models . We set Figure Knowledge of the selective pressure acting on the phenotype can help determine the contributions of adaptive selection and drift toward phenotypic differentiation among populations. Consequently, by comparing the location of plants and testing for pathogen resistance, the WhoGEM analysis facilitates a better understanding of phenotypic traits associated with quantitative disease resistance (QDR). Two types of disease resistance are described in plants: (i) complete resistance conditioned by a single gene  and (ii)M. truncatula is prone to infection by the soil-borne fungal vascular pathogen Verticillium alfalfae. Verticillium wilt response in M. truncatula is a QDR, regulated by QTLs that differ across resistant accessions and vary according to the fungal strains [http://www.plantwise.org/, accessed on October 19, 2017). Figure M. truncatula accessions when infected with the V. alfalfae strain V31–2 . The average MSS values of the “Spanish Coastal,” “Spanish-Moroccan Inland,” and “South Tunisian Coastal” genome components were found to be 1.04, 1.6, and 1.71, respectively, indicating resistant genomic backgrounds. The average MSS value of the “Greek” genome component is ≃3, making it a clearly susceptible genomic background. Interestingly, the predicted location of Jemalong-A17 within the “Spanish Coastal” population agrees with its resistant phenotype in response to V. alfalfae [M. truncatula using WhoGEM. The phenotypic difference between predicted resistant and susceptible accessions was around two points on a scale from 0 to 4, i.e., 50% of the phenotypic difference between extremes of the phenotype distribution. Given the estimated narrow sense heritability of the trait [V. alfalfae by the WhoGEM prediction machine outperforms the five major algorithms used for GS analyses .The findings Table a show thalfalfae . We havehe trait , 83, we Aphanomyces euteiches is another soil-borne pathogen of legume crops, mainly occurring north of the 45th parallel. Two closely linked major loci for resistance to A. euteiches root rot were reported by GWAS, which explain 23% of the genetic variance [r2 = 19.2 % of the variation in the phenotype and may provide a lower bound for heritability. Figure A. euteiches is controlled by a few major loci, because it was ascribed to two closely linked major loci [The oomycete variance . Using tjor loci . This maControlling for population structure may limit the power to detect true adaptive polymorphisms that are collinear with population structure , as evidM. truncatula ancestral populations to climatic zones defined by the Köppen-Geiger climate classification [M. truncatula populations. Different populations are present in the same climatic zone, while the “Greek” population is spread across several climatic zones . Pearson’s correlation coefficients between each admixture component and each bio-climatic or geographical variables are shown in Fig. Examination of the assignment of eight fication  suggestsr2 = 0.46; P ≤ 0.001), with climate being the major source of variation (41.4%). Variation explained by geography alone contributes to 5% only. This partition of genomic variation in response to climate is different to A. thaliana, in which climate variation among sites of origin explained only slightly more genomic variation than geographical distance [Next, redundancy analysis (RDA)  was useddistance , 89.Linking specific genomic variations to selective traits in plants, animals , and humans  is a key task for many fields from ecology, plant and animal breeding, to individualized health care and drug discovery. The quantitative phenotypic variability found in natural populations is due to a complex underlying genetic interplay of multiple, often unknown, loci with allelic effects affected by environmental conditions , 45, 90.WhoGEM is a powerful method that can be used to study natural variation. The method predicts quantitative phenotypes, not focusing on identifying causative variations or inferring the relative parts of demography and selection in the evolution of quantitative phenotypes. The method uses population admixture proportions of individuals to explain variation in genetically complex traits. We explicitly consider admixture proportions to embed population differentiation due to neutral processes such as genetic drift, migration, and mutation . AdmixtuK) may be in accordance with the dataset, WhoGEM prediction machine will improve the inference of K using covariates. In this study, we took advantage of the knowledge of geographical coordinates of the accessions. Comparing the observed geographical locations and those predicted by ProvenancePredictor, we are able to propose a most parsimonious value of the number of admixture components. The admixture component pattern of each sample then provides a comprehensive summary of each genome and is used as linear predictors of quantitative phenotypes.The WhoGEM method relies on a thorough inference of ancestral populations that define the admixture components. Finding the most likely number of the ancestral populations and the optimal assignment of the samples to these populations is critical for WhoGEM’s efficiency. Because it often appears that several possible numbers of ancestral populations  or may not  result from natural selection. Plant pathosystems are convenient experimental systems to test for the existence of adaptive divergences among populations, especially when the co-occurrence (or absence thereof) of the plant species and pathogens is known [QDR is typically broad spectrum, making the arms race between hosts and pathogens probably not critical. Our results support the idea that QDR in plants is likely to result from changes to a large number of genes scattered throughout the genome and that this is reflected in admixture proportions. Because of the co-occurrence of both the plant species and the pathogen around the Mediterranean Basin, we hypothesize that the observed pattern of quantitative resistance in the act Fig. b. Intrig0, 2018; ). Hence,0, 2018; . An altei et al. , and, asis known . This adis known , 99 and K = 8 dimensional vectors for each accession. The vectors represent combinations of genes  manifested as alleles, copy-number variants, and some other genetic or epigenetic variants. These vectors can be considered as “whole-genome” models in providing information integrated across the entire genome. Thus, the WhoGEM prediction machine moves away from focusing on large impact variants [Here, we present a methodology for the prediction of quantitative traits using admixture components including covariates, such as geographical origins. This was achieved by converting the large 840 K genotype data into variants , 101 or variants , 103. Invariants , 105, orvariants . The Whovariants , 49, whiAs an alternative to the admixture approach, redundancy analysis can be applied to raw SNP data, instead of admixture proportions . Other gp value inflation in GWAS analyses. Furthermore, it will be interesting to use WhoGEM to analyze quantitative phenotypes in breeds of domesticated species, where population structure is often strong due to breeding history [The WhoGEM concept is likely to be expandable to other quantitative functional traits that involve complex genetic determinism. How one would determine whether WhoGEM prediction machine would perform better in a given context is an exciting follow-up topic to develop, in varying the biological models, heritability of traits, and priors on genetic architectures. Prediction of simply inherited traits, not related to population structure, will not be accurate using whole-genome population-based models. In those cases, GWAS analyses are likely to be the most efficient way. We anticipate that an appropriate model to identify major-effect loci for some quantitative traits would be to run mixed effect models where admixture components, as determined here, would be used as fixed co-factors. Moreover, the method we use to improve population structure analyses by using covariates holds strong interest to correct  history .This study demonstrates the rationale of our WhoGEM prediction machine: population admixture integrates the effects of demography  and of natural selection toward adaptation and thereby explains more phenotypic variation than GS- or QTL-based approaches. The method is thus indifferent to the source of genetic similarity among samples—local adaptation or demographic history. Typically, predicting phenotypes on the basis of genome admixture components will help in inferring future trends of adaptation related to global climate change, where controlling for population structure may limit power to detect true adaptive polymorphisms that would be collinear with current population structure , 86. FinMedicago truncatula accessions [http://www.medicagohapmap.org). Quality checking and LD pruning was done using PLINK [A set of 262 genuine cessions  was usedng PLINK  with theThe strategy used to identify populations combines three steps: admixture-based tools, discriminant analysis of principal components (DAPC), and ProvenancePredictor.n proportions that sum to one, n being the number of admixture components . Computations were conducted independently twice and produced almost identical results. Second, the most suitable number of populations was assessed using discriminant analysis of principal components (DAPC) [First, we use the ADMIXTURE software package  applied s (DAPC) . DAPC com = 2. . N to the sample’s genetic make-up might also contain ties. The computation of the weight ProvenancePredictor is an adaptation of the admixture-based geographic population structure (GPS) algorithm  to plantK and a linear model fitted. Given the (relatively) small distances across the Mediterranean Basin, we computed a “naive” geographical distance using pairwise Euclidean distance based on the longitude/latitude reported for the accessions.To convert genetic distance based on admixture proportions to geographical distance, the correlation between geographic and genetic distances between pairs of individuals was estimated for each value of jth sample from the ith population that consists of ni individuals. For all populations, excluding the individual in question, the average admixture proportions and geographical coordinates were calculated as sth individual from the mth population, and nm is the size of the mth population. For the ith population, the adjusted average will be K.To estimate the assignment accuracy of ProvenancePredictor, we used the “leave-one-out” approach at the individual level. In brief, we excluded each reference individual from the data set, recalculated the mean admixture proportions of its reference population, predicted its biogeography, computed the geographical distance between predicted and reported locations, tested whether it is within the geographic regions of the reported origin, and then computed the mean accuracy per population. More specifically, we index our individual as the M. truncatula accessions with geographical coordinates (latitudes and longitudes) served as the reference set for ProvenancePredictor. Seventeen accessions, among which the Jemalong-A17 accession that is used as the reference genome [A set of 245 genuine e genome , were ofM. truncatula accessions were calculated by applying ADMIXTURE in the supervised mode. Accessions were then clustered into populations using hierarchical clustering based on their genome admixture proportions, using Euclidean distance and the “average” link. Relationships among accessions were based on genetic distances computed from the 840 K SNP dataset (R package SNPRelate), and a dendrogram was computed and drawn using R packages ape and geiger.To provide definitive population identification, the final admixture frequencies of the eight components for the 262 Maps and sample locations were drawn using the rworldmap, rgdal, mapplots, and maptools R packages.M. truncatula has been assessed for their response to Verticillium wilt, including 242 already sequenced accessions from the HapMap project [M. truncatula seeds were from our own collection or obtained from the INRA Medicago truncatula Stock Center . All the M. truncatula accessions have been phenotyped using an augmented randomized block design in three independent replicates for the already sequenced (reference) accessions and two replicates for the other accessions. Between four and ten plants per genotype were used in each replicate. Ten-day-old plants were root inoculated as described in Ben et al. [yijk = μ + blocki + accessionj + ϵijk  using R.A set of 313 accessions of  project . M. trunn et al. . Diseaseα = 5 % as the benchmark for using a predictor.The relationships between genome components and phenotypes were estimated using linear models. Because of dependencies among the predictors (the proportions of genome components must sum to one), a systematic search for the best minimum model was done using the leaps R package or use of the step function with both directions, employing a significance level of http://www.worldclim.org/current) were extracted for each accession’s location, using the reported latitude and longitude for that accession (raster R package). For each accession, the admixture components are the fractions that each of the eight sub-populations contributes to the accession’s genome. Thus, we have an Nx8 numeric matrix, assuming there are N accessions. Then, each of the eight sub-populations  is represented by an N-dimensional vector. On the other hand, each bio-climate variable was also represented by an N-dimensional vector as the bio-climate variables were extracted at each accession’s location. So, the correlation can be calculated. The relationships between genome components, the 19 WorldClim bio-climatic variables, and geography  were modeled using redundancy analysis (RDA). RDA of admixture proportions with bio-climatic variables conditional to geography was also computed to estimate effects of climate “corrected for” the geography. Total inertia explained by the RDA model was partitioned among geography and climate, separately or combined. The RDA was computed using the vegan R package.The 19 WorldClim bio-climatic variables , kinship-BLUP (G-BLUP), BayesB, reproducing kernel Hilbert space (RKHS), and least absolute shrinkage and selection operator (LASSO) regression were computed using the rrBLUP , BGLR 11, and glWe optimized the training sets by stratified sampling , that isUnless otherwise stated, all computations were done using the R statistical environment .Additional file 1:Figure S1. Geographical location of the 245 Medicago truncatula accessions used in this analysis. Figure S2. ADMIXTURE proportions for 262 Medicago truncatula accessions, by increasing putative K. Figure S3. Bayesian information criteria (BIC) as a function of increasing values of K, using discriminant analysis of principal components (DAPC) applied on the 840 K SNP dataset for 262 M. truncatula accessions. Figure S4. ProvenancePredictor indicates K = 8 as the first minimum number of admixture components to minimize the median distance between predicted and recorded location  and maximize correct assignment to country of origin . Figure S5. Relation between geographical and genetic distances among 245 M. truncatula accessions with known location, for K = 8. Figure S6. Dendrogam of genetic relationships between the 262 M. truncatula accessions of the eight M. truncatula populations, based on analysis of the 840 K SNP dataset. Figure S7. Geographical repartition of phenotypic values for several quantitative functional traits in 226 M. truncatula accessions. Figure S8. Assessing M. truncatula initial sampling based on the relationship between admixture component and partial resistance to V. alfalfae. Figure S9.M. truncatula accessions repartition, with admixture components visualized as pies, within the Mediterranean Basin climatic zones following Köppen-Geiger climate classification. (PDF 11091 kb)Additional file 2:Table S1. SNP selection process for admixture analysis. (PDF 48 kb)Additional file 3:Table S2. Classification of 262 M. truncatula accessions in eight populations. (CSV 26 kb)Additional file 4:Table S3. Maximum symptom score of 262 M. truncatula accessions in response to root infection by V. alfalfae. (CSV 6 kb)Additional file 5:Table S4. Maximum symptom score of 71 previously uncharacterized M. truncatula accessions in response to root infection by V. alfalfae. (CSV 3 kb)Additional file 6:Table S5. Mean comparisons for quantitative resistance, among groups of M. truncatula accessions. (PDF 73 kb)Additional file 7:Code 1. R code for the ProvenancePredictor function (R (txt) (R 13 kb)Additional file 8:Code 2. R code for the ProvenancePredictor cross-validation function (R (txt) (R 13 kb)