VOC production and fruit texture, assessed by multiple factor analysis, showed a contrasting behavior in apple. A GWAS approach dissected regulation of the volatilome and identified QTLs co-locating with important candidate genes. MdAAT1 and MdIGS. The integration of volatilome and fruit texture data conducted with a multiple factor analysis unraveled contrasting behavior, underlying opposite regulation of the two fruit quality aspects. The association analysis using the first two principal components identified two QTLs located on chromosomes 10 and 2, respectively. The distinction of the apple accessions on the basis of the allelic configuration of two functional markers, MdPG1 and MdACO1, shed light on the type of interplay existing between fruit texture and the production of volatile organic compounds.Fruit quality represents a fundamental factor guiding consumers’ preferences. Among apple quality traits, volatile organic compounds and texture features play a major role. Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), coupled with an artificial chewing device, was used to profile the entire apple volatilome of 162 apple accessions, while the fruit texture was dissected with a TAXT-AED texture analyzer. The array of volatile compounds was classed into seven major groups and used in a genome-wide association analysis carried out with 9142 single nucleotide polymorphisms (SNPs). Marker–trait associations were identified on seven chromosomes co-locating with important candidate genes for aroma, such as  Fruits are important components of the human diet, supplying important elements such as sugars, organic acids, vitamins and fiber. These elements change during the fruit developmental process to render the fruit more attractive and palatable, especially at the onset of ripening. Overall, the array of these compounds defines the quality of a fruit, as a degree of excellence . Fruit qPhysiologically, fruit texture depends on the dismantling process occurring on the polysaccharide cell wall architecture co-ordinated by cell wall-modifying proteins. Fruit texture is composed of a series of subtrait components, classified into mechanical and acoustic properties . Beside in vitro VOC profile  and functional principal component analysis (FPCA)], together with a high-density single nucleotide polymorphism (SNP) genotyping platform, enabled a genome-wide association study (GWAS) with the aim of making progress in understanding the genetic relationship between these two important fruit quality traits.In this investigation, a collection of 162 apple accessions was chosen within the germplasm repository available at the Fondazione Edmund Mach . Each genotype, planted in triplicate, was in the adult and fruit-bearing phase at the time of the analysis. Trees were maintained with standard agronomic practices for fruit thinning, pruning, and pest/disease control. Apples were harvested at the commercial ripening stage determined following the changes of skin and seed color as well as the degradation of the chlorophyll content assessed non-destructively with a Da-Meter .A subset of five apples per genotype was used for the characterization of the fruit texture subtraits. The measurements were carried out with a Texture Analyzer . The protocol was given in detail in –1. VOCs were then assessed by direct injection of the headspace mixture into a commercial PTR-ToF-MS 8000 apparatus , set with the conditions described in To profile the emission of VOCs during artificial chewing, another batch of five apples per genotype was assessed according to the method described by Young leaves collected from each apple accession were used for nucleic acid extraction. Genomic DNA was isolated with a Qiagen DNeasy Plant Kit. DNA quantity and quality were measured with a Nanodrop ND-8000 . The 162 accessions were genotyped with the 20K Infinium SNP Array (Illumina), ad hoc designed for apple  were jointly analyzed in a marker–trait association approach. For this purpose, the software TASSEL v3.0 was employed and the GWAS was computed implementing two models: the general linear model (GLM) and the mixed linear model (MLM). The GLM , u is a vector of random additive genetic effects for multiple background QTLs, X and Z are the known design matrices, and e is the unobserved vector of random residuals. Significant associations were selected on the threshold of P-value ≤0.05, corrected for multiple comparisons according to the false discovery rate (FDR) procedure reported by where The array of protonized VOC masses was reduced by applying noise and correlation coefficient thresholds. The first removed peaks with mean intensity <25 ppbv and not significantly different from blank samples . FurtherP<0.01; R>0.50) was conducted by the generation of a correlation analysis network with Cytoscape , aldehydes , phenylpropenes (i.e. m/z 134.072 and 149.097), and esters . Among these classes, esters, of both straight and branched types, are to date recognized as the most relevant VOCs in apple aroma  and alcohols  were rapidly released after crushing the fruit structure, while for other molecules, such as acetaldehyde (m/z 45.033) and acetone (m/z 59.049), the emission was less influenced by disruption of the sample. In addition to this, several other compounds, mainly C6-aldehydes, such as hexanal (m/z 83.086 and 101.097) and hexenals (m/z 81.070 and 99.081), revealed a third trend characterized by a constant and linear production, delayed by ~20 s after the initial tissue disruption .In this study the interplay between fruit texture and aroma was investigated by a comprehensive high-resolution phenotyping assessment (Supplementary Table S2). Fruit were analyzed after a period of cold storage (2 months), also taking into account that both the volatilome and texture undergo important changes during the post-harvest phase : those pwas used . The PTRle aroma . Similarle aroma b, VOCs win vitro mastication, we initially compared only three specific time points of the entire VOC dynamic: (i) before the artificial chewing (0 s); (ii) 20 s after the fruit processing; and (iii) 60 s after the fruit processing  and amyl alcohols (i.e. m/z 71.085), while negative values are linked to a greater abundance of methanol (m/z 33.033), acetaldehyde (m/z 45.033), ethanol (m/z 47.049), and C6-aldehydes (m/z 81.07 or 83.086). Beyond the differentiation of apple cultivars based on their aromatic profiles, samples are also differentially distributed on the PCA-hyperspace according to the VOC assessment carried out at three specific time points after chewing . Although the VOC magnitude was enhanced by mastication, a genetic and physical (apple flesh structure) regulation was suggested.As a first attempt to simplify the analysis of the entire VOC profile during the ocessing . The effocessing , the firP≤0.01, threshold 0.05) among the set of 33 masses, identified seven main groups of VOCs (m/z 33.033 and its water cluster m/z 51.044) and phenylpropenes (m/z 134.072 and 149.096), together with unknown fragments. Methanol, similarly to ethanol and acetaldehyde, is positively correlated (R=0.63) with an ester included in group 1, in agreement with the involvement of methanol in the methyl ester synthetic pathway .To reveal the general behavior existing among the VOCs assessed within the germplasm collection, the apple volatilome was analyzed through a correlation network . The net of VOCs , and it  of VOCs . While a of VOCs . The amo of VOCs . The thim/z 43.017, a common ester fragment selected to represent group 1, identified a major QTL on chromosome 2 . In several fruits, AAT is essential to control flavor biogenesis during the fruit ripening phase. It has in fact been documented  within each network analysis group, based on the Q–Q plot. The choice of the representative mass/cluster is also justified by the fact that the PTR-ToF-MS device accurately detects the nominal mass of a molecule, important to identify an array of compounds with similar structure and thus with a similar quality impact. The association between the set of SNPs and m/z 43.054 and m/z 57.069). These two compounds were selected on the basis that they did not show a high correlation value (R=0.35), indicating the possibility of the identification of two distinct groups. For m/z 43.054, statistically significant SNPs were found in chromosomes 2, 5, 15, and 16  gene was annotated (MDP0000523942). The action of ADH is to reduce aldehydes (previously reduced by acyl-CoA) to alcohols  was also identified. This element, which belongs to the 1-aminocyclopropane-1-carboxylate family, is involved in the early phase of apple ethylene production , supporting the interplay between alcohols and esters in apple. Chromosomes 2 and 15 were already associated with the accumulation rate of alcohol compounds in apple by m/z 57.069), a QTL was located on chromosome 11 (dehydrogenase reductase3b-like gene (SDR) was annotated (MDP0000313884). This gene encodes one NAD(P)(H)-dependent enzyme characterized by a wide range of substrates, including alcohols and aromatic compounds .For the alcohol group  was annotated on chromosome 7. This association, together with the aforementioned reported genes, strengthens the role of this region in the regulation between alcohol and aldehyde VOC categories.From the group of aldehyde compounds (group 4), QTLs were identified and located on chromosome 1 and 7, respectively , 11 (FB_0086581_L11_PA), and 16 (FB_0362423_L16_PA), respectively. While for the QTLs on chromosomes 5 and 16 no significant gene involved in phenylpropene synthesis was annotated, an isoeugenol synthase-1 like gene (MDP0000141131) was found on chromosome 11. This gene is involved in the biosynthetic pathway of phenylpropanoid (PhP-Vs), a VOC category with multiple roles, from attractors to pollinators and defense, to important contributors of the typical ‘spicy/smoky’ aroma of fruits  catalyzes the final formation of t-anethol and estragole. The latter compound was shown to be highly accumulated in ripe fruit of ‘Royal Gala’ apple (de novo biosynthesis (NSGT1 (NON-SMOKY GLYCOSYLTRANSFERASE1), a gene involved in the conversion of the non-cleavable triglycoside form of phenylpropanoids , revealed a distinct classification of apple cultivars based on these parameters. All 162 cultivars are uniformly spread over the PCA score plot defined by the first two PCs, together expressing 97.9% of the total variability. PC1 (82.6%) corresponds to the overall apple textural performance, while PC2 (15.3%) mainly refers to the more subtle classification based on the prominence of mechanical (i.e. flesh firmness) or acoustic (i.e. crispness) parameters. Although this characterization has been performed in previous investigations . This gene, as reported above, is a major candidate in the formation of esters. The association between MdAAT1 and PC2 is instead more related to the type of aroma , which in apple is for the most part related to the ratio between ester and alcohol (MdPG1 supports the role of fruit texture structure in the release of aroma in apple.To genetically dissect the VOC control in apple, the FPCA components were further employed as phenotypic traits in GWAS computation. The inclusion of PC1 in the analysis enabled the detection of a major QTL on chromosome 10, with a cluster of SNPs exceeding the FDR adjusted threshold (in apple . The facalcohol acyltransferase gene has recently been developed (MdAAT1; MdPG1 is involved in the control of fruit texture, which depends on the degradation of the cell wall polysaccharide structure. Firm apples are therefore characterized by a more intact and solid cell wall, which can prevent the release of VOCs due to tighter cell compartmentation. On the other hand, MdACO1 is the last gene governing the final biochemical formation of ethylene, a plant hormone triggering and co-ordinating several ripening processes (Within the several aspects of apple fruit quality, fruit texture and flavor are dominant features for their effect on the post-harvest performance and consumer preference . Despite MdAAT1; . Amongst MdAAT1; . While m MdAAT1; , 5. Acco MdAAT1; . The var MdAAT1; , with throcesses . The amoThe exploitation of the genetic variability existing within the apple germplasm can allow a valuable combination of alleles for the selection of a high quality ideotype. This goal, however, can be achieved only with a more informed and precise identification of the best performing cultivars to be used as superior parental lines.Supplementary DataClick here for additional data file.


Population-based identity by descent (IBD) mapping is a statistical method for detection of genetic loci that share an ancestral segment among “unrelated” pairs of individuals for a disease. As a complementary method to genome-wide association studies, IBD mapping is robust to allelic heterogeneity and may identify rare inherited variants when combined with sequence data.Our objective is to identify the causal genes for diastolic blood pressure (DBP). We applied a population-based IBD mapping method to 105 unrelated individuals selected from the family data provided for the Genetic Analysis Workshop 19. Using the genome-wide association study data , chromosome 3 was scanned for IBD sharing segments among all pairs of these individuals. At the chromosomal region with the most significant relationship between IBD sharing and DBP, the whole genome sequence data were examined to identify the risk variants for DBP.p = 0.0016), although it did not achieve the chromosome-wide significance level (p = 0.00012). This chromosomal region contains 1 gene, ZPLD1, which has been reported to be associated with cerebral cavernous malformations, a disease with enlarged small blood vessels (capillaries) in the brain. Although 24 deleterious variants were identified at this region, no significant association was found between these variants and DBP (p = 0.40).The most significant chromosomal region that was identified to have a relationship between the IBD sharing and DBP was at 3q12.3 (We presented a mapping strategy which combined a population-based IBD mapping method with sequence data analyses. One gene was located at a chromosomal region identified by this method for DBP. However, further study with a large sample size is needed to assess this result. As more sequence data become available, one of the challenges is how to identify the disease-causing variants among hundreds of deleterious variants an individual carries . VariousTraditional identity-by-descent (IBD) mapping methods have been successfully applied in studies of Mendelian and complex diseases using related cases . With thTo date, a few groups have applied IBD mapping methods as a complementary method to genome-wide association studies for complex diseases in large, unrelated samples , 9–11. FThe population-based IBD mapping methods may also be applied to quantitative traits . In thisThe genome-wide association study (GWAS) data from the families, which had 959 individuals and 472,049 genetic markers, were used for quality control procedures, including relationship and population structure analyses. Unrelated individuals and markers from chromosome 3 of the GWAS data were used for IBD mapping. Chromosome 3 was chosen according to the suggestions from the Genetic Analysis Workshop 19 (GAW19) data contributors.The DBP from the first time point was used as the outcome. For the individuals with medical treatment for hypertension, 5 mmHg was added to the original DBP values . Multiplp value of less than 0.0001. The markers were further pruned (with r2 < 0.33) for relationship and population structure analyses. These quality control procedures were performed using PLINK v1.07 [For the GWAS data, individuals were excluded if they had a genotype missing rate of 5 % or greater or a Mendelian error rate of 1 % or greater, and markers were excluded if they were insertion/deletion, had no unique physical location, a missing rate of 5 % or greater, a Mendelian error rate of 1 % or greater, a minor allele frequency (MAF) of 1 % or less, a MAF of 5 % or less with a missing rate of 1 % or greater, or a Hardy-Weinberg equilibrium (HWE) NK v1.07 .The PLINK “–genome” results were used to select unrelated individuals. Because Mexican Americans have ancestors with different ethnic backgrounds and because the genetic variants related to DBP may have different allele frequencies in different ethnic groups, the computer program Eigensoft (v4.2)  was applibdlength (the minimum IBD length to be detected), was set to 1 cM. Other parameters, window (the sliding window that determines the memory usage), overlap (overlap between sliding windows), ibdtrim , and ibdwindow (sliding window for IBD sharing detection), were set to be the number of markers in 12, 1.5, 0.15, and 0.2 cM, respectively. Default settings were used for the remaining parameters. IBD sharing estimation was run 3 times using different seeds. Results from these 3 runs were combined using the ibdmerge.jar utility program (see “Website resources” below).Beagle Refined IBD (v4.0.r1128) was used to estimate IBD sharing at each location on chromosome 3 . The parThe physical map was from build GRCh37 of the Genome Reference Consortium 2009. The genetic map was generated by Dr. Brian Browning based on the International Haplotype Map Project (HapMap) Phase II genetic map (see “Website resources” below). If a marker was not in this map, its genetic location was interpolated based on its physical location.i at marker j, and 1 if IBD sharing was observed for pair i at marker j.A Perl script was written to create a matrix with the number of unrelated individual pairs × the total number of markers on chromosome 3. The observations in the matrix only had 2 values: 0 if no IBD sharing was observed for pair D) and squared trait sum (S) for the DBP residuals for each unrelated pair. Then, for each marker in the matrix, 2 simple linear regressions were performed:Following the IBD mapping methods proposed for quantitative traits using sibpairs –17, we cD on the IBD sharing status (π) with estimated slope D2Regression 1: YS on π with estimated slope S2Regression 2: YThe weighted overall slope estimate is:The estimate of the standard error of i.where t test of the slope estimate. Under the null hypothesis of no linkage, the slope was zero whereas under the alternative hypothesis, the locus was linked to the trait and the slope was negative. The t statistic was calculated as the weighted overall slope estimate divided by its standard error:Linkage was tested using a one-sided Because the pairs of individuals were not independent from each other, the significance threshold for the real data was obtained using a permutation procedure.For the chromosomal region with the most significant relationship between IBD sharing and DBP, the sequence data were examined to identify the risk variants for DBP. First, variants with no variation (MAF = 0), more than 2 alleles, or a missing rate of more than 15 % were excluded. The remaining genetic variants were annotated with the Combined Annotation-Dependent Depletion (CADD) scaled scores . As recop < 0.05); however, the first principal component had a p value of 0.1 .After the quality control procedures, 914 individuals and 374,179 markers were selected for relationship and population structure analyses; and 105 unrelated Mexican Americans and 52,216 markers from chromosome 3 of the GWAS data were selected for IBD mapping  of them had detectable chromosomal segments shared by IBD. The average length of the IBD sharing segments was 1.47 cM with a range of 1.00 to 6.28 cM. The average chromosome-wide IBD sharing rate was 0.0029 with a standard deviation of 0.0025 and a range of 0 to 0.019. For each region, the rate was calculated with the number of pairs with IBD sharing at this region divided by the total number of pairs .p values of less than 0.01  located at 101,901,465 to 102,620,049 bp, a region bounded by the GWAS markers that had IBD mapping ZPLD1), which is located at 101,818,088 to 102,196,462 bp. There were 3811 variants at the region after the quality control procedures, 24 of which were identified as deleterious (2 within the gene ZPLD1). Based on the result from SKAT-O, these variants were not associated with DBP (p = 0.40).Of the 105 unrelated individuals, 71 had the whole-genome sequence data. The most significant region overlapped with 1 gene, the zona pellucida–like domain containing 1 gene (n = 105), statistical power to detect the causal chromosomal region was low, and the most significant IBD mapping result did not reach the estimated significance level.We applied a population-based IBD mapping method to a quantitative trait, DBP. Because of the relatively small sample size  in the brain, in a patient with a balanced translocation [ZPLD1 and MIR548A3) and systolic blood pressure (SBP) using unrelated individuals from the family data (n = 122) and the GWAS genotypes. The Spearman correlation coefficient between the DBP and SBP residuals was 0.59 (p <0.0001) in our data. Unfortunately, we were not able to identify causal variants in this gene for DBP. Further study with a large sample size is needed to assess this result.Interestingly, however, the gene at the most significant IBD mapping region, location . From Gelocation  also repUsing the same IBD sharing estimate method, Browning et al.  reportedIn summary, we demonstrated a gene mapping strategy for quantitative traits which combines a population-based IBD mapping method with sequence data analyses. However, as a result of the small sample size in this study, more information about this approach and its ability to prioritize causal variants for sequence data analyses should be further explored.


Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding. a priori prediction of deleterious alleles correlates well with effect sizes for grain yield and that variants predicted to be more damaging are on average more recessive. We develop a simple model allowing for variation in the heterozygous effects of deleterious mutations and demonstrate its improved ability to predict both phenotypes and hybrid vigor. Our results help reconcile alternative explanations for hybrid vigor and highlight the use of leveraging evolutionary history to facilitate breeding for crop improvement.A key long-term goal of biology is understanding the genetic basis of phenotypic variation. Although most new mutations are likely disadvantageous, their prevalence and importance in explaining patterns of phenotypic variation is controversial and not well understood. In this study we combine whole genome-sequencing and field evaluation of a maize mapping population to investigate the contribution of deleterious mutations to phenotype. We show that  Understanding the genetic basis of phenotypic variation is critical to many biological endeavors from human health to conservation and agriculture. Although most new mutations are likely deleterious , their iIn addition to its global agricultural importance, maize has long been an important genetic model system  and centModern maize agriculture takes advantage of hybrid maize plants that result from the cross between two parental inbred lines . These cIn this study, we set out to investigate the contribution of deleterious alleles to phenotypic variation and hybrid vigor in maize. We created a partial diallel population from 12 maize inbred lines which together represent much of the ancestry of present-day commercial U.S. corn hybrids , 30. We ha−1), with all observations taken from the inside two rows to minimize effects of shading and maturity differences from adjacent plots. We measured plant height , height of primary ear , days to 50% pollen shed (DTP), days to 50% silking (DTS), anthesis-silking interval , grain yield adjusted to 15.5% moisture , and test weight .We formed a partial diallel population from the F1 progeny of 12 inbred maize lines , S1 Fig.Yijkl is the phenotypic value of the lth genotype evaluated in the kth block of the jth replicate within the ith year; μ, the overall mean; iς, the fixed effect of the ith year; δij, the random effect of the jth replicate nested within the ith year; βkij, the random effect of the kth block nested within the ith year and jth replicate; αl, the fixed genetic effect of the lth individual; iς · lα, the random interaction effect of the lth individual with the ith year; and ε, the model residuals. We calculated the broad sense heritability (H2) of traits based on the analysis of all individuals  following the equation:i = 3 (number of years) and j = 3 (number of replicates per year).We estimated Best Linear Unbiased Estimates (BLUEs) of the genetic effects in ASReml-R  with the following linear mixed model:P values >0.05, r = 0.98 for DTS and DTP, The BLUE values for each cross can be found in i and j. Note that for ASI, lower trait values are considered superior. General combining ability (GCA) was estimated following Falconer and Mackay [We estimated mid-parent heterosis (MPH) as:d Mackay , and theWe extracted DNA from the 12 inbred lines following  and sheahttps://github.com/vsbuffalo/scythe) and for quality and sequence length (≥20 nucleotides) with Sickle (https://github.com/najoshi/sickle). We mapped filtered reads to the maize B73 reference genome (AGPv2) with bwa-mem [We trimmed raw sequence reads for adapter contamination with Scythe . Because these parents are highly inbred, knowing their homozygous genotype also allows us to know the genotype of the F1 derived from any two of the parents.We called single nucleotide polymorphisms (SNPs) using the samtools . To dealsamtools , SNPs wesamtools ; for allsamtools ; and forsamtools . Alignmewww.panzea.org)) obtained from alignments to a set of sequencing reads ascertained from a broad germplasm base [r = 0.995), suggesting the degree of relatedness among lines is not sensitive to using B73 as the reference genome.To test whether alignment to the B73 reference introduces a bias in relatedness estimation, we computed kinship matrices using both our SNP data as well as genotyping-by-sequencing data . We define “GERP-SNPs” as the subset of SNPs with GERP score >0, and at each SNP we assign the minor allele in the multi-species alignment as the likely deleterious allele. Finally, we predicted the functional consequences of GERP-SNPs based on genome annotation information obtained from SnpEff [http://ensembl.gramene.org/Zea_mays/Tools/AssemblyConverter?db=core).We used genomic evolutionary rate profiling (GERP)  estimatem SnpEff . The mulhttp://www.panzea.org/) and allele frequencies from the > 1,200 maize lines sequenced as part of HapMap3.2 [To compare GERP scores  to recombination rate and allele frequencies, we obtained the NAM genetic map  from theapMap3.2 . To compapMap3.2  for moreYi is the BLUE value of the ith hybrid, μ is the average genotypic value, αj is the allele substitution effect of the jth GERP-SNP, dj is the dominant effect of the jth GERP-SNP, Xij = {2p, 2p − 1, 2p − 2}, ε is the model residuals, and Wij = {−2p2, 2p(1 − p), −2(1 − p)2} are the genotype encodings for genotypes A1A1, A1A2, and A2A2 in the ith hybrid for the jth GERP-SNP with p of the A1 allele.We estimated the additive and dominant effects of individual GERP-SNPs using a GBLUP model  implemena = α − 2p(1 − p)d) and dominant (d) effects from the GVCBLUP output file  or complete dominance (α = 0). Then, to assess correlations between SNP effects and GERP scores, we calculated the degree of dominance (k = d/a) [k, we truncated GERP-SNPs with |k = d/a|>2 for all further analyses.The additive and dominance SNP encoding ensures that the effects are independent for a given GERP-SNP. We extracted additive   for SNPsTo compare the variance explained by our model to that explained by random SNPs, we used a 2-dimensional sampling approach to create 10 equal-sized datasets of randomly sampled SNPs (including SNPs with GERP score < = 0) matched for allele frequency (in bins of 10%) and recombination rate (in quartiles of cM/Mb). For each dataset we fit the above model separately and estimated SNP effects and phenotypic variance explained by each SNP.s by assuming that yield is a measure of fitness. We assigned the yield-increasing allele at each GERP-SNP a random dominance value in the range of 0 ≥ k ≥ 1 and calculated its equilibrium allele frequency p under mutation-selection balance using k > 0.98 and k ≤ 0.98. We then simulated datasets using binomial sampling to choose SNPs in a sample of size n = 12 inbreds.To test the relationship between GERP score and dominance under a simple model of mutation-selection equilibrium, we estimated the selection coefficient We imputed missing data and identified regions of identity by descent (IBD) between the 12 inbred lines using the fastIBD method implemented in BEAGLE . We thenhttps://github.com/yangjl/zmSNPtools); blocks with no SNPs with positive GERP scores were excluded from further analysis. For a particular SNP with a GERP score g, the homozygote for the conserved (major) allele was assigned a value of 0, the homozygote for the putatively deleterious allele a value of 2g, and the heterozygote a value of (1 + k) × g, where k is the dominance estimated from the GBLUP model above.Because there is no recombination in an inbred parent, this allows us to project the diploid genotype of each F1 based on the haplotypes of the two parents. In the projected diploid genotype of each F1, haplotype blocks were weighted by the summed GERP scores of all GERP-SNPs  and validation sets (20%) 100 times. After model training, we obtained prediction accuracies by comparing the predicted breeding values with the observed BLUE values in the corresponding validation sets. For comparison, we permuted GERP scores using 50k SNP (≈ 100Mb or larger) windows which were circularly shuffled 10 times to estimate a null conservation score for each IBD block. We conducted permutations on all GERP-SNPs as well as on a restricted set of GERP-SNPs only in genic regions to control for GERP differences between genic  and intergenic regions . We conducted permutation cross-validation experiments using the same training and validation sets.r) used for the cross-validation experiments, where the latter is defined as the correlation between real and estimated values; the two statistics will converge to the same value when there is no error in SNP/haplotype effect estimation [We estimated the posterior phenotypic variance explained using all of the data to derive correlations between breeding values estimated from the prediction model and observed BLUE values. Note that the correlation used here is different from the prediction accuracy .Sequencing data have been deposited in NCBI SRA (SRP103329) database, and code for all analyses are available in the public GitHub repository  of its phenotype from mixed linear models  to contra priori, we made use of Genomic Evolutionary Rate Profiling (GERP) [We resequenced the 12 inbred parents to an average depth of ≈ 10×, resulting in a filtered set of 13.8M SNPs. Compared to corresponding SNPs identified by previous studies see , we obseg (GERP)  scores og (GERP) . GERP scg (GERP) . Sites wTo compare the burden of deleterious variants between our elite maize lines and traditionally cultivated landraces, we used genotypes from the maize HapMap3.2  for our Population genetic theory predicts that deleterious variants should be at low overall frequencies, and that such variants should be enriched in regions of the genome with extremely low recombination . Using dper se , from ≈ 0 for flowering time traits to as much as 24% for grain yield   modelingllel see . GERP-SNllel see . We founin yield . Distribk=da see  across tk=da see  and matck) for grain yield .We then evaluated the relationship between GERP score and SNP effect size, dominance, and contribution to phenotypic variance. We found weak or negligible correlations between effect size and GERP score for flowering time and grain quality, but a strong positive correlation for fitness-related traits . The varin yield , such thWe investigated a number of possible caveats to the results presented in a priori as putatively deleterious, we implemented a genomic prediction model that evaluates complementation at the haplotype level by incorporating GERP scores of individual SNPs as weights (see k) estimated from our GBLUP model explained a greater amount of the posterior phenotypic variance for most traits per se .et al. [et al. find no evidence for hitch-hiking associated with selective sweeps during domestication, and the differences we observe in the burden of deleterious variants closely mimic results from simulations of partially recessive deleterious alleles in populations that have recently undergone demographic bottlenecks, but no additional selection [Using GERP scores as a proxy for deleterious alleles, we then ask whether our elite maize inbreds show an increased burden of deleterious alleles compared to a set of traditional landrace varieties. We find that modern inbreds are characterized by an increase in the proportion of deleterious variants fixed within the population , consistet al. , supportelection .We next use the set of SNPs with GERP > 0 scores (or GERP-SNPs) to investigate the phenotypic effects of deleterious variants. Across phenotypes, our results largely mirror previous work, finding that dominance contributes substantially to grain yield  while trWe also find that more strongly deleterious alleles (those with higher GERP score and a larger negative effect on yield) are more likely to be recessive for grain yield. Such a genome-wide relationship between dominance and fitness has not been well demonstrated in other multicellular organisms, but supports previous empirical evidence from gene knockouts in yeast , 63. Thiet al., [After showing that GERP-SNPs explain a substantial portion of the observed phenotypic variation when combined with our estimates of dominance and effect size, we more rigorously test the direct utility of GERP scores using cross-validation prediction methods. We show that for both plant height and grain yield, our GERP-enabled prediction model has significantly improved accuracy compared to randomized data, even when only considering SNPs within genes . As genoet al.,  for a reet al., , and ourper se among hybrids [et al., [et al., [Finally, our results also have implications for understanding the genetic basis of heterosis. We observed substantial heterosis for a number of traits in our diallel , includi hybrids . We argu hybrids . Combine hybrids  Fig 1d)per se am hybrids , 53, enr hybrids , and eve[et al.,  and Huan[et al., ), but wein silico enables us to predict grain yield and other traits, including heterosis, with greater accuracy. Together, these results help reconcile alternative explanations for hybrid vigor and point to the utility of leveraging evolutionary history to facilitate breeding for crop improvement.In this study, we use genomic and phenotypic data from a partial diallel population of maize to show that an incomplete dominance model of deleterious mutation both fits predictions of population genetic theory and explains phenotypic variation for fitness-related phenotypes and hybrid vigor. We find genome-wide support for hypotheses predicting that more damaging variants are more recessive. Finally, we show that leveraging evolutionary annotation information S1 Figa) Twelve maize inbred lines were selected and crossed in a half-diallel fashion. Each inbred lines was used as both male and female and the resulting F1 seed was bulked. (b) Density plots of normalized BLUE values for the seven phenotypic traits. We used the “scale” function in R to normalize the BLUE values by first centering on zero and then dividing the numbers by their standard deviation. The seven phenotypic traits are plant height (PHT), height of primary ear (EHT), days to 50% pollen shed (DTP), days to 50% silking (DTS), anthesis-silking interval (ASI), grain yield adjusted to 15.5% moisture (GY), and test weight (TW).((PDF)Click here for additional data file.S2 Figr) and the asterisks (*) indicate the correlation coefficients are statistically significant . Units for various traits are plant height , height of primary ear , days to 50% pollen shed (DTP), days to 50% silking (DTS), anthesis-silking interval , grain yield adjusted to 15.5% moisture , and test weight .The upper right panels show the scatter plots of all possible pairwise comparisons of two traits. The red line is a fitted loess curve. In the lower left panels, the numbers are the Spearman correlation coefficients ((PDF)Click here for additional data file.S3 FigIn the upper panel, regions in red are IBD blocks identified by pairwise comparison of the two parental lines of a hybrid. The vertical dashed lines define haplotype blocks. In the lower panel, hybrid genotypes in each block are coded as heterozygotes (0) or homozygotes (1).(PDF)Click here for additional data file.S4 FigRed solid and grey dashed lines define the best-fit regression line and its 95% confidence interval.(PDF)Click here for additional data file.S5 Figots indicate mean GERP scores of putatively deleterious SNPs (GERP scores > 0) carried by the 12 parental maize lines (bin size = 1 cM). Vertical red lines indicate centromeres.(PDF)Click here for additional data file.S6 FigAdditive and dominance effects are indicated by red and blue colors respectively.(PDF)Click here for additional data file.S7 Figk) in each trait. Red lines show the phenotypic variance explained using the observed k.Histograms show the results for the randomly shuffled (10 times) degrees of dominance ((PDF)Click here for additional data file.S8 FigSolid and dashed lines represent significant and non-significant linear regressions, with grey bands representing 95% confidence intervals. Data are only shown for SNPs which explain more phenotypic variance than the genome-wide mean.(PDF)Click here for additional data file.S9 FigSolid and dashed lines represent significant and non-significant linear regressions, with grey bands representing 95% confidence intervals. Data are only shown for GERP-SNPs which explain more variance than the genome-wide mean and found in regions above the first quantile of the recombination rate (cM/Mb).(PDF)Click here for additional data file.S10 Fig(a) Total per-SNP variance explained for grain yield per se by deleterious (red lines) and randomly sampled SNPs (grey beanplots). (b) Density plots of the degree of dominance (k). Extreme values of k were truncated at 2 and -2 for visualization. (c-e) Linear regressions of additive effects (c), dominance effects (d), and degree of dominance (e) of seven traits per se against SNP GERP scores. Colors in (c-e) are the same as the legend for (b). Solid and dashed lines represent significant and nonsignificant linear regressions, with grey bands representing 95% confidence intervals. Data are only shown for deleterious alleles that explain more variance than the genome-wide mean.(PDF)Click here for additional data file.S11 FigThe solid blue line indicates the regression line fitted to data simulated under mutation-selection balance (see (PNG)Click here for additional data file.S12 Figper se  and heterosis  under additive , dominance , and incomplete dominance  models. Prediction accuracy using real data is shown on the left (green) and permutation results on the right (grey). Horizontal bars indicate mean accuracy and the grey dashed lines indicate the overall mean accuracy. Stars indicate real data having significantly  higher cross-validation accuracy than permuted data.Beanplots represent prediction accuracy estimated from cross-validation experiments for traits (PDF)Click here for additional data file.S13 Figper se  and heterosis  under additive  and dominance  models. Prediction accuracy using real data is shown on the left (red) and permutation results on the right (grey). Horizontal bars indicate mean accuracy of each trait and the grey dashed lines indicate the mean accuracy of all traits. Stars indicate real data having significantly  higher cross-validation accuracy than permuted data.Beanplots represent prediction accuracy estimated from cross-validation experiments for traits (PDF)Click here for additional data file.S14 FigEach line represents the posterior breeding values of a diploid hybrid (red circle), its best parent (black diamond), and predicted breeding values of simulated AAB triploid (blue square) and ABB triploid (green triangle) plants based on estimated effect sizes and dominance values for each SNP.(PDF)Click here for additional data file.S1 TableAbbreviations for phenotypic traits are plant height , height of primary ear , days to 50% pollen shed (DTP), days to 50% silking (DTS), anthesis-silking interval , grain yield adjusted to 15.5% moisture , and test weight .(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(CSV)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(CSV)Click here for additional data file.


Eleven of them replicate (5% FDR) in five independent longevity studies combined; all but three are depleted of the life-shortening alleles in older Biobank participants. Further analysis revealed that brain expression levels of nearby genes  might be causally implicated in longevity. Gene expression and caloric restriction experiments in model organisms confirm the conserved role for RBM6 and SULT1A1 in modulating lifespan.The enormous variation in human lifespan is in part due to a myriad of sequence variants, only a few of which have been revealed to date. Since many life-shortening events are related to diseases, we developed a Mendelian randomization-based method combining 58 disease-related GWA studies to derive longevity priors for all HapMap SNPs. A Bayesian association scan, informed by these priors, for parental age of death in the UK Biobank study ( Along with various environmental factors, a complex genetic architecture influences human lifespan. Here, McDaid and colleagues reveal novel loci associated with human lifespan and linked biomarkers by Bayesian association scan. Human lifespan is a highly variable and heterogeneous traitWhile most determinants of life expectancy are still environmental  and FOXO3 (rs10457180) loci, although not at genome-wide significance level. A meta-analysis of European longevity cohorts confirmed APOE  and revealed a new locus near EBF1 Genome-wide association studies (GWAS) have been instrumental in revealing disease susceptibility036 ref.  and n=7,729 ref. , revealeCHRNA3/5 (P<10−16), known to associate with lung cancer linked to smokingAPOE region (P<10−19), also associated with Alzheimer’s diseaseFOXO3 or EBF1. These findings suggest disease-associated loci may serve as good candidates for lifespan studies. Indeed, a recent GWAS on extreme longevityP value combined over 14 disease-related traits is lower. This approach enabled the discovery of three novel loci implicated in extreme longevity.Two recent studies133/5 P<10−, known tHere we improve and extend these approaches using: (i) a Bayesian approach to derive a meaningful prior effect size for each SNP; (ii) estimating and accounting for all effect directions ; (iii) analysing lifespan in a general population cohort of the UK Biobank study101112The key aims of our study are to (i) discover new variants impacting mortality and doing this by developing a mechanistic model to estimate morbidity priors for each SNP; (ii) understand through which disease-predisposition they act; (iii) reveal the discrepancy between the life-shortening effect of a SNP and its effect on various life-shortening diseases; (iv) shed light on transcriptome biomarkers that may be causally involved in this process; (v) elucidate whether these processes are shared between mice and human.We used parental lifespan-association summary statistics (Z-statistics of the linear regression) from the UK Biobank studyP value for each observed test statistic, comparing the observed test statistic to the large sample of ‘null’ test statistics. In this framework, the goal is to compute a novel test statistic which is more powerful than the conventional lifespan Z-statistic of prior. The prior is a prediction of the lifespan effect size, based on the effect sizes of the same SNP for other disease-related traits and an (out-of-sample) estimate of the causal effect of these traits on lifespan. The steps taken, in order, are: (1) Impute Z-statistics for all SNPs of interest in lifespan and in other traits of interest. (2) Estimate the causal effects of traits on lifespan using Mendelian randomization (MR). (3) Build the prior for each SNP. (4) Compute the BFs for each SNP. (5) Sample the ‘null’ BFs. (6) Compute P values by comparing the observed BFs to the ‘null’ BFs. All these steps are explained in details in the ‘Methods section’.For each SNP, we compute a novel test statistic, based on a Bayes factor (BF). With simulations, we draw a large sample of how these statistics would be distributed if the null hypothesis were true for every SNP, that is, no effect of any SNP on lifespan. This allows us to compute a The first step is performed with a standard summary statistic imputation methodP values control the false discovery rate (FDR) regardless of the correctness of the priors. Aside from these permutation-based P values, all P values in this paper are based on a normally distributed test statistic and a null hypothesis of no effect of the SNP (or trait) on lifespan. The GWAS data we used for non-lifespan traits includes P values; from these P values and the effect direction we inferred the Z-statistics.Next, the derived priors were combined with the observed longevity association statistics to calculate the BF. The final step was to compare the obtained BFs to the distribution expected under the null hypothesis, allowing us to compute empirical P value threshold and strength of linkage disequilibrium (LD) pruning. We have explored multiple settings (P<10−5) and moderate LD pruning (LD<0.2). The multivariate MR identified 11 traits with significant causal effects on lifespan -cholesterol level and shorter stature improves lifespan; but higher body mass index (BMI), low-density lipoprotein (LDL)-cholesterol, triglyceride or glucose levels, susceptibility to coronary artery disease, major depression, type 2 diabetes (T2D) or schizophrenia all shorten lifespan. Smoking intensity (measured as number of cigarettes per day) showed a clear negative impact on lifespan. Less intuitively, the link between tall stature and shorter lifespan has been shown in several studies, some explaining the link by cancer riskAPOE, rs7256200) on the residual qq-plot for all the 22 leave-one-chromosome-out estimation (The instrument (SNPs) selection, a key component of any MR analysis, depends on the association settings  and, as lifespan . Note thlifespan . The 11 timation . Due to While many of the obtained causal effect estimates seem plausible and in line with literature evidence, we do not claim that these causal effect estimates are unbiased, neither that no instruments violated MR assumptions. This step was necessary only to obtain sensible priors for the downstream analysis. If these estimates are wrong, our prior is less informative, yielding fewer discoveries.P value <10−5 with at least one of the 11 traits identified in the previous step to be causally linked to lifespan. For each of these SNPs we calculated the product of its effect on a given trait  and the estimated causal effect of the trait on lifespan. These products were then summed up for all the 11 traits and used as prior effect estimate of the SNP on lifespan. For illustration we show how the effect sizes of these 78 K SNPs for four important traits compare to their effect on lifespan  survive the more stringent Bonferroni threshold (5 × 10−8).Combining the priors with the observed longevity summary statistics we obtained BF for all 2.3 million SNPs and generated 1000 null GWAS associations to derive null BFs. The generated 2.3 billion  null BFs were used to assign g 5% FDR . For eacof 1.002 . If we uP values . If one FTO gene also features in the list of 16 top hits. However, three of them  have not yet been associated with any trait at genome-wide significant level.Details of these top hits are shown in APOE/APOC1 (rs4420638) variant has HR=1.038 and each copy of the G allele decreases life by ∼5 months.In APOE and CHRNA3/5) we have only 16% (for the FTO variant) to 73% (for the SNP near BSND) power to replicate our findings with a P value below 0.05/16 in a study very similar to the combined CHARGE data set. For this reason we call our analysis as a lookup, given that strict replication is impossible due to power considerations.Given the large sample size of the UK Biobank compared to other previous independent studies we first assessed power to replicate our findings in the combined CHARGE studies. First, we performed power calculations assuming a replication study with 14,000 samples selected from the top and bottom 5% tail of a general lifespan distribution and a SNP explaining ∼1% of lifespan variability. These calculations showed that apart from the top two hits  seemed to have larger effects on extreme longevity  impacting lifespan only in the normal range, but with negligible effect on extreme lifespan.Some of these 16 SNPs  survive Bonferroni correction (0.05/16).In summary, 12 out of the 16 hits replicate in at least one of the replication studies with nominal significant one-sided P=0.0021) and five of them have nominally significant association with participant age (P=8.09 × 10−5). This observation is concordant with the selection bias of study participants: those that are able to make it to population cohort studies are in better health than the overall population. Hence, people carrying life-shortening (or disease-associated) alleles are less likely to make it to a cohort at an older age resulting in lower life-shortening allele frequency as a function of participant age in the UK Biobank  did not replicate in our study (P=0.41). The longevity-associated EBF1 variant (rs2149954-T) found in the largest European GWASP value=0.16) and was assigned zero prior in our analysis.As expected, the well-established P<10−5. None of these SNPs attained a nominally significant P value in our study , SH2B3/ATXN2 (rs3184504-G), ABO (rs514659-A) and HLA (rs3763305-A). Remarkably, the first two variants replicated in our analysis with (one-sided) P values P=4.34 × 10−6, P=1.08 × 10−3, P=0.03, P=0.51, respectively.A recent extreme longevity studySULT1A1, CHRNA5 and RBM6) showed significant negative causal effects  on lifespan, that is, lower expression extending lifespan  at the 16 loci that had more than five independent eQTL SNPs in a GTEx ref.  tissue tlifespan .P<10−4) of age-associated genesP values. This set of SNPs, however, showed no significant enrichment in low P values. Similarly, we found no significant enrichment of lower than expected lifespan P values among SNPs that are methylation QTLs27P<10−4) for at least one of the 353 age-associated methylation CpG sitesIf the expression of a gene correlates with chronological age, it may be an indicator for the biological age and may be linked to lifespanSULT1A1, CHRNA5 and RBM6) that emerged as strong candidates, and analysed whether their expression levels (in brain) could be used as an early biomarker for longevity. Specifically, we looked for evidence whether their expression or the suppression thereof may be linked to lifespan in animal models.In this section we focus on the three genes . The other two genes showed no significant association with lifespan.We compared the lifespan and the expression level of these focal genes in prefrontal cortex at 72 days of age in 35 strains of LXS mouse linesC. elegansD. melanogaster36SULT1A1 expression levels are up-regulated upon CR diet in mouse (GSE11291), , defined as a reduction of ∼20–40% of the regular caloric intake, can extend lifespan in a large range of organisms (E11291), . No signAPOEε4) trigger diseases with very late onset (such as Alzheimer’s), hence these markers would not impact lifespan below 70 years of age. To check to what extent the genomic landscape of lifespan and extreme longevity overlap, we applied LD-score regressionrG=0.73, s.e.=0.11) between these two kinds of longevity measures, indicating mostly shared genetic mechanisms.One key question is whether there are specific genetic factors associated with extreme longevity10P values, but reduce multiple testing burden to only 4% of the genome that had non-zero prior assigned, still only three of the 16 loci would have been recovered at 5% FDR  decreases lifespan primarily through (beyond the well-known effect on Alzheimer predisposition) increasing LDL levels, but surprisingly protects from type 2 diabetes. The CHRNA3/5 SNP (rs951266) mainly impacts lifespan through smoking and schizophrenia predisposition; the SNX29 SNP rs729583 seems to have little impact on most of the 11 traits; the FTO SNP rs9939973 essentially exerts its effect on BMI and its downstream traits. Fourth, the actual effect size estimates of the discovered SNPs are quite far from both the priors and their individual effect on a trait . This is not unexpected, as longevity seems to be extremely complex, impacting multiple diseases and processes.Given the size of all other published longevity GWAS, we had low power to replicate our findings. Despite this fact many of the discovered loci replicated in independent data sets101218P values . Only thSurprisingly, the MR analysis did not yield a significant causal effect of alcohol consumption on lifespan, despite epidemiological observationsrG=0.73). Second, the squared genetic correlation between lifespan and the  weighted combination of 11 traits/diseases examined in this study, calculated by LD-score regressionThe follow-up analyses revealed several novel insights into the genetic underpinnings of lifespan regulation. First, we found that while genetic effects on extreme longevity are generally larger than on general lifespan, their global genetics are largely shared  in our MR analysis, it revealed a much stronger causal evidence for lifespan than the earlier studyN=101,069). (v) Combining our results with expression QTL analysis does not provide definite evidence of the causal involvement of certain transcripts in lifespan modulation. In addition, tissues with larger sample size have more chance to be picked up than others. For example, in case of the lifespan-associated rs646776 at the 1p13.3 locus (near CELSR2) we could not detect a causally implicated mediator gene in any tissue. However, a previous studySORT1 in the liver. We conducted MR analysis to confirm this gene as the best candidate for LDL, but not for lifespan: The reason for this is that SORT1 has a much stronger eQTL SNP in liver (rs7528419), which is not associated with lifespan in our study. Thus unless rs7528419 is an invalid instrument, our observation excludes SORT1 as causal gene for lifespan, despite being a strong candidate for LDL.Our approach has several weaknesses: (i) Since the UK Biobank study is much larger than any other lifespan study ever conducted, meaningful replication is not possible due to these previous studies being massively underpowered for this purpose. (ii) We cannot identify longevity-associated SNPs whose effects are non-disease mediated or mediated by diseases whose genetic background is largely unknown or confounded by environmental factors. (iii) When applying MR we did not strictly verify whether all instruments are valid as our aim was only to obtain a sensible causal effect estimate to define the priors, rather than accurately establishing the true causal effect size. (iv) Sample size of the available GWASs impacts our ability to detect traits causally affecting lifespan. This limitation precluded us from including most cancer GWASs to build the priors. Another example is educational attainment: when we used the recently published study on educational attainmentWe expect that the growing number of traits subjected to GWASs and their increasing sample size will strengthen our approach to reveal many more longevity-associated SNPs that act through disease modulation. Importantly, our analysis strategy is not specific to longevity and can provide a new statistical framework to improve the power of future GWA studies of any disease with well-studied risk factors.P values under the null hypothesis of no SNP affecting lifespan. This procedure allows us to control the FDR. To compute the BF for a SNP, we need an alternative distribution of the SNP’s effect on lifespan, alongside the conventional null distribution of zero effect. We use a prior distribution as our alternative distribution, as presented in A BF is computed for each SNP, compared to the distribution of BF obtained for simulated The primary longevity study used is based on 116,279 individuals in the UK Biobanket al.As replication we combined two  independent studies performed by the CHARGE ConsortiumP value, effect allele, effect size or direction) defined. The direction of the effect (±), which is sufficient for our method, is defined as the sign of ‘beta’ fields (for continuous traits) or whether ‘odds ratio’ fields are greater (+) or less (−) than 1 (for binary traits).To build priors for lifespan, we assembled a compendium of meta-analytic GWASs that are not related to lifespan nor longevity. We began with 207 studies, made up of three groups . The larWe also included the 35 publicly available studies from the Psychiatric Genomics Consortium (PGC), and another collection of 36 studies of various consortia: anthropometric (GIANT), lipid (GLGC), metabolic , cardiovascular (CARDIOGRAM) and other traits.P<5e-4) as estimated by LD-Score regressionOf these studies, 60 demonstrated heritability significantly different from zero , and we applied it to the 58 studies with strong instruments. We established a set of SNPs that are strong instruments  for at least one of the 58 studies.This method requires, for each SNP, an estimate of its effect on lifespan, which is independent of its summary statistic in the parental lifespan study. We refer to this estimate as the The conventional method to estimate the causal effects of diseases on lifespan is to use the data on each individual’s  lifespan and on each individual’s disease status. We do not have access to such detailed data, but we describe here how we derive equivalent estimates of the causal effect by using the Z-statistic data that is available to us, which gives us the estimates of the effect of all the SNPs on lifespan and on the various diseases.n individuals, m SNPs and d diseases define the n × m matrix Z which records the genotype of each individual, an n × d matrix X which records the disease status of each individual for the d diseases, and a vector y of length n with the parental lifespans. Every column of X, Z and y is assumed to have been standardized to have mean zero and variance one.Given Using two-stage least squares, we first defineet al.With such data, as described in equations (8–9) in Section 8.3.4 of Green m, reporting the estimate of the effect of each SNP on lifespan. Also, for the disease traits, we have an m × d matrix The summary statistic data, which we do have, takes the form of a vector i on trait t, that is, These estimates of the standardized effects come from the GWAS summary statistics and were calculated assuming a linear model for the relationship between each SNP and each trait (including lifespan). For example, each extra minor allele will increase or decrease the log-odds of a binary trait, or linearly increase or decrease continuous traits. We infer the standardized effect estimate of SNP To compute where This last equation gives us estimates for the causal effects in terms of quantities that we have access to.The covariance of this estimator, assuming homoscedastic errors, iswhereet al.et al.ρ in their paper) are in represents the variance of the outcome trait unexplained by the causal predictor, which in reality is very close to 1. The covariance estimator for heteroscedastic errors requires individual level data and hence not suitable for summary statistics-based MR estimation. Note that Burgess C by the identity matrix I, which improves numerical stability and simplifies the equations. It gives us the expressions we estimate the causal effects and the covariance of those estimates, where S to select the relevant subset of rows from Instead of using every SNP, we use only those which are strong instruments, and we use only a subset of those strong instruments that are spread further apart on the chromosomes in order that their genetic correlation is low. This separation allows us to approximate S, the subset of strong instruments where there is a low level of genetic correlation (LD) between instruments, we began with the SNP with the most significant P value  and then discarded any SNP in LD (r2>0.2). Repeating this pruning procedure results in 6,443 independent SNPs used for the estimation of causal effects. To avoid weak instruments the standardized effects for a SNP-trait pair with pt,i>10−5 was set to zero. Define pit<10−5, and we can then simplify To select P value>0.05, which resulted in 11 traits. In other words, 11 traits were identified to have a significant causal effect on lifespan. For the remainder of the method, computing the prior and the BF, only these 11 traits were used. In all these regression analyses we forced the intercept to be zero, but fitting the intercept too did not change the estimated causal effects , will be non-zero and hence they do have a prior, albeit very weak.Also, as mentioned earlier, most of the 2.3 million SNPs will have zero prior, N. When using standardized effect estimates, their distribution under the null is nl is the sample size in the longevity study. The prior In Bayesian analysis, we also consider the likelihood under an alternative hypothesis to compare it to the likelihood under the null. While under the null distribution the prior has zero mean and zero variance, the prior distribution under the alternative hypothesis is N).A conventional GWAS will consider how likely the observed lifespan or longevity z-statistic coming from a standard normal distribution, δ, which takes the place of the true mean, we use integration to compute the marginal likelihood,For each of the 2.3 million SNPs, we can now compare the effect estimate observed in the longevity GWAS to the prior computed above. The BF is defined as the ratio of the likelihoods of the observed longevity effect size estimate, L is the density of γ under the corresponding Gaussian distributionwhere http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf], the ratio of above is equivalent toAs shown elsewhere [ P values to the obtained BFs. As standard procedure with any test statistics (BF is a test statistic), we used a permutation-based approach to calculate P values corresponding to each BF ) for SNP i). In formula, the BF for SNP i for the k-th random trait is defined asTo define a set of SNPs that survive 5% FDR we first needed to assign  BF ref. . In pracZi representing the standardized genotype vector for SNP i and yk) null BFs emerging from the exact same SNP set with the same priors to compute empirical P values for each observed BF. These P values were then subjected to Benjamini–Hochberg step-up procedure to select the largest set that survives 5% FDR. Using the null BFs, we also estimated the per comparison error ratewhere et al.et al.We followed the same protocol to calculate Cox proportionate HRset al.P values. Since effect sizes and directions are unknown for the iGWAS, we used the Fisher’s combined probability testP values from the first three studies with the P values from the two iGWAS cohorts. To account for the sample overlap we used 236,383 off-target SNPs  to estimate the null cumulative density function for the sum of the −2ln(P) values, instead of using a χ2 distribution as done in the standard Fisher’s method. Note that for the iGWAS NECS study we used the best available proxies for 14 of the 16 SNPs ,P values. Analogously, we also searched for SNPs associated27P values to a random set of the same size. This analysis is meant to test whether most age-associated biomarkers (expression or methylation) are causally linked to lifespan.Next, we asked whether SNPs associatedP values fell below 0.05 and compared it to the expected number through the binomial test.If these 16 SNPs are truly associated with lifespan, their life-shortening alleles should be depleted in older individuals in general population cohorts, since people carrying these alleles would have less chance to survive in the cohort at older ages due to selection bias. We tested this hypothesis by modelling age as the outcome in a linear regression with regressors such as SNP allele dosage, gender, 15 ancestry principal components and batch indicator in 120,000 genetically white British participants of the UK Biobank. We then asked what fraction of the one-sided ad libitum feedingMale mice from 41 ILSXISS (LXS) recombinant inbred strains were maintained For each species, Affymetrix raw .CEL files were downloaded from GEO databasehttp://wp.unil.ch/sgg/bayesian-lifespan-gwas/.The disease GWAS summary statistics are publicly available and the sources are provided in How to cite this article: McDaid, A. F. et al. Bayesian association scan reveals loci associated with human lifespan and linked biomarkers. Nat. Commun.8, 15842 doi: 10.1038/ncomms15842 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Supplemental Digital Content is available in the text To examine factors influencing retinal vasculature in two environmentally contrasted, cross-sectional studies of adult participants of European descent and to estimate the extent and specificity of genetic contributions to each retinal vasculature feature.Retinal images from 1088 participants in the Orkney Complex Disease Study and 387 in the CROATIA-Korčula study, taken using the same nonmydriatic camera system and graded by the same person, were evaluated. Using general linear models, we estimated the influence of an extensive range of systemic risk factors, calculated retinal traits heritabilities and genetic correlations.Systemic covariates explained little (<4%) of the variation in vessel tortuosity, substantially more  of the variation in vessel width and monofractal dimension. Suggestive not well trodden associations of biological interest included that of urate, tissue plasminogen activator and cardiac PR interval with arteriolar narrowing, that of carotid intima–media thickness with less-tortuous arterioles and of cardiac QT interval with more tortuous venules. The genetic underpinning of tortuosity is largely distinct from that of the other retinal vascular features, whereas that of fractal dimension and vessel width greatly overlaps. The previously recognized influence of ocular axial length on vessel widths was high and can be expected to lead to artefactual genetic associations [genetic correlation with central retinal arteriolar equivalent: −0.53 (standard error 0.11)]. The significant genetic correlation between SBP and central retinal arteriolar equivalent, −0.53 (standard error 0.22) , augurs more favourably for the discovery of genetic variants relevant to vascular physiology. Retinal vessels are thought to reflect pathological processes occurring in the less readily accessible coronary and cerebral microvessels of similar scale (∼100–250 μm in diameter) –6. LongiGenetic factors are important to gain etiological insights and provide instruments to test causal relationships, but genetic research in retinal microcirculation is still not extensive. Several studies have reported the heritabilities of retinal vessel widths, all indicating substantial genetic influence on these traits. These include twin studies: a Danish study of 210 young healthy individuals , the AusATXN2, has also been associated with SBP and DBP, but, to our knowledge, the predicted genetic overlap between retinal microvasculature features and blood pressure (BP) has never been quantified.Efforts to map genetic determinants for CRAE and CRVE have already started: through linkage in two studies, with little overlap of findings ,17 and mHere, using primarily the Orkney Complex Disease Study (ORCADES) population-based cohort, we aimed to consolidate basic knowledge on retinal vasculature traits. In total, seven retinal measures of retinal vessel width, tortuosity and global pattern of branching were studied . Systemic covariates with well established associations with retinal vessel features, as well as more sporadically investigated ones, were selected on the basis of published retinal vessel research. They included two ocular physical measures; axial length of the eye and intraocular pressure (IOP); four body shape traits including stature together with three obesity measures ; six cardiovascular traits including SBP, DBP, pulse pressure (PP) and three markers of atherosclerosis ; three quantitative measures of cardiac physiology that have been little explored previously; and also 14 blood biochemical markers of inflammation, haemostasis, lipid and glucose metabolism. We estimated heritability for all retinal traits, investigated the degree to which these traits are influenced by overlapping genetic variants and quantified the genetic overlap between retinal vasculature features and associated systemic risk factors.Orkney is an archipelago in northern Scotland, with a total population of ∼20 000 people. ORCADES is a family-based study comprising 2078 richly phenotyped and genotyped orcadian participants. Fundus images from 1088 genotyped individuals were successfully graded. The analysed sample, with a mean age of 52 years, included 57% women, 3.8% people with type 2 diabetes and 36.8% hypertensive participants (52% of whom were taking antihypertensive medication). Individuals were classified as hypertensive if they satisfied at least one of these criteria: SBP at least 140 mmHg, or DBP at least 90 mmHg or using antihypertensive medication. Some very deep pedigrees were available. These were verified for genotyped participants and corrected if necessary using genotype-derived kinship coefficients. After removing uninformative individuals, the pedigrees were still too large for analysis; therefore, they were trimmed to smaller informative pedigrees with a maximum bitsize of 50 using the software PedCut . In the The island of Korčula is located in the southern Croatian coastal region of Dalmatia. A total of 969 individuals were recruited in this study and most underwent complete eye examination. Family history data and pedigrees were not available. Data on numerous quantitative traits associated with cardiovascular, metabolic and other diseases were also collected. The eye examination included fundus image photography, but only 421 images were gradable. Successfully graded fundus images from 387 genotyped individuals were included in the analysis. The analysed sample, with a mean participant age of 54.3 years, included 68.5% women, 10.6% people with type 2 diabetes and 51.6% hypertensive participants (44% of whom were taking antihypertensive medication).Both studies received approval from the relevant ethics committees in Scotland and Croatia and complied with the tenets of the Declaration of Helsinki. All participants were volunteers and gave written informed consent.A high-resolution (12 Mpixel) digital nonmydriatic retinal camera  was used to capture fundus photographs centred between the macula and optic disc in participants of the ORCADES and CROATIA-Korčula studies. Retinal fundus images were obtained at a 45° angle, and all images were stored in joint photographic experts group or tag image file format. Images centred overly towards the macula, with optic disc on the side of the field, were not used in the image analysis. Furthermore, only retinal images with at least four major arteries and venules present in the zone of grading were included. Images of very poor quality (e.g. out of focus and overexposure of the lens) or from participants with known disorders influencing image quality  were also excluded from the image analysis. Two different software packages were used for the semiautomated computer-assisted capture of retinal vessel parameters: VAMPIRE (beta version v2.0), a platform developed in the Universities of Dundee and Edinburgh  and SIVADbox) calculated using the box-counting technique  were measured in serum; C-reactive protein (CRP) and fasting glucose were measured in ethylenediaminetetraacetic acid (EDTA) plasma; fibrinogen and tissue-type plasminogen activator (tPA) were measured in citrated plasma; and glycated haemoglobin A1c was measured in whole blood in EDTA. LDL cholesterol levels were calculated using the Friedewald equation.ABPI is the ratio of the BP in the lower leg to the BP in the arm. The measurements used in the analysis were calculated as the lowest posterior tibial (ankle) SBP divided by the highest brachial SBP.Augmentation index was the only trait selected from PWA. PWA was performed after 1 h of supine rest. Radial arterial tonometry was performed using the SphygmoCor Px system  with a Miller tonometer  continuously for 30 s in a resting state, and an average waveform was generated. Four readings were performed in total (two successful readings followed by 5 min rest followed by two further readings). The augmentation index is a ratio calculated from the PP waveform and one of the most commonly used noninvasive measures of arterial stiffness. It is defined as the ratio of augmentation pressure to PP. The stiffer the arteries, the more quickly the reflected pressure wave travels and is superimposed on the forward wave, thus augmenting the pressure.CIMT was assessed by ultrasound examination of the common carotid artery 2 cm below the carotid bifurcation. The mean of three measurements was used as a final trait in the analysis. This measure was only available in the ORCADES study.SBPs and DBPs were measured twice using digital sphygmomanometers  and the second measurements were used in analysis as continuous variables, without correction for medication taken. Smoking information was collected  through questionnaires. Bodyfat% in ORCADES was measured using bioimpedance with Tanita scales. Bodyfat% in CROATIA-Korčula was calculated from four skin folds measurements using Harpenden skin callipers. Linear regression equations were used to calculate body density , and Sir.Electrocardiogram (ECG)-derived measures included QT, the interval reflecting duration of ventricular depolarization and repolarization; PR, the interval reflecting the duration of atrioventricular conduction; and RR, the interval between two ventricular contractions in milliseconds. Heart rate (HR) in beats per minute was derived as 60 000/RR. Digital 10-s ECGs were taken after at least 10 min of supine rest, using a computer link with QT and RR intervals calculated using CardioView software . QT was corrected for HR, using the Bazett's formula n = 890), the Illumina Omni1 (n = 304) and Illumina OmniExpress beadchips (n = 1073) for ORCADES and 370CNV-Duo and Quad beadchips for Korčula, following the manufacturer's standard recommendations. Genotypes were determined using the Illumina BeadStudio or Genome Studio (Hap/Omni) softwares. Analysis using genotype data in ORCADES were performed on the common set of markers across the three arrays used.DNA was extracted from peripheral blood. Extraction was done using BACC3 Nucleon kits  by the WTCRF  for ORCADES and at the Medical School, University of Split for CROATIA-Korčula participants. Participants were genotyped using dense single nucleotide polymorphism (SNP) arrays: a mix of the Illumina HumanHap300v2, 370CNV-Quad beadchips (http://www.r-project.org). Individuals with less than 97% genotyping rate were removed from further analysis. SNPs with call rate below 98% and P value for Fisher's exact test of Hardy–Weinberg equilibrium below 10–6 were also removed. In addition, samples with excess autosomal heterozygosity or sex inconsistency were removed because of possible sample contamination or mix up. Ethnic outliers were removed on the basis of principal components analysis of genotypic data. After these quality control steps, the number of genotyped individuals available with retinal trait measures was 1088 for Orcades and 387 for Korčula.Genotyping rates for both SNPs and individuals were checked using the GenABEL package implemented in R (http://www.r-project.org). Normality of distributions of retinal traits was tested using the Kolmogorov–Smirnov normality test (lillie.test) implemented in the nortest package in R, in addition to graphical representation of each trait distribution. Traits for which the distribution of residuals strongly deviated from normality (after adjusting for standard covariates) were transformed using quantile normalization using the rntransform  function of the GenABEL R package  and TORT , suggesting a substantial portion of common genetics underlying vessel anatomy. These remained strong after further adjustment for SBP . Monofractal dimension derived from the VAMPIRE software, Dbox, showed 100% genetic correlation with that measured by SIVA, Df, and shared common genetic determinants with the vessel calibres . The tortuosity measures displayed significant heritabilities, 0.55 (SE = 0.08) for arterioles and 0.21 (SE = 0.07) for venules . Genetic correlations between CRAE as well as CRVE and axial length of the eye were high and highly significant, whereas genetic correlations between CRAE and SBP or DBP were low but strengthened and reached significance after adjusting for axial length of the eye. The genetic correlation between SBP and CRAE was then −0.53 , and the genetic correlation between CRAE and DBP was −0.37 , quantifying the expected contribution of shared genetic determinants to both arteriolar vessel widths and BP. These values remained unchanged after adjusting vessel widths for hypertensive medication effect (not shown). The genetic correlations for the other pairs of traits investigated, including CRVE and SBP, were low and NS in the sample analysed.Genetic correlations between retinal traits and the most important systemic risk factors were calculated . In combination, BP and age explained 11, 16 and 27% of the interindividual variation, for, respectively, Df, CRAE and Dbox.pulation , as obseSBP effects on age-adjusted retinal vessel widths in Korčula  and Orkney   were all strongly negative, in line with the well established association between high BP and retinal arteriolar narrowing. A more novel finding is the negative association between atrioventricular conduction time (PR) and CRAE, which is in line with the reported association of lower CRAE at baseline and incidence of atrioventricular conduction anomalies  in the multiethnic study of atherosclerosis (MESA) prospective study , as wellAll venular measures and the arteriolar tortuosity measures were less influenced by BP or showed no relationship. Of note, however, the influence of BP on CRVE was stronger in both our studies in hypertensive individuals, and the negative association of SBP with CRVE reached statistical significance in the largest sample, ORCADES, when the analysis was restricted to hypertensive individuals. This stronger effect was found in all hypertensive ORCADES participants regardless of them taking antihypertensive medications. A different magnitude of effect of SBP (not DBP) on CRAE was also observed between hypertensive and nonhypertensive subgroups, but this was more clearly modulated by antihypertensive medications intake and compatible with the reported dilating effect on CRAE of several antihypertensive treatments ,41.Df, not studied in , confirming common genetic factors shaping both arterioles and veins, independently of BP , and in agreement with the first CRAE GWAS hit described, close to the MEF2C gene, also influencing CRVE [Vessel width heritability estimates  were in line with published population-based estimates and, as previously observed lower than the twin study estimates, in agreement with the fact that twin-based studies provide an upper limit heritability estimate. A general consistent trend across the various studies is that of higher heritabilities for venular width, compared with arterial, which is possibly reflected in the higher GWAS success for CRVE than CRAE so far. This would agree with haemodynamic and metabolic changes, of possible large environmental component, influencing arterioles more than venules. Heritability estimates for AVR  were very similar across studies . This perhaps is not surprising as AVR gives a ratio of the two widths and is therefore devoid of across-study noise common to both measurements (e.g. ocular magnification). CRAE and CRVE displayed both high phenotypic (.65–0.77 ) and higfamilies , presenting CRVE .et al.[h2 = 0.82); however, this was a twin study and included only 218 participants. We estimated the heritability of arterial TORT to be 55% in ORCADES, the highest amongst the retinal traits analysed. The trait was not influenced by the axial length of the eye but was associated by CIMT, independently of age, reflecting modulation by atheromatous processes in the arterial wall. Venular tortuosity heritability was lower (h2 close to 21%), possibly due to a thinner vessel wall that is more responsive to systemic changes, compared with the wall of the arterioles. Venular tortuosity was inversely associated with ocular axial length, and positively with QTc, a marker of myocardial health. The effect of age on arteriolar but not venular tortuosity is in line with the generally stronger effect of age on arteriolar compared with venular retinal traits. It could be in part due to the inherent alteration of elastin with age, which is more abundant in the arterioles, contributing to age-dependent rigidification [ρg = 0.59, SE = 0.15). The low genetic correlations between tortuosity and width or fractal dimension traits, on the other hand, suggest little common causes. No reported data on the genetic contribution to the variance of fractal dimensions exist, and this study represents the first report of this trait's heritability. Monofractal measures from VAMPIRE (Dbox) and SIVA (Df) had similar low heritability, close to 22%, and high genetic correlation highlighting a good agreement between the two methods. This suggests that meta-analysis of genetic effects on data generated using the two different softwares can be carried out to maximise sample size. Monofractal dimensions shared genetic determinants with the vessel calibres, to a bigger extent than AVR, and as they were not associated with axial length may be very practical measures to use in genetic association meta-analyses. The venular component, Dfv, displayed the lowest and least significant heritability (h2 = 0.15).Taarnhoj et al. reportedfication . DespiteIn conclusion, our study reinforced some features of retinal trait genetic architecture that have been described in other studies and discovered some novel features worthy of further investigation when large dataset will be available. The estimates of heritability and genetic correlations suggest genetics to be a fruitful area of investigation. These analyses should help to guide and interpret future genetics studies, a long-term goal of which is to better understand the nature of the relationship between retinal features and cardiovascular outcomes.ORCADES acknowledges the invaluable contributions of the research nurses in Orkney who carried the field work as well as the administrative team in Edinburgh University; the Wellcome Trust Clinical Research facility  for DNA extraction; and the Helmholtz Zentrum Munchen  for some of the genotyping.CROATIA-Korčula field work was made possible through the involvement of the staff of several institutions in Croatia, including but not limited to The University of Split and Zagreb Medical Schools and the Croatian Institute for Public Health. We would also like to acknowledge the invaluable contributions of the recruitment team in Korčula, the administrative teams in Croatia and Edinburgh and the participants. The SNP genotyping was performed in Helmholtz Zentrum München, Neuherberg, Germany.We thank the Singapore Eye Research Institute staff for providing training to use the SIVA software, especially Prof Tien Wong and Dr Haslina Hamza for their continuous support. Finally, we thank Dr Shona Kerr for helpful editing and comments.The ORCADES study was supported by the Chief Scientist Office of the Scottish Government (CZB/4/276 and CZB/4/710), the Royal Society, the Medical Research Council (UK) and the European Union framework program 6 EUROSPAN project (LSHGCT2006018947). The CROATIA-Korčula study was supported by grants from the Medical Research Council (UK), the Republic of Croatia Ministry of Science, Education and Sports (216-1080315-0302) and the Croatian Science Foundation (8875); the study genotyping was funded by the European Union framework program 6 project EUROSPAN (LSHGCT2006018947). M.K. received a Global Research Scholarship from the University of Edinburgh, R.N. an MRC IGMM studentship.There are no conflicts of interest.