Purpose: A classic twin study to evaluate the relative contributions of genetic and environmental factors to resting pupil size and reactivity.Methods: Pupillometry was performed on 326 female twins (mean age 64 years) from the TwinsUK Adult Twin Registry, assessing resting pupil diameter in darkness and increasing levels of ambient light, alongside dynamic pupillary characteristics. Maximum-likelihood structural equation models estimated the proportion of trait variance attributable to genetic factors.Results: Mean (SD) pupil diameter in darkness was 5.29 mm (0.81), decreasing to 3.24 mm (0.57) in bright light. Pupil light reaction (PLR) had a mean (SD) amplitude of 1.38 mm (0.27) and latency of 250.34 milliseconds (28.58). Pupil size and PLR were not associated with iris colour, intraocular pressure or refractive error, but were associated with age . In darkness the resting pupil size showed a MZ intraclass correlation coefficient of 0.85, almost double that of DZ (0.44), suggesting strong additive genetic effects, with the most parsimonious model estimating a heritability of 86% [95% confidence interval (CI) 79–90%] with 14% (95% CI 10–21%) explained by unique environmental factors. PLR amplitude, latency and constriction velocity had estimated heritabilities of 69% (95% CI 54–79%), 40% (95% CI 21–56%), and 64% (95% CI 48–75%), respectively.Conclusion: Genetic effects are key determinants of resting pupil size and reactivity. Future studies to identify these genetic factors could improve our understanding of variation in pupil size and pupillary reactions in health and disease. Pupil size is important not only because it controls the amount of light that is able to reach the retina but also because it affects the levels of chromatic and spherical aberration, thereby optimising visual perception . Pupil sCertain medical conditions such as diabetes and pseudoexfoliation can also decrease the effect of dilating drops on the size of the pupil . DiffereIn addition to this variability in resting pupil size, clinically there is wide variation in the dynamic pupillary light reflex (PLR) in healthy individuals. A more rigorous assessment of the PLR is now possible using pupillometric measurements using infrared video techniques. Measurements obtained include the amplitude of the light reflex, latency , velocitOver recent years, automated pupillometry has evolved into a robust and reliable measure of pupillary evaluation. This has removed the subjectivity of pupillary evaluation, providing data which is more accurate and clinically significant. Its use has been supported in the monitoring neurointensive care patients, for whom subtle changes can be a predictive factor of neurological deterioration . More reTwin studies provide us with the unique ability to examine the relative contribution of environmental and genetic factors to trait variation. Heritability is the quantification of the overall phenotypic variation that is attributable to genetic factors. Heritability estimates allow us to define boundaries for the ability of genetics to predict traits or disease and indirectly provides insights into the role of genes . HeritabThe purpose of this study was to undertake a classical twin study evaluating the relative contributions of genetic and environmental factors to resting pupil size under varying degrees of background illuminance and the PLR (the dynamic pupil response to a transient light stimulus).Healthy twin volunteers were recruited from the TwinsUK registry based at St Thomas' Hospital, London . Twin paInclusion criteria included a minimum age of 18, the capacity to understand the study and consent to participate. Patients were excluded if they were a known diabetic given the well-accepted association with autonomic pupillary dysfunction. The study had appropriate local research ethics approval (EC04/015) and was conducted in accordance with the tenets of the Declaration of Helsinki.All pupillometry measurements were taken using the Procyon P3000 pupillometer (Procyon Instruments Ltd.). This is a binocular dynamic pupilometer that allows for customised stimulus protocols and uses video cameras running at 25 Hz with a spatial resolution of 0.03 mm. This device and similar protocols have previously been described elsewhere . Individual sub models are subsequently compared with the full model with the best fitting model determined by identification of the model with the lowest Akaike information criterion (AIC). The AIC is a measure of the relative quality of statistical models for a given data set. Heritability is consequently estimated from the best-fitting model from the total contribution of genetic factors to trait variance.The approach utilises structural equation modelling to separate the observed phenotypic variance into additive (A) genetic effects, dominant genetic effects (D) or common environmental (C) effects, and unique environmental (E) effects, the latter also including measurement error. By subsequently dividing these individual components by the total variance you are able to determine the different standardised components of variance. To adjust for the effects of age on studied pupil traits, we performed linear regression models with pupil traits and age at visit, from which we obtained the residuals. Heritability models were then performed using these residuals. Structural equation modelling is used to estimate best-fit model that explains the variance and covariance of a trait, using the phenotype information from each twin pair and knowing their genetic relationship. Only three variables can be inferred to fit the data from two twins, so an ACDE model cannot be used; ACE or ADE models have to be used separately. An ADE model implies non-additive genetic effects, which may the case when the MZ correlation is more than twice the DZ correlation- otherwise an ACE model is chosen, implying additive genetic effects explain the greater MZ twin correlation. Univariant models were initially created using all specified parameters (ACE/ADE) and then using a stepwise approach, individual parameters were dropped, testing the deterioration in model fit accordingly. This creates three parsimonious models with increasing degrees of freedom . Twice the difference in log likelihoods between the full and sub models follows a http://openmx.psyc.virginia.edu) package in R (http://www.R-project.org), with results from the left eye reported.Statistical analysis was performed using STATA . Twin modelling was completed using the OpenMx (n = 7), diabetic (n = 30) or if incomplete measurements were obtained for the parameters investigated (n = 22). In total results from 326 twins were included [70 MZ (43%) and 93 DZ (57%) twin pairs] in heritability calculations. Zygosity was determined by genotyping in 94% of the population (n = 305) and through PPQ in 6% (n = 21). Mean age at time of examination was 63.7 years [Standard Deviation (±) 7.9 (range 37–81)] and all participants were female (100%). Only one twin pair was not of European ethnicity (99% European). Self-reported iris colour was available in 232 twins, of which 144 were blue (61.2%), 50 brown (22.4%) and 38 green (16.5%). Mean spherical equivalent was −0.25 D (±2.9) in the right eye and −0.25 D (±2.8) in the left eye. Mean IOP of both eyes was 15.1 mmHg (±3.3).Three hundred eighty-five twins were considered for our study. Participants were excluded if they were unpaired or if zygosity was unknown (n = 5), dry eye disease (n = 25) and other (n = 2)]. Eighteen (5.5%) patients had other forms of eye surgery including squint surgery (n = 5), oculoplastic procedures (n = 6), retinal detachment surgery (n = 4) and undefined laser procedures (n = 3). There was no significant difference in the means of any of the pupil measurements between MZ and DZ twins. Full details can be seen in Regarding medical history, 1 (0.3%) had a neurological disorder and 10 (3.1%) were pseudophakic. Thirty-two patients took regular eye drops , it conThe lower heritability of dynamic PLR characteristics in comparison to resting pupil size may be in part limited by measurement error, environmental factors or unknown shared family factors. Pupil latency, for instance, was seen to have relatively low heritability, however, we believe this is reflective of inaccuracy in its measurement. The pupilometer used possesses a more accurate spatial resolution in comparison to its frequency resolution. Potentially explaining any imprecise results. Given these limitations, we believe our results warrant further exploration on a larger scale, to determine if these conclusions are applicable to the wider population.Future work should include genome-wide association studies to identify genetic polymorphisms associated with pupil diameter variance. A greater understanding of the genetic factors and biological mechanisms underlying pupil size and reactivity might allow greater clinical utility of assessing normal pupil size and reactivity in health and disease. Assessing pupillary responses can also be a useful clinical tool in monitoring certain neurological conditions or identifying those that warrant further investigation. As an example, unilateral macular diseases rarely show a relative afferent pupillary defect (RAPD) of more than 0.5 log units, thus if a measured RAPD is greater than this, additional pathologies such as compressive optic neuropathy must be considered . AdditioIn conclusion, this study provides a current, well-phenotyped normative dataset of pupillary size and reactivity in a largely European female population. Twin modelling demonstrates that genetic effects are a key determinant of both resting pupil size, particularly in dim light, and reactivity characteristics including latency, constriction velocity and amplitude. The knowledge that pupil size and reactivity is predominantly genetically determined should direct future research into understanding functional mechanisms influencing pupil size and ultimately improve our ability to use these measures clinically.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by King's College London - research ethics approval (EC04/015). The patients/participants provided their written informed consent to participate in this study.FB and CH were involved in the conception and design of the study. FB and JV were involved in data collection. JV, CH, AA, FB, and KW carried out the statistical analysis and data interpretation, provided critical review of the manuscript, and contributed to the final write-up. AA and KW drafted the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We identified five genetic loci associated with oral microbiota at study-wide significance (p < 3.16 × 10−11). Four of the five associations were well replicated in an independent cohort of 1439 individuals: rs1196764 at APPL2 with Prevotella jejuni, Oribacterium uSGB 3339 and Solobacterium uSGB 315; rs3775944 at the serum uric acid transporter SLC2A9 with Oribacterium uSGB 1215, Oribacterium uSGB 489 and Lachnoanaerobaculum umeaense; rs4911713 near OR11H1 with species F0422 uSGB 392; and rs36186689 at LOC105371703 with Eggerthia. Further analyses confirmed 84% (386/455 for tongue dorsum) and 85%  of host genome-microbiome associations including six genome-wide significant associations mutually validated between the two niches. As many of the oral microbiome-associated genetic variants lie near miRNA genes, we tentatively validated the potential of host miRNAs to modulate the growth of specific oral bacteria. Human genetics accounted for at least 10% of oral microbiome compositions between individuals. Machine learning models showed that polygenetic risk scores dominated over oral microbiome in predicting risk of dental diseases such as dental calculus and gingival bleeding. These findings indicate that human genetic differences are one explanation for a stable or recurrent oral microbiome in each individual.The oral microbiota contains billions of microbial cells, which could contribute to diseases in many body sites. Challenged by eating, drinking, and dental hygiene on a daily basis, the oral microbiota is regarded as highly dynamic. Here, we report significant human genomic associations with the oral metagenome from more than 1915 individuals, for both the tongue dorsum ( Oral metagenomic shotgun sequencing data has been available from the Human Microbiome Project (HMP)5, for rheumatoid arthritis6 and colorectal cancer3. Other diseases such as liver cirrhosis, atherosclerotic cardiovascular diseases, type 2 diabetes and colorectal cancer studied by metagenome-wide association studies (MWAS) using gut microbiome data also indicated potential contribution of the oral microbiome to disease etiology10.A healthy individual swallows 1–1.5 L of saliva every day16. The strongest signal in cohorts of European ancestry is the association between LCT1 and Bifidobacterium, explained by the metabolism of lactose by the commensal bacterium. These large-scale genome-wide association studies have mainly focused on the fecal microbiome; however, the influence of host genetics on the composition and stability of the oral microbiome is still poorly understood. Several studies based on 16 S rRNA amplicon sequencing and microarrays have reported that human oral microbiota are influenced by both host genetics and environmental factors20. Only two studies have identified limited human genes that affected oral microbial communities. One study identified that IMMPL2 on chromosome 7 and INHBA-AS1 on chromosome 12 could influence microbiome phenotypes18. The other study reported a gene copy number (CN) of the AMY1 locus correlated with the oral and gut microbiome composition and function21. These two studies used 16 S rRNA amplicon sequencing for a small number of samples. Therefore, the influence of human genes on the composition of the oral microbiome and genetic stability between different oral niches are still poorly understood.Controversy over whether human genetics or environments dominate the fecal microbiome is being clarified by an increasing number of studiesHere, we presented the first large-scale metagenome-genome-wide association studies (mgGWAS) for 2017 tongue dorsum samples and 1915 salivary samples from a cohort of 2984 healthy Chinese individuals with high-depth whole-genome sequencing data. We further validated the identified associations in an independent replication cohort of 1494 individuals with also metagenomic sequencing data and relatively low-depth whole-genome sequencing data. A large number of concordant associations were identified between genetic loci and the tongue dorsum and salivary microbiomes. The effects of environmental factors and host genes on oral microbiome composition were investigated. Host genetics explained more variances of microbiome composition than environmental factors. The findings underscore the value of mgGWAS for in situ microbial samples, instead of focusing on feces.The 4D-SZ cohort  at present have high-depth whole-genome sequencing data from 2984 individuals , Proteobacteria , Firmicutes , Actinobacteria , Fusobacteria , Patescibacteria , and Campylobacterota   test; Supplementary Fig. SShotgun metagenome sequencing was performed for the 3,932 oral samples, with an average sequencing data of 19.18 ± 7.90 Gb for 2017 tongue dorsum and 13.64 ± 2.91 Gb for 1915 salivary samples , and 10 million human genetic variants  ≥ 0.5%), 455 independent associations involving 340 independent loci (distance < 1 Mb and r2 < 0.2) and 385 independent taxa reached genome-wide significance (p < 5 × 10−8). With a more conservative Bonferroni-corrected study-wide significant p value of 3.16 × 10−11 (= 5 × 10−8/1583), we identified three genomic loci, namely APPL2, SLC2A9, and MGST1, associated with five tongue dorsum microbial features involving 112 SNP-taxon associations  in the same effect direction of the minor allele . Two of the three study-wide signals from the discovery cohort were well replicated: rs1196764 at APPL2 with Prevotella jejuni, Oribacterium uSGB 3339, and Solobacterium uSGB 315; and rs3775944 at SLC2A9 with Oribacterium uSGB 1215, Oribacterium uSGB 489, and Lachnoanaerobaculum umeaense.We also used a replication cohort to validate these associations. The replication cohort was comprised of 1494 individuals from multiple cities in China  positively associated with abundances of three species, namely Prevotella jejuni , Oribacterium unclassified SGB (uSGB) 3339 , and Solobacterium uSGB 315 . APPL2 encoded a multifunctional adapter protein that binds to various membrane receptors, nuclear factors and signaling proteins to regulate many processes, such as cell proliferation, immune response, endosomal trafficking and cell metabolism. APPL2-associated three taxa all positively correlated with high sugar/fat dietary frequency  when checking the correlation between oral taxa and phenotypic traits in this cohort, in line with the role of APPL2 in controlling glucose-stimulated insulin secretion25. Appl2 protein had been reported to play a negative regulatory role in inflammation26. Its associated three taxa also correlated with decreasing risk of dental calculus and gingival bleeding , thereby supporting a link between genetic variation in the APPL2 gene, immune response, and the abundance of these taxa.The strongest association identified by tongue dorsum mgGWAS was on rs1196764 located in the r2 = 0.99) in SLC2A9. Minor allele G of rs3775944 (MAF = 0.49) in SLC2A9 locus negatively correlated with Oribacterium uSGB 1215 , Oribacterium uSGB 489 , and L. umeaense . SLC2A9 is a urate transporter and SLC2A9 polymorphisms have been reported associated with serum uric acid and urine uric acid concentration in multiple studies29. We also looked at these top loci in Biobank Japan (BBJ)31, and SLC2A9 was correlated with lower serum uric acid concentration , ischemic stroke (p = 1.73 × 10−4), urolithiasis (p = 2.02 × 10−4), and pulse pressure (p = 6.86 × 10−4). The negative associations of SLC2A9 with serum uric acid concentration (p = 6.74 × 10−6) and urine pH (p = 8.75 × 10−4) were confirmed in this cohort , Oribacterium uSGB 489 , and L. umeaense . These results presented a potential explanation for SLC2A9 acting on three oral taxa through serum uric acid as intermedium  gene was a determinant of triglyceride concentration , triglyceride concentration correlated with abundance of Haemophilus D parainfluenzae A (p = 7.70 × 10−16), and consistently LPL exhibited significant association with Haemophilus D parainfluenzae A (p = 1.59 × 10−8). These findings suggested that host genes may regulate oral microbiota by mediating their relevant metabolites.The second strongest association was on rs3775944, which is a perfect proxy for the exonic variant rs10939650  were identified as the third strongest signal, with minor allele negatively associated with Streptococcus uSGB 2460 , followed by family Streptococcaceae and other eight Streptococcus SGBs, such as S. infantis and S. pseudopneumoniae, although not confirmed in the replication cohort. These variants were also positively associated with red blood cell count (p = 2.51 × 10−5) and asthma (p = 5.03 × 10−5) in Biobank Japan. Consistently, 84% (237/282) of the Streptococcus spp. were observed correlated with red blood cell count (p < 0.05), such as S. mitis (p = 1.99 × 10−12) and S. pseudopneumoniae (p = 4.51 × 10−12). These results suggested that commensal Streptococcus species might utilize red blood cells as camouflage to avoid being engulfed by phagocytic immune cells in addition to the well-known group A Streptococcus (S. pyogenes)32. Our results also supported previous findings that Streptococcus spp. are often involved in diseases of the respiratory tracts such as asthma33.Variants in ADAMTS9 with Simonsiella muelleri, rs59134851 near MST1L-MIR3675 with Streptococcus anginosus (playing important roles in respiratory infections34), chr22:41198300 in EP300-AS1 with Parvimonas micra , rs34555647 near MIR3622B-CCDC25 with Selenomonas sputigena . These replicated associations invited further investigation of the impacts of host–microbial interactions on disease.In addition to the above three study-wide significant loci, other well replicated genome-wide significant associations included rs17070896 in 36. We next tried mgGWAS analysis for the saliva microbiome. With the 1685 independent salivary microbial taxa , and 10 million human genetic variants (MAF ≥ 0.5%) in discovery cohort, 466 independent associations involving 374 independent loci (r2 < 0.2) reached genome-wide significance (p < 5 × 10−8). With a more conservative Bonferroni-corrected study-wide significant p value of 2.97 × 10−11 (= 5 × 10−8/1685), two study-wide significant independent loci were identified , given that 23 of the 466 independent associations identified in the discovery cohort with p < 5 × 10−8 were not available in the low-depth replication dataset . Two study-wide significant signals identified by this saliva mgGWAS were both well replicated . F0422 uSGB 392 negatively correlated with concentrations of serum amino acids such as cysteine and glycine and levels of blood microelements such as magnesium and lead, as well as serum testosterone levels and mental distress (impatience or tension) , while searching GWAS summary statistics from Biobank Japan and this study. The other locus, MTRNR2L1-LOC105371703-MIR4522, is associated with the genus Eggerthia . Eggerthia was most positively associated with frequency of gingival bleeding, dental calculus, frequency of tooth pain, and dental periodontitis, but negatively linked to serum hormones such as cortisone, aldosterone, and testosterone  when searching in the GTEx37 database, and their associated oral taxa both consistently correlated with serum testosterone levels and mental distress (impatience); PDXDC2P-NPIPB14P associated with species Centipeda sp000468035 linked to thyroid abnormality; LARP1 associated with species Aggregatibacter kilianii linked to mean corpuscular hemoglobin; SMARCA1 associated with species Veillonella parvula linked to pharyngeal mucosal congestion (PMC) , respectively, six were shared between them  were replicated with p < 0.05 in the same effect direction of minor allele for the salivary samples  concentration, showed the strongest association with SGBs belonging to Oribacterium (p = 5.09 × 10−13) and Lachnoanaerobaculum (p = 4.69 × 10−9) in tongue dorsum samples, and also a relative low association with that of Oribacterium (p = 0.001) and Lachnoanaerobaculum (p = 1.0 × 10−4) in salivary samples. Among the 466 independent and genome-wide significant variants-taxa associations for saliva samples, 391 associations (84%) also were replicated with p < 0.05 in the same effect direction of minor allele for the tongue dorsum . Our mgGWAS of the salivary microbiome further confirm and extend human genetic contribution to the oral microbiome. These results suggested tongue and salivary microbiome as niches in one oral cavity shared a high level of host genetic similarity in the coevolution process.Among 455 and 466 independent associations identified for tongue dorsum and salivary microbiome , followed by disease enrichment and tissue expression analysis. These mgGWAS analyses returned 221 and 261 genes (< 20 Kb for associated genetic loci) for tongue dorsum and salivary microbiome, respectively. Functional mapping of their separately related genes in the DAVID database suggested that tongue dorsum associated host genes mainly enriched in phosphatidylinositol-related pathways including phosphatidylinositol signaling system, biosynthesis, dephosphorylation, and phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase activity, and Ca2+ pathway including calcium ion binding, calcium channel regulator activity, and voltage-gated calcium channel activity  segment analysis in DAVID showed that both tongue dorsum and saliva mgGWAS signals were enriched in cardiometabolic diseases and traits such as tobacco use disorder, myocardial infarction, triglycerides, blood pressure, lipoproteins, coronary artery disease, and nervous system diseases such as schizophrenia, bipolar disorder, psychiatric disorders, and Parkinson’s disease  and S. infantis (p = 1.254 × 10−8), respectively. Next, we investigated whether miR-4693 could regulate S. oralis and miR-3977 could regulate S. infantis. We blasted the 5p strand of miR-4693 (miR-4693-5p) sequence against the whole-genome sequence of S. oralis and found that eleven genes were predicted to be targeted by miR-4693-5p, of which four genes  were 16 S ribosomal RNA genes and were the most probable targets with minimum mfe (minimum free energy) of –28.4 kcal/mol . A total of 35 and 53 host environmental factors were significantly associated with β-diversity (BH-adjusted FDR < 0.05) for tongue dorsum and salivary samples, respectively, via PERMANOVA analysis  and Streptococcus parasanguinis  in salivary samples. In addition, gender, serum metabolites such as glutamic acid, cystine, and testosterone, as well as geographic location (residing in northern or southern China), all showed strong effects on the oral microbiome composition. In total, 35 and 53 host environmental factors were able to infer 6.36% and 7.78% of the variance of microbiome β-diversity for tongue dorsum and salivary samples, respectively. When calculating the cumulative explained variance of β-diversity by using all the independent environmental variables, we found that 12.85% and 15.54% of the variance can be explained for tongue dorsum and salivary samples, respectively.We first investigated the contribution of the host environmental factors to oral microbiome β-diversity (based on genus-level Bray–Curtis dissimilarities), by using host metadata including age, gender, BMI, diets, lifestyles, drugs use, and health status questions, as well as blood measurements. We selected 340 independent variables out of the total 423 environmental factors for association analysis (correlation α-diversity and β-diversity using 10 million genetic variants (MAF ≥ 0.5%). Six genome-wide significant loci were identified for α-diversity of the oral microbiome . When searching its correlations with microbial taxa, it was mostly negatively associated with Leptotrichia A sp000469505 and Prevotella saccharolytica . It was mostly positively associated with Prevotella uSGB 2511 and family Bacteroidaceae  explained 7.78% of the β-diversity variance for salivary microbiome and 35 explained 6.36% for tongue dorsum microbiome. By applying the same number of host genetic variants as environmental factors, we found the top 53 and 35 SNPs that were most closely associated with β-diversity of the salivary and tongue dorsum microbiome explained 14.14 and 10.14% of the β-diversity variances for the two niches, respectively  and oral microbiome separately, as well as their combination, to predict dental diseases. We found two of the six dental diseases that occurred in over 5% of individuals to be significantly associated with the oral microbiome . Either salivary and tongue dorsum microbiome explained 20% of the variance for dental calculus. Salivary and tongue dorsum microbiome explained 13% and 15% of the variance for gingival bleeding, respectively. Compared with the oral microbiome, the genetic PRS showed significantly higher predictive efficiency with a mean R2 of 45%, ranging from the lowest of 25% for gingival bleeding to the highest of 60% for teeth loss. Furthermore, when incorporating the oral microbiome into the PRS model, the predictive efficiency is slightly improved, with a 4% increment of R2 for dental calculus and a 6% increment of R2 for gingival bleeding  were also at least nominally significant in the other niche (p < 0.05), consistent with our and previous findings that tongue dorsum and salivary microbiome communities exhibited high levels of similarity46, especially in the micron-scale structure of oral niches47. Not only an independent cohort but also different niches  in the oral cavity corroborated the robust and replicable host-microbe association results. The non-replicated associations may require more high-depth genome cohorts or other niches microbiome for further confirmation in the future. Consistent with previous studies48, the salivary microbiome showed higher alpha diversity than tongue dorsum. In combination with the fact that saliva comes into contact with all surfaces in the oral cavity and represents a fingerprint of the general composition of the oral microbiome, these results suggested that the salivary microbiome is more diverse and likely more dynamic. Thus, host genetic associations that are stronger with the salivary than the tongue dorsum community will further invite other omics studies, especially the proteome and the nitrogen cycle that could impact microbial growth.In summary, we performed the first large-scale mgGWAS for the oral microbiome and report unequivocal human genetic determinants for the oral microbiome. Based on the metagenome-assembled profile, we identified abundant genetic loci to associate with oral microbiota. Four out of the five study-wide and nearly 1/10 genome-wide significant signals could be replicated in a low-depth genome cohort also from China, highlighting the power of adding the independent cohort for genomic association analyses of the oral microbiome. Our mgGWAS analysis found 84%–85% concordant association signals shared by the tongue dorsum and salivary microbiome, with all genome-wide significant associations in one niche  in this 4D-SZ cohort. The observed strong correlations between the two sites were supported by a metagenome-wide association study of rheumatoid arthritis6, although the fact that the taxonomic compositions of the two sites were different49. This was also consistent with the report from the HMP project revealed that oral cavity and stool bacteria overlapped in more than 45% of subjects50. Second, gender49, age51, geographic regions53, diets55, and serum metabolites had been reported to affect the gut microbiome composition substantially. Consistently, all these host factors also exerted significant effects on oral microbiome communities  of the 62 prevalent salivary microbial genera  and 41% of the 75 prevalent tongue dorsum microbial genera significantly correlated with corresponding gut genera in abundance  according to the manufacturer’s protocol. Tongue dorsum and salivary samples were collected with an MGIEasy kit. For the salivary sample, a 2× concentration of stabilizing reagent kit was used and 2-mL saliva was collected. DNA of oral samples was extracted using MagPure Stool DNA KF Kit B (no. MD5115-02B). The DNA concentrations from blood and oral samples were estimated by Qubit (Invitrogen). About 500 ng of input DNA from blood and oral samples were used for library preparation and then processed for paired-end 100 bp sequencing using the BGISEQ-500 platform58.All the adult Chinese individuals in this cohort were recruited for a multi-omics study, with some volunteers providing samples from as early as 2015, which would constitute the time dimension in “4D”. The cohort included 2984 individuals with blood samples collected during a physical examination in 2017 in the city of Shenzhen, and all these individuals were enlisted for high-depth whole-genome sequencing  at BGI-Shenzhen, and all participants provided written informed consent at enrollment.59 (v0.7.15) with default parameters. The reads consisting of base quality < 5 or containing adapter sequences were filtered out. The alignments were indexed in the BAM format using Samtools60 (v0.1.18) and PCR duplicates were marked for downstream filtering using Picardtools (v1.62). The Genome Analysis Toolkit’s  BaseRecalibrator created recalibration tables to screen known SNPs and INDELs in the BAM files from dbSNP (v150). GATKlite (v2.2.15) was used for subsequent base quality recalibration and removal of read pairs with improperly aligned segments as determined by Stampy. GATK’s HaplotypeCaller was used for variant discovery. GVCFs containing SNVs and INDELs from GATK HaplotypeCaller were combined (CombineGVCFs), genotyped (GenotypeGVCFs), variant score recalibrated , and filtered . During the GATK VariantRecalibrator process, we took our variants as inputs and used four standard SNP sets to train the model: (1) HapMap3.3 SNPs; (2) dbSNP build 150 SNPs; (3) 1000 Genomes Project SNPs from Omni 2.5 chip; and (4) 1000 G phase1 high confidence SNPs. The sensitivity threshold of 99.9% to SNPs and 98% to INDELs were applied for variant selection after optimizing for Transition to Transversion (TiTv) ratios using the GATK ApplyRecalibration command.About 2984 individuals with blood samples were sequenced to a mean of 33× for the whole genome. The reads were aligned to the latest reference human genome GRCh38/hg38 with BWAp > 10−5; and (iii) genotype calling rate > 98%. We demanded samples to meet these criteria: (i) mean sequencing depth > 20×; (ii) variant calling rate > 98%; (iii) no population stratification by performing principal components analysis (PCA) analysis implemented in PLINK62 (v1.9), and (iv) excluding related individuals by calculating pairwise identity by descent  in PLINK. No samples were removed in quality control filtering. After variant and sample quality control, 2984 individuals  with about 10 million common and low-frequency (MAF ≥ 0.5%) variants were left for mgGWAS analyses.We applied a conservative inclusion threshold for variants: (i) mean depth > 8×; (ii) Hardy–Weinberg equilibrium (HWE) 63 and then imputed using Minimac3 (v2.0.1)64 with our previous 1992 high-depth WGS dataset56 as reference panel. We retained only variants with imputation info. > 0.7, Hardy–Weinberg equilibrium P > 10−5 and genotype calling rate > 90%. Similar to what we have done for the discovery cohort, samples were demanded to have a mean sequencing depth > 5×, variant call rate > 95%, no population stratification, and no kinship. Finally, 1430 individuals  with 8.6 million common and low-frequency variants (MAF ≥ 0.5%) from the replication cohort were left for association validation analysis.About 1494 individuals in the replication cohort were sequenced to a mean of 9× for the whole genome. We used BWA to align the whole genome reads to GRCh38/hg38 and used GATK to perform variants calling by applying the same pipelines as for the high-depth WGS data. After completing the joint calling process with CombineGVCFs and GenotypeGVCFs options, we obtained 43,402,368 raw variants. A more stringent process in the GATK VariantRecalibrator stage compared with the high-depth WGS was then used, the sensitivity threshold of 98.0% to both SNPs and INDELs was applied for variant selection after optimizing for Transition to Transversion (TiTv) ratios using the GATK ApplyRecalibration command. Further, we kept variants with less than 10% missing genotype frequency and minor allele count more than 5. All these high-quality variants were pre-phased using Eagle (v2.4.1)Metagenomic sequencing was done on the BGISEQ-500 platform, with 100 bp of paired-end reads for all samples and four libraries were constructed for each lane. We generated 19.18 ± 7.90 Gb (average ± standard deviation) and 19.90 ± 7.73 Gb raw bases per sample for tongue dorsum samples in discovery and replication cohorts, respectively  using Bowtie2 with parameters: “-end-to-end -very-sensitive -seed 0 -time -k 2 -no-unal -no-discordant -X 1200”, and the normalized contigs depths were obtained by using jgi_summarize_bam_contig_depths, then based on the correspondence of contigs and genome, the normalized contig depth was converted to the relative abundance of each species for each sample. Finally, we merged all representative species’ relative abundance to generate a taxonomic profile for the human oral population. The profiling workflow was implemented in the metapi jgi_profiling module (https://github.com/ohmeta/metapi/blob/dev/metapi/rules/profiling.smk#L305).The high-quality oral genome catalog was constructed in our previous studyThe nonparametric Wilcoxon rank-sum test was used to determine statistically significant differences in species α-diversity between tongue dorsum and saliva niches. We analyze the β-diversity (based on genus-level Bray–Curtis dissimilarity) difference between the two oral niches using PERMANOVA (adonis) in the ‘vegan’ package and visualize the two oral niches groups using ordination such as non-metric multidimensional scaling (NMDS) plots.−5. After filtering, the represented genera of these microbial taxa covered between 99.63% (tongue dorsum) and 99.76%  of the whole community in the cohort. As many oral microbial taxa are highly correlated and aim to reduce the number of GWAS tests, we then performed a number of Spearman’s correlation tests to obtain the independent taxa for mgGWAS analyses. Spearman’s correlations were calculated pairwise between all taxa, and the correlations were used to generate an adjacency matrix where correlations of > 0.8 represented an edge between taxa. A graphical representation of this matrix was then used for the greedy selection of representative taxa. Nodes (microbiota taxa) were sorted by degree and the one with the highest degree was then chosen as a final taxon (selecting at random in the case of a tie). The taxon and its connected nodes were then removed from the network and the process repeated until a final set of taxa sets were found such that each of the discarded taxa was correlated with at least one taxon. These filtering resulted in a final set of 1583 and 1685 independent microbial taxa for tongue dorsum and saliva, respectively, which were used for association analyses.After investigating the distributions of occurrence rate and relative abundance of all microbial taxa, we decided to filter the microbial taxa to keep those with occurrence rates over 90% and average relative abundance over 1 × 10n = 10). We calculated the average number of concordant replicated associations in the ten-time random iterations as expected value and then compared it to the actual replicated number using a one-sided χ2 test. The replication was considered significant if the χ2p was less than 0.05.We tested the associations between host genetics and oral bacteria using a linear model based on the relative abundance of oral bacteria. Specifically, the relative abundance was transformed by the natural logarithm and the outlier individual who was located away from the mean by more than four standard deviations was removed so that the abundance of bacteria could be treated as a quantitative trait. Next, for 10 million common and low-frequency variants (MAF ≥ 0.5%) identified in this cohort, we used a linear regression model to perform mgGWAS analysis via PLINK v1.9. Given the effects of environmental factors such as diet and lifestyles on microbial features, we included all potential cofounders that were significantly associated with the β-diversity (Benjamini–Hochberg FDR < 0.05) estimates in the below explained variance analysis, as well as the top four principal components (PCs) as covariates for mgGWAS analysis in both the salivary and tongue dorsum niches. We next performed the same association analyses in the replication cohort and further validated the significance of identified associations by the discovery cohort. To investigate whether the observed number of nominally significant concordant associations with effects in the same direction was more than expected by chance, we randomly selected 455 and 466 independent associations for tongue dorsum and saliva, respectively, with ten iterations  was utilized to perform functional and pathway enrichment analysis. DAVID is a systematic and integrative functional annotation tool for the analysis of the relevant biological annotation of gene lists and provides a functional interpretation of the GO enrichment and KEGG pathway analysis38. The p value < 0.05 was considered statistically significant. In addition, the mapped genes were further investigated using the GENE2FUNC procedure in FUMA39 (http://fuma.ctglab.nl/), which provides hypergeometric tests for the list of enriched mapping genes in 53 GTEx tissue-specific gene expression sets, 7246 MSigDB gene sets, and 2195 GWAS catalog gene sets39. Using the GENE2FUNC procedure, we examined whether the mapped genes were enriched in specific diseases or traits in the GWAS catalog as well as whether showed tissue-specific expression. Significant results were selected if a false discovery rate (FDR)-corrected p < 0.05 was observed.The significant genetic variants identified in the association analysis were mapped to genes using ANNOVARhttp://miRBase.org) were blasted against the genomes of Streptococcus oralis and Streptococcus infantis, respectively. The potential targets of two miRNAs were predicted by RNAhybrid42 depending on the minimum free energy (MFE) of secondary structure binding. The cutoff of MFE ≤ −20 kcal/mol and p value < 0.05 were used for selecting the final targeted genes.The sequence of miR-4693-5p and miR-3977 downloaded from miRbase V22.1  and Streptococcus oralis  were stored in China National Gene Bank (CNGB). Streptococcus orails and Streptococcus infantis were cultured in presence of 2 nm/mL miRNA in the Brian Heart Infusion (BHI) medium or not. They were cultured in an anaerobic chamber at 37 °C. Strains were monitored as absorbance at 600 nm (OD600) per 2 h. The clone numbers were calculated at 2, 4, 6, and 10 h, respectively. The experiment was repeated three times in each group, and the average number of clones in each group was calculated. The results were represented by the growth curve of strains.The sequence of miR-3977 is 5′-GUGCUUCAUCGUAAUUAACCUUA-3′. The sequence of miR-4693-5p is 5′-AUACUGUGAAUUUCACUGUCACA-3′. They are both 23 nucleotides sequences and single-stranded linear miRNAs. All of them are synthesized by Beijing Liuhe BGI Technology Co., Ltd. The microbiome β-diversity (between-sample diversity) based on genus-level abundance data were generated using the “vegdist” function (Bray–Curtis dissimilarities). Then, we performed principal coordinates analysis (PCoA) based on the calculated beta-diversity dissimilarities using the “capscale” function in “vegan”. Finally, associations for β-diversity (a two-axis MDS) were performed using the manova function from the “stats” package, in a multivariate analysis using genotypes and the same covariates stated above as variables.21. A total of 423 host environmental factors are available in this cohort. Environmental metadata were first log-transformed and checked for collinearity using the Spearman correlation coefficient. Collinearity was assumed if a Spearman’s ρ > 0.6 or ρ < −0.6. Collinear variables were considered redundant and one variable from each pair was removed from further analysis, resulting in a final set of 340 variables.As part of the 4D-SZ cohort, all participants in this study had records of multi-omics data, including anthropometric measurement, stool form, defecation frequency, diet, lifestyle, blood parameters, hormone, etc.10-transformed of the mean-normalized values was calculated for each quantitative phenotype  and a linear regression model for the quantitative trait implemented in the PLINK v1.9 was used for association analysis. Samples with missing values and values beyond 4 s.d. from the mean were excluded from the association analysis. For each binary phenotype , a logistic regression model was used for association analysis. Age, gender, and the top four PCs were included as covariates for each association analysis.To investigate the potential associations of top loci identified in microbiome GWAS with environmental variables especially for serum metabolites, we also performed GWAS analysis for the 340 environmental variables. Among the 340 environmental traits, the logWe next searched for associations between the 340 environmental variables selected above and the oral microbiome compositions. We performed Bray–Curtis distance-based redundancy analysis (dbRDA) to identify variables that are significantly associated with β-diversity and measure the fraction of variance explained by the factors, using the “capscale” function in the vegan package. The significance of each response variable was confirmed with an analysis of variance (ANOVA) for the dbRDA (anova.cca function in the vegan package). Only the variables that were significantly associated (Benjamini–Hochberg FDR < 0.05) with the β-diversity estimates in the univariable models were included in the multivariable model. The additive explanatory value (in %) of significant response variables  was assessed with a variation partitioning analysis of the vegan package .66 implemented in Python (v3.7.8) and a fivefold cross-validation scheme to construct risk-prediction models. In every fold of the fivefold cross-validation scheme, we calculated the associations between SNPs and dental diseases within the training dataset, and then selected independent and significant SNPs  to calculate the PRS as an unweighted sum of risk alleles, and finally we trained a model on the PRS and predicted the disease risk in the test dataset. During the process, we obtained the optimal values of the tuning parameters using fivefold cross-validation and evaluated the results using the coefficient of determination (R2) as variance explained and AUC as disease discriminative efficiency.To obtain the predictions of human genetics on dental diseases, we used gradient boosting decision trees from the LightGBM (v.3.1.1) packageSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7Supplementary Table S8Supplementary Table S9Supplementary Table S10Supplementary Table S11Supplementary Figures


A successful response was obtained after selection for ovulation rate during 10 generations in rabbits. However, no correlated response in litter size was observed due to an increase in prenatal mortality. This increase could be due to the reduction in fetus weights and/or an increase in variable asynchrony among fetus weights. Therefore, the consequences of the selection procedure on weight at 28 and 63 days old  and its variability are unknown. Using genetic trends and a cryopreserved control population for estimating correlated responses to selection, no relevant response on weight at 28 and 63 days old was observed. Similar results have been obtained for the variability of growth traits.The aim of this work was to estimate correlated responses in growth traits and their variabilities in an experiment of selection for ovulation rate during 10 generations in rabbits. Individual weight at 28 days old  and at 63 days old  was analyzed, as well as individual growth rate . The variability of each growth trait was calculated as the absolute value of the difference between the individual value and the mean value of their litter. Data were analyzed using Bayesian methodology. The estimated heritabilities of IW28, IW63 and IGR were low, whereas negligible heritabilities were obtained for growth variability traits. The common litter effect was high for all growth traits, around 30% of the phenotypic variance, whereas low maternal effect for all growth traits was obtained. Low genetic correlations between ovulation rate and growth traits were found, and also between ovulation rate and the variability of growth traits. Therefore, genetic trends methods did not show correlated responses in growth traits. A similar result was also obtained using a cryopreserved control population. Intensive rabbit production is produced by a three-way cross in which males, selected for growth traits from paternal lines, are mated with crossbred females from lines selected for reproductive traits . All theSelection for ovulation rate, proposed as a way to increase litter size , has beeThe objective of this study was to estimate the correlated response on growth traits and their variability using genetic trends methods and also by comparing the selected line with a cryopreserved control in a rabbit line selected for ovulation rate during 10 generations. Maternal and litter effects on these traits were also evaluated.All experimental procedures were approved by the Universitat Politècnica de València Research Ethics Committee, according to Council Directives 98/58/EC and 2010/63/EU.The animals used in the experiment came from a synthetic line selected for the phenotypic value of ovulation rate in the second gestation of each female during 10 generations. The selection procedure has been described previously by Laborda and coauthors .All animals were kept under constant photoperiod of 16 h light:8 h dark and controlled ventilation. The female nourished its kits during lactation period without fostering. Young rabbits for all parities were weaned at 28 days old and placed in flat-deck cages, 8 rabbits per cage, and fed ad libitum with a commercial diet . The fattening period was 35 days. At 63 days old, rabbits were slaughtered or selected and placed in individual flat-deck cages and fed ad libitum with a commercial diet  and they stayed there up to their reproductive age: 17–18 weeks of age.To produce a control population, embryos from 45 donor females and 10 males from the base generation were vitrified and stored in liquid nitrogen until they were transferred. Details of the procedure are presented by Laborda and coauthors . The offThe reproductive trait analyzed during the second gestation in the selection procedure during ten generations was ovulation rate (OR), measured as the number of corpora lutea. The OR was also recorded in the last gestation of all females, that did not die or were not culled, obtaining a total of 1478 records from 856 females. A total of 20,230 records were used to analyze individual weight at 28 days old  and 19,362 records were used to analyze individual weight at 63 days old  and individual growth rate . An approach to study variability of growth traits was performed using deviation of growth traits, which was estimated as the absolute value of the difference between the individual value for IW28, IW63 and IGR and the mean value of their litter, DIW28, DIW63 and DIGR in kg, respectively.All growth traits were also measured in the offspring of the control population and in contemporary animals from the 10th generation, obtaining a total of 1238 and 1142 records for IW28 and 1208 and 1123 records for IW63 and IGR, respectively.All analyses were performed using Bayesian methodology ,14.Correlated responses to selection have been estimated using two independent approaches: genetic trends and control population. Genetic trends use all data from all generations of selection but its estimations depend on the model, and therefore response to selection depends on the genetic parameters used in the model. In the control population approach, the number of data is smaller but differences between control and selected population will have only genetic bases if both populations are raised contemporarily.ijklm is the trait OR; YSi is the effect of year-season of the mating day (31 levels for OR); POj is the effect of the parity order (four levels for OR); Lk is the effect of lactation status at mating (two levels: lactating and nonlactating does when mated); aijkl is the additive value of the animal; pijkl is the permanent environmental effect; and eijklm is the residual effect.A repeatability model was used to analyze OR in the selection period:ijklmn is IW28, IW63 and IGR; YSi is the fixed effect of year-season in which the animal was growing (29 levels); POj is the effect parity in which the animal was born (five levels); NBAk is the effect of the number of rabbits born alive when the animal was born (17 levels); dijkl is the random dam  effect between parities (851 levels); cijklm is the common litter effect (2683 levels); aijklmn is the additive value of animal; and eijklmn is the residual effect.For individual weights and IGR, as well as for growth variability traits, the animal model used was:Univariate analysis for OR was performed to estimate the heritability of the selection trait. To account for the selection process and to estimate the heritability of each growth trait as well as the correlation between OR and growth traits, bivariate analyses including OR were performed.Data augmentation ,15 was pAfter data augmentation, the model for all traits was:Hence, sorting the data by individual, the residual (co)variance matrix can be written as The distribution of permanent environmental effects was assumed to be normal and of the form:The distribution of the random dam effects between parities was assumed to be normal and of the form:Finally, the distribution of the common litter effects was assumed to be normal and of the form:For all analyses, bounded flat priors were used for matrices To estimate the correlations between growth traits, trivariate analyses including OR and two growth traits were performed. Hence, sorting the data by individual, the residual (co)variance matrix can be written as 95% is the guaranteed value of the interval  containing the 95% of the probability, P is the probability of the estimation being higher (or lower) than 0.00, P0.10 and P0.30 are the probability of the estimation being higher (or lower) than 0.10 and 0.30, respectively, and Pr is the probability of relevance [2) is <10%. After some exploratory analyses, two chains were used, each of 1,000,000 iterations, with a burning period of 200,000 iterations. Only every 100th iteration was saved. Features of marginal posterior distributions of parameters were obtained using the package R code. Convergence was tested using the Z criterion of Geweke and Monte Carlo sampling errors were computed.Marginal posterior distributions of all unknowns were estimated using a Gibbs sampling procedure using the program TM . Differeelevance ,14. We celevance ,17. In tijklm is the trait OR; Linei is the effect of the line (two levels: control and selected); YSj is the effect of year-season (three levels); POk is the effect of parity (two levels: at second and fourth gestation); Ll is the effect of lactation state of the doe (two levels: lactating and nonlactating does when mated); pijklm is the effect of the doe (105 levels); and eijklmn is the residual of the model.The model assumed for analyzing OR using the offspring of the control and selected population was:ijklm is IW28, IW63 and IGR; YSi is the fixed effect of year-season in which the animal was growing (four levels); POj is the effect parity in which the animal was born (four levels); NBAk is the effect of the number of rabbits born alive when the animal was born (16 levels); dijkl is the random dam effect between parities (96 levels); cijklm is the common litter effect (302 levels); and eijklmn is the residual effect.The model assumed for analyzing individual growth traits and their variabilities was:I is a unity matrix and I is a unity matrix and Bounded uniform priors were used for all unknowns with the exception of the dam and common litter effects, which were considered normally distributed. Dam effect was with mean Descriptive statistics for all traits are presented in Phenotypic correlations between OR and the growth traits were low: 0.17, 0.15 and 0.09 for IW28, IW63 and IGR, respectively. The R-squared between OR and the other analyzed traits was lower than 5%. When the ratio between R-squared and R-squared maximum was estimated, different results were observed: 0.72 for IW28, 0.57 for IW63 and 0.29 for IGR.Phenotypic residual correlations between OR and growth traits and also between OR and the variability of growth traits were very low . Only co2), had relevance for IW28 (0.14), and it was close to zero for IW63 and IGR. However, the common litter effect, which is calculated as the ratio of the common litter effect variance with respect to phenotypic variance (c2), explained a greater part of phenotypic variance for all growth traits, with their values being close to 0.30. Genetic variation for the variability of growth traits was negligible, lower than 0.02, similarly to the maternal effects. The common litter effects for the variability of growth traits were close to 0.10.The heritability for OR was moderate, 0.17, with a probability to be higher than 0.10 of 94% and the highest posterior density region at 95% ranging from 0.08 to 0.27 (data not shown). The heritability values of IW28, IW63 and IGR were low: 0.09, 0.12 and 0.11, respectively . A heritAll high posterior density regions for genetic correlations between OR and growth traits were large . The estThe genetic correlation between IW28 and IW63 was positive (P > 0 = 100%) and high (mean = 0.83) . MoreoveThe correlated responses to selection were estimated at the end of the selection period as the difference of the average breeding values between the end and the beginning of the period. Correlated responses on IW28, IW63 and IW2863 were low , being 2Features of the marginal posterior distributions of the differences between the control and selected populations for growth traits and their variabilities are presented in 2, explained a greater part of the phenotypic variance for growth traits, close to 0.25, whereas low estimations were observed for the variability of growth traits. The maternal effect of the doe over all their parities, m2, had a non-negligible value for IW28 (0.10).Similarly to the estimations using genetic trends, the common litter effect, cDirect response to selection for ovulation rate was obtained in rabbits after 10 generations of selection, 0.13 ova per generation. However, prenatal survival decreased and no correlated response on litter size at birth was observed . The decDescriptive statistics obtained in the present work are similar to those previously reported in other rabbit maternal lines ,17,20 atLow heritabilities, close to 0.10, were obtained for IW28 and IW63, which corresponded to weaning and commercial time, respectively. Likewise, IGR, which is the most common selection criterion in sire lines in rabbits , who anaGenetic and phenotypic correlations were similar, and this is a generally observed phenomenon . GeneticConsidering all these genetic parameters, the correlated response to selection for ovulation rate during ten generations was estimated at the end of the selection period. Using genetic trends methods, no relevant correlated responses on IW28, IW63 and IW2863 were found, less than 1% of the trait per generation. These estimates agreed with previous results . SimilarThe increase in ovulation rate by selection did not reduce the weight of the young rabbits at weaning (28 days) and at marketing (63 days) and did not modify their variabilities. It can be inferred that female effects, which include maternal and common litter effect, were not modified after selection.


R2 = 0.39) relationship between genomic differentiation (FST) and phenotypic differentiation (STP) for loci and traits putatively under selection. This quantitative relationship between PST and FST for loci under selection in diverse taxa provides broad context and cross‐system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes “strong” differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between PST and FST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta‐analysis of studies examining population‐level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies  we examined, there was a moderate ( For a model in which the only independent variable is a categorical variable for population:fS = the F ratio for the population variable; νnS and νdS are the numerator and denominator degrees of freedom for the population variable; and Z represents all other covariates in the model.For any model including a variable for population:j indicates a population; js = standard deviation for population j; jN = number of individuals censused in population j; and Jx‐bar and x‐bar indicate population‐specific and metapopulation means, respectively.For mean and standard deviation data:QST is calculated the same way as PST, but only applies to phenotypes for which the heritable component has been isolated, usually via common‐rearing experiments.The keystone fact of the Modern Evolutionary Synthesis is the genetic basis for evolution Fisher, . While pDespite this progress, biologists have struggled to systematically associate genomic data with biologically relevant phenotypes, particularly when pleiotropy, polygenic inheritance, epistasis, and phenotypic plasticity confound their relationship , linkage, and gene interactions—across taxa and traits will also obscure the relationship between PST and FST  sequence repeats  of DNA in noncoding regions that vary in length between individuals and thus are generally regarded as neutrally evolving loci , some SNPs in coding regions can be highly influential. Sliding windows can be used to measure the collective QTLs are genomic regions that are significantly associated with a quantitative, or continuous trait with a polygenic basis. These regions are defined using experimental crosses in a process called QTL mapping . With this approach, a locus that contains multiple SNPs is then analyzed in a multiallelic framework, much like a microsatellite.22.1https://webofknowledge.com) and the search terms in Table PST, phenotypic differentiation, (2) neutral FST, neutral genomic differentiation, and (3) non‐neutral FST (henceforth: nnFST), genetic differentiation for loci putatively under selection   outlier tests are most commonly used to identify candidate loci. These tests assume that loci with genetic differentiation values that significantly exceed background  differentiation levels are under selection.Genetic differentiation  are one suite of methods that identify correlations between genomic data—usually high coverage genomic data—and fitness values, phenotypes, or environmental values (GEAS).PST) and (1) neutral and non‐neutral genetic differentiation , (2) proportion of loci identified as non‐neutral, and (3) several methodological choices. The result is several models that assess the degree to which phenotypic and genomic differentiation are congruent (in traits and loci putatively under selection), as well as the role of some potential confounding methodological factors.We used general linear models to examine the relationship between phenotypic differentiation , FST of neutral loci, FST formula, marker type, number of loci under selection, number of neutral loci, and method(s) used to determine which loci are under selection. For papers which included raw phenotypic measurements (16) we calculated PST using:We extracted the following information from each paper: species, phenotypic trait, F tests to confirm that all reported and calculated PST values were statistically different than 0, that is, implied phenotypic differentiation.The numerator is the sum of squared deviations of each individual's phenotype from the population mean, and the denominator is the sum of squared deviations of each individual's phenotype from the metapopulation mean. We used For papers with more than two study populations, we used all possible pairwise population comparisons for analysis.FST calculationsRaw: raw Bayesian: BayeScan and 2DSFSNon‐Bayesian: Arlequin, Lositan, fdist, dfdist, fstat, detsel, lnRVGWAS‐specific: other software used specifically for GWASWe organized genomic analysis software into four groups:2.3FST and PST data in this study. The logit transformation assumes that small changes at the ends of the range of possibilities  are more important than small changes in the middle of the range . Logit transforming FST and PST also standardizes their sensitivity to differentiation—doubling interpopulation variation produces an equal change in logit(FST or PST) regardless of the starting variation . This averaging resulted in a final 111 datapoints for 88 unique population‐level comparisons across 31 papers.Due to the large range in number of phenotypes measured in any given paper, we used paper‐specific averages for our global PST and average neutral FST or average non‐neutral FST (nnFST). As nnFST and FST are strongly correlated , we removed the interaction and refit the model. We also included a first‐order effect of proportion of non‐neutral loci in all subsequent analyses.PST and the PST‐nnFST slope. We did not test for an effect of nnFSTp‐value threshold, as p‐value threshold did not have a strong effect on nnFST , and analytical method —on T Figure . We testPST, nnFST, Nnn, and Ntotal are described above; β‐terms are coefficients determined during model fitting: βM indicates a method‐specific intercept and βFM indicates a method‐specific PST‐nnFST slope.We tested all effects in each model using type II likelihood ratio tests.PST‐nnFST trends within studies, with the goal of elucidating a PST‐nnFST relationship for individual phenotypes across numerous populations. We winnowed our master database down to all paper‐phenotype‐method combinations that had at least five PST‐nnFST datapoints . We then fit the following general linear model across all datapoints from the winnowed database:We also examined PST, nnFST, Nnn, and Ntotal are described above; β‐terms are coefficients determined during model fitting: βZ indicates a phenotype‐specific intercept and βFZ indicates a phenotype‐specific PST‐nnFST slope—that is, βZ and βFZ took a unique value for each paper‐phenotype‐method combination.βL  slope across all paper‐phenotype‐method combinations, rather than fitting a unique βL term for each (as we did for βZ) due to the overall small sample size and the lack of variation in proportion of non‐neutral loci for three papers. We tested the slopes of each phenotype‐specific PST‐nnFST relationship (βFZ) using t tests. As the relatively small number of points within each study certainly lowered the power to detect significant PST‐nnFST relationships, we also examined the distribution of PST‐nnFST slopes for the within‐study analysis.We fit one 3FST (nnFST) was superior to neutral FST in predicting PST, with higher slope (0.43 vs. 0.30) and R2 (0.20 vs. 0.12) . However, the nnFST model outperformed the neutral FST model, with the neutral model having a likelihood of 0.005 with respect to the nnFST model.Non‐neutral slope 0.4 vs. 0.30PST, but had no significant interaction with nnFST  increased the model R2 from 0.20 to 0.25.Proportion of non‐neutral loci also had a significant negative effect on T Figure ; Table 2STP  for AFLPs, then QTLs, which were closely followed by SNPs and msats. This model result does not, however, imply that some marker types caused higher PST, rather it indicates that PST was higher for a given estimate of nnFST  for some markers. Marker type did not have a significant interaction with nnFST.Of our four methodological variables , only marker type had a significant effect on  Figure ; Table 3PST and nnFST for any phenotypes, even with proportion of non‐neutral loci included in the model  despite the noise of diverse study systems and study designs—has encouraging implications for evolutionary biology. First and foremost, this relationship unsurprisingly supports the genomic basis for phenotypic evolution, suggesting that phenotypic differentiation often has some underlying genomic basis. Of course, this point does not rule out additional environmental contributions like phenotypic plasticity and transgenerational epigenetics. It also suggests that the genomic patterns behind contemporary phenotypic evolution are at least somewhat comparable among taxa on average, even if their characterization is incomplete. Specifically, despite the litany of confounding factors described below, we still found a significant relationship between PST and nnFST, and nnFST explained a meaningful proportion of the variation in PST. With further refinement of genomic methods, standardization and reporting of phenotypic data, and clearer details of how study systems differ in terms of genetic architecture , linkage, and gene interactions), this relationship should get clearer.Our major finding—of a positive relationship between PST and FST compared to the values predicted by our model. Finally, as mounting evidence suggests that adaptation in response to selection is generally nonparallel  and methods elucidating gene interactions , including phenotypic plasticity, can also muddle the PST‐FST relationship. Indeed, genotype–environment effects account for a large portion of variation in many phenotypes  would report a higher proportion of non‐neutral loci despite relatively low levels of PST. If this hypothesis is confirmed, more liberal classification of loci as non‐neutral may require down‐weighting of non‐neutral FST.T Figure . If mostMarker choice. Our results confirm that marker choice induces significant variation into the STnnF‐STP relationship. While marker choice did not significantly affect the PST‐nnFST slope, including an effect of marker choice on PST raised the model R2 from 0.25 to 0.39. This apparent effect may be due to the correlation of marker type and genomic coverage, as high genomic coverage  resulted in a lower value of PST for a given value of nnFST than low genomic coverage . This result suggests that historical low‐coverage approaches associated with certain marker types may have underestimated nnFST, leading to higher PST values for a given value of nnFST .Estimating FST. While our results indicated no particular best FST estimation method in terms of linking PST to nnFST, we note that our observed PST‐nnFST relationship is almost certainly muddied by noise generated by varying software and software settings used to estimate FST.Different methodological approaches to determining genetic differentiation associated with selection introduce noise to genotype–phenotype relationships between studies. However, as the genome is a relatively concrete feature of an organism, the bias introduced by the choice of genetic and analytical methods can be reduced by systematically identifying appropriate methodology.4.2.3Data standardization. Variable methods for measuring complex phenotypes can make comparisons across studies challenging. For example, features like “body shape” may be quantified several different ways even within a particular clade. As relevant phenotypes are study‐dependent, some variability in measurements is to be expected. However, it is crucial that authors report methods detailing their phenotyping protocols so that phenotypic data that are comparable across studies can be more easily extracted. Even methods for calculating PST vary, seemingly arbitrarily, from paper to paper, often without reporting of assumed values of some variables included in calculations (such as heritability). Some disciplines, like macroecology and studies of vertebrate museum specimens, utilize simplified, standardized phenotypic measurements for a particular taxon  that is used by the human genetics community —are often not published in sufficient detail. The inclusion of metadata and workflows for bioinformatic and statistical analyses will improve reproducibility and ensure data are accessible for future analyses as technologies evolve , including sampling of as many biologically relevant and evolutionarily independent phenotypes as feasible.A standardization of traits documented within taxonomic groups.Based on our ability to infer a general PST and nnFST across a spectrum of differentiation ensures that genotypes and phenotypes are not only associated, but clearly differentiate congruently across landscapes, providing more thorough evidence for the genome–phenome functional link. Having a gradient of differentiation  avoids potentially spurious genome–phenome relationships generated by cherry‐picking highly differentiated populations, which may be responsible for the observed weak PST‐nnFST relationship within studies. Measuring numerous phenotypes increases the likelihood of finding a phenotype that is strongly determined by diverging loci, as long as appropriate statistical corrections are used to avoid false positives, and also helps us better understand how a given trait diverges in reference to the rest of the phenome. With enough careful measurement, we should be able to describe the relationship between nnFST and nnPST, the non‐neutral components of phenotype. Currently, our only approach to this is to use our limited understanding of the system to select what we judge as the most differentiated traits. Through careful methodological choices, broader measurement of phenotypes, and a metapopulation approach, genome‐to‐phenome associations in natural populations can become a more powerful and accessible tool for understanding contemporary evolution.Correlating The authors declare no conflicts of interest.Project design: All authors. Project funding: BJO, BLK, AIK, MTK. Literature search: AKW, ZTW. Data collection: ZTW, AKW, KLB, JDC. Analysis: ZTW. Figure making: ZTW, AKW, KLB, JDC. Lead writing: ZTW, AKW, KLB, JDC, JJH. Editing and revision: All authors.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.


Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes. Genome-wide association studies (GWAS) have become a powerful approach in the genetic dissection of quantitative traits in human, animal and plant genetics . A numbep-values ≤ 0.005 and then includes them in a multi-locus model for true QTN detection  , Elasticetection , iteratietection , hybrid etection , a fast etection , and muletection . AlthougFortunately, the score test can greatly decrease computational time. Furthermore, a major advantage of the score test is that it only requires imputation under the null model of no association, and working within the framework of the score test makes other extensions feasible. In this study, we proposed an efficient association analysis approach by integrating the score test with Empirical Bayes, named ScoreEB, under the framework of the mrMLM  and FASTy denotes an n × 1 quantitative phenotype vector for n individuals; X denotes the n × c fixed effect design matrix, c denotes the number of covariates, including unit vector, population structure :K matrix makes Tscore follows the chi-square distribution with one degree of freedomp-values can be computed via Davies method  is considered, which is obtained by integrating over the QTN effects s matrix  defined nd σe2 . The int problem , i.e., cs method .y, X, b and ε are the same as those in Model (1); q is the number of markers selected in single-locus scanning; βi is the random effect for marker i, and xi is the corresponding designed matrix for βi. Obviously, the parameters of interest to be estimated are We conduct variable selection in a multi-locus modelβi is viewed as random. With the Bayesian hierarchical model, a normal prior is adopted for 1) Initial-step: Assign initial values to parameters with2) Expectation-step: QTN effect can be estimated by3) Maximization-step: Update parameters Empirical Bayes ’s prior . The folβi. Because Empirical Bayes is a multi-locus model, there is no requirement for Bonferroni correction  = 3.0 was set up (Repeat 2) and 3) until convergence. All the markers with rrection . Insteads set up . This crith QTN, ith QTN, i from the sth repeat, and i.We performed three simulation experiments to validate ScoreEB. In the first simulation experiment, 216,130 SNPs in In the second simulation experiment, the phenotypes without polygenic effect were simulated by the model In the third simulation experiment, we intended to investigate the influence of the sample size on the running time. The sample size was set to 200, 500, 1,000, and 2,000, respectively. Meanwhile, the number of markers was fixed at 50,000. And repetition times was set to 100. In addition, five-fold cross-validation test was performed to decide the choice of two hyperparameters in Empirical Bayes step of ScoreEB at different sample sizes .Arabidopsis thaliana, rice, maize, cattle and pig.We use previously published datasets from multiple species that includes Arabidopsis thaliana dataset consists of 199 accessions each with 216,130 genotyped SNPs (Sus scrofa genome build 11.1. The growth performance related phenotype AGE (days to 100 kg) is re-analyzed , which has 8 cores and 16 threads. The versions of R and gcc are r-base-4.0.5 and 7.5.0, respectively, based on the Ubuntu 18.04 operating system.To validate the performance of ScoreEB, three simulation experiments and five real datasets analysis were carried out. Each experiment was analyzed by six methods: a fast score test integrated with Empirical Bayes (ScoreEB), multi-locus random-SNP-effect mixed linear model (mrMLM), fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). We performed simulated and real data analysis using six GWAS methods on the same computer  for ScoreEB, mrMLM, FASTmrEMMA, ISIS EM-BLASSO, HRePML and GEMMA methods were 0.4405, 0.4651, 0.4583, 0.4020, 0.4385 and 0.3358, respectively, showing that ScoreEB along with mrMLM and FASTmrEMMA has the similar power, which are significantly higher than GEMMA  to measure the accuracy of QTN effect estimation. The smaller the MSE, the better the accuracy of the method. We evaluated the accuracies for all of the six simulated QTNs across six methods. In the first simulation experiment, results demonstrated that the average MSEs with ScoreEB, mrMLM, FASTmrEMMA, ISIS EM-BLASSO, HRePML and GEMMA were 0.1001, 0.1075, 0.4084, 0.1132, 0.2055 and 5.6487, respectively . The aveArabidopsis data set consists of 199 accessions each with 216,130 genotyped SNPs , and that of the second stage is O(tqN2), here, t is the number of iterations required for expectation-maximization (EM) method to converge, q is the number of markers selected in the first stage, and t, q is much smaller than M. The time complexity of mrMLM and FASTmrEMMA in the first stage is difficult to make sure because they call other complicated algorithms, and that of ISIS EM-BLASSO is mainly limit to iterative modified-sure independence screening (ISIS) step, however, the time complexity of these three methods in the second stage are the same with that of ScoreEB. The time complexity of HRePML is greatly affected by limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. And the time complexity of GEMMA is O(MN2). The multi-locus method ScoreEB along with mrMLM, FASTmrEMMA and ISIS EM-BLASSO is constrained by Empirical Bayes in the second step, ScoreEB has a high computational efficiency when the number of individuals is not very large .We compared time complexity over M markers and N individuals among the above six methods. In ScoreEB, the time complexity of first stage is In the first simulation experiment, the dataset consists of 199 individuals and 216,130 single nucleotide polymorphism (SNP) markers with 1,000 replicates. The total running time for ScoreEB, mrMLM, FASTmrEMMA, ISIS EM-BLASSO, HRePML and GEMMA methods were 5.6419, 25.0795, 25.9247, 22.7102, 19.6781 and 17.0846 h, respectively . The ScoIn the third simulation experiment, the dataset consists of 50,000 markers with 200, 500, 1,000 and 2,000 samples, respectively. And repetition times was set to 100. ScoreEB is always the most fast with 0.3708, 0.7041, 2.6965 and 6.7638 h at different sample size, and GEMMA and HRePML are always the second and the third fast, respectively . With saArabidopsis, rice, maize, cattle and pig data, confirmed the effectiveness of ScoreEB, which identified the most candidate genes  . It shouComplex genetic architecture plays a key role in influencing the statistical power, which often leads single-locus methods to perform poorly. However, multi-locus methods can identify and account for complex genetic architectures, such as, allelic heterogeneity, and rare variant architecture . InteresN is not very large , such as, most plant researches. For researches with millions of individuals, we recommend BOLT-LMM (via a link function. 2) Further explore the issue of fewer identical genes being identified compared to different methods.Although we found that ScoreEB is an efficient and powerful multi-locus method, our approach is not free of limitations. ScoreEB is currently only suitable for analyzing quantitative traits, and is not available for analysis of binary traits. Binary traits are common, for example, stress tolerance in plants and case-control in human beings, and are mostly based on logistic or generalized linear models. ScoreEB detected a small number of genes also identified by the other methods . AlthougBOLT-LMM  or fastGBOLT-LMM . In respIn this paper, we demonstrated that ScoreEB is a fast and powerful GWAS method for quantitative trait analysis. In addition, ScoreEB has the ability to accurately estimate the QTN effect and effectively control the false positive rate. Using ScoreEB analysis can contribute to increasing our knowledge of the underlying mechanisms of complex traits and to predicting more candidate genes for molecular assisted breeding.


Schistocerca gregaria, a model organism for the study of insect polyphenism, we quantified changes in additive genetic variation elicited by lifetime thermal stress for ten traits, in which evolutionary significance is known. Irrespective of their category, traits under strong stabilizing selection showed genetic invariance with environmental stress, while traits more loosely associated with fitness showed a marked increase in additive genetic variation in the stressful environment. Furthermore, traits involved in adaptive phenotypic plasticity (growth compensation) showed either no change in additive genetic variance or a change of moderate magnitude across thermal environments. We interpret this mitigated response of plastic traits in the context of integrated evolution to adjust the entire phenotype in heterogeneous environments . Altogether, our results indicate, in agreement with theoretical expectations, that environmental stress can increase available additive genetic variance in some desert locust traits, but those closely linked to fitness are largely unaffected. Our study also highlights the importance of assessing the proximity to fitness of a trait on a case‐by‐case basis and in an ecologically relevant context, as well as considering the processes of canalization and plasticity, involved in the control of phenotypic variation.Under environmental stress, previously hidden additive genetic variation can be unmasked and exposed to selection. The amount of hidden variation is expected to be higher for life history traits, which strongly correlate to individual fitness, than for morphological traits, in which fitness effects are more ambiguous. However, no consensual pattern has been recovered yet, and this idea is still debated in the literature. Here, we hypothesize that the classical categorization of traits  may fail to capture their proximity to fitness. In the desert locust,  Schistocerca gregaria), we show that ecologically relevant environmental stress increases available additive genetic variance only for traits that are not directly linked to individual fitness. Our results suggest that using a life history versus morphology categorization of traits as a proxy for their link to fitness might be too simplistic, and instead, traits' proximity to fitness should be known specifically in the studied populations. Our study also highlights that traits' control of phenotypic variation  should not be overlooked.The idea that environmental stress can unmask cryptic additive genetic variation and expose it to selection is well accepted. Still, no consensus has been reached on the potential magnitude of these stress‐induced changes. In the desert locust ( However, this measure scales VA over the residual nongenetic variance components (VR) that may be sensitive to environment  studied. Under global changes, most environmental alterations are expected to be gradual, sustained changes in the frequency of environmental experiences. However, most studies focus on either extreme, lethal stresses on resistance traits , and morphological traits . The rationale behind this comparison is that the level of connection of a trait to individual fitness may determine its level of additive genetic variation (including hidden variation) because purging selection is assumed to be stronger on traits that are tightly linked to fitness ), an agricultural pest and a model organism for the study of insect polyphenism. Crucially, phenotypic integration has been scrutinized in this species for the past century  likely generates noise in the meta‐analyses' data, blurring the nature of the observed relationship between environmental stress and Brodie's  meta‐anaVA would be unaffected for traits strongly and directly related to fitness , while it would be increased in traits whose link to fitness is weak  or indirect presumably through adaptive phenotypic plasticity . However, expected change for those latter plastic traits is not easy to quantify, though it might be rather low on average, with responses likely to be more specific, depending both on the intensity at which phenotypic compensation is compromised with stress and on the genetic constraints in which they evolve. Beyond evaluating VA predictions in relation to environmental stress in a desert locust population, thereby expanding the range of taxa for which values of genetic parameters have been obtained, our results should help to predict more accurately the future range and severity of attacks by this agricultural pest in response to global change over long timescales .Our laboratory desert locust population was derived from 57 fertilized wild females collected in Mauritania in December 2010 . The temperature treatments included some daily variation, with colder nights than days . The warm treatment (34℃) was chosen to optimize nymphal growth. The cold treatment (26.5℃) was set to stress it, while limiting strong negative effects on survival   or 1 = 6 instars. Development Time was the age (in days) at which an individual reaches its adult emergence. Maximal Nymphal Weight was the maximal weight attained during the last instar. Nymphal Viability was the probability of hatchlings to reach adulthood recorded as a binary indicator: 0 = failure or 1 = success.We monitored the development of all G5 individuals from hatching day to adult emergence. Each day, we weighted the nymphs to the nearest milligram and recorded any molting event. 2.5Dark Pigmentation) and the percentage of green color (Green Pigmentation), which are estimates of the melanin and background polyphenisms of an individual  and tested the significance of factors present in the final model with an ANOVA (type III) using the MASS package. We then ran the quantitative genetic analyses, using ASReml‐R  was then decomposed into additive genetic (VA) and residual (VR) components, such that VP = VA + VR. The relatedness matrix was based on a pedigree spanning 5 generations, which incorporated not only data from the full‐sib and half‐sib relatives of the 4th‐generation families, but also data from genealogical relatedness since the field capture of individuals (see Section 2.3 in the main text and Section VA using a likelihood‐ratio test (LRT) against a null model. We report observed p‐values and their Shannon information transforms S‐value = −log2, which measures the degree of incompatibility between the data and the null hypothesis of the model  of information against the null hypothesis of the model. Since heritability estimates tend to increase with the number of fixed effects in the model . Note that using mixed‐effects models including covariates did not lead to any significant change in VA or h2 estimates (results not shown). Only Growth Rate was analyzed including nymphal stage as a covariate and individual identity as random effect, because of the need to account for the longitudinal structure of the dataset  with VR = 1  scale . We simulated individual phenotypic value of the ith individual such that iY = iA + iε. iA was the breeding value of the individual i and was normally distributed assuming V[iA] = VA (A is the additive relationship matrix computed based on the pedigree). iε was the random residual variation and was normally distributed with variance VR  varied between the 10 traits studied in the desert locust population . Narrow‐sense heritability (h2) was substantial for Nymphal Viability, Development Time and Extramolting , but very low for Growth Rate . In contrast, the model including a VA effect was not well supported for Maximal Nymphal Weight in both temperature treatments , suggesting that this trait did not display any heritable variation. Evolvability estimates for nymphal life history traits (when computable) were above 0.2%, sometimes reaching nearly one percent. Even body weight showed high evolvability in both optimal and stressful environments , in spite of low heritability. These values agree with most studies in insects  temperature for most morphological traits . This resulted in low heritability  and low evolvability  in the optimal environment. The single exception was Green Pigmentation, a proxy of the background pigmentation, with indication of some additive genetic variance  and quite elevated h2 (0.396) and IA (0.323%) values. Under the stressful low temperature, the heritability and evolvability values for Green Pigmentation more than doubled, with h2 reaching 0.957  and a S‐value showing an eightfold increase. The two morphometric ratios, Length of the hind femur/Maximum width of the head and Vertical diameter of eyes/Width of the vertex between eyes, which had nearly null values in the optimal treatment, showed the largest changes in standardized genetic parameters with the sublethal treatment: Their heritability reached 0.35 and 0.135, and evolvability reached 0.126% and 0.276%, respectively, with S‐values reasonably supporting models including a VA effect . In contrast, the proxy of melanin pigmentation, and the ratio Length of the fore wing/Length of the hind femur still showed negligible raw and standardized genetic parameters in the cold environment, with null S‐values.The model including a 3.2.3h2) remained at similar level between temperature treatments not only for life history traits but also for the morphological traits related to dispersal and melanism , this was due to constant VA and VR values and phenotypic means across optimal and stressful temperatures , was almost fully compensated by a change in similar direction in phenotypic mean (and VR) . In contrast, the three other morphological traits showed marked increases in both standardized levels of additive genetic variance under the cold treatment  for h2 = 0.25 and 0.5 for the maximum (243) and minimum (108) sample sizes studied, respectively . Note that, for any given sample size, our experimental design  is adequate for estimating low levels of heritability , as is sometimes observed for similar traits in other species , and the dispersal‐related trait . We did not observe either a stress‐induced increase in environmental variation  showed both phenotypic plasticity in response to temperature and significant s Nolte, . Thermald Brodie  and can Second, the release of genetic additive variance for adaptive plastic traits depends on the intensity at which phenotypic compensation is compromised in the stressful environment  strongly limits the number of individuals that can be reared, monitored, and measured under a certain family structure . In particular, we might have been able to obtain statistical support for a Drosophila sp.; Wood & Brodie, Because these logistical constraints are common, most animal stress evolution studies have focused on a few model organisms  under environmental stress, our results should help predict more accurately how global change might affect populations' dynamics and invasions  and during hours around solar noon ; funding acquisition ; investigation (supporting); methodology ; supervision ; writing‐original draft (lead). Benjamin Pélissié: Conceptualization ; data curation (lead); investigation (lead); methodology ; writing‐review & editing . Cyril Piou: Conceptualization ; data curation (supporting); funding acquisition ; investigation (supporting); methodology ; supervision . Floriane Chardonnet: Data curation ; methodology (supporting). Christine Pagès: Investigation . Antoine Foucart: Investigation (supporting). Elodie Chapuis: Methodology (supporting). Hélène Jourdan‐Pineau: Data curation (supporting); methodology ; writing‐review & editing .Supplementary MaterialClick here for additional data file.