Drosophila melanogaster, and investigated the contribution of interactions between mitochondrial and nuclear genotypes (mito-nuclear interactions) on this function. In addition, we assessed the effects of nutrition and sex on the cuticle barrier function. Based on a dye penetration assay, we find that cuticle barrier function varies across three fly lines that were captured from geographically separated regions in three continents. Testing different combinations of mito-nuclear genotypes, we show that the inward barrier efficiency is modulated by the nuclear and mitochondrial genomes independently. We also find an interaction between diet and sex. Our findings provide new insights into the regulation of cuticle inward barrier function in nature.An important role of the insect cuticle is to prevent wetting  and also to prevent penetration of potentially harmful substances. This barrier function mainly depends on the hydrophobic cuticle surface composed of lipids including cuticular hydrocarbons (CHCs). We investigated to what extent the cuticle inward barrier function depends on the genotype, comprising mitochondrial and nuclear genes in the fruit fly  Drosophila melanogaster, where the ratio of two CHC isomers varied with climatic conditions (D. melanogaster strains from north (Maine) to south (Florida) along the US east coast paralleling increasing temperatures and desiccation threat  contribute to variation in CHC profiles, which in turn affects their courtship behaviour  on the phenotype can differ across nuclear genomes, across sex and with environmental factors such as diet . It is wDrosophila lines with distinct mito-nuclear genotypes reared on different diets. We coupled this with a new in situ method that generically measures the cuticle inward barrier function  (B) (formerly Dahomey), Africa (C) (https://en.climate-data.org/), Coffs Harbour lies in a zone with a humid subtropical climate , Dundas lies in a warm-summer, humid continental climate  zone and Benin lies in a zone with tropical monsoon climate . We initially crossed 45 virgin females from the source population with the desired mitochondrial genotype to 45 males from the source population with the desired nuclear genotype to create the first generation of each mito-nuclear line. To avoid skewed effects of mito-nuclear combinations due to non-random sampling from each source population, we created each line three times independently  and kept them separated from each other since then. To generate the second generation  we backcrossed 45 virgin female offspring from the line  , Benin  . AccordiWolbachia. This was confirmed for all lines by diagnostic PCR for Wolbachia- specific primers after infected lines were treated with Tetracycline (0.3 g/l added to the food) for three generations  and 0.1% Triton X-100) and incubated them at 55 °C for 30 min. We washed the Eosin Y-stained flies three times with distilled water, isolated wings using tweezers and mounted them in 50% glycerol on glass slides. We collected images using a Leica DMI8 microscope with a built-in camera and the software LAS X. For quantification of staining, we converted the images to 8-bit format and recorded the mean grey values that reflect the staining intensity using the Fiji software (D. melanogaster (We used 10 flies (20 wings) of each combination (per line/food/sex) for wing staining. We carried out Eosin Y staining according to software . We compnogaster . If the lme4 package  occurred in only seven out of 1108 individuals distributed across four of the nine mito-nuclear combinations. We omitted these seven measurements from our analyses and used the remaining 1101 measurements assigned to one of the three staining pattern as ordinate response variable  for our analysis. We analysed the data with generalised linear mixed models (GLMMs) using the  package  in R 3.4 package . We star package . We stopPRJNA532313) and the origin of the nuclear genome. In brief we ran PCAs on the frequencies of the three observed staining patterns using 34 SNPs and the nuclear background as explanatory variables. The aim of the PCAs was to see whether and how the 27 lines cluster in the plane of the first two principal components. Based on the results from the PCA we grouped the fixed factor mitochondrial genotype to three levels . Mitochondrial genotype, nuclear genotype, sex and food type, with all their higher-level interactions, were fixed effects of the full model and line was used as random factor to avoid pseudo-replication of data in the analyses. As the response variable was ordinal (see above), we used a Binomial distribution with a log-link error function to analyse the data. We further changed the number of maximal iterations for the model to converge from 1,000 to 500,000 in the glmer Control to assure model convergence. To find the optimal grouping of the fly lines we ran a principal component analysis (PCA) using SNP analyses of the mitochondrial genomes from all our mito-nuclear lines  genotypes, as well as the interaction between sex and food (p < 0.001), best explained the frequency of the wing-staining pattern (p = 0.141).The final model revealed that nuclear  and the lowest frequency of both wing areas being stained (43.30%) among all three nuclear genotypes . Flies wFor different mitochondrial genotypes, the staining frequency for the rear area was similar in all three mitochondrial genotype groups  , whereasThe significant interaction effect between food and sex is underlined by the striking differences of a male–female difference in wing staining pattern for plant food or yeast food . SpecifiThe physiological and genetic mechanisms involved in the constitution of the inward barrier function of the insect cuticle are largely unexplored. To contribute to our understanding of these mechanisms, we investigated the effects of nuclear and mitochondrial genetic variation, sex and diet and their interactions on the function of the wing cuticle inward barrier. These factors had an effect on cuticle barrier function at different hierarchical levels.According to our statistical model, if we consider sex or diet alone (as main effects), they do not have any significant effect on the frequency of the wing-staining pattern and, by consequence, on the cuticle inward barrier efficiency. Yet, they are involved in a statistically significant interaction, with the effects of each of these factors is contingent on the other. Simply, this suggests an important role of sex on cuticle barrier function that is modulated by diet. Thus, with respect to the cuticle barrier function, we conclude that the responses to food quality between males and females are different. In other words, food utilization for barrier construction and optimization  varies between males and females. Indeed, in recent studies it was shown that the impact of the food source on the CHC pattern differed in males and females . Thus, iNext, we need to determine sex-specific single nucleotide polymorphisms (SNPs) and identify the CHC composition in males and females of our lines on different food sources to better understand inward barrier function in dependence of diet and sex. These experiments would also allow us to reveal possible differences and similarities between the molecular constitution of the inward and the outward barrier for which rich data is available.D. melanogaster. Due to specific modifications of CHC composition after induction of desiccation resistance, the survival rate of females is higher than the survival rate of males under prolonged desiccation conditions  correlates with a wing cuticle inward barrier that is more efficient than in flies with the Dundas  or with the Benin  nuclear genotype, which correlates with the lowest cuticle inward barrier efficiency. Rough temperature or humidity profiles of the locations  do not explain these correlations arguing that the cuticle inward barrier may not be directly dependent on these factors. In any case, we conclude that the inward barrier function depends largely on varying fly line-specific expression profiles of nuclear genes, some of which presumably are needed for CHC production and deposition including genes coding for Cyp proteins, fatty acid synthases and elongases. Nuclear genetic variations that comply with differences in CHC composition in inbred or geographically separated lines of ait loci  or SNPs ait loci . The genmitochondrial climatic adaptation hypothesis (We found a significant effect of the mitochondrial genotype on the wing-staining frequency patterns. Cuticle inward barrier efficiency was similarly reduced in flies with a Dundas mitochondrial genotype. Rough climate profiles of the original locations of our fly populations are insufficient to explain the correlation between the mitochondrial genotype and cuticle inward barrier efficiency. More detailed assessment of the local climate situation and the use of more geographically disjunct fly populations are thus necessary to allow relating cuticle inward barrier efficiency to any climatic factor. A relationship between cuticle inward barrier efficiency, mitochondrial genotype and a climatic factor would, in turn, allow the testing of whether this relationship is in agreement with the pothesis . This hyD. melanogaster lines engineered from laboratory and natural populations (D. melanogaster. Together, we conclude that the function of the inward barrier is sensitive to variation in the mitochondrial genomes of the lines tested. We also conclude that the mitochondrial-dependent inward barrier function is possibly not mediated by CHCs.In a recent work, no significant effect of the mitochondrial genotype on lipid content was found in ulations . That stD. melanogaster or refine the quantification of our dye-penetration assay in our analyses to uncover whether any subtle mito-nuclear effects on cuticle barrier function exist.Interestingly, Aw and colleagues , 2018 diIn summary, in this study we find that Eosin Y penetration through the wing cuticle of mass bred populations from three global populations is variable and that the pattern of variability differs between the lines. Our data indicate that along with CHCs, other cuticle components are sensitive to genetic variation within the nuclear and mitochondrial genomes as well as to sex-diet interactions. Candidates are factors acting in the Snu-Snsl pathway that contribute to the construction of the outermost cuticle layer termed envelope that serves as a physical barrier against penetration and desiccation . Nuclear10.7717/peerj.7802/supp-1Figure S1The mitochondrial genotypes were grouped based on the result by a PCA. We distinguish three groups. The mitochondrial genotypes AA1, AA2, AB1-3 and AC1-3 were grouped and named type ‘A’, the mitochondrial genotypes BA1-3, BB1-3, BC1-3, CA1 and CA3 were grouped and named type ‘B’ ,the mitochondrial genotypes AA3, CA2, CB1-3 and CC1-3 were grouped and named type ‘C’.Click here for additional data file.10.7717/peerj.7802/supp-2Figure S2Relative frequencies by sex are: male no staining: 21.61%, rear area stained: 6.79%, both areas stained: 71.61%; female no staining: 11.46%, rear area stained: 16.45%, both stained: 72.09%. Staining frequencies by food: plant food no staining: 11.07%, rear area stained: 3.51%, both areas stained: 85.41%; yeast food no staining: 22.56%, rear area stained: 20.11%, both areas stained: 57.33%.Click here for additional data file.10.7717/peerj.7802/supp-3Table S1∘ ∘ indicates that none of the two areas was stained, ∘ • indicates that the rear wing area was stained and • • indicates that both areas on the wing were stained. Empty cells indicate that the specific combination of all four factors did not occur in our experiment after recording of the mitochondrial genotypes to five levels based on the sequence data.Click here for additional data file.10.7717/peerj.7802/supp-4Data S1Click here for additional data file.


Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high‐density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome‐wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS‐identified loci were assessed between partially resistant and susceptible genotypes through a RNA‐seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA‐seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.White mould of soya bean, caused by  Sclerotinia sclerotiorum (Lib.) de Bary has a broad host range and is documented to infect at least 408 plant species , which distributed on 17 chromosomes (LGs) of soya bean. Among these QTLs, only six loci were identified under field conditions  mapping in bi‐parental derived population is a method commonly used to dissect the genetics basis of white mould resistance in soya bean. The previous mapping studies have identified a total of 103 QTLs (Glycine max var. Williams 82 (2n=40) began in the middle of 2006 and was completed early in 2008. Approximately 978 million base pair (Mb) is captured in 20 chromosomes, with a small additional amount of mostly repetitive sequence in unmapped scaffolds  to identify loci associated with resistance to white mould via GWAS, (ii) to explore candidate genes located at GWAS‐identified loci through differential expression analyses and (iii) to assess the potential of marker‐based prediction model as a new approach in soya bean breeding. We believe that genetic dissection in two different germplasm panels will provide complementary information for understanding of mechanisms underlying white mould resistance.Greenhouse evaluations of the two panels of germplasm for resistance to white mould revealed a broad range of resistance levels Table . As a myG × E.In field tests, averaged over 2 years, a large variation in white mould resistance was also observed across assayed soya bean accessions in both panels. Disease severe index (DSI) had a mean of 31.2 and 30.8 for PIs and improved lines, respectively, with more than a 20‐fold difference among the resistant and susceptible lines Table . ANOVA fet al., r = −0.22 in 2014, r = −0.12 in 2015) but statistically significant correlation (P < 0.05) with live node number measured in the greenhouse for improved lines (Table A previous study demonstrated that maturity groups (MGs) significantly affected disease incidence  analysis and GWAS for the improved lines and PIs, respectively. Moreover, population structure analysis was based on 4549 SNPs with minor allele frequency (MAF) >20% and physical distance >60 kb.et al., r2 declining to half its maximum value, was 240 kb and 370 kb in the two subsets of PIs and improved lines, respectively. These LD decay estimates are larger than previously published values in landraces of 187 kb and in improved lines of 233 kb  between the two grouping factors. For example, PIs from Japan were mainly (63%) clustered in Cluster 4, whereas Cluster 1 contained 31 accessions, of which 19 were from northern China; improved lines belonging to MG II dominated Cluster 4, whereas Cluster 3 contained eight accessions, of which all were from MG III . The MLM model resulted in a good approximation to expected cumulative distribution of P value  comprised four SNPs covering 44.5 kb around 7.2 Mb on Chromosome 16. In the panel of PIs, the locus most significantly associated  with number of live nodes comprised six SNPs covering 270 kb at 36.7 Mb on Chromosome 7.GWAS was conducted using the phenotypic variation data from greenhouse and field trials in a mixed linear model (MLM), which accounts for both population structure  and familial relatedness  comprised of two SNPs (ss715605011 and ss715605026) covering 190 kb around 49.5 Mb on Chromosome 9. In the panel of PIs, the SNP showing the highest association  with DSI comprised of three SNPs  covering 14 kb around 29 Mb on Chromosome 16. Only one locus (Locus #7) had a significant association with white mould resistance across both panels. One explanation for this unexpected result is that the two populations had a different genetic background and molecular mode of action underlying resistance. A NJ tree showed that the panels of PIs and improved lines formed highly differentiated populations  between resistant and susceptible genotypes Table . As for et al. , the sumet al. . OverallGlyma.05G044000), phosphatase (Glyma.09 g281900 and Glyma.14G049600) and methyltransferase (Glyma.16 g134700), were prominent  were also significantly higher in abundance in the resistant lines at both time points studied. The NB‐ARC domain is believed to be a functional ATPase domain, and its nucleotide‐binding state is proposed to regulate activity of the resistance protein  and a UDP‐glucosyltransferase (Glyma.16G158100) involved in secondary metabolism biosynthesis. The expression of this gene implies that a detoxification battle is being waged between host and pathogen  was found located at a GWAS‐identified locus, and it was up‐regulated with log2 (fold change) = 2.8 (FDR <0.05) in the resistant line's transcriptome but had no significant change in susceptible line's transcriptome across time points. Furthermore, three additional oxalate exchanger‐related genes  exhibited elevated levels of transcripts in resistant line's transcriptome after inoculation with S. sclerotiorum, but did not overlap with GWAS‐identified loci. Future studies will focus on functionally validating effects of these genes, uncovering the molecular mechanisms of complex white mould resistance in soya bean.As mentioned above, about half (58) of the differentially expressed genes were more abundant in the resistant line's transcriptome compared to the susceptible line's transcriptome. Among these up‐regulated genes, those encoding defence‐associated proteins, such as pectate lyase (Prediction accuracies of MAS using the loci identified via GWAS for DSI were investigated. For MAS by multiple linear regression (MLR) method, 12 and 14 SNPs identified from improved lines and PIs were investigated, respectively. At the same time, the prediction accuracies estimated from an equal number of randomly selected SNPs were used as a control. The prediction accuracies of MAS in the improved lines ranged from 0.47 to 0.51 (average of 0.50) for the 12 SNPs, which was 26% higher than that of the random SNPs (average of 0.37) Figure .et al., Based on the above analysis, it is clear that white mould resistance in soya bean is a complex trait and controlled by multiple genes with small effects. Our MAS model showed relatively low prediction accuracy for DSI. Moreover, it was recently suggested that MAS had failed to significantly improve complex traits . The Illumina SoySNP6K iSelect BeadChip , which consists of 5361 SNPs, has recently been developed for use specifically within soya bean breeding/research programmes . These accessions were collected from multiple geographic origins including the United States, China, Japan, Korea, Kyrgyzstan and Russia. All of those accessions were selected to represent the variation and maintain the diversity of the collection, based on SNPs detected by the SoySNP50K BeadChip , which consists of 52 401 SNPs. The quality of each SNP was checked manually as previously reported by Yan et al. . The SNP3 growth stage  was used for inoculations. The experiments were conducted in the winter of 2012 and 2013. The drop‐mycelium method developed by Chen and Wang ij + (L × E)ik + εijk, where Yijk is the observed phenotype for the kth line in the jth replicate of the ith environment; Lk is the random effect of the kth line; Ei is the random effect of the ith year; R (E)ij is the random effect of the jth replicate in the ith year; (E × L)ik is the random interaction effect of the ith year and the kth line, and εijk is the error. The heritability estimates were calculated using variance components obtained by the BLUP linear model  of 2014 and 2015. Consistent heavy white mould disease symptoms had been observed historically in the disease nursery. Ninety seeds were planted in single‐row plots, 6 m long with 0.58 m row spacing, at a depth of 3.8 cm with three replications. Plots were rated for disease severity based on the rating system developed by Kim et al.  at appro et al., . All plaNyquist, .et al., r2) of alleles between markers. The extent of LD decay was measured as the chromosomal distance at which the average pairwise correlation coefficient (r2) dropped to half its maximum value.Principal component and neighbour‐joining tree analysis were applied to infer population stratification. A pairwise distance matrix derived from a modified Euclidean distance for all polymorphic SNPs was calculated to construct neighbour‐joining trees using TASSEL 5.0 software  ≤0.05 was used to identify significant associations.A unified mixed model was used to perform GWAS with the control of both population structure and relative kinship. The MLM can be expressed as Sclerotinia sclerotiorum in greenhouse with the drop‐mycelium method , the most susceptible line (V28N8RR) and resistance  and susceptible  check were grown and inoculated with ® RNA Assay Kit. Libraries were constructed and sequenced by MOGENE , and their sequencing reads were analysed as described by  was then used to produce the merged transcriptome file. Cuffdiff  according to the manufacturer's instructions in conjunction with DNase treatment. The quality of total RNA was determined using RiboGreeny = 1nμ + Zg + e, where y is a vector of phenotypes, 1n is a vector of ones, μ is the mean, Z is a design matrix allocating records to genetic values, g is a vector of additive genetic effects for an individual, and e is a vector of random normal deviates σ2. Analyses were performed with the software TASSEL 5.0.A genomic best linear unbiased prediction (gBLUP) model was used to predict genomic estimated breeding values (GEBVs) of white mould resistance. The model for gBLUP is given by et al., As for the MAS model, MLR was employed to predict DSI  plot of MLM for living node and DSI in two panels.Figure S4 Manhattan plots of MLM for DSI in improved lines (a) and PIs (b).Figure S5 Functional category annotations for candidate genes and their respective percentages identified via GWAS as significantly associated with white mould resistance.Figure S6 Comparison of predication accuracy between significant SNP and randomly selected SNP.Figure S7 Scale used for phenotyping white mould disease severity (DS).Table S1 Correlation analysis of DSI and agronomic traits in improved lines and PIs.Table S2 Distribution of accessions in each subgroup based on genetic distance in improved lines and PIs.Click here for additional data file.Table S3 Candidate genes showing statistically significant induction in response to Sclerotinia sclerotiorum inoculation among genotypes tested.Click here for additional data file.Table S4 Nucleotide differences found between resistant and susceptible genotypes result in an amino acid change at GWAS‐hit loci.Table S5 Soya bean germplasm accessions analyzed in this study.Click here for additional data file.


Inconsistencies between published estimates of dominance heritability between studies of human genetic isolates and human outbred populations incite investigation into whether such differences result from particular trait architectures or specific population structures. We analyse simulated datasets, characteristic of genetic isolates and of unrelated individuals, before analysing the isolate of Cilento for various commonly studied traits. We show the strengths of using genetic relationship matrices for variance decomposition over identity-by-descent based methods in a population isolate and that heritability estimates in isolates will avoid the downward biases that may occur in studies of samples of unrelated individuals; irrespective of the simulated distribution of causal variants. Yet, we also show that precise estimates of dominance in isolates are demonstrably problematic in the presence of shared environmental effects and such effects should be accounted for. Nevertheless, we demonstrate how studying isolates can help determine the existence or non-existence of dominance for complex traits, and we find strong indications of non-zero dominance for low-density lipoprotein level in Cilento. Finally, we recommend future study designs to analyse trait variance decomposition from ensemble data across multiple population isolates. This suggests the presence of genetic constituents in the architectures of such traits and leads to an obvious question: for a pair of individuals, can one describe a relationship between their degree of relatedness (genomic sharing) and the degree of similarity of their trait values? Fisher unravelled this question by proposing a decomposition of the variance of a trait, with components attributed to each individual’s genome and to the amassment of environmental exposures in each individual’s history. This genetic component of the variability is known as the heritability of the trait which Fisher connected to the correlation of trait values between relatives. Heritability has been estimated extensively for a multitude of traits and through diverse models and study designs. Importantly, the recent availability of dense genetic data in large cohorts has enabled the estimation of heritability from samples of unrelated individuals whereas previous estimations had been driven by studies of close relatives such as twins or nuclear families. A review of heritability estimation in related individuals can be found in Tenesa & HaleyH2) and the more commonly communicated narrow-sense heritability (h2). This stems from the innovative modelling of complex traits by Fisher who demonstrated the interest of splitting the genetic variance of a trait into additive, dominant , and epistatic (interaction between genotypes of multiple loci) components3. For details on more elaborate models, we refer the reader to Abney et al.4 and Young & Durbin5. Briefly put, h2 describes the additive contributions of each allele received from one’s parents while H2 encompasses the effect of one’s whole genome and is the sum of h2 and the contributions of non-additive effects. For the purposes of this study, we term this non-additive fraction of variance as ‘dominant’ as we do not here consider epistasis or higher order variance terms; we will denote this component as H2 − h2). In terms of phenotypic similarities between family members, the parent/offspring correlation is equal to An important distinction is to be made between broad-sense heritability . For a setting of We then are able to estimate the heritabilities as follows:N × N matrices K and D. Historically, K and D are defined in terms of identity-by-descent (IBD) probabilities7. K is equal to 2φ, where φi, j is the kinship coefficient of individuals i and j, defined as the probability of two alleles, randomly sampled from each of individuals i and j, at the same locus will be IBD. Di, j is the probability that individuals i and j share exactly two pairs of alleles IBD at a given locus. Both φi, j and Di, j are themselves expressions of Jacquard’s nine coefficients of identity: Di, j = Δ1 + Δ76. In studies of family data or isolated populations, these coefficients have been classically estimated from pedigree information but with the advent of dense genomic information, they can now be estimated reliably from genotype data by either estimating genome-wide IBD sharing probabilities or detecting and counting IBD segments10. Such methods have also been developed for studies of unrelated individuals11, though the predominant approach in such studies is to use moment estimators of K and D by taking correlations between each pair of individuals’  additive and dominant genetic components, respectively13. These latter estimators are known as genetic relationship matrices (GRMs) and can be used in any study design.There are various possible choices of the K and D which both come with potential drawbacks. If IBD probabilities are used to estimate K and D, they represent the level of relatedness between pairs of individuals based on the presence of recent common ancestors but if K and D are estimated as GRMs, then they represent simply the correlation between pairs of individuals’ genotypes. For the former interpretation, coefficients of identity can only be approximated either by their expected values based on the pedigree structure linking individuals or by estimating the proportions of IBD-sharing between individuals based on their genotypes. However, exhaustive pedigree information is never available and indeed the concept of IBD is similarly problematic due to the ambiguity of how many generations to consider when looking back for evidence of shared genetic ancestors. After many generations, mutations and recombinations cause the IBD segments to become increasingly short and not completely identical and thus difficult to distinguish from background genetic variation16. For the latter interpretation involving GRMs, there is the immediate problem that such correlations are computed from a large set of variants which are not specific to the trait being studied in the hope that these variants will be representative of the unknown set of causal variants via linkage disequilibrium (LD) (correlations between variants)17. Consequentially, if heritability is estimated with GRMs, it corresponds to only a proportion of the phenotypic variation coming from the subset of causal variants that are in LD with the genotyped variants18. This can lead to downwardly biased estimate of heritability as causal variants may often be held at low frequencies by selection20 and so will be in weak LD with common genotyped variants. Furthermore, if there exist relatively few causal variants, the large numbers of non-causal variants used to estimate the genetic correlations might mask the desired correlation of causal variants between individuals21. Genomic-based IBD methods applied to unrelated individuals has been suggested as an approach to improve upon genetic correlation methods as detected stretches of IBD can cover some un-typed genetic variation11.This leads to two distinct interpretations of the matrices 22. However, there has been a trend towards using genomic-based estimates even when pedigree data is available due to the increased precision of relatedness estimation from genetic data, both in human studies27 and in animal/plant studies31.The main motivation for employing GRMs is that this allows for the estimation of heritability from unrelated individuals, thus leveraging data from large cohorts and avoiding shared environment biases32 and thus often only estimates of et al.12 illustrated this characterization of diminutive dominant genetic variance for 79 traits in two large samples of unrelated individuals. This result was then re-enforced in Nolte et al.33. Yet, many others have presented incongruent results on this subject. Chen et al.34 compared the same approach as Zhu et al.12 with a twin-based analysis and concluded that whilst the genetic variances of 19 traits were predominantly additive, dominant genetic components were nonetheless more prominently apparent than when described elsewhere. Aside from these studies, dominance heritability estimation using GRMs has rarely been carried out, and the authors who are more interested in dominance tend to rely on family data36. Of particular note is the observation that significant non-additive genetic components for many traits have been found in some studies on population isolates: Abney et al.37, Pilia et al.38, and Traglia et al.39  and indeed such confounding between estimates for dominance and shared environmental factors between relatives has recently been observed by Zaitlen et al.41 who performed a study on extended genealogies from the Icelandic populations, itself a moderate isolate.Studying dominance in samples of human twins or siblings can be problematic due to confounding between the sharing of genotypes and shared environmental factors47. Negative results regarding the improvement of prediction given by including genetic dominance have also been presented  and indeed debate continues in regards to the practical value of non-additive variation; for recent reviews we refer the reader to Varona et al.49 and Wolak & Keller50. The increased interest in non-additive variation in this domain suggests that there may be value in not discounting such variation in human studies.Genetic dominance has often been considered in the study of various animal species . Here, by design, confounding with shared environmental factors can often be avoided and extensive and highly accurate pedigree data can be recorded. For many traits, dominance heritability is often found to be significantly different from zero and the inclusion of dominance has been shown to give improved performance of prediction models in animal studiesK and D in an isolate as well as the effect of shared environmental factors between siblings on the estimation of We propose to compare heritability estimations in a range of simulated study designs in order to contrast studies in population isolates and in samples of unrelated individuals. In this way we hope to determine whether the differences between studies in isolates and in unrelated samples stem from particular trait architectures, specific population characteristics, or non-equivalence between interpretations of heritability in differing study settings. We will also assess different methods for estimating the matrices mentclass2pt{minim51. We simulated phenotypes with the following characteristics: M causal additive variants, and M causal dominant variants. Causal variants are selected at random and effect sizes are drawn from normal distributions. Full details of the simulation of genotypes, phenotypes, and population structure are given in the Methods section. We chose 200 values of M between 1 and 1,000,000, and for some values of M we repeated the simulation 500 times in order to empirically estimate the standard errors of the estimates of K and D for each population as GRMs from a dense set roughly 5.8 million of frequent UK10K variants (MAF > 0.05). We also performed the simulation with K and D calculated on roughly 170,000 single nucleotide polymorphisms (SNPs) which are those also available in the real data of Cilento ” which mimics the population structure of the genetic isolate of Cilento from Southern Italy (this cohort is described fully in the Methods section), along with three simulated outbred populations, “Oubred(1444)”, “Outbred4332)”, and “Outbred(8644)” where the numbers in parentheses indicate the sample sizes. All populations are formed from mosaic haplotypes arising from the UK10K imputation panel444” whic”, and “OK and D. However the, precision of the estimates was low. The estimates in all of the simulated outbred populations were evidently downwardly biased when causal variants were selected completely at random and therefore included many rare variants as in the UK10K panel , over 50% of the variants have a MAF below 0.01. As the size of the outbred population increases, the precision of the estimates increases but downward biases remain, even when all causal variants are non-rare. The number of causal variants for each variance component (M) did not affect the results other than we observed that a small number of causal variants led to lower precision in the results obtained when simulations were repeated. This is shown by the diamonds representing empirical standard errors measured for certain values of M shown in Figs Fitting the LMM for Isolated1444) resulted in accurate estimates of 444 resulentclass1pt{minimaentclass1pt{minimaWe observed increased precision in the estimation of heritability components as we increased the size of the simulated outbred population Figs  and 2. TSubsequent analyses will focus on the population Isolated(1444). This will be of particular interest as for this population results are directly comparable with analyses of the real data of Cilento.K and D in a population isolate, we performed similar simulations of phenotypes and tested the estimation of K and D calculated from the pedigree of Cilento, K and D calculated from exact IBD-sharing recorded during the data simulation (true IBD), K and D calculated as GRMs, and finally K and D calculated using either the IBDLD9 or GIBDLD52 software (see Methods section). Comparisons of off-diagonal elements of these matrices are given in Supplementary Fig. K as estimated by a GRM was very similar to the true IBD-sharing probabilities but there were some differences for the matrix D  of egy Fig. .Figure 4MAF > 0.05) UK10K positions with estimates of K and D using either pedigree information or true IBD-sharing information  based methods are not suitable for millions of variants and so were applied to the set of roughly 170,000 SNPs present in all three Cilento villages. These methods were compared to the use of GRMs based on the same set of variants and to using pedigree information or true IBD-sharing information  indicating pairs of siblings in the sample for K and D as these were predominantly the methods used in aforementioned studies that calculated dominant genetic components for widely studies traits , creating a sibling free population which we label as “Isolated(5136)_nosibs”. Full details of the simulation of this population are found in the Methods section. From this population, we observed improved estimates of both K and D using different approaches and then compared the resulting values. We calculated K and D using either the pedigree information, or as GRMs using genotype data before or after imputation. Results were in accordance with those from the simulated population isolate Isolated(1444) . Several traits displayed significant dominant genetic components and our results . Full details are given in the Methods section; as above in the simulation study, the model names indicate the variance components included in the LMM. The orthogonality between the additive and non-additive genetic components is apparent as estimates for K and D had equivalent estimates for the additive genetic components to the MLEs found when using GRMs, however the dominant genetic components were always estimated as equal or greater when using pedigree information.Following quality control and imputation ; we fitted LMMs to the data in Cilento having estimated matrices ts Table  are not ts Table . LMMs weThe traits of BMI, LDL, and Total Chol were all estimated as having dominant genetic components higher than their respective additive genetic components in the KD model. By examining the 95% confidence regions, there is some indication that the dominant genetic components are unlikely to be equal to zero. This is due to the observation that the red zones either do not intersect or only briefly intersect the upper left boundary xes Fig. .Adding the shared environmental component between siblings drastically changed the estimates of entclass1pt{minimaure Fig. . EquivalFor BMI, the unadjusted heritability estimate was distant from previously reported results, but once we allow for a shared environmental component between siblings, we find similar estimates for Across all analyses, whether on simulated or real Cilento data, we observed that estimates of Isolated populations exhibit favourable characteristics for uncovering the contribution of K and D. In the simulated isolate, the precision of the estimates of 53 when estimating We compared different methods to estimate the covariance matrices 55.Foreseeably, the simulated outbred populations led to underestimation of both 39  unrelated individuals. To create isolate type data, for each chromosome, we randomly selected 200 UK10K haplotypes, from which 2,940 mosaic haplotypes were simulated in order to simulate the 1,470 founders of the combined pedigree of Cilento. This set of founder haplotypes were supplied to the software Genedrop (part of the MORGAN66 package) along with the pedigree of Cilento in order to simulate phased genetic data for the 1,444 genotyped members of Cilento. Our gene-dropping approach was identical to the methods used in Herzig et al.67 We have made comparisons on four potential populations: the 1,444 individuals from Genedrop with isolate type data, labelled “Isolated(1444)”, and three possible sets of the 8,664 simulated unrelated individuals: “Outbred(1444)”, “Outbred(4332)”, “Outbred(8664)”, that represent outbred populations of the same size as Cilento, three times the size, and six times the size, respectively. We chose this range of samples sizes based on an analysis of the variance of eigenvalues68 of GRMs estimated on the populations Isolated(1444) and Outbred(1444)  autosomal haplotypes would be appropriate for the generation of simulated data for Campora and we decided to scale this up to 200 for the generation of simulated data for the three villages. We checked that this created simulated data with a similar structure as the observed data in Cilento  and Isolated(8664), respectively. In addition, we randomly discarded one individual from each sibling pair of the population Isolate(8664) to create a population with no sibling pairs of size 5,136, labelled as “Isolated(5136)_nosibs”.Phenotypes were simulated repeatedly for each population as the sum of normally distributed errors Equation .3\documeGA and GD are the additive genetic components of the genotypes of the randomly selected M causal additive variants and the non-additive genetic components of the randomly selected M causal dominant variants, respectively. Effect sizes βA and βD were drawn from normal distributions. Variants may exhibit both additive and dominant effects and a maximum of 2M variants could have non-zero effect sizes. We varied the heritability by scaling the effect-sizes accordingly in the knowledge that 69 and estimated parameters τA, τD, and 70. Matrices K and D were estimated using the method-of-moment techniques described in Zhu et al.12, and we either used all variants present on the UK10K, or the variants present in the real data from all three Cilento villages. The exact set of variants used for these calculations were those with MAF > 0.05 and those passing a quality control threshold on the Hardy-Weinberg p-values (>10−5).To estimate the variance parameters, and hence heritability, we fitted the model of Equation K and D from the pedigree structure of Cilento using software IdCoefs4 that calculates 71. Furthermore, we were able to record the origin of every mosaic segment simulated during the HapGen based and gene-dropping stages. This allowed us to calculate true proportions of IBD-sharing between every pair of individuals in the Isolated(1444) population. We also tested the software IBDLD9 and GIBDLD52 which directly estimate In the case of Isolated(1444), we also estimated S, which has values of 1 on the diagonal and at every off-diagonal element corresponding to pairs of siblings in the sample; all other entries are zero. To simulate phenotypes for the population Isolated(1444) with additional correlation between pairs of siblings, approximating an effect of shared environmental exposure, we simulated phenotypes under the same model as Equation Here we introduce the sibship matrix, denoted as K and D were again estimated on the basis of pedigree or genetic information. To calculate GRMs from genetic data, we were restricted to using the set of variants on the intersections of the two arrays used for genotyping of Cilento data. As this set was relatively sparse, we also performed genetic imputation with the following pipeline: phasing by SHAPEIT272 with the “duohmm” option73 and informed by the Haplotype Reference Consortium74 (HRC) reference panel followed by imputation by IMPUTE475 with the HRC as the reference panel. K and D were then computed on hard called imputed genotypes77 after removing variants with imputation quality scores below 0.7.After quality control on both phenotypes and genetic data , we used the same approach as with the simulated data to estimate the heritabilities of the seven traits considered in this study. The only difference being that for the analyses of Cilento data, we added the following covariates to the LMM: age, sex, age × sex, and indicators of village membership . For one trait (Triglycerides) we transformed the phenotype to a logarithmic scale, whereas other traits were left untransformed after excluding very small numbers of outliers. LDL and Total Chol were both pre-adjusted for use of lipid-lowering medication. Matrices et al.78 presented an IBD-based method for nuclear families in the Icelandic population aimed at eliminating environmental bias by looking at deviations in observed kinship from expected values. In Cilento data, the sample size precluded this approach as there are insufficient numbers of pairs of individuals with the required expected level of IBD-sharing and with both sets of parent’s genotypes. However, we are able to add a shared environment effect by adding into our model a variance component indicating pairs of individuals who share the same mother. A similar approach was shown to lead to unbiased results in many simulation settings in Young et al.78 As pairs of siblings have by far the highest probability of sharing two alleles IBD as each locus (one chance in four), correlations caused by shared environmental exposures between siblings are very likely to confound the estimation of In a recent study of the Icelandic population, Young Supplementary Materials


Understanding the genetic basis of fitness-related trait variation has long been of great interest to evolutionary biologists. Secondary sexual characteristics, such as horns in bovids, are particularly intriguing since they can be potentially affected by both natural and sexual selection. Until recently, however, the study of fitness-related quantitative trait variation in wild species has been hampered by a lack of genomic resources, pedigree, and/or phenotype data. Recent innovations in genomic technologies have enabled wildlife researchers to perform marker-based relatedness estimation and acquire adequate loci density, enabling both the “top-down” approach of quantitative genetics and the “bottom-up” approach of association studies to describe the genetic basis of fitness-related traits. Here we combine a cross species application of the OvineHD BeadChip and horn measurements  from harvested thinhorn sheep to examine the heritability and to perform a genome-wide single-nucleotide polymorphism association study of horn size in the species. Thinhorn sheep are mountain ungulates that reside in the mountainous regions of northwestern North America. Thinhorn sheep males grow massive horns that determine the social rank and mating success. We found horn length, base circumference, and volume to be moderately heritable and two loci to be suggestively associated with horn length. The genetic basis of trait diversity is a fundamental area of inquiry in evolutionary biology. Knowledge regarding the generation, inheritance, and maintenance of variation strikes at the core of our understanding of evolution. Genes that underlie fitness-related traits are of particular interest since selection is thought to act most strongly on these relationships . Key areRecent advances in genomic technologies have ushered in a new age of inquiry into the genetic basis of traits by dramatically lowering the per-unit cost of obtaining genetic data, particularly for nonmodel organisms . This drSecondary sexual characteristics are intriguing targets of inquiry since they are potentially under both natural and sexual selection. While beneficial alleles may be expected to be driven to fixation by either process, the presence of both may yield counterbalancing selection pressures that maintain variation . Horns iRXFP2), which has been shown to strongly influence horn development in domestic sheep and underwent strong positive selection due to artificial breeding for individuals lacking horns . In a feral breed of domestic sheep, the Soay sheep of St. Kilda archipelago (Scotland), RXFP2 has been found to be strongly associated with discrete and quantitative variation in horn phenotype  in game management units 5, 7, and 9 in Yukon, Canada , where r1 and r2 represent the base radii at either end of an annual growth segment, and H is the length of the segment  may be legally harvested  and year 2 growth to be recorded as a single growth increment, resulting in an overestimate of year 1 growth and underestimate for the subsequent years. Furthermore, age determination will also be in error. To mitigate the effect of misidentified or missing first annulus, we removed individuals with biologically implausible growth increments, that is, year 1 growth >160 mm and sum of year 1 and 2 > 420 mm . Horns w® HD SNP BeadChip following the manufacturer’s protocol (Qiagen). The Ovine Infinium® HD SNP BeadChip is an SNP array containing 606,006 markers originally designed for use in domestic sheep (Ovis aries) by the International Sheep Genomics Consortium (ISGC) , using the software GENOMESTUDIO (Illumina). We also used GENOMESTUDIO to cull low-quality genotype calls using a GenCall (GC) score threshold of 0.8. The GC score of genotype call is an assessment of cluster quality based on how tightly clustered the raw signal of that genotype call is compared to other calls of identical genotype and can range from 0 to 1 (higher is better). Post GC score quality-controlled genotype calls were exported in PLINK format using a custom plug-in (Illumina).We extracted DNA from 192 samples of Dall’s sheep rams with the Qiagen DNeasy Blood and Tissue Kit (Qiagen) using standard protocol. We quantified the extracted DNA using the Qubit Fluorometer (Life Technologies) and normalized to 50 ng/µl in preparation for genotyping. We genotyped the sampled individuals using the Ovine Infiniumm (ISGC) . O. arieears ago . Raw sigWe used PLINK v1.07  to removWe used the R packages GENABEL v1.8  and its Va), permanent environment (Vpe), cohort (Vyb), year of measurement (Vym), and residual variation (Vr) as random effects and age associated with each horn size measurement as a fixed effect. Horn size variation is thus broken down into five components, Vp = Va + Vpe + Vyb + Vym + Vr. Permanent environmental effect was calculated from repeated measures of the same individual to account for variation associated with environment condition effects specific to that individual. Additive genetic variation was estimated using a genomic relationship matrix (GRM) constructed by calculating the proportion of shared alleles at each locus weighted by allele frequencies. Narrow sense heritability was calculated as the ratio of additive genetic variation and overall phenotypic variation (h2 = Va/Vp). Next, the covariance structure of our linear model is used to test for associations of individual SNPs with horn size measures using ordinary least squares using REPEATABEL. P values for SNP associations were calculated using Wald tests. We defined genome-wide significance and suggestive significance of SNP association using 0.05/nSNPs and 1/nSNPs, respectively (nSNPs − number of markers). In association analysis containing repeated measures of potentially related individuals, there are concerns that significance may be inflated because (1) population stratification may overestimate SNP effects and (2) repeated measures of the same individual may be correlated. By using the (co)variance matrix constructed in the first model-fitting step, we can account for relatedness by using the GRM and within individual variance via the estimation of permanent environmental effects .Repeated-measures data generally result from longitudinal studies; however, annualized pattern growth due to the cessation of horn growth each winter allows for the estimation of horn measurements in previous years. The use of repeated measures allows for better estimates of within-individual variation and has been found to increase the power of genome-wide association studies by providing year-to-year variation in a trait measurement . The met effects . We calcP values . We reviWe acquired SNP genotypes and horn size measurement data for 192 Dall’s sheep rams from southwestern Yukon, Canada Table 1.Marker-based estimates of heritability for horn measures ranged from 0.33 to 0.36 Table 1.GFRA2 (GDNF family receptor α). In domestic sheep, GFRA2 codes for receptors that interact with glial cell line–derived neurotrophic factors (GDNFs) (FIGNL2 (fidgetin-like protein 2). In mammals, fidgetin-like proteins belong to a family of ATPases associated with embryonic development (No loci were found to be associated with any of the horn size traits at the genome-wide significance level S3. Two  (GDNFs) . OAR3_13elopment .Ovis canadensis). For example, In this study, we investigated the genetic basis of fitness-related traits in Dall’s sheep by genotyping 192 animals using a cross-species application of a domestic sheep high-density SNP array . We achieve a typical conversion rate of about 1% , which rGFRA2 and FIGNL2 in the domestic sheep genome. In humans, GFRA2 is a protein-coding gene for a coreceptor in the GDNF family of neurotrophic ligands , which belongs to a superfamily of genes that code for ATPases associated with diverse cellular activities proteins (AAA proteins). This superfamily is made up of a wide range of molecular chaperones that facilitate cellular functions such as proteolysis and membrane fusion characterized by a common conserved ATP-binding domain of ∼240 residues  and modest marker density  in this study indicate caution against overinterpretation. Previous research has shown that at sample and marker sizes like ours SNP effects may be upwardly biased and can produce false positives , have found a strong candidate for horn morphology in the gene p genome . QTLs ofic sheep . Our poselection . Similaric sheep . So while sweeps .While quantitative genetic and association studies have long been performed for some wild animals, most are part of large longitudinal studies. These types of projects can be costly and logistically challenging to operate, thus putting them out of the reach of most wildlife researchers. By taking advantage of the “genome-enabled”  status oThe data is available at EVA under the following accessions - Project: PRJEB34082; Analyses: ERZ1066075.ZS conceived of the study. DC and ZS designed the study. ZS performed the data analysis and wrote the manuscript. DC provided input in results interpretation and provided substantial feedback on the 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.


Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity. Aleksandr Zenin et al. present a genome-wide association study and genetic risk model for human healthspan, the length of morbidity-free life. They identify 12 loci associated with healthspan and osbserve genetic correlations between healthspan and life-history and lifestyle traits, such as obesity and smoking. Likewise, extreme longevity in human cohorts is associated with a delayed incidence of diseases: Kaplan-Meyer curves of disease-free survival, stratified by age, demonstrate a consistent delay in the onset of age-related diseases with increasing age of survival2. Therefore, the emerging premise is that aging itself is the common driver of chronic diseases and conditions that limit the functional and disease-free survival3. Healthy and morbidity-free lifespan, often termed “healthspan”, is thus a promising phenotype for longevity research4 and possibly a target for future anti-aging interventions5. The thorough delineation between the healthspan and lifespan is more than of academic interest: the last century saw a dramatic increase in lifespan, not necessarily followed by a matching improvement in the healthspan6.Age is the most important single risk factor for multiple diseases, see, e.g., ref. 5. The increasing number of available genomes of very old people9, though representing a rather specific and a relatively small sub-group of exceptionally successfully aging individuals, can provide an insight into the genetic architecture of exceptional life-spans and health-spans by use of Genome-Wide Association Studies (GWAS). While such studies suggested a fair number of loci, the APOE locus is probably among the few consistently implicated in multiple studies, see ref. 10 for a review. GWAS of the disease-free survival has been performed in relatively large cohorts , however, without producing genome-wide significant associations11, highlighting the complexity of healthspan phenotype. Further gains can be naturally achieved by increasing the population size with the help of proxy phenotypes, such as a search for genetic variants that predispose one to age-related disease and hence are depleted in long-lived persons compared to controls8. Another promising alternative involves GWAS of parental lifespans14.Genomics provide a hypothesis-free approach to study the biology of complex traits, including aging15. The close association between disease and mortality risk dynamics suggests the possibility of a single underlying mechanism, that is aging. We hypothesize that the incidence of the selected diseases is therefore a natural measure of the organism resilience and hence of aging process progression. Accordingly, the disease-free survival, the healthspan, is expected to be a useful phenotype, directly associated with the rate of aging. To reveal the genetic determinants of the healthspan, we built a proportional hazards model to predict the age corresponding to the incidence of the first disease from the “Gompertzian cluster” depending on an individual’s age, gender, genetic variation, and a number of more “technical” covariates. We used the sub-population of 300,447 genetically confirmed white British ancestry individuals (hereafter referred to as GCW-British) as a discovery cohort for a GWAS and identified 12 loci associated with healthspan at the whole-genome level of significance. The genetic signature of healthspan has high and significant genetic correlations with GWAS of obesity, type 2 diabetes, coronary heart disease, traits related to metabolic syndrome, and all-cause mortality . We conclude by noting that the healthspan phenotype offers a promising new way to investigate human aging by exploiting the data from large cohorts of living individuals with rich clinical information.In this paper, we focused on aging and morbidity in mid-life using clinical histories for over 300,000 people, aged 37 to 73, and participating in the UK Biobank (UKB) cohort. We checked the for incidence of chronic diseases and identified a cluster of the top eight morbidities strongly associated with age after the age of 40 and ranked by the number of occurrences. We observed that the risk of the selected diseases increases exponentially at similar rates. The corresponding doubling time is approximately eight years, close to the mortality risk doubling time from Gompertz law of mortality2 and selected the top eight morbidities strongly associated with age after the age of 40, having a discrete clinically apparent outcome  and ranked by the number of occurrences. The shortlist included Congestive Heart Failure (CHF), Myocardial Infarction (MI), Chronic Obstructive Pulmonary Disease (COPD), stroke, dementia, diabetes, cancer, and death . Iterative multiple imputation method17 that is often used for comparison of survival data gives ρ = 0.573 (0.530–0.613 95%CI).We chose to define healthspan as the age of the onset of the first disease from our list of the selected “Gompertzian” diseases or death. As expected, the first morbidity incidence rate also increases exponentially with age  to test statistical associations between specific genes and disease risks. In subsection Cox-Gompertz proportional hazards model and healthspan we explain how to use a maximum likelihood version of Cox-Gompertz model to predict the age corresponding to the end of healthspan for each study participant.Next, we assumed there is a group of genetic factors, predisposing individuals to the early onset of chronic diseases and identified gene-variants associated with the shorter healthspan. Since the incidence of the first morbidity risk grows exponentially with age, we propose to employ the Cox-Gompertz proportional hazard model , with a corresponding healthspan difference of approximately three years. The genetic principal components PC4 and PC5, and some of the assessment center labels were also highly significantly associated with the healthspan , and the ethnicity-related genetic variables . A maximum likelihood optimization produced the best fit proportional hazards model parameters. The morbidity incidence growth rate was found to be 0.098 per year, which corresponds to a doubling time of seven years and is compatible with the mortality rate doubling time of approximately eight years from Gompertz mortality law. As expected, being male is a significant risk factor  = 0.26 at the significance level of p < 5 × 10−8 , and the LD score regression intercept was 1.053 . After adjusting the results of the discovery GWAS for genomic control of 1.053, a total of 328 SNPs positioned in 12 loci remained statistically significant at the genome-wide level .A total of 394 SNPs at 14 loci achieved a genome-wide significance threshold of n = 81,099), African (n = 3073), South Asian , Chinese (n = 1422) and Caribbean (n = 3799). Using meta-analysis for the selected subsets , we performed the analysis on the 12 genome-wide significant SNPs for the replication group (Supplementary Data p < (0.05/12). We subsequently refer to these five SNPs as ‘replicated’.For replication, we used a combination of the UK Biobank participants not included in the discovery set whose self reported ancestry was European  between the healthspan and 46 traits (Supplementary Data rg > 0.4) were found in association with coronary artery disease (CAD)23 (rg = 0.62), Type 2 Diabetes24 (rg = 0.58), glycated hemoglobin level (HbA1C)25 (rg = 0.42), cigarettes smoked per day26 (rg = 0.44), and insulin resistance index (HOMA-IR)27 (rg = 0.41). The strongest negative correlations (rg < −0.4) were for the age of first birth28 (rg = −0.43), father’s age at death, mother’s age at death, and combined parental age at death defined as a sum of standardized mother’s and father’s age at death29  former vs. current smoker26 (rg = −0.48) and HDL related traits30 . Figure rg| > 0.3 and p < 4.3 × 10−5) fall into four distinct clusters: (1) the group of sociodemographic factors (including education), lifespan traits, smoking, CAD and lung cancer; (2) HDL-related traits; (3) the cluster of obesity-related traits including BMI and (4) Type 2 diabetes-related traits. The healthspan itself clusters together with CAD and parental age at death (a sub-cluster of cluster 1). We note, however, the absence of any substantial genetic correlation between the healthspan and Alzheimer disease  rg = 0.5, glycate31 annotation are presented in Supplementary Data HLA-DQA1, HLA-DQB1, LPA, MC1R (TUBB3 exon 1), SPATA33, and CASP8 genes.For the five replicated loci we selected SNPs that most likely include the functional variant (99% credible set). In total, we picked 924 SNPs  associations at the same or at strongly (r2 < 0.8) linked SNPs. The results are summarized in Supplementary Data ALS2CR12) associated with melanoma skin cancer35 and esophageal squamous cell carcinoma36. Next, loci on chromosome 6 at 0.4 Mb (IRF4) associated with different aspects of pigmentation, such as color of skin, eye and hair, pigmentation, tanning and freckles38, but also with non-melanoma skin cancer38 and the mole count in cutaneous malignant melanoma families39. Two loci  were associated with coronary artery disease, myocardial infarction, LDL and cholesterol levels40. The remaining replicated locus on chromosome 10 at 114 Mb (TCF7L2) was associated with glucose levels, BMI and type 2 diabetes42.Finally, we investigated the overlap between associations obtained here and elsewhere, using the phenoscaner v1.1 database43, and disease-free survival11 are also associated with healthspan in our data , which have been reported to be associated with (extreme) longevity in refs. 13 were also significantly associated with the healthspan. Two of these variants reached genome-wide significance and were independently discovered as healthspan loci in this study.We have compared whether SNPs previously reported to be associated with lifespan, (extreme) longevity44. In practice, there is no widely accepted definition of healthspan45. Practical use of the terms “healthspan” and “disease-free survival” varies depending on the scope of a research or the availability of the relevant data. For example, Walter et al.11 defined the disease-free survival as the time to the first of the following adjudicated events: myocardial infarction, heart failure, stroke, dementia, hip fracture, cancer, or death. In the interest of consolidating terms, in this study, we followed a more empirical, data-driven, definition of healthspan as the age of occurrence of the first prevalent disease with a discrete clinical manifestation and following Gompertz dynamics. To do so, we used the UKB clinical information and systematically investigated the incidence of the most prevalent chronic diseases. We found that the risks of the most prevalent age-related diseases  grow exponentially with age at nearly the same “Gompertzian” rates. The first morbidity signifies the end of the functional or disease-free period, the healthspan, and may signal a transition into a biologically or clinically distinct and relatively short-lived state, linked with the progressive accumulation of frailty, multimorbidity, and death. The manifestly close relation between the prevalent chronic diseases and mortality suggests that the healthspan may be a very relevant aging phenotype.Survival free of major disease and healthspan are related, broadly and almost interchangeably used terms that are commonly understood as the age of first chronic disease, or disability-free life-expectancy46 and adopted here. It resembles a regression of the independent variable  against the martingale residuals of the proportional hazard model, the difference between the predicted and the observed morbidity, see, e.g., ref. 12. We obtained explicit analytic expressions for the regression coefficient and statistics for the specific case of parametric Cox-Gompertz mortality model, see Eqs. , while possible biases introduced by collection of disease incidence information from multiple heterogeneous sources may introduce some—most likely, negative—bias in incidence rate estimates. Also, on the technical side, the replication sample included people with different ethnicity, similar to ref. 12. This allowed us to achieve larger size of replication sample, hopefully, increasing the power of replication, although it can bias the results toward the confirmation of effects that are common to different human populations. At the same time, the total size of non-European ancestry sub-sample was much smaller than of the European ancestry , and we expect the bias, if any, to be small.Using healthspan for quantitative studies relies on the availability of the accurate information regarding the age corresponding to onset of the diseases involved. The actual date, however, may never be known. Diagnosis always lags behind onset, and the difference may lead to a systematic bias towards later ages for diseases with gradual or hidden symptoms. MI, stroke and death from our list of morbidities have the smallest possible lag between the condition onset and corresponding diagnosis/event. Conversely, cancer, dementia, COPD, CHF and diabetes may develop gradually and hence it is difficult to obtain accurate age corresponding to the onset of these conditions. The discrepancy between the the actual and the reported ages is random and yet, for large enough cohorts, the incidence statistics should still provide a good estimation of real incidence rates. Moreover, the events are defined based on information coming from multiple sources, such as registries, hospital records, and interviews, which introduces additional sources of bias50. The number is very close to our healthspan difference estimate. It is therefore intriguing to see if this numerical coincidence is a model artifact, or if indeed the observed difference in the lifespans could be attributed to the difference in healthspan. Four of the 12 loci identified here as associated with healthspan demonstrated significant differences of effects between males and females, see Supplementary Data 52.Since the first morbidity risk grows exponentially with age, we proposed to employ the probabilistic language of Cox-Gompertz proportional hazard model to test for associations between the demographic and genetic variables, on the one hand, and healthspan, on the other. For example, the Cox-Gompertz model estimates that the healthspan is 2.5 years lower for males than females, while the lifespan difference—using the same methods and cohort–is estimated as 3.2 years. Indeed, females in the UK (the population relevant to this study) live longer than males, although the gap between the sexes has decreased over time and is now 3.7 yearsp = 5.74 × 10−33) and the age of the first birth (p = 2.37 × 10−22) could be indicators of such possibilities. One possible way to deconvolute the effects of human development, diseases and longevity could thus involve using longitudinal clinical data to see if there are gene variants responsible for the rate of aging or biological aging acceleration separately in every age group to negate the effects of accumulation in the course of development.It is tempting to consider the results of our GWAS as informative for potential anti-aging targets. The healthspan, as well as lifespan, however, is an integrated quantity and therefore may depend on the gene activation patterns during subsequent development stages and/or associated with life-long exposure. Therefore, our GWAS ‘hits’ may not necessarily be good targets for an intervention at advanced ages. The appearance of significant genetic correlations with such traits as the years of schooling , we re-run the GWAS considering death as a censoring event. The results changed only marginally. For example, the genetic correlations of newly defined healthspan with individual lifespan, parental, maternal and paternal age at death, became 0.80, −0.74, −0.65, and −0.74, respectively . All the twelve loci that were genome-wide significant . While genetic correlation between these traits is high (ρ > 0.7), the remaining ‘uncoupled’ variance leaves room for genetic variants affecting healthspan and lifespan  in distinct ways.The notable absence in our study of the gene variants around the  57 Fig. . These fHLA-DQB1 is significantly (p = 4.18 × 10−8) associated with COPD, diabetes, cancer and dementia in this study and was demonstrated to be associated with parental survival earlier in ref. 13. The gene variant near TYR are predictors of death in the prospective UKB cohort and has been implicated in earlier onset of macular degeneration, a notable example of age-related disease60, not present in our healthspan definition. Most notably, the chromosme 20 locus containing C20orf112 was not associated with the incidence of any of the disease at the full-genome level  also control the dynamics of multiple morbidities later down the road. The comparison and better understanding of the results of such studies will help to differentiate the biology of health- and life-span. Human development and aging is a multi-stage process, and therefore longevity emerges as a genuinely complex trait. The presented study highlights a need for further systematic advances in aging GWAS methodology to elucidate the practical potential of genetics in diagnosis of aging and, subsequently, help to shape the anti-aging therapeutic target space.62. Participants, aged between 37 and 73, were invited to one of 22 centers across the UK between 2006 and 2010. Blood, urine and saliva samples were collected, physical measurements were taken, and each individual answered an extensive questionnaire focused on questions of health and lifestyle. All participants gave written informed consent and the study was approved by the North West Multicentre Research Ethics Committee. UKB has Human Tissue Authority research tissue bank approval, meaning separate ethical approvals are not required to use the existing data. UKB provided genotyping information for 488,377 individuals. Data access to UKB was granted under application #21988. Phenotypes and genotypes were downloaded directly from UKB.UK Biobank is a prospective cohort study of over 500,000 individuals from across the United Kingdom63. 49,950 samples were genotyped as part of the UK BiLEVE study using a newly designed array, with 438,427 remaining samples genotyped on an updated version (UK Biobank Axiom array), both manufactured by Affymetrix (96% of SNPs overlap between the arrays). Samples were processed and genotyped in batches approx. 5000 samples each. In brief, SNPs or samples with high missingness, multi-allelic SNPs and SNPs with batchwise departures from Hardyâ€“Weinberg equilibrium were removed from the data set. After quality control, genotypes were available for 488k subjects at 805k sites. UKB provided 40 principal components (PCs) of genetic relatedness (UKB field id 22009) and a binary assessment of whether subjects were genetically confirmed European Ancestry (UKB field id 22006), based on principal components analysis of their genetic data.UKB participants were genotyped on two slightly different arrays and quality control was performed by UKBWe have computed Pearson correlations between self reported ethnicity (UKB field id 21000), coded as binary variable, and the 40 principal components in UKB data set of 488,363 participants with genetics principal components analysis data available. The estimates could be found in Supplementary Data 64 modified to allow for very large sample sizes. Imputation was carried out using IMPUTE265 using the merged UK10K and 1000 Genomes Phase 3 reference panels to yield higher imputation accuracy of haplotypes. The imputations resulted in 92,693,895 SNPs, short indels and large structural variants, imputed in 488,377 individuals63.Imputed data were prepared by UKB. In summary, autosomal phasing was carried out using a version of SHAPEIT3N = 407,208). This cohort also represents the largest possible unrelated individuals subset63 with all relatives of third degree or closer removed. For the discovery set we selected 300,447 genetically confirmed white (GCW) British individuals according to the genetic principal components provided by the UK Biobank who were not included in UK BiLEVE study (UKB Resource 531). For replication, we used a combination of the UK Biobank participants not included in the discovery set that comprised rest of European ancestry individuals , individuals of African ancestry , individuals of South Asian ancestry , Chinese individuals (n = 1422) and Caribbean individuals (n = 3799). Remaining self-declared ethnicities that were mixed, or were ambiguous  were not analyzed. To reduce the risk of bias due to population stratification, all groups were analyzed separately followed by a meta-analysis. Total resulting sample size for replication was 96,313 individuals. Additionally, we checked that there is no individuals with kinship coefficient r > 0.01 between discovery and replication cohorts, using relationship data provided by UKB (UKB data category 100315). For more details see Supplementary Data For the discovery and replication we used only the data from PCA cohort  of replication was estimated using the fact that under alternative hypothesis (H1:β ≠ 0) the test statistics T2 from replication sample is expected to follow the λLDSC, Nrep is the sample size of the replication cohort and Ndisc is the sample size of the discovery cohort. The the power of replication is equal to the probability that such distributed statistics would exceed the threshold value k = 8.2 that corresponds to right-hand integral of The replication threshold was set as m(t) = Δt−1Nd/Nh(t) when Δt is sufficiently small. Here t is the age, Nh(t) is the number of people healthy at the age t and Nd is the number of people diagnosed between the ages t and t + Δt (both Nh and Nd are presumed to be large). This definition does not rely on any specific underlying model. In practice, datasets are of limited size and the interval Δt cannot be made arbitrarily small, and therefore precautions should be taken to avoid possible artifacts in the calculation. To compute the incidence rate at a given age t, one shall consider a set of participants Υ defined as those who are healthy at the age t and whose health status is available in the whole age range [t, t + Δt): u is the participant’s id, δu = 1 if the participant was diagnosed and δu = 0 otherwise, Nh(t) = |Υ| and We used in-patient hospital admissions data (UKB data category 2000) and self-reported diagnoses obtained via verbal interview (UKB data category 100074) to extract information in relation to the disease history, the nature of and the age at the available diagnosis. For each of the condition, we follow the instructions similar to the ones given by the UK Biobank outcome adjudication group for algorithmic-defined stroke and MI (UKB data category 42). For each selected condition, except for cancer and death we compile a list of hospital data codes  definitions. Finally, our prescription relies on the implicit assumption, that the diagnosis does not influence the enrollment. This is not always true. If someone is dead, this would, naturally, prevent that person from being enrolled at a greater age. This can be addressed by the following modification: Υ for all diseases and conditions, Υ' participants set was only employed for the mortality rate calculation.The maximum follow-up age δn = 1, if the participant is already diagnosed with any of the diseases, δn = 0, otherwise.By design of the UKB study, every participant is admitted into the cohort at the age t is xn is a vector of age-independent parameters, characterizing the participant. Here h0, Γ, and β are the baseline morbidity incidence, the Gompertz exponent and the log-odds-ratio regression coefficients vector, the model parameters. The (negative log of) likelihood of the data can be presented in the following form:Under then Cox-Gompertz proportional hazards model the risks of frailty acquisition or healthspan end at the age L.Given a necessary amount of data the model parameters could be obtained by the likelihood maximization or, equivalently, minimization of the cost function p = 5 × 10−301) corresponding to an average healthspan difference of about five years. The genetic principal component PC4 was highly significant log(HR) = 3.4 × 10−2, p = 9.2 × 10−23. PC5 was also highly significant log(HR) = 4.6 × 10−2, p = 1.7 × 10−40. The average healthspan or lifespan can be estimated from Cox-Gompertz model parameters as γ = 0.577 is the Euler-Mascheroni constant, see, e.g.,66.We built the first version of the Cox-Gompertz healthspan model by including GCW-British UKB participants information, including gender and the first genetic principal components variables, assessment center codes and genotyping batch labels .If the participants state vector h0 and the rate of aging Γ is an ill-defined mathematical problem66. Only the combination of the two parameters, the change in the life expectancy can be determined with accuracy. We therefore fix the Gompertz exponent Γ to its most probable value in the zeroth order model and allow for all other model parameters adjustment. The perturbation theory expansion for the small effect βs associated with the gene variants yields (the derivation is not shown):δs2〉ρ for the corresponding weighted average. The effect determination errorWe note, however, that the simultaneous determination of the weak effects of a gene on the baseline hazard 67 for discovery cohort with–info2 option) more than 0.7.In our analyses, we used imputed variants with the expected effective minor allele count  more than 200 for discovery cohort genotypes and imputation info score  summary statistics and a reference sample that is utilized for the LD estimation. The method starts with the “top SNP”  as provided by the summary-level data and then the p-values for all the remaining SNPs are calculated conditional on the selected SNP. The algorithm then selects the next top SNP in the conditional analysis (provided p < p0) and proceeds to fit all the selected SNPs in the model dropping all those SNPs with p-values > p0. The iteration continues until no SNP is added or dropped from the model thus finding a subset of associated SNPs with a threshold for LD (r2 < 0.9) among SNPs. Finally, a joint analysis of the subset of associated SNPs is performed. We had performed analyses with p0 = 5 × 10−8 and p0 = 1 × 10−5.Conditional and joint analysis (COJO) as implemented in the program GCTAAs the LD reference, we used a sub-sample of 10,000 people, randomly chosen from the total set of 120,286 people used for GWAS discovery phase. Additional to our previous SNP filters described in the Association testing section, in selecting LD reference data, we further filtered out the SNPs with imputation info scores less than 0.7 and minor allele frequencies (MAF) less than 0.002.n = 137,469, females: n = 162,978) for 12 genome-wide significantly associated SNPs from Table 68  were considered statistically significant. Pair-wise genetic correlations between all the traits selected as described above were obtained from the LD-hub. To focus on the largest magnitude genetic correlations, we selected only the traits with absolute values of genetic correlations with HS more than 0.3. This filtering led to the total of 36 traits (including HS). Clustering and visualization was carried out using corrplot package for R and basic hclust function. For clustering, we estimated squared Euclidean distances by subtracting absolute values of genetic correlation from 1 and used Ward’s clustering method.We used LD hub and ldscFor genetic correlation analysis between each disease comprising healthspan phenotype and healthspan itself we used LDSC (LD Score) v1.0.0 software. Genotype calls were filtered by MAF > 0.01 using LDSC ‘munge-sumstats’ script to produce total 659,079 variants valid for downstream analysis. Genomic reference was constructing by randomly sampling 10,000 individuals from the UKB population. Then, we ran LDSC genetics correlation analysis with default parameters and input data as described above. Cross-correlations can be seen at Fig. λ19 and genetics correlations we have used SNPs defined by overlap between our set of SNPs and ‘high quality SNPs’ as suggested by the authors of the LD hub 20, 1,162,742 SNPs in total).For analysis of heritability, genomic control inflation factor 69 to prepare the set of SNPs for VEP annotation. For this analysis, we provided PAINTOR with clumping results, LD matrices and annotation files calculated using the same 10,000 UKB individuals reference set that we used for COJO analysis. With PLINK70 and we performed clumping analysis with ‘p1’ and ‘p2’ p-value threshold parameters set to 5 × 10−8, ‘r2’ set to 0.1 and MAF > 0.002. Then, we generated pair-wise correlation matrix for all SNPs in each region in clumping analysis results using plink–r option. When running PAINTOR, we did not use annotations; we changed options controlling input and output files format only, and otherwise we have used default parameters. We choose 159 SNPs marked by PAINTOR as 99% credible set for further analysis. In the next step, each SNP was extended with a list of proxy SNPs with R2 > 0.8 calculated using EUR cohort from 1000 Genomes Project Phase 371 (N = 503) with 84.4 million variants as reference set. Total 924 SNPs was chosen for functional annotation by VEP with GRCH37 genomic reference.We used PAINTOR software32 with following parameters: flag_loci = 1; flag_genes = 1; flag_genesets = 1; flag_tissues = 1; param_ncores = 10. Independent (as selected by COJO procedure) variants with p < 5 × 10−8 (14 SNPs) and p < 10−5 (135 SNPs) has resulted from these analyses. We have used UKB subset of 10,000 individuals for computations of LD .For prioritizing genes in associated regions, gene set enrichment and tissue/cell type enrichment analyses, we have used the DEPICT software v. 1 rel. 19434. For five replicated SNPs (Table p < 5 × 10−8) association at the same or at strongly (r2 < 0.8) linked SNPs.We investigated the overlap between associations obtained here and elsewhere, using PhenoScaner v1.1 databasehttps://github.com/azenin/healthspanpaper.All computer code used in this research is available at Further information on experimental design is available in the Reporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19