ARPP21, RP11-62H7.2, MFHAS1, RHEBL1). The Mendelian randomisation analysis suggested that increased DASH score, which reflect a healthy diet style, is causal of lower glucose, and insulin levels. These findings further our knowledge of the pathways underlying the relationship between diet and health outcomes. They may have significant implications for global public health and provide future dietary recommendations for the prevention of common chronic diseases.Diet is a modifiable risk factor for common chronic diseases and mental health disorders, and its effects are under partial genetic control. To estimate the impact of diet on individual health, most epidemiological and genetic studies have focused on individual aspects of dietary intake. However, analysing individual food groups in isolation does not capture the complexity of the whole diet pattern. Dietary indices enable a holistic estimation of diet and account for the intercorrelations between food and nutrients. In this study we performed the first ever genome-wide association study (GWA) including 173,701 individuals from the UK Biobank to identify genetic variants associated with the Dietary Approaches to Stop Hypertension (DASH) diet. DASH was calculated using the 24 h-recall questionnaire collected by UK Biobank. The GWA was performed using a linear mixed model implemented in BOLT-LMM. We identified seven independent single-nucleotide polymorphisms (SNPs) associated with DASH. Significant genetic correlations were observed between DASH and several educational traits with a significant enrichment for genes involved in the AMP-dependent protein kinase (AMPK) activation that controls the appetite by regulating the signalling in the hypothalamus. The colocalization analysis implicates genes involved in body mass index (BMI)/obesity and neuroticism ( In 2016, the World Health Organization (WHO) estimated that more than 1.9 billion adults were overweight or obese with alarming projections indicating that by 2030, nearly 60% of the worldwide population could be either overweight or obese . Since bhttp://www.nealelab.is/uk-biobank/ accessed on 1 April 2022) conducted GWAs of thousands single food intakes  in 361,000 unrelated individuals in UK Biobank (UKB).To estimate the impact of diet on individual health, epidemiological and genetic studies have so far focused on different aspects of dietary intake such as macronutrient composition, curated measures of single food intake, multivariate dietary patterns described by principal component analysis (PCA) and food liking. Five genome-wide association studies (GWAs) have been conducted on macronutrient intake ,9,10,11,However, analysing individual food groups in isolation presents some limitations since the complexity of the diet pattern as a whole is not considered . Indeed,Over recent decades, the research has shifted towards the analysis of dietary patterns (dietary indices). Dietary indices enable a holistic estimation of diet and account for the intercorrelations between nutrients or foods and for the possible synergic effects of nutrients . We haveFollowing up on these findings ,19, hereWe carried out a GWA analysis for DASH index that was calculated based on the UK Biobank 24 h dietary recall questionnaire . In the A total of 203,581 individuals replied to one or more questionnaires. DASH diet scores were computed using the individual food intakes for each food group included in the DASH index  Supplem. Scores For these subjects we computed the averages of their scores. We excluded from the analysis subjects who fell in each of the following category: (i) incomplete diet questionnaire, (ii) nutritional data not credible  and (iii) low energy intake reported (energy intake < 1.1 × resting metabolic rate (RMR), with the RMR calculated by Mifflin St Jeor equation ). After UK Biobank is a large prospective cohort including genome wide genotyping, deep phenotyping and molecular data on over 500,000 individuals recruited throughout the UK between 2006–2010 . BrieflyThe genome wide association analysis was performed in BOLT-LMM  using a https://fuma.ctglab.nl/) [p-value (p < 5 × 10−8) that are in approximate linkage disequilibrium with each other at r2 < 0.1. The independence of these signals was directly assessed using the imputed genotype dosage for the lead SNPs as a covariate along with the other covariates from the primary GWA using a linear mixed model in BOLT-LMM . We defined a novel locus if all the variants and the genes mapping in the identified loci were not previously reported associated with DASH by querying NHGRI-EBI GWAS Catalog [We used the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) web-based application (lab.nl/)  to ident Catalog , PhenoSc Catalog  and Open Catalog .p < 2.86 × 10−6 . To validate MAGMA results, we also conducted gene analysis and gene set analysis using VEGAS2 (version 0.2) [p < 2.55 × 10−6 . For each GWA locus, we performed a colocalization analysis with the ‘‘coloc’’ R package  [We used Multi-marker Analysis of GenoMic Annotation (MAGMA) (Version 1.8), implemented in FUMA web-based application, applying standard settings to identify the most likely causal genes . The staion 0.2)  using thAustria) .−4; PP for association in the eQTL (P2): 1 × 10−4; and PP for association in both dataset (P12): 1 × 10−5).We performed the “coloc” analysis using the publicly available genome-wide expression quantitative trait locus (eQTL) data from 31,684 whole-blood samples deposited on the eQTLgen portal ,37. We iTo investigate the SNP functional relevance on DASH, we applied the Summary-data-based Mendelian Randomization (SMR) (Version 1.03) approach , integraPHEIDI < 0.05) indicates that the association might be due to two distinct variants in high LD. We performed SMR using the recommended default options and utilised the genotypes from 3601 independent TwinsUK samples to estimate the LD structures. To account for multiple testing, SMR p-values were adjusted using the Benjamini and Hochberg method [pSMRFDR < 0.01 were considered statistically significant, while pSMRFDR < 0.05 and ≥0.01 were considered “suggestive”.The heterogeneity in dependent instruments (HEIDI) test evaluates the existence of linkage disequilibrium (LD) in the observed association. Rejection of the null hypothesis (g method . Associag) between DASH and complex traits were estimated using linkage disequilibrium score regression (LDSC) through LD-hub (Version 1.9.3) software performed using the LD-Hub online portal (http://ldsc.broadinstitute.org/) [g between one phenotypic trait of interest and 855 diseases or other phenotypic traits whose summary-level GWAs results are deposited in the database. To account for multiple testing, the significance for the LDSC analysis was set at pLDSCFDR < 0.01.Genetic correlations (rte.org/)  that autWe performed a two-sample bidirectional Mendelian randomization analyses utilising the Generalised Summary-data-based Mendelian Randomization (GSMR) (Version 1.0.9)  implemenp < 1 × 10−8) to select a minimum number of instrument SNPs (n > 5) to perform the GSMR analysis. Genotypes of unrelated TwinsUK cohort participants were used as reference to estimate the LD structures. To further validate the GSMR results we also conducted IVW-MR and MR-Egger analyses [The HEIDI test implemented in GSMR was used to detect and remove variants showing independent effects on both exposure and outcome , because they do not satisfy the assumptions for valid instruments. The HEIDI test is more conservative than excluding SNPs that have an outlying association likely driven by locus-specific pleiotropy. GSMR is more powerful than other MR methods (i.e inverse-weighted MR (IVW-MR) and MR-Egger) because it takes account of the sampling variation of both the exposure and outcome effects . GSMR alanalyses  using thanalyses  package 2 ± 4.6 kg/m2), had average daily energy intake of 2106.6 kcal ± 595.4 kcal and a mean DASH score of 24 ± 4.2. Half of the samples were females (54.5%) and smokers (56.6%) (DASH = 1.2 and the linkage disequilibrium score regression (LDSC) intercept of 0.997 ± 0.0099 is consistent with the expectations of polygenicity and large sample sizes [p < 5 × 10−8)  . Some of females .5% and sle sizes  and indi × 10−8) , cluster × 10−8) .p < 5 × 10−8) or suggestive (p < 1 × 10−7) p-value.To investigate the presence of multiple independent sources of associations within the respective associated regions, we performed conditional analyses on each of the seven regions, adjusting for the effect of the lead SNPs that were included in models as covariates. We did not detect any additional independent SNP with either significant (p = 7.6 × 10−18) (NEGR1) gene, which has previously been associated with psychiatric [p = 1.3 × 10−14) harbouring FTO (p = 6.9 × 10−10) mapping on chromosome 3p22.3 in the intronic region of CAMP Regulated Phosphoprotein 21 (ARPP21)) gene (p = 5.3 × 10−10) on chromosome 8p23.1 (p = 1.9 × 10−8), 12q13.12  and 18q21.32 ,  . The 1p3chiatric , behaviochiatric , nutritichiatric  and metachiatric . We alsoring FTO , best kn1)) gene  which hae 8p23.1 . Chromosp < 2.66 × 10−6) with DASH. While most of these genes (15 out of 19) mapped within the identified loci, MAGMA analysis also found four genes which are physically distant (>250 kb) from the lead SNPs. Using MAGMA , we perfpadjusted = 2.8 × 10−2) (padjusted ranging from 2.7 × 10−17 to 4.4 × 10−2), in particular, the “general factor of neuroticism” which was one of the most significantly enriched entries (padjusted = 2.7 × 10−17)  . We also× 10−17) .ARPP21 and RHEBL1) were probably sharing the same common causal variant (PPH4 ranging from 1 to 0.80) with the associated loci (H3 ranging from 1 to 0.80). The COLOC-PP for NEGR1 (PPH4 = 0.68), PRKAG1 (PPH4 = 0.65) and FTO (PPH4 = 0.74) eQTLs showed a suggestive probability for a causal variant shared with chromosome 1p31.1, 8p23.1 and 16q12.2 loci, respectively. Finally, the analysis of eight eQTL transcripts showed no colocalization (PPH0 or PPH1 ranging from 1 to 0.80) or failed to support any tested hypotheses (All PPs < 0.80)  .p < 5 × 10−8) to be utilised as a genetic instrument and therefore were excluded from the analysis (pSMRFDR <0.01) for fifteen genes (pSMRFDR ranging from 1.03 × 10−20 to 2.06 × 10−3) with eight genes showing a suggestive SMR (0.05 < pSMRFDR ≤ 1 × 10−2) (pSMRFDR ranging from 3.15 × 10−19 to 3.34 × 10−3)  were consistent with either pleiotropy or causality (p ranging from 6.8 × 10−13 to 4.4 × 10−2).To further evaluate whether any of the cis-eQTLs mediated the association between genetic variants and DASH, we applied SMR  on the Ganalysis . We obse × 10−2) . The SMR × 10−2) . We obta × 10−3) . Next, wausality  while fog) between DASH and other complex traits, we performed LD score regression (LDSC) analyses on 855 other phenotypic traits or diseases [pLDSCFDR < 0.01) with 193 of them . We also found significant positive genetic correlations with other measures of educational attainment . Among the strongest negative correlations (rg < −0.35), “Time spent watching television (TV)” was the trait showing the strongest and most significant genetic correlation with DASH  (To compute the amount of shared genetic correlation (rdiseases  and obse of them . Among ttainment . “Averag× 10−56) We previously described the association between DASH and cardiometabolic traits  and nextDASH->Insulin (standard error (SE)) = −0.041 (0.01); pGSMR = 3.86 × 10−4) and glucose levels (Beta DASH->Glucose (SE) = −0.036 (0.01); pGSMR = 8.82 × 10−3) ) = 0.101 (0.02); pGSMR = 2.15 × 10−8) (pGSMR = 7.82 × 10−2) and glucose (pGSMR = 0.821) levels on DASH score (n < 5) to perform the GSMR analyses. For CAD, neuroticism score, body fat percentage, HOMA-IR, triglycerides, LDL, HDL and total cholesterol levels the GSMR analyses were not significant when considering DASH as exposure  (HDL->DASH (SE) = −0.066 (0.03) pGSMR = 2.32 × 10−2), high LDL levels (Beta LDL->DASH (SE) = 0.112 (0.03) pGSMR = 1.75 × 10−5), increased levels of total cholesterol (Beta TC->DASH (SE) = 0.071 (0.03) pGSMR = 8.65 × 10−3), increased CAD risk (Beta CAD->DASH (SE) = 0.081 (0.03); pGSMR = 2.57 × 10−3) and increased body fat percentage (Beta BODY FAT%->DASH (SE) = 0.426 (0.15); pGSMR = 4.83 × 10−3) when considering DASH as outcome (n < 5) to perform the GSMR analyses. Finally, for BMI, we found significant bidirectional effect with DASH  . Our res × 10−8) . We did SH score . We coulctively) . We also outcome . We coulith DASH . This reith DASH . As diffARPP21, RP11-62H7.2, MFHAS1 and RHEBL1) whose expressions were potentially associated with DASH due to causality or pleiotropy. Interestingly, most of these genes have been consistently associated with cardiometabolic diseases [In this first GWA, we investigated the genetic influences on DASH on over 170,000 subjects of European ancestry and identified seven associated loci, which provide new insights into the genetic basis of this dietary pattern. By leveraging these genetic findings, we performed Mendelian randomization analyses to assess the causal relationship between DASH and health outcomes. Our results indicate that a healthy diet style may causally lead to reduced levels of glucose and insulin. The gene-based analysis revealed nineteen genes associated with DASH. We identified four genes  intakes based on the principal component (PC) of UK Biobank generic diet questionnaire and identified a number of genetic loci associated with either DC1 (a meat-related diet) or DC2 (a fish/plant-related diet) . MoreoveNEGR1 and FTO genes, respectively). These two genes have been consistently associated with BMI [NEGR1 expression levels are associated with lower DASH score. Decreased expression level of Negr1 in murine periventricular hypothalamic areas lead to an increase in body weight [Similarly, the variant rs56094641) on chromosome 16q12.2 is associated with lower DASH score as well as lower non-oily fish intake/liking ,14. Simiwith BMI ,63 obesiwith BMI . Althoug1 on chroy weight  and may Gene-set enrichment analyses provide evidence that the genes annotated to the variants associated with DASH participate in the “Activation of AMPK downstream of NMDARs” pathway. The AMP-dependent protein kinase (AMPK) is highly expressed in the hippocampus  and is aOur study should be interpreted in the context of the following limitations. First, our SMR analyses is based on cis-eQTL effects estimated in peripheral blood because the currently available brain eQTL studies have very limited statistical power due to their small sample sizes. However, Ting Qi et al. demonstrated that, when the genes are expressed in both brain and blood, then using cis-eQTL effects estimated in blood as proxies of those in brain increase the power to identify putative functional genes for brain-related complex traits and diseases . AdditioIn conclusion, this study provides novel insights into the genetic architecture of DASH and highlighted its putative causal relationship with health outcomes. These findings extend our knowledge of the genetic pathways underlying DASH and may have significant implications for global public health providing future dietary recommendations for the prevention of common chronic diseases and mental health disorders.


Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73–33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut ( Groundnut is a nutritious crop with high protein (12% to 36%) and oil (36% to 54%) content and an important food crop worldwide. Groundnut seed also plays a crucial role in providing, vitamins, minerals, unsaturated oil and plant protein for many people in Ghana . The nutSimilarly, in Ghana and other African countries, the breeding programs are in critical need for innovative techniques to improve yield and quality of groundnut. Application of RGB-image method, i.e., the science of making measurements from photographs, for automatic phenotyping may overcome the flaws of the current conventional phenotyping methods. RGB-image method saves significant time, decreases the cost of data collection, and offers the benefits of non-destructive measurements, regular assessment, accurate observations and direct storage of data . RGB-imaTwo experiments were conducted between June 2020 and October 2020 at Nyankpala, located in the Tolon district of Northern region of Ghana. Nyankpala is located at 09° 25′ 41″ N, 00° 58′ 42″ W, and altitude of 183 m above the sea level. The soils of the experimental site belong to Ferric Luvisols of the Tingoli series with a brown color, moderately drained, and free from concretions . The Nort(i + 1)-ti is the number of days between two successive scores .Observations were recorded on the number of pods per plant (Pods/PLT), i.e., at the physiological maturity, pods obtained from five harvested plants were individually counted and averaged. Pod weight per plant (PW/PLT) was also performed at the physiological maturity, when pods from five plants were dug manually and hand-stripped, cleaned from soil, then air dried to constant weight, and pod weight was taken using an electronic scale ; produced as part of Image J/Fiji  depending on the light available. Green area (GA = H 60–120°), greener area (GGA = H 80–120°), H angle, and crop senescence index [CSI = (100*(GA-GGA)/GA); Data analysis was performed using the mixed linear model with R statistical program, version 4.0. 2 . This waEstimation of Genotypic Variance (VG) was done using the formula: ications .Calculation of phenotypic variance (VP) was done using the formula:ications .The formula question . GCV valquestion .Calculation of phenotypic coefficient of variation (PCV) was also carried out using the formula interest . PCV valinterest .Estimation of broad sense heritability was done using the formula ariances . Broad sariances .Calculation of expected genetic advance (EGA) was performed using the formula: GA = (K) бP H2 where GA is the expected genetic advance, K is the selection differential (2.06 at 5% selection intensity) and бA is the phenotypic standard deviation .The genetic advance as percentage (GAM) the of mean was calculated as: AX)×100 . GAM wasAX)×100 .The area under disease progress curve was used to understand the incidence and progression of ELS and LLS diseases among the groundnut genotypes using leaf spot severity scores taken at 70, 80, 85, and 95 DAP. However, the best associations with the traditional measurements were at 95 DAP, for which we primarily present here the data recorded at 95 DAP. The genotypes exhibited different levels of resistance to ELS and LLS diseases. For the medium duration population, the area under disease progress curve for ELS_AUDPC ranged from 32 for genotype 73–33 to 73 for Sen-DOK IT with a mean of 57 . In the Genotypic differences were observed for the pod weight plant-1 (PW/PLT) and number of pods plant-1 (Pods/PLT). Among the genotypes, Mal-ICG 14630 had the lowest PW/PLT of 5.5 g, and Zam-ICGV-SM-07599 the highest of 17.5 g. The population mean was 10.5 g plant-1 . For the2g) and phenotypic (Ϭ2p) variance and coefficient of variation , broad sense heritability (H2), expected genetic advance (EGA) and genetic advance as percentage of the mean (GAM) for the traits estimated in this work are presented in 2g were in the range of 0.01 for GAA at 70 DAP (GGA_70) to 353.1 for LLS_AUDPC whiles values for Ϭ2p were in the range of 0.01 for GGA_70 to 384.1 for LLS_AUDPC  among the parameters studied. The analysis showed a negative linear association between the RGB-image method traits  and the LSD scores (ELS_AUDPC and LLS_AUDPC) for both populations (p < 0.001), GA_85 and LLS_AUDPC , and GA_85 and PW/PLT , GGA_85 also showed significant associations with ELS_AUDPC , LLS_AUDPC , Pods/plant , and PW/PLT . For the early duration population, significant correlations were observed between GA_85 with LLS_AUDPC  ELS_AUDPC , and PW/PLT , LLS_AUDPC , and PW/PLT . RGB-image methodRGB-image method.The Pearson correlation matrix was employed to assess the relationship between the RGB-image method and conventionally measured traits for both, the training and validation populations. There were significant correlations  as the most important traits influencing this principal component. For the validation population, the first three principal components  were those regarded as having a significant contribution to the total observed variation existing among the genotypes judging by their eigenvalues, and they accounted for 87.5% of the total variation  was used to identify the most important traits in this study. For the training population, Principal components one (PC1) and two (PC2) were those considered with the greatest contribution to the observed variability among the genotypes based on their eigenvalues . These tariation . PC1 conThe challenge to feed the growing human population in the face of numerous constrains for the agricultural production including biotic and abiotic stresses is an uphill task. Efficient phenotyping can help breeding programs develop more rapidly productive and resistant varieties of crops. In this study, 60 medium duration and 192 short duration accessions from the AGGC were used to develop improved phenotyping approaches for the leaf spot resistance and yield in groundnut. Originated under diverse agroecological conditions, these accessions represent an important genetic resource for biotic and abiotic stress resistance, which is the key to genetic gain and crop improvement . Indeed,The estimated phenotypic coefficients of variation were higher compared to the genotypic coefficients of variation for the studied parameters. This observation was not different from the findings of p < 0.05) among the studied parameters indicating that RGB-image method has the potential to replace or complement the conventional methods of data collection due to the easy application and less expensive nature of the technology. Findings from this research suggest potential for automatization of disease severity and yield components assessment that will enable faster data collection at multiple/relevant time points throughout the growing season. The positive correlation between GA, GGA and Hue and yield components (PW/PLT and Pods/PLT) indicates that improving GA, GGA and Hue will directly lead to improvement in yield components. The strong association between GA and LLS_AUDPC, GGA and LLS_AUDPC and Hue and LLS_AUDPC provides an opportunity for the development of LLS resistant cultivars through indirect selection. Because of polygenic nature of ELS and LLS  were observed between predicted and the observed disease scores for all six prediction models. GA, GGA, and Hue angle, representing the number of green RGB-image method.Significant correlations (This study showed the potential of using RGB-image method as a high-throughput tool for phenotyping leaf spot diseases and yield components estimation in groundnut. Fast progress in groundnut improvement could be achieved when RGB-image method traits are use as surrogate traits for selecting leaf spots resistance in groundnut breeding programs in Ghana. GA, GGA, Hue, and CSI can successfully replace or, at least, complement the conventional methods for leaf spot diseases phenotyping in groundnut. This study reveals that photogrammetric techniques were more effective at the later rather than early stages of vegetation and when disease symptoms are ample.Genotypes 73–33, Gha-GAF 1723:201909, Zam-ICGV-SM-07599:201909, and Oug-ICGV 90099:201909 were identified as promising sources for leaf spot diseases resistance and high yield components for the groundnut breeding programs in Ghana.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.ES conducted the experiments, analyzed the data and wrote the manuscript. RO-F coordinated the project, provided the materials, supervised the experiments, and reviewed and edited the manuscript. YK designed the experiments, assisted in data collection and statistical analysis, review and edited the manuscript, DP, JA-D, and AM assisted in trials establishment, data analysis, reviewed and edited the manuscript. AD and KO as academic supervisors, supervised the experiments and reviewed and edited the manuscript. MB acquired the funds, coordinated the project, supervised the experiments, and reviewed and edited the manuscript. AC helped in statistical analysis, reviewed and edited the manuscript. DH and JR acquired funding for the project, reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.This project was funded by the USAID, Feed the Future Peanut Innovation Lab.AC was employed by company Bayer Crop Science.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Humans have adapted to diverse environments, but few studies have linked regions of the genome with evidence of selection to phenotypes. We generated an integrative dataset of genomic, transcriptomic, and biomarker data for the Tsimane and the Moseten—two indigenous Amerindian populations in Bolivia. From analyses focused on Tsimane individuals, we found evidence for adaptation at genes and traits involved in immunity, which makes sense given that infection has strong effects on physiology and fitness in this population. Using phenotypic data from both populations, we were able to link genotypes to the immune and metabolic traits that are potentially advantageous. This study expands our knowledge of natural selection in Amerindians and uncovers previously undescribed loci of evolutionary and biomedical relevance. PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits.A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype–phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection . Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing  EPAS1, which enable Tibetan highlanders to persist at high altitudes  they inhabit a high pathogen environment, which is known to affect physiology, fertility, and mortality –11, lead1519ortality , 23, 76.The Tsimane largely maintain a traditional, subsistence-level lifestyle supported through slash‐and‐burn horticulture, fishing, hunting, and seasonal foraging . They haThis high pathogen burden shapes many immune and other physiological traits in the Tsimane. Relative to individuals living in the Global North, the Tsimane exhibit elevated levels of C-reactive protein (CRP), various immunoglobulins, B cells, natural killer cells, and eosinophils , 27. Pre8Here, we use a three-pronged approach to test the hypothesis that traits that shape, or are shaped by, immune defenses are under selection in the Tsimane. First, we used genome-wide genotype data to scan 203 Tsimane genomes for evidence of selective sweeps. Second, to functionally validate the candidate regions identified by our selection scans, we drew on existing biomarker and blood mRNA-sequencing (mRNA-seq) data to ask if any of these regions contained loci associated with 1) gene expression levels in whole blood n = 242) and/or 2) relevant physiological biomarkers . To maximize power, these follow-up analyses included data from a nearby, genetically and ethnolinguistically related population, the Moseten , 24  to gene expression and/or biomarker variation. Together, our work leverages an integrative dataset from a genetically understudied Native American group to identify genotype–phenotype connections of evolutionary and biomedical significance.In total, our analyses identified 21 nonoverlapping regions that are candidates for selective sweeps, as well as several immune traits that show evidence for polygenic selection . Our candidate regions included or were near genes that code for key immune  and nd ABCA1 –45]. Impr < 0.125, n = 203; see Methods). We merged these data with publicly available whole-genome sequences from the 1000 Genomes Project  . Before  Project  and the  Project . As expe Project . No Tsim Project .2 = 0.84, P < 10−16; SI Appendix, Fig. S1). Consistent with our PCA results, both approaches suggest minimal evidence of European ancestry (mean ± SD of RFMix estimate: 4.276 × 10−3 ± 0.014) or African ancestry . To identify signals of strong selective sweeps, we searched for variants with both high allele frequencies and extended haplotype homozygosity in the Tsimane relative to reference populations from the 1000 Genomes Project. More specifically, we calculated 1) the integrated haplotype score (iHS)  . This procedure left us with 39 50-kb regions, which we further collapsed into 21 nonoverlapping candidate regions .To ensure that small amounts of European admixture did not bias our analyses, we excluded seven individuals with >5% European ancestry inferred from RFMix, and we masked SNPs in any remaining individuals’ genomes that were putatively inherited from a European ancestor (see re (iHS) , 2) the able S1) . For eacable S1) ; we thenMethods and SI Appendix, Table S4 for demographic model parameters). Across 100 simulated genomic datasets, we rarely observed selection statistics as extreme as those calculated from the real data. More specifically, when we calculated the median FCS for each of our 21 candidate regions, we found that these values were more extreme than 99.5% ± 0.76% of the combined FCS distribution from 100 simulated datasets .In addition to positive selection, allele frequency and homozygosity patterns in a population are determined by neutral and demographic processes. To confirm that our results could not be explained by these forces alone, we used the demographic models and the general approach of  to simulSI Appendix, Table S5), including immune-related genes. For example, MUC21 and MUC22 are mucin family genes involved in protecting the epithelium from microorganisms (PSD4 plays a key role in major histocompatibility complex (MHC) class II antigen presentation  cholesterol levels .To further understand the putative biological functions of genes overlapping our candidate regions, we used public catalogs of genome-wide association study (GWAS) summary statistics to ask whether these genes had been previously associated with phenotypic traits. As expected, given the Tsimane’s high pathogen environment, we found that many of our candidate genes are associated with immune-related traits such as eosinophil counts (9/18 genes) and percentages (7/18), lymphocyte counts (7/18), and overall white blood cell counts 9/18; . For pheSI Appendix, Table S7 for biomarker-specific sample sizes). For the 2,329 SNPs that fell within our candidate regions and passed our filtering criteria (see Methods), we tested for correlations between genotype and each biomarker as well as between genotype and gene expression levels . While our gene expression and biomarker datasets include both Tsimane and Moseten individuals to boost power, our results are very similar whether Moseten individuals were included or excluded . We focus here on results that were statistically significant in both analyses.To understand the phenotypic effects of genetic variation within our candidate regions, we drew on whole-blood mRNA-seq data (n = 242) and immune and metabolic biomarker data for Tsimane and Moseten individuals. Specifically, our biomarker data included: lipid panels measuring low- and high-density lipoproteins, total cholesterol, and triglyceride concentrations , 28; fasSI Appendix, Fig. S6). As expected, most of the genes expressed in blood have some reported immune function; one notable exception was PFKP, which codes for the platelet isoform of the enzyme involved in the rate-limiting step of glycolysis (TOX2  and PSD4 . Given that many of the candidate genes are not expressed in whole blood and are presumably active in other tissues, we caution that absence of evidence in this analysis is not evidence of absence in terms of a selected variant regulating gene expression.Nine of the 18 candidate genes are expressed in whole blood and could thus be tested for eQTL (ycolysis . ThroughABCA1 that correlate with HDL cholesterol levels (SNP = chr9:104794136 (hg38); beta = 2.858, P = 3.82 × 10−6), 2) within ANXA6 that correlate with triglyceride levels  and total cholesterol levels , and 3) near MUC21 and MUC22 that correlate with total lymphocyte counts . When analyzing Tsimane samples alone, we also find evidence for an association between regulatory variants in SLC25A21 and fasting blood glucose levels . The associations between selected variants in ABCA1 and HDL cholesterol levels  within l levels F and bete levels D are nothttps://pan.ukbb.broadinstitute.org/) and 2) genes whose products are known to physically interact with 16 families of viruses . This phenotypic validation is essential for understanding the functional consequences of natural selection, for prioritizing loci for follow-up work, and for separating false- and true-positive signals. As complementary phenotypic information is rarely available in genomic studies of natural selection, the current study highlights a major strength of combining genomics with integrative, long-term anthropological research  predicts reduced HDL . ABCA1 is a rate-limiting factor for HDL assembly  exhibit triglyceride levels that are 2.7 times higher than individuals with two copies of the major allele . While this tag SNP does have an appreciable worldwide minor allele frequency (41%), we note that it could be linked to population-specific genetic variation in the Tsimane. More generally, we cannot claim that any of the SNPs highlighted in the present study have causal effects on phenotypes in the Tsimane. We also note that the genotype–phenotype associations we uncover are on average small in terms of their effect sizes, but this is not unexpected given that 1) variants with large, detrimental effects are selected against and are thus expected to be rare and 2) complex immune and metabolic traits are both highly polygenic and strongly influenced by nongenetic factors  Future studies using whole-genome sequencing as well as gene editing techniques and/or functional assays are needed to identify causal loci and to understand their effects on biological processes in the absence of environmental heterogeneity.We also found evidence for selection on ransport . Knockdoe levels , but to SI Appendix, Table S11). These are all viruses that are known to be at especially high prevalence, or to have disproportionate effects, in Native Americans. CMV prevalence is generally high in South America , which are elevated in the Tsimane relative to post-industrial populations, especially at earlier ages . We therefore did not include Moseten individuals in our evolutionary analyses, and we could not perform a separate evolutionary analysis for this population due to sample size limitations. However, we did include Moseten individuals in eQTL and genotype–biomarker association analyses to boost sample size ; these analyses all explicitly controlled for any genetic structure between Tsimane and Moseten individuals using statistical methods that are well established for GWAS of structured populations (Mapping Genotype–Phenotype Correlations at Candidate Regions). We also repeated all association analyses with and without Moseten individuals to confirm our results .While this study focuses on understanding how natural selection has shaped genetic and phenotypic diversity in the Tsimane, we also included samples from a neighboring indigenous horticulturalist population, the Moseten, in some analyses. The Moseten and Tsimane inhabit adjacent territories, speak very similar languages that are part of an isolate language family , 83, andulations  . Our work was approved by the institutional review boards at the University of California, Santa Barbara and the University of New Mexico . All work was also discussed with and approved by the Tsimane government (Gran Consejo Tsimane), community leaders, and the study participants themselves. We also developed materials to communicate study results to participant communities, which are posted at Between 2006 and 2015, 1,286 blood samples were collected from individuals belonging to the Tsimane n = 1,112) and Moseten (n = 174) communities by the THLHP  were successfully genotyped. To assess reproducibility, 14 samples were processed in duplicate and the mean concordance between technical replicates was estimated at 99.99%.SI Appendix, Materials and Methods.We used Plink  to filteSI Appendix, Fig. S1). Second, we generated local ancestry assignments using RFMix . For all downstream selection analyses, we pruned our set of 203 unrelated individuals down to 196 individuals with >95% Native American ancestry inferred from RFMix, and we masked SNPs in regions that were not confidently assigned to Native American ancestry. These analyses are further detailed in the SI Appendix, Materials and Methods.We merged our filtered, phased data with 1000 Genomes Phase 3 data  as well ng RFMix , with poWe identified genomic regions under recent positive selection in the Tsimane using an outlier approach and three statistics: the iHS , the PBSfill-aa tool from VCFtools . The set of 50-kb candidate/outlier regions identified by our two summary approaches were highly overlapping . The union set with overlapping regions collapsed is summarized in SI Appendix, Table S3.To identify candidate regions that have putatively been under positive selection, we used two approaches derived from the literature to combine our iHS, XP-EHH, and PBS results and identify outlier regions . Next, for traits associated with more than six genes in our dataset, we used a hypergeometric test to ask whether the number of candidate genes in the Tsimane that were also associated with the focal trait was more than expected by chance. In cases where several GWAS have investigated the same trait, we used summary statistics from the study with the largest sample size . To perform these tests, we used the R function phyper after calculating 1) the number of genes associated with any 50-kb window in our dataset, 2) the total number of candidate genes, 3) the number of genes associated with the focal trait at P < 5 × 10−8, and 4) the overlap between #2 and #3. Results from these analyses are presented in SI Appendix, Table S6. We only considered tests that passed a 10% false discovery rate (FDR) to be significant.We considered candidate genes for positive selection to be protein-coding genes that overlapped each candidate window; for anyone who wants to consider a broader distance between genes and variants , we also provide a list of genes whose transcription start or end sites are within 250 kb of each candidate window in https://pan.ukbb.broadinstitute.org/) and 2) genes that code for structures known to biochemically interact with 16 families of viruses . We considered a window to be associated with the response to a particular virus if it contained any portion of the coding region of a virus-interacting gene or any portion of the 10-kb putative regulatory regions upstream and downstream of a virus-interacting gene.To test for polygenic selection on a particular quantitative immune trait or viral response, we first obtained all 50-kb windows associated with a given trait and summarized their genomic properties. We split the genome into nonoverlapping 50-kb windows, and for each window calculated 1) the total number of SNPs, 2) the number of conserved SNP positions , 3) thex windows (x being the number of windows associated with the trait) among windows with a similar number of total SNPs, similar number of conserved SNPs, and similar recombination rate. To generate this null distribution, we only sampled from windows that were not associated with the focal trait, and we defined “similar” as within the same quartile of the total SNP, conserved SNP, or recombination rate distribution. We performed 10,000 samples for a given trait, and then computed a P-value for each trait by calculating the proportion of resampled windows for which the median FCS value was higher than that observed for the real trait-associated windows. All P-values were adjusted for multiple testing using a Benjamini–Hochberg FDR approach . We found a significant result for basophil counts , but ultimately excluded this from our final results set; we did so because the trait was associated with an order of magnitude fewer testable loci in our dataset relative to all other quantitative immune traits (~17-fold on average) and was thus an extreme outlier.After we obtained all 50-kb regions associated with a given trait , we generated a null distribution using two permutation schemes. In the first scheme (which is presented in the main text), we randomly sampled approach , and traP-value for each virus by calculating the proportion of samples for which the median FCS value was higher than that observed for the real virus-associated windows. All P-values were then adjusted for multiple testing . Therefore, for a given family of viruses, we 1) randomly sampled the same number of genes associated with the response to that virus in our real dataset, 2) obtained all 50-kb windows that overlapped the protein coding or regulatory region of genes in this sampled list, and 3) calculated the median FCS value across all 50-kb windows. As in the first permutation scheme, we only sampled from windows that were not associated with the virus in our real dataset. We performed 10,000 samples, and then computed a  testing . We provSI Appendix, Table S4 provides a description of all fixed and open parameters.To confirm that the results we obtained from selection scans could not be explained by neutral processes, we inferred demographic parameters for the Tsimane and simulated genetic data in the absence of selection. We used fastsimcoal2  to consthttps://github.com/isaacovercast/easySFS) was used to infer the joint, folded site frequency spectrum for the Tsimane, Peruvians, and Han Chinese using our set of unrelated and unadmixed individuals. As input loci, we used SNPs filtered for: nonautosomal SNPs, SNPs that were not biallelic, SNPs that were not in the Hardy–Weinberg equilibrium (P < 10−8), SNPs that were not genotyped across >90% of individuals, genic regions (defined as within coding regions or within 100 kb of any transcription start or end site), CpG islands, GERP conserved elements  and combined the three statistics into an FCS for each SNP. We performed these simulation and analysis procedures 100 times and combined the FCS values derived from all runs to create the distribution plotted in HBB, HBA1, and HBA2 genes from our dataset. We then filtered for expressed, protein-coding genes, normalized the data with the R package limma  and Tsimane individuals (n = 154) and used to generate mRNA-seq data using the Lexogen QuantSeq 3′ FWD mRNA-Seq Library Prep Kit and the Illumina NovaSeq S4 platform. We used standard pipelines for read trimming , mappingge limma  and corrP < 10−8), and SNPs in strong LD . Association testing was performed using GEMMA .For each of the candidate regions putatively under positive selection, we mapped eQTL using the processed mRNA-seq data and MEGA genotype data for Tsimane and Moseten individuals. For each population, we removed SNPs with MAF < 5%, SNPs not in Hardy-Weinberg equilibrium . For each candidate 50-kb window and each trait, we used GEMMA (P < 10−8), and SNPs in strong LD . However, our analyses are intended to provide evolutionary insight rather than to identify genetic variants for immediate diagnostic or clinical use. All genotype–phenotype associations passing the 10% FDR threshold are presented in SI Appendix, Tables S9 and S10 provides the top tag SNP , for a given region and trait combination. In cases where several SNPs in LD were tied for the lowest p-value, we report all of them in SI Appendix, Table S10, and we randomly chose one for visualization. Finally, for all of the top tag SNPs reported in the main text and used for visualization, we also reran our analyses after permuting the genotype labels 1000 times; we did so to confirm that our empirical null distributions were uniform as expected .For all tests associated with a given trait, we extracted the p-value associated with the genotype effect and corrected for multiple hypothesis testing . We conshttps://github.com/AmandaJLea/Tsimane_selection.Analysis code is available at Appendix 01 (PDF)Click here for additional data file.Dataset S01 (XLSX)Click here for additional data file.Dataset S02 (XLSX)Click here for additional data file.Dataset S03 (XLSX)Click here for additional data file.Dataset S04 (XLSX)Click here for additional data file.Dataset S05 (XLSX)Click here for additional data file.Dataset S06 (XLSX)Click here for additional data file.Dataset S07 (XLSX)Click here for additional data file.Dataset S08 (XLSX)Click here for additional data file.Dataset S09 (XLSX)Click here for additional data file.Dataset S10 (XLSX)Click here for additional data file.Dataset S11 (XLSX)Click here for additional data file.Dataset S12 (XLSX)Click here for additional data file.