Partners resemble each other in health behaviors and outcomes such as alcohol use, smoking, physical activity, and obesity. While this is consistent with social contagion theory suggesting partner influence, it is notoriously difficult to establish causality because of assortative mating and contextual confounding. We offer a novel approach to studying social contagion in health in long-term partnerships by combining genetic data of both partners in married/cohabiting couples with longitudinal data on their health behaviors and outcomes. We examine the influence of the partner’s genetic predisposition for three health outcomes and behaviors  among married/cohabiting couples. We use longitudinal data from the Health and Retirement Study and the English Longitudinal Study of Ageing with data on health outcomes and genotypes for both partners. Results show that changes over time in BMI, smoking, and drinking depend on the partner’s genetic predispositions to these traits. These findings underline the importance of people’s social surroundings for their health and highlight the potential of targeting health interventions at couples.The online version contains supplementary material available at 10.1007/s10519-023-10147-w. The partner’s lifestyle has considerable associations with one’s own lifestyle across a variety of domains, including obesity, physical activity, eating, alcohol consumption, weight loss, and smoking  for both partners and testing whether ego’s phenotype is associated with the PGI of ego’s partner while controlling for ego’s own PGI. However, this approach is known to suffer from confounding by assortative mating  and the English Longitudinal Study of Ageing (ELSA). Both studies follow a nationally representative sample of adults aged 50 and older every two years. HRS does this for people in the United States since 1992 and ELSA for people in England since 2002. DNA samples have been collected in both studies, and if a participant has a partner, this partner is automatically selected to participate in the study as well, even if he or she is younger than 50 years. We examine three outcomes for which extensive information on genetics is available and that are important indicators of a healthy lifestyle; namely BMI  is a longitudinal household study that follows a nationally representative sample of adults aged 50 and older in the United States every two years since 1992  is measured by dividing weight in kilograms by length in meters squared. Drinking is measured by the number of alcoholic drinks per week (DPW). Smoking was measured by the number of cigarettes per day (CPD) and non-smokers were set to zero. We take the natural logarithm for DPW, left anchored at 1 (ln(y + 1)).Genetic propensity for BMI/DPW/CPD is measured by the use of polygenic indexes (PGI). A PGI is the aggregation of many small genetic effects scattered across the genome on a phenotype. It is computed by weighting the alleles at the different loci across the genome with their association to the phenotype of interest, and then summing these weighted alleles. Information on the association between alleles and phenotypes is derived from recent large-scale publicly available GWAS  for each outcome that was available in the public domain and that was created in an identical way for both HRS and ELSA to facilitate reproducibility and to maximize comparability. The PGIs were available through the Polygenic Index Repository or the HRS and/or ELSA studies  using random effect regression models controlling for ego’s first observed scores for each phenotype in the partnership (BMI/CPD/DPW respectively). For example, if ego’s phenotype is first observed in wave 2, then this first phenotype will be controlled for in the subsequent waves and ego will only be included in the analyses after wave 2. In addition, the analyses control for PCs of the partner, which provides a broad control for genetic similarity between partners. Individuals can occur as both ego and partner in the data, so each couple can occur twice at each wave (directed dyads). We adjust for repeated observations within individuals/couples over time by estimating a random intercept for dyads (directed). We use robust standard errors clustered on the household level based on sandwich estimators White  because We conducted sensitivity analyses to evaluate whether findings are affected by outliers in BMI (< 20 or > 40), heavy smokers (≥ 20 CPD), or heavy drinkers , and by excluding non-drinkers and non-smokers. In addition, we performed analyses in which we control for the baseline difference between partners in the phenotype of interest . The results of these analyses were not substantially different from the main analyses . We also conducted analyses in which we interact the social genetic effects with the number of years between the first and the current ego phenotype. We find that this interaction is insignificant for all outcomes .We repeat our social genetic effects analysis but for height as phenotype, as a negative control. We should not expect a social genetic effect for height because height is largely fixed at the time of partner choice, but there is assortative mating on height  analyses, in which the partner’s polygenic index  acts as an instrumental variable for the partner’s actual health behavior or outcome . We also conduct multilevel multivariate analyses, in which data from the three different phenotypes  are combined into one analysis per data source (HRS and ELSA). Finally, we conduct analyses using change scores, where the outcome variable is the difference between ego’s current behavior and the behavior at baseline.r = 0.23, DPW r = 0.48, and CPD r = 0.33; ELSA, BMI r = 0.20, DPW r = 0.50, and CPD r = 0.25). To examine genetic similarity, we examine the correlation between ego’s and partner’s PGIs for each of these three health behaviors and outcomes, controlling for partner’s and ego’s age and sex and ego’s 20 first principal components. We first confirmed that the PGI were associated with their corresponding phenotypes. Standardized estimates of ego’s PGI were largest for BMI , then DWP , and then CPD . The genetic correlation between partners is insignificant for all three health outcomes in the ELSA, but is significant for BMI and CPD in the HRS . What is more, the estimates for these two genetic similarities are significantly different from what would be expected under phenotypic assortative mating , we see that the point estimates are very similar between the HRS and ELSA, but the confidence intervals are much narrower in the HRS than in the ELSA. The HRS has more participants and a longer period of observation compared to ELSA , which increases statistical power compared to ELSA. In all but one model (CPD without controlling for ego PGI), the social genetic effects are significant in the HRS. In the ELSA, all effects are also in the expected positive direction, although the smaller number of participants and shorter observation window leads to lower power to detect significant effects. Most importantly, the meta-analytic estimates combining the data from the HRS and ELSA show that the social genetic effects are significant for all three health outcomes, regardless of whether we control for ego’s PGI or not. Hence, we find robust evidence for social genetic effects when we combine data sources to obtain sufficient power. In contrast, our negative control – the social genetic effect for height – shows a null effect even when we combine data sources . Sex-stratified analyses are provided in the supplementary material . They provide little evidence for differences in social genetic effects by sex. Only for CPD, there is some evidence suggesting stronger social genetic effects for men than women.The results of the IV analyses suggest that the partner’s health behaviors and outcomes are a plausible mechanism for our observed social genetic effects (Table S8). That is, whenever we observe a significant social genetic effect in our main analyses, we also observe a significant effect in the IV analyses where the partner’s behavior is instrumented by the partner’s polygenic index. The results of the multilevel multivariate analyses (Table S12) and change score analyses (Table S9) corroborate our main analyses; we find evidence for social genetic effects, with generally larger effect sizes and smaller standard errors in the HRS than in the ELSA.2 of both partners, sex interacted with age and age2, and relationship duration  and sex, age and ageion Fig. , making We examined genetic similarity and social genetic effects among married and cohabiting partners for three widely studied health behaviors and outcomes  using genome-wide data of couples in the HRS and ELSA, large nationally representative samples of the American population (HRS) and the English population (ELSA) aged 50 and older. We confirm previous research showing the existence of limited genetic similarity between partners for BMI . Similar mechanisms can be thought of for BMI and drinking. For example, if one partner stops drinking because of liver damage accumulated over the years, this might cause the other partner to stop as well; and if one partner starts exercising because of weight gained over the years, this might induce the other partner to start exercising. Moreover, nowadays there is more information on, and awareness of, the negative effects of unhealthy lifestyles compared to the previous decades . However, maybe such effects should not be ruled out, because interpreting social genetic effects as the effects of fixed predispositions of a partner on ego would include such socialization effects. Finally, we studied the three health behaviors in isolation and did not examine cross-trait effects. It is plausible that there may be cross-trait social genetic effects given large phenotypic and genotypic correlations between BMI, drinking, and smoking  we navigate in life are populated by others who shape and mold these environments. These environments can be seen as genetic landscapes, which may give insights into the role of colleagues  positive spill-over effects on partners. These findings highlight the potential of targeting health interventions at couples, at partners of people who have already been identified as being at high risk, and at people most susceptible to social influence of the partner.Previous research suggests that marriage and cohabitation can bring important health benefits, e.g., in the form of self-reported health, happiness, and reduced mortality (Johnson et al. Below is the link to the electronic supplementary material.Supplementary Material 1


Butia (Arecaceae). First, we conducted a bibliometric studywith selected articles, by revising the fundamental contributions to unravelingphenotypic traits that have been used for describing the phenotypic variationwithin and among populations. Moreover, we sought to comprehend the patterns ofgenetic diversity and structure that have been presented so far, based onmolecular markers. Finally, we conducted a review of the gene sequencesregistered to NCBI for Butia. Overall, morphologicaldescriptors have been proposed to depict population-level variability, but themost significant results are available from chemical properties andcharacterization of metabolites, revealing important traits to being explored.Yet, limited information is available to describe population variation and theirgenetic components. On the molecular level, almost all studies so far providedresults with classical molecular markers. The literature of SNP markers forButia species is virtually non-existent. Given the currentendangered state of Butia species, it is urgent thatresearchers pursue updated genomic technologies to invest in in-depthcharacterizations of the genetic diversity and structure of jelly palms. Thecurrent state of population fragmentation urges effective measures toward theirconservation. We compiled studies that addressed morphological and physicochemical traits, aswell as population genetic studies involving jelly palms, genus Butia (Becc.) Becc. is a genus of palm trees (Arecaceae)autochthonous to South America. Trees are popularly known as “butiás”, pindo palmsor jelly palms. The species are naturally distributed in southern and centralBrazil, eastern Paraguay, northeastern Argentina and northwestern and southeasternUruguay  Becc., that was first identifiedin 1826 as Cocos capitata. Originally, Martius described the palmfrom their observations near the town of Montes Claros, in Minas Gerais state .All populations north from that and located by the coastal plain of the Atlantic aredesignated as B. catarinensis  as worthy of conservation. The need to conservegenetic diversity within populations is based on two premises: (i) genetic diversityis a key factor for evolution to occur; (ii) and the expected relationship betweenheterozygosity and population fitness, that is, genetic variation an importantfactor for fitness .MoleculButia palms, a wide phenotypic diversity is observed, whichprovides fruits varying for biometric, physicochemical and sensorial traits  plant morphology, (ii) physicochemical propertiesand nutrition, and (iii) population genetic studies and cytogenetics. Moreover, wesearched the NCBI database to look into gene sequences that involvedButia palms. Our main goal is to provide a panorama of thefundamental knowledge acquired through the years and prospect their utility fornovel endeavors toward conservation and breeding of jelly palms.This review was developed to address the current state of art of knowledge onphenotypic and genetic variation on A bibliometric study and a systematic review of articles were employed toencompass the knowledge produced about the morphological, physicochemical andmolecular variation of jelly palm species. Four main steps were conducted in oursurvey: (i) keyword search through bibliometric platform; (ii) article selectionafter analyzing title and abstract, avoiding irrelevant contents to our subjectsas well as redundant publications; (iii) categorization of papers according toscope. For that, three main categories were pre-defined: plant morphology,physicochemical properties and nutrition and population genetic and cytogeneticstudies; (iv) categorization according to the object of research, journal ofpublication, year, objectives, main results and species involved.Butia genus . One article was excluded for not beingavailable on the platform and another was excluded for being written in German. The review was conducted through the “Web of Science” data platform, using thekeyword “Butia” to search for all the results published up to 2023. The choiceof “Web of Science” was due to the feasibility of conducting the search on theplatform and the full access to the published articles. Our search presented 227results, which were analyzed for title, abstract, and keywords. After readingand manual filtering, 47 articles were excluded for not being related to theAfter this selection, 178 papers were analyzed concerning the three differentcategories here proposed:Plant morphology: papers with phenology of the genus, biometricvariables or anatomical and, eventually, even some physiologicalstudies; Physicochemical properties and nutrition: papers addressing chemicalcompounds in fruits, nutritional analyses and chemical compositionof fruits and seeds;Population genetics and cytogenetics: karyotype description, geneticdiversity and structure.After classification, 68 papers were selected. Of the total, 13 articles featuredmorphological analyses, 40 specifically dealt with physicochemical propertiesand nutrition, 11 referred to population genetic or cytogenetic studies, and 4could be fitted into more than one category . Following bibliometric approaches, further analyses of the articles wereconducted to determine: (i) a list of publication sources and the distributionof articles among them; (ii) discrimination of journal impact factors; (iii)production per year from the first to last publication date; (iv) ratios ofpublications per period and the categories defined as per our study; (v) ratioof annual publications per number of citations of each article. The qualitative analysis of the studies sought the validation and interpretationof scientific evidence relevant to the topics studied, and the identification ofgaps in the literature, which may guide future studies. A few additional studieswere also considered for the purpose of contextualization or discussion. https://www.ncbi.nlm.nih.gov/popset/?term=butia) in order tofind all the sequence information already available. Searching for the term“Butia”, we found 54 results  in population sets (PopSets).After manually filtering the results, excluding non-related subjects, 43 popsetswere checked for the gene sequences and the species involved.We also searched the GenBank from NCBI  and iNaturalist as of August 3rd, 2023. Public databases on records for Among the 68 articles that matched the scope of this research, 39 papers werepublished each in different journals . The remth, 2023)among all journals was for “Energy Conversion and Management” (IF = 11.5) andthe lowest was for “Revista Chilena de Nutricion” (IF = 0.1). The highest numberof studies in frequency (N = 8) were published in a low impact journal (IF =1.1) - “Revista Brasileira de Fruticultura”. Other four publications (N = 4) areavailable from a journal with a high impact factor (IF = 7.4) - “Food ResearchInternational”. The highest impact factor  were published in the last eight years, with the highest number (N = 7) in2022, and 25% were published in the last three years. As per our categorization, most studies in the period were devoted tophysicochemical and nutritional analyses (N = 39). Plant morphology was theobject of 18 studies, while population genetics and cytogenetics involved 12articles. Among all papers, four were placed into more than one category, asthey dealt with both morphology and physicochemical properties of jelly palms.Based on the frequency per category, an increase in physicochemical andnutritional studies is notable for the last five years (N = 17). B. capitata. The second most cited article (103 citations)was of B.eriospatha. In total, 8 articles received no citations until thetime of this research and have been published in journals with an impact factorfrom 0.9 to 7.4. The selected articles were cited 971 times throughout the entire period (updatedup to April 2023), with the highest number of citations corresponding to studiesof physicochemical properties and nutrition (N = 670), followed by populationgenetics and cytogenetics (N = 132) and plant morphology (N = 98) . The artB.capitata, followed by B. eriospatha (N = 17) andB. odorata (N = 16). In fact, the number of papers thatactually studied B. odorata was higher. This is because before2010, B. capitata encompassed both B. capitataand B. odorata. It was B. odorata, while thoselocated in Cerrado and areas from Central Brazil remained as B.capitata. That taken into consideration, we counted the number ofpapers that studied B. odorata to 26 and that studiedB. capitata to 19, as there were 10 articles which namedB. odorata as B. capitata of samples fromRio Grande do Sul state. However, that may depend on the type of paper,especially for those that were more concerned with physicochemical analysesrather than the specifics of the sample origin. Other papers were dedicated tostudy B. catarinensis (N = 8), B. yatay (N =8), B. paraguayensis (N = 7), B. lallemantii(N = 3), B. purpurascens (N = 3) and B.witecki (N = 2). B. archeri, B. campicola, B. exospadix, B.leiospatha, B. leptospatha, B. lepidotispatha, B. marmorii, B.matogrossensis, B. microspadix and B. pubispathawere involved in only one publication - and (Z)-hex-2-enal,methoxyphenyloxyme, (E)-β-ocimene, α-fenchene and octyl methyl ether  life cycle by protectingand reversing hydrogen peroxide-induced oxidative damage. B.capitata species, on the other hand, demonstrated functionality asan inhibitor of colorectal cancer cells (HT-29) in an in vitroantitumor activity analysis   . In the nd C33a) , the fruof B. capitata were compared. A large amount ofvitamin C, quercetin and kaempferol derivatives were found as the mainflavonoids present in the fruits. On the other hand, the frozen commercial pulpsshowed lower contents of bioactive compounds and antioxidant capacity comparedto the fresh fruit  had the same number of chromosomes (2n = 32). The speciesalso have the same karyotypic formula: 14 metacentric, 12 submetacentric and 6acrocentric chromosomes.Up to date, few molecular genetic studies with B. paraguayensis, B.lallemantii, B. yatay and B. eriospatha, and foundhigh variability within populations of B. paraguayensis,B. lallemantii and B. yatay, possibly dueto gene flow, past hybridization or life history traits. Population genetic studies are available, but very limited to the end of theyears 2000 and the 2010s, using RAPD, AFLP, ISSR and SSR markers. Using ISSRmarkers, B.odorata  and found that83.68% of the genetic variability was attributed to variation within populationsand 13.67% to differences between populations within the regions investigated.The results suggest that the populations have a common origin and may haveundergone selection, drift, geographic isolation and mutations that caused thedifferences between them, structuring them into subpopulations  than in an urban population (HE = 0.62)  .B. catarinensis,with 86% of the markers successfully amplified. Moreover, the results indicatedthat there is a high potential for transfer of SSR markers between species ofthe same genus in the Arecaceae family. As for genome sequencing, we identifiedonly one paper that characterized the plastidial genome of B.eriospatha. The complete sequence was 154,048 bp in length, withthe typical quadripartite structure, consistent with other six species fromtribe Cocoseae . Moreover, a few sequences were registered asB. aff yatay and B. aff.paraguayensis, probably due to difficulties in determiningtheir classification. We also found sequenced registered at the genus levelonly, as Butia sp. (phytochrome B (PHYB); trnQ-rps16 intergenic spacer; trnD-trnTintergenic spacer; ribulose-1,5-bisphosphate carboxylase/oxygenase largesubunit; NADH dehydrogenase subunit F (ndhF)), and for populationgenetics and evolution (e.g. microsatellite but10). After searching GenBank for sequences available, at least one sequence has beenregistered for utia sp. . Of the Butia, but rather to comprehend therelationships among palms in general. Two of the most important molecularphylogenetic studies used WRKY genes . By searching GBIF, the numberof registered species is higher that what we found on Web of Science. The databaseprovided records for 27 distinct species, that can be explained mainly by olderpublications and registered observations that can be found on the database, as wellas hybrid taxa. In fact, there are substantial morphological differences amongspecies, such as  species .Butia species demonstrate to be great sources of bioactivecompounds and antioxidants, with potential as functional food for the treatment ofcancer cells and glycemic control in patients with diabetes  should be the goal of novelendeavors toward unraveling more specific as well as a broader context of range ofdistribution of jelly palms, so that a detailed panorama of their geneticconservation status might be described. Whole genome sequences ofAnother line of studies that has been increasingly employed among plant species isepigenetics or epigenomics. Since epigenetics deals with changes in gene expressiondue to modifications of DNA that do not alter the nucleotide sequence, such as DNAmethylation, histone acetylation and other mechanisms, the environment is animportant component to affect cellular mechanisms beyond genetic determination. Asper our review, jelly palms have a broad phenotypic variation and populations areusually separated by considerable distances and in distinct climates and soils.Therefore, we believe that not only genetic adaptive mechanisms, such as Butia taxa. At the genomic level, wehave little information available for conducting phylogenomic analyses to furtherdetermine the levels of differentiation among the taxa. Either ways or both waystogether, we need rapid and increasing studies toward conservation of jelly palms.Population viability analyses should be conducted coupling genomic resources as wellas an in-depth characterization of the phenotypic variation available, as theliterature showed that individuals of several populations are frequently old and noregeneration is occurring. This will ultimately lead to much more damage that couldbe repaired, so urgent measures need to be pursued for conservation of the geneticresources of these important and unique habitats that butiás compose, the palmgroves. The prominent morphological differences among jelly palms led taxonomists todistinguish more than 20 species to this date. In fact, the differences areremarkable when we compare


Castanea sativa (sweet chestnut), a Neogene relict growing in the Caucasus global biodiversity hotspot. We generated genetic and ecological metrics for 21 sites in Georgia and Azerbaijan, which cover the natural range of sweet chestnut across the region. We demonstrated that climate primarily drives the pattern of genetic diversity in C. sativa, proved with a significant isolation‐by‐environment model. In future, climate change may significantly reorganize the species' genetic diversity, inducing even some genetic loss, especially in the very distinct eastern fringe of the species range in Azerbaijan. Based on our combined approach, we mapped populations suitable for ex situ and in situ conservation, accounting for genetic variability and the location of future climate refugia.The climate drives species distribution and genetic diversity; the latter defines the adaptability of populations and species. The ongoing climate crisis induces tree decline in many regions, compromising the mitigation potential of forests. Scientific‐based strategies for prioritizing forest tree populations are critical to managing the impact of climate change. Identifying future climate refugia, which are locations naturally buffering the negative impact of climate change, may facilitate local conservation. In this work, we conducted the populations' prioritization for  Castanea sativa growing in the isolated Caucasian range. We combined genetic and ecological metrics to conduct conservation prioritization for the species in the region of high conservation importance.Studies were conducted in the Caucasus hotspot of biodiversity. Using methods of landscape genetics, we identified the drivers of genetic diversity and differentiation in  The species was extensively cultivated in southern Europe for nuts and timber since the Roman Empire  M.E.Barr  landscape genetics methods to investigate drivers of genetic diversity and differentiation in C. sativa to help assess future risks, (ii) Species Distribution Models (SDMs) to predict future species' distribution and locate areas where climate changes pose the greatest risks across its range in the South Caucasus, (iii) map populations at higher risk of genetic erosion using effective population size (Ne) index, and (iv) use genetic and ecological metrics to prioritize populations for future in situ and ex situ conservation. Considering the region's great climatic variability, we expect to find climatic variables mostly to structure the genetic diversity of the species, according to the IBE model. Previous studies in the region indicated the significant role of a glacial refugium in Colchis (western Georgia) in shaping diversity patterns in trees growing in the region  and the descriptive statistics, such as the number of alleles per locus (A), allelic richness (Ar), private alleles (Ap) and a matrix pairwise FST values.We re‐analyzed the genetic dataset previously generated by Beridze et al.  for land2.2oro., geographic location—geo., climate—clim., and species genetic ancestry—anc.) in structuring the neutral genetic variation in C. sativa in the South Caucasus. The analysis was applied to untangle confounded and constrained contributions of the above explanatory variables on a variation in dissimilarity matrices. It explores the linear relationship between the dissimilarity matrix and explanatory variables and measures in a nonlinear relationship . As a response matrix, we used Slatkin's linearized pairwise FST values (FST/1−FST).We performed a series of distance‐based redundancy analyses (dbRDA) to assess the relative role of the landscape variables (orography—clim.) on the observed differentiation pattern, we tested for IBE. For each locality, 19 bioclimatic variables for the current period were downloaded from CHELSA  on the genetic differentiation of populations, we tested the IBD model using the coordinates of the population (latitude and longitude) as a proxy. However, since IBD considers only the “flat landscape,” an unrealistic assumption in the rugged topography of the South Caucasus, we tested the impact of orographic complexity (oro.) in the frame of the IBR model based on circuit theory  from principal component analysis (PCA) performed on the set of Q‐membership values using the prcomp function in R  was considered. Here, we used the STRUCTURE results previously performed for studied populations by Beridze et al. . Specifiscale function in R, and the correlation between them was assessed using the cor function  method and refers to the recent past, a few generations back in time. In NeEstimator, a random‐mating model was chosen for analysis; low‐frequency alleles (≤0.02) were excluded from the procedure. In contrast, Migrate‐n computes NeCOAL using the Bayesian inference method based on the coalescence theory  and True Skill Statistics (TSS) were used to evaluate each model's accuracy  set to 2 to draw the first two PCs. Also, we explored the species autecology by comparing bioclimatic and environmental variables. For this analysis, we divided the species' natural range into six distributional domains: Western and Eastern Caucasus, Anatolia, and Europe's three main Peninsulas . We collected the natural georeferenced occurrence of sweet chestnut  from the GBIF database  based on the available data on the species' natural range . The tested K varied from 2 to 5 and was chosen based on the results of STRUCTURE's generic clustering performed by Beridze et al.  developed by  might be a crucial factor in pollinators' movement and gene flow. The values of forest cover data in the Caucasus were gained from Buchner et al.  period according to the scenario ssp585. We used precipitation of the warmest quarter (bio18) and precipitation of the coldest quarter (bio19) to calculate Ci as they showed the highest contribution to SDMs  displayed significantly lower allelic diversity (p < .05) in comparison to western‐located stands (LC and WGC). There were no private alleles in more than half of the populations studied, and the distribution of Ap displayed an apparent west–east gradient with the highest number of private alleles in the western range.Details of the genetic diversity of populations can be found in Beridze et al. . The ave3.2The correlation values among explanatory variables used in the dbRDA analysis can be viewed in supplementary materials Appendix . Only twclim., geo., oro., and anc.) explained 60% of the total variance, showing a strong statistically significant association  (Table oro.) was not significant , neutral genetic structure (anc.), and geography (geo.) significantly explained 54% of the total variance. However, significant variation was attributed exclusively to the pure effect of geography  and climate .The full model I, which incorporated all explanatory variables (p = .084), the effect of climate was highly significant when controlling for geography and explained 19% of total genetic variation (40% of the variation explained by the full model III), while geography itself was not significant  varied widely from 25.6 in CGC1 to 2718.6 in WGC3  and the lowest in WGC5 (11700). In the majority of populations, we noted signs of a bottleneck (p < .05); the exceptions were WGC6, WGC7, and CGC1  are given in the Appendix n Figure , Table 4s Figure . The hig2) compared with the current period. Areas with high suitability (≥70%) might be lost, especially in the Lesser Caucasus; the exceptions, however, are the Adjara and Pontic Mts. in east Türkiye where the optimal conditions for the sweet chestnut will still be available  for the lower threshold (≥15%) but a decrease in the area with high suitability (Table 2) for a threshold ≥15%. However, the area with high suitability would be reduced almost in half, amounting to 7527 km2  in the Caucasian and European ranges. Most West Caucasian populations experience much higher precipitation in winter and summer than those in the Iberian and Apennine peninsulas. The Eastern Caucasian populations create a small pocket of climatic space with much lower precipitation values in the warmest and the coldest quarters compared with the Western Caucasian subgroup.The ecoplot Figure  visualizPOPS revealed four genetic clusters for the current period Appendix A. West GSpatially explicit simulations showed a loss of variation through time in response to climate changes Figure . The sig3.5DIVA‐GIS analysis indicated that populations from the western part of the species range have the highest conservation priority Figure . AccordiCi category 1 had populations LC3, LR1‐2, WGC2, and CGC1; the next group with Ci category 2 had LC2, LC4, WGC1, WGC3, and WGC5. The remaining populations had categories from 3 to 5.A second analysis, which combined genetic and ecological metrics, delivered the same pattern that the western populations are of the highest conservation priority, although it differed in detail. Specifically, populations LC1 and LC2 located in the Lesser Caucasus and WGC5 and WGC7 from the Greater Caucasus lost their high conservation values due to expected significant climate shifts. The exception was population CGC1, which was suggested to experience the lowest climate change among the studied locations. Generally, Central and Eastern Greater Caucasus populations attained a low conservation index. Apart from having decreased genetic diversity, those stands also had low values of ecological indices—relatively low forest continuity and pronounced future climate shift Appendix . Overall44.1Aesculus hippocastanum L., growing naturally in the Pindos Mts., Greece  and eastern range (East Georgia and Azerbaijan) in the South Caucasus grows under different precipitation conditions. Studies have shown that the neutral genetic diversity of sweet chestnut in the Caucasus and the Iberian Peninsula are organized along the precipitation gradient  with a confounding effect of geographic distance (21.1%) were responsible for sweet chestnut's extant neutral genetic structure, which denotes the pattern of IBE. Our results also demonstrated that the occurrence of sweet chestnut in the South Caucasus is affected more by precipitation than temperature. The regional climates in the study area, especially precipitation, are controlled by the orography and distance from the Black and Caspian Seas  in 2041–2070. Next, up to 2100, we predict even worse effects of climate change for the extreme ssp585 scenario with an almost 70% loss. Range contraction would likely be most drastic in Eastern Georgia and Azerbaijan, putting the population at the easternmost locations at risk of extinction. Similar to other global hotspots of diversity, the Caucasus is considered highly vulnerable to the adverse effects of climate change  could have intensified drift and selection; if strong enough, selection may also lead to neutral diversity loss  had the highest category of Ci. These have a relatively high genetic diversity and are within continuous forest, but the current climate conditions also overlap with the future ones, giving a high probability of in situ survival. Priority 2 of Ci was ascribed to population CGC1 from the Greater Caucasus, which in the Reverse Selection was also considered for conservation due to high allelic diversity; here, the environmental indices and climate predictions were relatively high, giving justified prospects for future survival. Sweet chestnut populations from the Lesser Caucasus in West Georgia with the highest allelic diversity acquired low Ci. At those locations, the climate components are expected to shift too drastically in future, which questions their in situ survival. For LC1 and LC2, precipitation values of the warmest quarter are projected to decrease by ca. 52% and 70%, respectively, and precipitation of the coldest quarter by ca. 28% and 56%, respectively. POPs also predicted a loss of the genetic cluster containing populations from this part of the species range. However, the highest allelic diversity noted in LC1 and LC2 makes them perfect objects for ex situ conservation or using advanced methods such as assisted migration. The populations from the eastern locations, except for CGC1, were classified as of marginal attention in conservation management. Besides possessing low genetic diversity indices, the future climate will likely fail to provide suitable habitats, precluding their in situ persistence. Therefore, the easterly located populations may face the greatest vulnerability to climate change and the most significant risk of future extirpation. Ex situ conservation actions may be the only way to preserve those genetic resources.The complementary approach using the modified 4.4Castanea sativa is already declining in the South Caucasus ; data curation ; formal analysis ; investigation ; methodology ; software ; visualization ; writing – original draft ; writing – review and editing . Katarzyna Sękiewicz: Conceptualization ; data curation ; formal analysis ; funding acquisition (supporting); investigation ; methodology ; project administration (supporting); resources (supporting); software ; supervision (supporting); validation ; visualization ; writing – original draft (supporting); writing – review and editing . Łukasz Walas: Data curation (supporting); formal analysis ; methodology (supporting); resources (supporting); software (supporting); supervision (supporting); validation (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Peter A. Thomas: Conceptualization (supporting); investigation ; resources ; validation ; writing – review and editing . Irina Danelia: Funding acquisition (supporting); methodology (supporting); resources ; validation ; writing – review and editing (supporting). Vahid Fazaliyev: Funding acquisition (supporting); methodology (supporting); resources ; writing – review and editing (supporting). Giorgi Kvartskhava: Funding acquisition (supporting); methodology (supporting); writing – review and editing (supporting). Jan Sós: Writing – review and editing (supporting). Monika Dering: Conceptualization (lead); data curation ; formal analysis (supporting); funding acquisition (lead); investigation ; methodology ; project administration (lead); resources (lead); software ; supervision (lead); validation ; visualization ; writing – original draft ; writing – review and editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics  were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.  Ginkgo biloba L.), which has edible, medicinal, timber, and landscaping functions [Forests are the largest, most widespread, and most complex terrestrial ecosystems . Not onlunctions , is communctions . It is ounctions , widely unctions . Ginkgo unctions , and breIn recent years, with the concept of sustainable management of plantations , a largeUsually, the whole process of forest breeding is time consuming, with the genetic variation being evaluated in multiple repeated selections . To acceOur study subject is a ginkgo timber plantation that lost seed source information. This study demonstrates a practical example of superior tree screening that combines genetic analysis and high-throughput phenotypic extraction. Individual trees were grouped according to the genetic diversity and population structure analysis based on SSR. Meanwhile, growth structural characteristics were extracted using TLS data (of high-density point clouds). Comprehensive evaluation was performed by principal components analysis (PCA) of growth structural characteristics to screen for superior individual trees in each genetic group. The specific objectives are (a) to analyze the genetic diversity and population structure of the Ginkgo plantation so that individual trees can be grouped; (b) to use 3D point cloud data for individual tree growth structural characteristic extraction, thereby verifying the availability of TLS point cloud data in quantifying individual tree growth; and (c) to select superior individual trees with the best growth and preserve genetic diversity as much as possible, thus providing breeding materials for sustainable utilization of the plantation. This study demonstrates the feasibility of terrestrial LiDAR in extracting growth structural characteristics of individual tree. In addition, this study innovatively combines genetic analysis with high-throughput phenotypic acquisition to preserve genetic diversity and phenotypic advantages at the same time. This study therefore reveals the prospect of the combination of remote sensing technology and genetic analysis, provides technical and methodological support for the sustainable management and precise cultivation of plantation, and provides enlightenment for the application of remote sensing technology to accelerate the forest breeding.In this study, SSR markers were used to analyze the genetic diversity and population structure of the 102 individual trees in a ginkgo timber plantation. The individual trees were divided into different genetic groups. Meanwhile, canopy height model (CHM) was derived from point clouds obtained using TLS. Height (H), crown volume (CV), and crown area (CA) of individual trees were automatically extracted according to optimal individual tree segmentation results obtained by comparative shortest-path (CSP) algorithm. The diameter at breast height (DBH) was obtained by the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Crown width (CW) and height to living crown (HLC) were extracted according to the point cloud data. To obtain trunk volume (TV), we reconstructed the 3D branch geometry of the individual tree using the quantitative structure model (QSM). The biomass of all components was calculated using allometric models. Then, the accuracy of H and DBH was evaluated according to the field-measured data. Finally, best individual trees in different genetic groups were assessed using PCA. The workflow of this study is shown in Fig. G. biloba, Metasequoia glyptostroboides, and Populus deltoids, which are in different stages of development. The experimental stand is a 14-year-old ginkgo timber plantation that lost seed source information.The experiment site is located in the Dongtai Forest Farm , which is on the eastern coast of Dongtai City, Jiangsu Province, China. The Dongtai Forest Farm experiences a mild climate, with an average annual temperature and precipitation of 14.6 °C and 1,050 mm, respectively. The farm covers 4,156.93 ha, with 85% forest cover, mainly planted with As shown in Fig. Genomic DNA was extracted using the Magen HiPure SF Plant DNA Mini Kit following the manufacturer’s protocol. DNA solution (1 μl) was taken to determine the purity and concentration using Spectrophotometers  and was normalized to 30 ng/μl for polymerase chain reaction (PCR).2O, 1 μl each of SSR forward and reverse primers, and 1 μl of genomic DNA template. The PCR program involved predenaturation at 95 °C for 3 min, denaturation at 95 °C for 10 s, appropriate annealing temperature for 10 s, extension at 72 °C for 30 s, 34 cycles, extension at 72 °C for 5 min, and storage at 4 °C.The selected primers were derived from 2 parts: 13 pairs used in ginkgo-related studies in the published literature  and 8 paThe bands were assigned and counted, and those with equal distance and bright and clear bands on the electrophoresis map were counted as 1, while those without bands or with faint bands were counted as 0, forming a matrix of 1 and 0. The number of observed alleles (Na), the number of efficient alleles (Ne), the observed heterozygosity (Ho), the expected heterozygosity (He), adherence to Hardy–Weinberg equilibrium (assessed via a chi-square test), and Shannon's information index (I) were calculated by GenAlEx 6.5 . PolymorK values were set to 1 to 20. Estimates were acquired using Markov chain Monte Carlo (MCMC) method with 500,000 iterations followed by a burn-in period of 100,000 iterations. The iterations were run 10 times independently. The resulting data were submitted to STRUCTURE HARVESTER [K value was determined based on the distribution of ΔK [To estimate individual tree ancestry, we used STRUCTURE 2.3.4 to perform Bayesian clustering analysis on the sample set . The genARVESTER , and theon of ΔK . Repeateon of ΔK , and DISon of ΔK  was usedTo denoise the original point cloud data, we set a height threshold and classified the ground point clouds  using anAfter individual tree segmentation, the individual tree characteristics such as H, CA, and CV were extracted. H is the vertical height from the ground to the treetop. HLC, which is the vertical height of the first living branch from the ground surface to the tree crown, was calculated according to the vertical distribution of tree point cloud. The CW in the north–south and east–west directions was extracted from the point cloud projection and averaged as the final value. CA was obtained by calculating the crown projection area, and CV was obtained by calculating the volume of fill, cut, and cut-and-fill. Since some trunk point clouds were incomplete or obscured by branches or leaves at breast height, the point clouds fitted automatically did not match the actual point clouds, so the sliced point clouds at 1.3 m were selected from the detected trunks, and DBH was obtained by DBSCAN algorithm , root mean square error (RMSE), and relative root mean square error (rRMSE) [ix is the value of a characteristic, n is the number of individual trees.The accuracy of H was evaluated on the basis of field-measured data by adjusted coefficient of determination (Adj- (rRMSE) , and theProcessed by R language, correlation analysis was performed using Spearman's method. PCA can remove redundant information through projection and dimensionality reduction without any artificial weighting, and assigns corresponding scores to each characteristic of individual trees, so it is commonly used as a comprehensive evaluation method. Growth structural characteristics were evaluated via PCA using SPSS 25.0 within each genetic group. Due to the far genetic distance among groups, we ensured that the screened trees were genetically distant from each other among groups, which allowed the screened superior trees to retain a certain amount of genetic diversity. Trees with the highest comprehensive ratings were selected as the superior tree (the inclusion rate set at 10%).Fourteen pairs of SSR primers Table  with gooP < 0.01). The high number of valid alleles for E-SSR202, C394, and C388 indicates their higher importance in the population structure of the population, while E-SSR32 and E-SSR354 have fewer effective alleles and play a less important role. In general, the 14 primers selected have high genetic diversity and could meet the corresponding analysis requirements.The polymorphism analysis of 14 SSR primers Table  shows thNeighbor-joining clustering is used to obtain the relationship of Nei's genetic distance of 102 ginkgo individuals. The results Fig.  show thaK is determined for 102 individual trees according to the ΔK result. The optimal K value is 3 when ΔK reaches the peak  load matrix and variance contribution rates are shown in Tables 1, X2, X3 ……X13X representing the standardized values of the original data of the indicators, and nF representing the score of the nth PC, the linear equation of the scores of each PC is obtained according to the eigenvector matrix (Table These PCs reveal the comprehensive conditions of ginkgo individual tree growth. With ix Table . The scoData of the 102 individual trees of different genetic groups are standardized, and their scores on the selected PCs are obtained Tables  to S6. AIn this study, we applied TLS high-throughput phenotype acquisition technology for the selection and breeding of Ginkgo plantations, which is seldom seen in the previous literature. We used TLS data to characterize the growth of individual trees in a ginkgo plantation and divided individual trees into 3 genetic groups according to the genetic distance. The superior individual trees with the best growth of each genetic group were selected using PCA. Our study enables the screened individual trees to retain the genetic information of each group as much as possible. Combined with phenotype screening, the screened population of superior trees can preserve both phenotypic advantages and genetic diversity. The results reveal the potential and feasibility of ground-based LiDAR in acquiring and analyzing high-throughput phenotypic characteristics of individual trees, as well as the prospect of combining remote sensing technology with genetic experiments for analysis.Many studies showed that the ginkgo population has high genetic diversity ,44,45, aThe complex genetic background may be caused by the evolutionary history of ginkgo. The population structure of plant populations is influenced by various factors such as life history, geographic distribution, and mating system ,48. The TLS has been identified as an efficient technique for extracting highly detailed and accurate structural information of individual trees. Currently, it can provide 3D point cloud data with the best detail and accuracy for extracting forest-related variables . Since TTree growth structural characteristics can be extracted using TLS. The results of scatterplots indicated that, based on the accurate segmentation result, there was a high correlation between H and DBH within different genetic groups, indicating that LiDAR-extracted H and DBH could reveal the real situation well. For the calculation of biomass, there are direct measurement methods and indirect estimation methods. The former is more accurate but costly and highly destructive to local resources ; the latThere is quite a bit of variation in the growth structural characteristics of individual trees in this plantation, according to the statistical analysis. The dispersion degree is large for each growth structural characteristic, which may be caused by the high genetic diversity of the population. However, since most growth structural characteristics, such as height , biomassCombining genetic analysis and remote sensing technologies is beneficial for the in-depth understanding of plantation resources, further providing a basis for the strategy of genetic resource conservation and utilization, and accelerates the screening and breeding of superior individual trees. In this study, genetic analysis and remote sensing technology were combined to accelerate forest breeding. We first divided the tree into different genetic groups according to the genetic distance, then quantified the growth structural characteristics using TLS, and finally used PCA to screen out trees with superior growth structural characteristics in each genetic group, thus preserving as much genetic diversity as possible while guaranteeing the best growth structural characteristics. It is likely that these ideas could be applied to other tree species planted under similar frameworks in the future, especially for large-scale plantations where no information on seed sources is available. With the development of computer science, surveying technology, and geographic information technology, the integration of remote sensing with various disciplines is opening up new possibilities for forest resource management . Using rIn this study, we displayed the ability of high-density TLS to characterize the growth of ginkgo plantation individual trees in a breeding process. Combined with genetic analysis, 102 individual trees were comprehensively assessed using PCA and 11 superior individual trees were screened out for future breeding. The rational use of plantation resources is an essential issue that deserves in-depth consideration by researchers and forest resource management departments. Remote sensing provides an effective way to phenotype forests due to its fast operation and easy deployment. The results of the study showed the high genetic diversity and complex population structure within the ginkgo plantation. Meanwhile, the accuracy of TLS in extracting individual tree structural characteristic information was demonstrated. In addition, the combined utilization of genetic analysis and TLS enabled the screened trees to have as much phenotypic advantage and genetic diversity as possible, which provides insights into the application of remote sensing technologies to accelerate the forest breeding process.


Camels have a high tolerance to hunger, thirst, cold, and drought, as well as the ability to travel over long distances. They are indispensable animals for agricultural production in desert areas in China. Old World camels have served humans in uses such as cross-continental caravans; transporting people and goods; connecting different cultures; providing milk, meat, and wool; and serving as draught animals. In view of the severe reduction in Bactrian camel germplasm resources, people now realize that it is particularly important to scientifically evaluate and protect the genetic integrity and diversity of native species. At the same time, there is a growing demand for intensive and sustainable meat and milk production. Using modern genomic tools for selection to improve milk and wool production is warranted. In this study, GBS technology was used to analyze the genetic diversity and genetic structure of Tarim and Junggar camels, and identify the genes important in their evolution. On this basis, the effects of resource conservation were evaluated by analyzing the differences between individuals and populations at the genome level. Screening candidate genes that may be responsible for the biological characteristics of Bactrian camels will aid research into genetic resource protection, important economic traits, and the mechanisms underlying biological characteristics, molecular breeding, and disease.n = 60) and Junggar (n = 58) populations using simplified GBS genome sequencing. The results show that Tarim and Junggar Bactrian camels have high nucleotide diversity. A phylogenetic tree constructed using structural analysis, principal component analysis (PCA), and the adjacency method (NJ) showed that Tarim and Junggar Bactrian camels were clustered together. The selection signals revealed that the Tarim and Junggar Bactrian camels shared 108 genes under positive selection, including WNT1, WNT10B, CD14, SEC61A2, DPAGT1, FOXO6, etc. These selected genes were widely involved in the immune system, embryonic development, lipid metabolism, and other processes. From a genomic analysis perspective, the genetic relationship between TLM and ZGE camels is close, with an average Fst of 0.048 and a relatively low average differentiation coefficient between the two populations. In addition, shared selected genes in the long-term depression pathway were significantly enriched in Tarim and Junggar. These findings will offer support and assistance for the exploration of genetic resource preservation, economically significant traits, and the mechanisms underlying biological characteristics, molecular breeding, and disease.In view of the severe reduction in Bactrian camel germplasm resources, scientific evaluation, protection, and utilization is particularly important. Therefore, it is necessary to investigate the genetic diversity and genetic structure of this species, and identify the genes that have played important roles in its evolution. In this study, 21,971 SNPs were identified in 118 domestic Bactrian camels from the Tarim ( Bactrian camels are mainly distributed in desert and semi-desert areas of western and northern Asia, and are strongly adapted to their ecological environment. They have a strong ability to survive due to their high temperature tolerance, drought tolerance, and salt tolerance, and can feed on prickly and alkaline plants ,2. Old WWith rapid development in sequencing and bioinformatic technologies, these have been widely used in the study of population genetics, genetic mapping, and whole-genome selection breeding. In the study of genetic differentiation in cattle, horses, and sheep, different methods are selected according to different research purposes. Rapid development in DNA-based approaches, specifically sequencing methods such as GBS, has provided important information. This information includes species’ taxonomy, distribution, evolutionary history, genetic diversity, population genetic structure, conservation genetics, molecular breeding, and germplasm conservation ,7. The BHowever, economic factors (low economic benefit from raising camels), environmental factors (droughts), and the camels’ own biological factors (a slow breeding speed and long breeding cycle) have led to a serious decline in the quantity and quality of Bactrian camels . This isThe experimental study was conducted in 2022. A total of 118 Bactrian camels were randomly selected from two conserved populations of Tarim Bactrian camels (TLM) and Junggar Bactrian camels (ZGE). Samples of TLM were collected from Keping County, Aksu, southern Xinjiang, and samples of ZGE were collected from Fuhai County, Altay, northern Xinjiang. (Detailed information is listed in Camelus bactrianus)  was used as the reference genome, and the BWA (version 0.7.15) software (parameters: mem-t 4-k 32-M) was used to align the valid paired-end data to the reference gene set and calculate the average alignment rate, average sequencing depth, and average coverage. The GATK standard process was used to detect the variation sites and obtain the site variation file.DNA was extracted from the blood samples using a blood genomic DNA extraction kit  Co., Ltd., Shanghai, China), following the manufacturer’s protocol. The genome of Bactrian camels  and expected heterozygosity (He) were calculated using PlinkV1.90. The expected heterozygosity (HE), observed heterozygosity (HO), polymorphism information content (PIC), effective number of alleles, and minimum allele frequency (MAF) were used to analyze the genetic diversity.The fixed coefficient F of the population reflects the level of allele heterozygosity in the population. The genetic differentiation index (Fst) of each SNP locus in all of the individuals was analyzed. Based on the high-quality SNP information obtained after screening, the PLINK software was used to detect the selection signal. The sliding nucleotide diversity (π) ratio and Fst were calculated using a 500 Kb window and 20 Kb step size. The top 5% of the π ratio and Fst values were used as thresholds to analyze the population selection signal, and the intersection of the top 1% was used as the candidate site. GO and KEGG enrichment analysis were carried out for the genes where the selected sites were located.Genome-wide sequential homozygous fragment (ROH) analysis was conducted. The ROH-based inbreeding coefficient was calculated by calculating the total length of the ROH segments in an individual as a proportion of the total length of the autosomal genome. The ROH length was calculated for each sample using Plink (V1.90). The inbreeding coefficient of each individual based on the long homozygosity (ROH) was obtained by counting the ROHs of each individual; the inbreeding situation of the sample population was analyzed, and the genetic relationship was inferred.GBS sequencing was performed on 118 Bactrian camel blood genomes whose parameters all met the sequencing criteria. In total, 327.52 Gb of raw data were generated, with an average of 806.69 Mb per genome. After filtering, a total of 327.49 Gb of valid data remained, i.e., 99.99% of the original reads, averaging 805.88 Mb per genome. The proportions of bases with Q20 quality ranged from 90.25 to 97.58%, with the average being 95.65%. The GC base contents ranged from 37.1 to 38.93%, with an average of 38.09%. The effective read lengths were 1.94~17.08 Mb, with 5.60 Mb being the average. The ratios of the effective reading lengths to the length of the reference gene were 89.02~95.71%, and the average ratio was 94.75%. The ratio of the total sequenced bases to the genome size, that is, the minimum sequencing depth, was 5.65×; the maximum sequencing depth was 31.64×, the average sequencing depth was 14.33×, and the average coverage was 3.57%. The GC distribution was normal, the samples were free of contamination, the sequencing quality was high, and the data were good.SNP site quality control was carried out to retain the best-quality typing sites for subsequent analysis. The specific quality control conditions and results are shown in The phylogenetic tree provides insight into the genetic relatedness and ancestral relationships among individuals in the Bactrian camel population. The neighbor-joining method was employed to construct the phylogenetic tree. The results indicate that TLM and ZGE share a recent common ancestor, suggesting a close genetic relationship with a short evolutionary divergence time. The results of the phylogenetic tree analysis are shown in The results of the principal component analysis indicate that the populations of TLM and ZGE can be divided into four clusters. The results of the first principal component analysis indicate that individuals from TLM and ZGE populations tend to cluster together, indicating high similarity between the majority of individuals from both populations. However, the results of the second principal component analysis indicate that a small number of individuals from TLM are more scattered; this suggests a lower similarity between these individuals and other individuals. Overall, the differentiation between the two populations is not pronounced, with only a few TLM individuals showing higher distinctiveness from all of the other individuals. The PCA results are shown in The genetic distance between individuals in the Bactrian camel population was analyzed using genetic markers or the similarity of alleles. The results indicate that TLM and ZGE have a close genetic distance. The results of the genetic distance analysis are shown in The statistical analysis of the heterozygosity in genomic loci included the analysis of the effective population content (Ne), polymorphic marker fraction (PN), expected heterozygosity (HE), observed heterozygosity (HO), polymorphism information content (PIC), effective number of alleles, and minimum allele frequency (MAF). Effective population content (Ne) analysis was performed using the computational method proposed by Herrero-Medrano et al. (2013) and Sved . The minA long ROH may indicate a more recent relationship, and the greater the number of such segments, the higher the likelihood of inbreeding within the family. A short ROH indicates a distant relationship, and the more such segments there are, the less likely inbreeding is to occur within the family. The ROH length and number of ROHs were calculated for each sample using Plink (V1.90). In terms of ROH distribution, there were more ROHs on each of the chromosomes 1, 3, 6, and 14. The distribution results are shown in The longer the total length or number of ROHs in an individual, the higher the inbreeding coefficient of the individual. The inbreeding coefficient value of each individual was obtained by counting the ROHs of each individual in the population. The proportions of samples corresponding to different inbreeding coefficients can be visualized, and are shown in By sliding a 500 kb window across the genome, the differences in population genetic information (SNPS) in the sliding window were analyzed. The population π values reflect the genomic base diversity of the Tarim and Junggar Bactrian camel populations. The π analysis results are shown in The genetic differentiation index is suitable for a comparison of diversity among subpopulations and is used to measure the degree of population differentiation. The red and blue lines represent the FST threshold values of 0.25 and 0.5, respectively. FST values range from 0 to 1, where a higher FST value indicates a greater genetic differentiation among populations. The results of the FST analysis of the Tarim (TLM) and Junggar Bactrian camels (ZGE) are shown in Selection signal analysis was carried out according to the Fst and π between the TLM and ZGE populations, and the top 5% region with the largest Fst value was selected to be screened. The smallest 5% region in the nucleotide polymorphism analysis was also selected to be screened. Functional enrichment analysis was performed on the selected regional annotated genes from the TLM and ZGE populations. We took the intersection of the two parts of the selected region as the final selected region. A total of 1564 SNPs were screened from the selected regions, and 108 genes were selected for GO functional annotation analysis. These selected genes were mainly enriched in cell–cell signaling, cell–cell recognition, nervous system development, the negative regulation of cell fate specification, the positive regulation of the canonical Wnt signaling pathway, homophilic cell adhesion via plasma membrane adhesion molecules, and other biological processes. The main cell components involved were as follows: integral components of plasma membrane, presynaptic membrane, and endoplasmic reticulum. The main molecular functions involved included metal binding and peptide binding. The GO enrichment results are shown in This was performed due to the need for species conservation. Combined with the results of the genomic genetic relationship analysis and cluster analysis, the existing genetic relationships between female camels and male camels were used to divide them into different families. In total, 93 female camels were found to be far from the detected male camels, and so they were classified as “other”. The cluster analysis results of all of the Bactrian camel samples are shown in Bactrian camels are a rare and valuable genetic resource in northwest China, playing an important role in development in animal husbandry and ecological balance. This is the main motivation for the protection of this genetic resource, but there is also the promise of the implementation of scientific and effective measures for breed protection, as well as research into Bactrian camels’ genetic diversity and genetic structure. Genetic diversity is one of the key issues in the protection of Bactrian camel resources in this region. With rapid development in molecular biotechnology, sequencing-based technology has become mainstream, which makes it possible to study the genetic structure and genetic diversity of Bactrian camels at the genomic level. At the same time, the effects of efforts to protect Bactrian camels can be monitored more comprehensively and accurately. Therefore, in this study, the genetic diversity, genetic structure, genetic relationships, and degrees of inbreeding of 60 Bactrian camels from Tarim and 58 from Junggar were investigated using GBS. The sequencing results included 327.49 Gb of valid data, and 21,971 high-quality SNPs were obtained. The detected loci were distributed on each autosome, and the number of loci basically corresponded to the length of the chromosome, which indicated that the obtained variation information was representative. These data will provide a basis for the preparation of a breeding SNP microarray for Bactrian camels. The genetic relationships and inbreeding coefficients obtained from the study were based on analysis at the genome level, truly reflecting the actual conservation situation of the populations of TLM and ZGE, and providing a powerful technical means for the protection of germplasm resources and optimization of the selection and matching scheme.Population structure is an important method for studying the evolutionary processes and relationships within a species. In order to evaluate, protect, and utilize the genetic resources of Bactrian camels scientifically, Han et al.  used resIn a study involving the full genome sequence data of six camels from the Arabian Peninsula and the genotyping-by-sequencing data of forty-four camels from Sudan, genome admixture and principal component analyses indicated distinct geographic separation between the camels from Sudan and those from the Arabian Peninsula . HoweverThe genetic differentiation, reproduction modes, and generation lengths among species are closely related. There are similarities among Bactrian camel populations in China, Mongolia, Iran, and Kazakhstan, which share several common haplotypes. This phenomenon does not seem to bear much relation to regional location. It is foreseeable that domestic camels may often travel between different geographical areas. TLM and ZGE Bactrian camels are selected via open sub-breeding. The use of superior non-closely related male camels in the generation breeding process will result in a high degree of genetic variation among individuals in the population. The results of the phylogenetic tree analysis and genetic relationship matrix of the TLM and ZGE Bactrian camels show that the genetic relationships and genetic distances between TLM and ZGE Bactrian camels are very different. The differentiation between TLM and ZGE Bactrian camels showed an unstable and chaotic state, with some crossbreeding and individual outliers, indicating that the genetic structure of TLM and ZGE Bactrian camels has changed greatly over time. It is speculated that this may have been caused by the small size of the conservation population.Abundant genetic diversity is the basis through which species cope with environmental changes. There are many genetic variations in camels that can be used to improve economically important traits. Bactrian camel breeds distributed in different ecological regions have formed different characteristics and potential development value. The evolutionary relationships and genetic diversity of Bactrian camels have been studied. Ishag et al. (2010) reported the SNP 419 C>T in the non-coding region (intron 1) of the GH gene of Kenani, Lahwee, Rashaidi, Anafi, Bishari, and Kabbashi camel breeds of Sudan. This could aid in the determination of genetic relationships among the different camel breeds . Zhang DAccording to seven microsatellite markers, the genetic diversity among the Ourdhaoui Médenine, Ourdhaoui Tataouine, and Merzougui camel subpopulations in Tunisia revealed a mean number of alleles (MNA) of 6.5. The expected heterozygosity (He) ranged from 0.76 to 0.84, while the observed heterozygosity (Ho) was 1.0. The subpopulations showed a moderate genetic structure based on the fixation index (FST = 0.052) . Four drRecently, there has been increased interest in the field of camel immunology. Bactrian camels are also a valuable genetic resource, with a rich gene pool that may be the perfect answer to adapting to harsh climatic conditions and efficiently dealing with the challenges that they present. Bactrian camels are highly adapted to the extreme desert ecosystem, with higher resistance to a wide range of pathogens compared to many other species from the same geographical region ,33. ReseOontomyces sp. CR2) isolated from the rumen contents of Bactrian camels has the highest bioconversion potential for lignocellulose, thus indicating that the rumen of Bactrian camels may be a good source for isolating anaerobic fungi in industrial applications [In addition, there is another important consideration. Camel milk (CM) is known for its benefits in the human diet and for health. Milk traits are very important in camel breeding, considering that, on average, the amount of milk produced by camels is six times greater than that produced by the indigenous cattle in drylands. Camel milk has some health benefits attributable to the camels’ consumption of high-quality desert plants. Camel meat is also highly valued for its nutritive and healthy contents ,43. Relaications . These rIn order to utilize the potential of camels, there is a need for genetic improvement using marker-assisted selection, while sustaining the camels’ genetic diversity and resilience. In this study, GBS was used for the analysis of population structure, genetic diversity, and gene selection signals. The results show that the genetic resource conservation effects of the TLM and ZGE Bactrian camel populations were clear, and they had high nucleotide diversity and rich genetic diversity. TLM and ZGE have a close genetic relationship, and there has been obvious genetic exchange between them in the past for a long time. After years of geographic isolation, the degree of genetic exchange has decreased. The genetic variation information for Bactrian camels and 108 candidate genes related to Bactrian camels’ biological characteristics were obtained at the genome level, such as WNT1, WNT10B, FOXO6, CD14, SEC61A2, DPAGT1, etc. These findings will provide a basis for determining the mechanisms underlying important economic traits and biological characteristics, and prove useful for molecular breeding and disease research in Bactrian camels, particularly TML Bactrian and ZGE Bactrian camels.


Aedes aegypti is of major concern for global public health as the viruses that it transmits affect millions of people each year worldwide. Originating in Africa, Ae. aegypti has now spread throughout much of the world. While the genetic makeup of Ae. aegypti in the New World has been extensively studied, there is limited knowledge on its genetic diversity in Africa, particularly at a microgeographical level.The impact of the arbovirus vector Ae. aegypti populations from Benin and employed wing morphometric analyses as a cost-effective and reliable tool to explore population structure. Our sampling encompassed various areas of Benin, from the southern to the northern borders of the country, and included urban, semi-urban, and sylvatic sites.We investigated mitochondrial cytochrome oxidase I of four We observed a notable level of genetic diversity (haplotype diversity of 0.8333) and nucleotide diversity (0.00421986), and identified seven distinct haplotypes. Sylvatic and semi-urban sites exhibited a greater number of haplotypes compared to urban sites. Utilizing 18 wing landmarks, we calculated the centroid size, which revealed significant variation among the three landscape types. However, principal component analysis, employed to assess wing shape variation, did not demonstrate significant differences between populations based on landscape type.Ae. aegypti populations in Benin, and provide insight into important biological characteristics of these populations with respect to their potential to transmit viruses. To the best of our knowledge, this is the first study undertaken in Africa to integrate genetics with morphology to analyse the population structure of the major arbovirus vector Ae. aegypti.Our findings indicate substantial genetic and morphological diversity among The online version contains supplementary material available at 10.1186/s13071-023-05943-6. Aedes aegypti  is a significant global vector responsible for transmitting arboviruses. Originally hailing from Africa, this species is found in tropical and subtropical regions, particularly in urban areas, and serves as a primary vector for dengue, yellow fever, chikungunya, and Zika viruses [Ae. aegypti (subspecies Aedes aegyptiformosus) preferred non-human hosts and inhabited tropical forests, with larvae breeding in tree holes. However, as human populations expanded in Africa, Ae. aegypti populations underwent evolutionary changes to adapt to human habitats. These changes involved the use of artificial water containers as larval habitats and a preference for humans as blood meal sources [Aedes aegyptiaegypti) was introduced into the New World approximately 500 years ago through slave trade ships originating from Africa [Ae. aegypti formosus exhibiting darker body colour and Ae. aegypti aegypti displaying lighter body colour [Ae. aegypti occupying different ecological niches, which makes the use of these patterns for subspecies discrimination challenging [m Africa . Initialy colour . HoweverAe. aegypti and populations found outside of Africa. Extensive studies have examined populations outside of Africa, revealing distinct genetic structures within and across continents, indicating high genetic diversity even at a microgeographical scale [Ae. aegypti, particularly in West Africa, remains limited [Ae. aegypti at finer scales remain poorly understood. Understanding the factors that influence the population structure of Ae. aegypti can aid in the prevention and control of the diseases that it transmits, and preparedness for potential future threats posed by this species.Genetic data clearly demonstrate a significant genetic differentiation between ancestral African populations of al scale . However limited . Kotsaki limited  and Glor limited  exploredAe. aegypti [The genetics of mosquito species are known to influence important traits such as vector competence, which in turn affect the potential for transmission, spread and establishment of arboviruses. Intraspecific genetic diversity is primarily shaped by fluctuations in population size , which,  aegypti , 14. TheAedes albopictus in Thailand and found that wing size is influenced by climatic factors, while wing shape provides insights into heritable intraspecific and geographic differences. Geometric morphometric (GM) analysis provides information on phenotypic biomarkers which, when combined with genetic data, can provide precise information on population structure. Furthermore, GM analysis can aid in the prediction of critical biological characteristics of mosquitoes, such as flight capacity, gamete production, and virus transmission potential [Ae. aegypti populations in Brazil and Thailand revealed associations between population structure and degrees of urbanization and land use [Morphological analysis, specifically that of geometric characteristics, offers another monitoring tool for the study of inter- and intraspecific variation. Wing size and shape are known to be the first morphological traits influenced by environmental and genetic factors , 16. Morotential . GM studland use .Aedes aegypti is recognized as the primary vector of dengue virus in Benin, but there is limited information on the burden of this disease there due to transmission by this vector [Ae. aegypti to investigate the influence of geography and landscape type on its populations across multiple regions in Benin. The results should contribute to a better understanding and characterization of local adaptations in this crucial vector species, which could ultimately inform targeted vector control measures.In Benin, West Africa, two distinct biogeographic zones can be identified: the Sudanian zone in the north, characterized by an annual rainfall of 600–1200 mm, and the Guinean zone in the south and centre, with an average annual rainfall of 1200–2200 mm. s vector . The aimAe. aegypti mosquitoes were collected from four sites in Benin  and dry seasons. Human activities, especially slash-and-burn agriculture, have modified woodland and wooded savannas in this region. Calavi, located in the Atlantique Department, is approximately 18 km north of Cotonou. The trapping site was within the botanical garden of the University Abomey-Calavi, which is surrounded by an urban environment and a lush flora and rich fauna. Porto Novo, an urban trapping site, is characterized by densely populated neighbourhoods with sparse vegetation and limited animal presence. Dassa, the capital of the Collines Department, is situated on a peneplain covered by areas of savanna, trees, shrubs, and intermittent deciduous or semi-deciduous forests. The trapping was conducted on a property on the outskirts of the city, which had abundant vegetation and trees. The northernmost trapping location, W National Park, is a wooded savanna in the Sudanian region. This region, located south of the Sahel, is characterized by isolated trees and wooded savannas, with a dry season lasting 5–7 months . Mosquitoes were captured at a sylvatic site within the wooded savannas, approximately 3 km from the nearest town. Adult mosquitoes were collected with BG-Sentinel mosquito traps . The traps were equipped with BG-Lure  as an attractant and were set in the afternoon for 24 h.Aedes aegypti wing could be found in the Additional file Specimens were morphologically identified using the key of Becker et al. . For furD were computed with DnaSP 10.0.19045 [For the genetic analysis, 10 specimens from each trapping site were selected and DNA was extracted from one leg. Individual legs were titrated with 500 µl of cell culture medium  and zirconia beads . Legs were homogenized for 4 min in a vortexer. The suspension was clarified by centrifugation for 1 min at 8000 r.p.m. and 4 °C, and DNA was extracted with a QIAamp viral RNA mini kit according to the manufacturer’s protocol . A fragment of the COI region with about 710 base pairs was amplified using the primers LCO1490 (5ʹ-GGTCAACAAATCATAAAGATATTGG-3ʹ) and HCO2198 (5ʹ-TAAACTTCAGGGTGACCAAAAAATCA-3ʹ) (5). Polymerase chain reaction products were subjected to Sanger sequencing. Sequences were processed and aligned with Geneious 9.1.8  and trimmed to 501 base pairs. Sequences were submitted to the National Center for Biotechnology Information GenBank and the Basic Local Alignment Search Tool was used to compare the nucleotide data with previously reported sequences. The number of haplotypes, haplotype diversity, nucleotide diversity and Tajima's .0.19045 . To cons.0.19045 .Ae. aegypti populations in Benin. The specimens were carefully examined for white scales on the first abdominal tergite, but none were found. Therefore, based on their morphology alone, the analysed specimens were classified as Ae. aegypti formosus. A total of 24 COI sequences were obtained and uploaded to GenBank (accession numbers OQ991342-OQ991365). Genetic variability analysis of the 24 COI gene sequences revealed that 494 out of 501 (98.6%) sites were identical. Seven segregating sites defined seven distinct haplotypes (H1–H7), indicating a high level of genetic diversity (haplotype diversity of 0.8333) and nucleotide diversity (0.00421986). The most prevalent haplotype, H4 (29.16%), was found across all four sampling sites  was lower than the number observed at the semi-urban site Dassa (n = 5) and the sylvatic site W National Park (n = 5). Tajima's D statistics were generally positive but not statistically significant . Positive values of these statistics may indicate balancing selection or a recent population contraction. A summary of the statistics for COI gene polymorphism by location is given in Additional file We examined the genetic and morphometric characteristics of four Ae. aegypti displays high genetic diversity. Gloria-Soria et al. [Ae. aegypti formosus) and domestic (Ae. aegypti aegypti) subspecies in Africa. It exhibits rapid evolution and adaptability to various ecological conditions [Compared to other species in the family Culicidae, a et al.  showed tnditions . In the nditions . (ii) Linditions . (iii) Unditions . (iv) Thnditions . To valiF2.80 = 6.419, P = 0.0026). The semi-urban site displayed the highest CS, while the urban sites had the lowest. Regarding wing shape, there was only a tendency for a difference among landscape types, which was not statistically significant   gene polymorphism by location.


The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM. Although it has been known for over 100 years that assortative mating (AM) can influence the results of genetic studies , in receOur approach is based on three observations. First, regardless of the AM sorting process and history, AM affects estimates of genetic studies through its effects on linkage disequilibrium (LD). Second, because chromosomes segregate independently, the correlations between polygenic indexes (PGIs) constructed from single-nucleotide polymorphisms (SNPs) on different chromosomes identify the cross-chromosome component of AM-induced LD . Third, Our approach can be applied to single traits to estimate how much the genetic variance is inflated by AM relative to the panmictic  genetic variance. Likewise, it can be applied to pairs of traits to assess how much the genetic correlation between traits is inflated relative to the panmictic genetic correlation. To illustrate, we study education and health-related traits, an important context with an expansive AM literature e.g., . ApplyinSNP-selection bias, is often larger than the bias from AM-induced LD.While our approach can be used to adjust genetic associations that are often used in MR studies, we find that it is inadequate for two reasons. First, in this setting, the adjustment is very sensitive to estimation error in the PGIs, producing additional biases to the MR estimates. Second, even if an error-free PGI were available, AM causes a selection bias in choosing which SNPs to use as instruments, which can generate upwardly biased MR estimates. We derive conditions under which we may expect to observe this bias. In simulations, we validate this theory and show that the latter bias, which we call We define AM very generally: AM is when mates have correlated traits or genotypes, regardless of how these correlations arise. For example, mates may sort directly on the trait of interest, or they may sort geographically across regions that have different mean levels of the trait . For all types of AM, AM influences genetic studies through its effect on LD. Consider an additive model derived by projecting the trait onto the set of genotypes among individuals in a population. Formally, for a pair of traits, There are many ways that pairs of traits can be jointly analyzed in genetic studies; in this paper we consider genetic variance, genetic correlation, regressions with polygenic indexes (PGIs), and Mendelian randomization studies. The primary way that AM influences these analyses is by inducing LD between pairs of SNPs that would otherwise be uncorrelated in a randomly mating population. This LD arises because the maternally-inherited genotypes are correlated with the paternally-inherited genotypes, and these correlations can compound over many generations of AM. We consider the impact of AM-induced LD on each type of analysis below.effect correlation , the most common being the rrgZ,gY) . The effThe genetic correlation can be written:This expression contains many covariance terms corresponding to pairs of unlinked SNPs  that would be uncorrelated in a randomly mating population. However, in an AM population, these terms may be nonzero. Thus, AM can generate a genetic correlation between traits that would be uncorrelated in a randomly mating population.AM influences regression analyses that include PGIs as covariates in a similar way as it affects genetic correlation. A PGI for trait  weights . So a reBecause the genetic covariance is in the numerator of this expression, the same factors that influence genetic correlation will influence PGI regression coefficients.There are two potential sources of bias due to AM in MR studies. The first is by inducing violations of MR’s assumptions. Most MR studies use GWAS summary statistics from a subset of SNPs to infer the causal effect of a “treatment” trait on an “outcome” trait. Formally, suppose the true causal model isIn most applications of MR, many SNPs are used as instruments, and there are a variety of MR methods that combine the MR ratios in different ways . The formulas for these expressions—which we validated in a series of simulations see –S2—are cIn We propose a new method that estimates the fraction of the genetic correlation  that is due to AM in order to better interpret the results of such studies. At a high level, our methods use the amount of cross-chromosome LD for PGIs as a proxy for the amount of AM-induced LD in the genome. While AM also influences LD within a chromosome, as illustrated in The fraction of the genetic correlation between a pair of traits that is due to within-chromosome LD isIn these expressions, We approximate Where Multiplying an unbiased genetic correlation estimate by this value would adjust the estimate to what the genetic correlation would be in a randomly mating population. We obtain standard errors for this AM adjustment using a bootstrap procedure see .The above adjustment would be perfect if ment see . A summaIn the randomly mating population, the post-adjustment error appears mean zero for all levels of error, though the error variance becomes substantial for higher levels of ment see . CompletIt is possible to adjust GWAS associations using a similar approach to the one described above. To see how, we useUsing this approximation, the adjusted GWAS coefficients areAdjusted GWAS coefficients can then be used to produce adjusted MR estimates. For example, adjusted MR-median estimates would be obtained by taking the ratio of the adjusted GWAS estimates and finding the median ratio. The adjusted MR estimate will be valid if the PGI is a good proxy for the true additive genetic factor. However, because the adjustment factor is a ratio of two small values, even small amounts of error in the PGI can lead to highly unstable adjusted GWAS estimates, and this instability is inherited by the MR estimate. In simulations summarized in outcome trait and where the outcome and treatment traits have a positive panmictic genetic correlation, treatment trait, In addition to bias due to AM-induced LD inflating GWAS associations, AM can further lead to bias by how SNPs are selected for inclusion in MR analyses. Generally, in order to avoid weak instrument bias, only SNPs that meet some significance criteria are included in MR studies .We used our adjustments to obtain estimates of how much AM affects the genetic variances and genetic correlations for education and health in real data.  of the genetic correlation is estimated to be due to AM. The inflation of the EA-height genetic correlation is small and statistically weak , but for all other health traits, the inflation greater than 5%, with a mean across traits of 16%. However, due to noise in the PGIs used in the AM adjustment, these estimates should be considered an upper bound of the actual inflation. We further highlight that although some of the genetic correlation estimates cannot be explained by AM, this does not necessarily imply that there is a causal effect of EA on health (or vice versa) or even that there is a common underlying biological pathway since these results could also be explained by non-pleiotropic effects and local LD. However, for most of the health traits considered, we can rule out that the genetic correlation is primarily driven by AM.Much of modern genomics effectively ignores the role of AM, leading to potential misinterpretations of key results. Under random mating, these studies would have a much clearer interpretation. For this paper, we extended the existing theory on assortative mating and developed a general approach to adjust genetic studies to produce estimates equal to what they would have been in a randomly mating population.The theoretical framework developed in this paper is based on behavioral sorting models from the economics literature , which gHowever, our theory also allowed us to fill in gaps missed by the extensive simulations presented previously, confirming some conjectures and refuting others. For example, based on a series of robustness analyses, In addition to the consequences of AM described in detail above, the theory we derived may suggest other additional consequences of AM, potentially implicating any method that compares patterns of genetic effects across the genome. For example, some studies note the similarities in the associated biological pathways between pairs of traits  in their MR analysis . HoweverFuture work may extend procedures like those proposed here, by accounting for within-chromosome LD and SNP selection bias, or by understanding how AM can also bias genetic analyses that we did not study. This work will be essential in efforts to better understand the complex interactions between genes, behavior, and health.ex ante expected contribution of any particular SNP pair to the genetic (co)variance is equal for all pairs, the expected contribution of just within-chromosome SNP pairs will beAM can influence LD within a chromosome, but the contribution of within-chromosome SNP pairs to the total genetic (co)variance is expected to be negligible. Assuming that the p) distribution, where p = 0.5 is the allele frequency that was the same for every SNP.We used a simple simulation framework to assess the effect of AM and the performance of our adjustment, which is summarized here. A more detailed description of the simulation procedure is found in the Supplementary Note. In our simulations, we began by simulating the genotypes of 2000 SNPs  for a population of 10,000 individuals. We assumed that the population had been randomly mating up until this generation such that each genotype for each person was drawn from a Binomial and a constant using ordinary least squares. We selected the set of SNPs that had a p-value of less than 0.05/2000 = 2.5 × 10We adjusted the GWAS summary statistics using the same PGIs described in the The polygenic indexes used in our real-data analysis of education and health came from the SSGAC’s Polygenic Index Repository . SpecifiThe genotypes used to make the PGIs for the real-data analysis came from the Tranche 1 of the UK Biobank .The educational attainment variable we considered was the number of years of school completed, constructed as in For the MR analyses, we performed a GWAS of each trait in the Tranche 1 sample of the UK Biobank . We keptAll confidence intervals in the real data analyses were constructed using a bootstrap approach.th and 97.5th percentile of these replications as the 95% confidence interval.For the genetic correlation analyses, we drew from the UKB sample (with replacement) a new sample of 148,506 individuals. With this bootstrap sample, we calculated the genetic correlation adjustment and saved the results. This was repeated 100 times. We report the 2.5th and 97.5th percentile of these replications as the 95% confidence interval.For the MR analyses, we likewise drew a bootstrap sample of 148,506 individuals, and performed a GWAS in the bootstrap sample. We adjusted the GWAS estimates using PGIs from the bootstrap sample as well. Using the unadjusted and adjusted estimates, we applied our set of MR estimators as done in the MR analyses described in the previous section. This was repeated 100 times. We reported the 2.5Supplement 1Supplement 2Supplement 3