Significant human diseases/phenotypes exist which require both an environmental trigger event and a genetic predisposition before the disease/phenotype emerges, e.g. Carbamazepine with the rare SNP allele of rs3909184 causing Stevens Johnson syndrome, and aminoglycosides with rs267606617 causing sensory neural deafness. The underlying genotypes are fully penetrant only when the correct environmental trigger(s) occur, otherwise they are silent and harmless. Such diseases/phenotypes will not appear to have a Mendelian inheritance pattern, unless the environmental trigger is very common (>50% per lifetime). The known causative genotypes are likely to be protein-altering SNPs with dominant/semi-dominant effect. We questioned whether other diseases and phenotypes could have a similar aetiology.We wrote the fSNPd program to analyse multiple exomes from a test cohort simultaneously with the purpose of identifying SNP alleles at a significantly different frequency to that of the general population. fSNPd was tested on trial cohorts, iteratively improved, and modelled for performance against an idealised association study under mutliple parameters. We also assessed the seqeuncing depath of all human exons to determine which were sufficiently well sequenced in an exome to be sued by fSNPd - by assessing forty exomes base by base.We describe a simple methodology for the detection of SNPs capable of causing a phenotype triggered by an environmental event. This uses cohorts of relatively small size  with the phenotype being investigated, their exomes, and thence seeks SNP allele frequencies significantly different from expected to identify potentially clinically important, protein altering SNP alleles. The strengths and weaknesses of this approach for discovering significant genetic causes of human disease are comparable to Mendelian disease mutation detection and Association Studies.The fSNPd methodology is another approach, and has potentially significant advantage over Association studies in needing far fewer individuals, to detect genes involved in the pathogenesis of a diseases/phenotypes. Furthermore, the SNP alleles identified alter amino acids, potentially making it easier to devise functional assays of protein function to determine pathogenicity.The online version of this article (10.1186/s12864-017-4325-y) contains supplementary material, which is available to authorized users. Clear examples are: the discovery of highly penetrant, but often very rare, pathogenic mutations causing Mendelian diseases , 2; Assoherapies , 7. We dHLA-B*1502) which are phenotypically silent until after Carbamazepine absorption when Stevens Johnson syndrome  occurs, HIV progression and G allele of rs1799987, and mitochondrial genome SNPs rs267606617 (m.1555A > G) and rs267606618 (m.1095 T > C) causing sensory-neural deafness after amino-glycoside administration [fSNPd relies upon SNPs, which are widespread and frequent variations found in the genomic sequence of individuals of a species. fSNPd does examine autosomal and sex-chromosome SNPs. The term SNP has, by common usage, come to also include small insertions and deletions (INDELS) in genomic DNA. SNPs have a widespread distribution throughout the human genome . For eacstration .We hypothesised that other SNPs could also cause environmental triggered diseases and phenotypes. We have developed a simple methodology for their discovery using small cohorts and exomes which we report here.The “exome” we used contained 19,214 genes , and approximately 121,000 SNPs where rare alleles were called , and of which 20% were protein changing.We compared these allele frequencies against “control” allele frequencies obtained from population database catalogues. Firstly, we used the data in the European subset of the 1000G for SNP allele frequencies. And secondly, and separately, the SNP allele frequency data from the EVS , 13. WheThe number of SNPs that could be accurately sequenced in our exomes was determined, and the results are detailed in Additional file To assess the performance of fSNPd for allele frequency determination we analysed 100 SNPs, chosen from two study cohorts, where allele frequencies were found to be statistically altered and checked the results by use of the Integrated Genome Viewer (IGV ); all frp value of <10−8. If we made the rare allele frequency in the general population 0.005 then fSNPd detected the rare SNP alleles if there were one to ten causative SNPs. If there were only 30 subjects in the cohort, or if we simulated half the cases of a cohort of 100 were non-genetic, or if the SNP rare allele was only penetrant in 50% of cases, then fSNPd could detect the SNP alleles if there were one to four causative SNPs. And, if our cohort had 3000 individuals Association Studies would detected the SNP alleles when there was one to five causative SNPs, and fSNPd one to ten causative SNPs.We performed fSNPd simulations, assuming that a rare SNP allele was fully penetrant when present (unless otherwise stated), see Additional file p value of <10−8. Secondly, deafness caused by aminoglycoside antibiotics found to be due to the mitochondrial genome SNP rs267606617 (m.1555A > G), would not have been detected by the fSNPd method, as the mitochondrial genome is not included in an “exome”, nor was the cohort size of six individuals sufficient [p value of <10−6.Finally we illustrate the potential use of fSNPd by re-examining two of the known examples of human diseases/phenotypes which require both an environmental trigger event and a genetic predisposition before the disease/phenotype emerges. Firstly, 44 patients were reported who had Stevens–Johnson syndrome after taking carbamazepine, all had the G allele of the HLA-B SNP rs3909184, where the allele frequency in the general population 8% [A number of clear examples are known of disorders which have both an essential environmental and an essential genetic component, and for some of these the etiology is binary; a single environmental trigger and a single genotype are both required. However, it is unclear how many diseases/disorders/phenotypes could have this joint and equal environmental – genetic architecture. Doubtless this architecture will be a simplification. As a consequence we have concentrated on the situation of a single environmental trigger  and potentially a number of different, but each highly penetrant, genetic changes (which could be in the same or different genes). Adverse drug reactions are an example of this situation, particularly idiosyncratic Type B reactions . Some offSNPd has the potential to detect diseases and phenotypes that require both an environmental trigger event(s), and the inheritance of a specific susceptible genetic background. The genetic causes of the phenotypes which fSNPd is designed to detect are assumed to be complex: environmental triggers that may be multiple and involve (at least) age, sex and disease state; phenotype penetrance may be incomplete (as for many dominant Mendelian conditions); and multiple independently-penetrant SNP alleles could each cause a clinically indistinguishable phenotype. The SNP alleles that can be discovered by fSNPd would be expected to range from relatively common (<50%), to very rare (<0.1%).Our comparison of fSNPd to Mendelian gene discovery and Association Studies Table  shows thWe suggest that the fSNPd approach should be considered when seeking the highly penetrant genetic components for apparently sporadic phenotypes which can be triggered by environmental events. As the common causative SNP alleles will be discovered in one experiment, and each SNPs is within a single gene, and the SNP alleles cause protein changes, then fSNPd has the potential to facilitate the rapid discovery of underlying pathophysiology.Our aim was to find gene changes with fully penetrant effects that would cause pain phenotypes. We designed our approach based on three tenets. Firstly, the SNP alleles that would individually cause fully penetrant (and detectable) effects were most likely to be exonic and protein changing . Secondly, an exome would detect the majority of these SNPs. Thirdly, smaller cohorts are easier to collect, phenotype and curate.We wrote the fSNPd program to analyse multiple exomes simultaneously with the purpose of identifying common SNP alleles that exist in a population of interest at a significantly different frequency than that of the general population. To do this, the program first establishes a catalogue of common SNPs based on an existing cohort; we used the 1000 Genome Project or the Exome Variant Server (EVS) as both were available at the time to download. Confounding variables can be limited by choosing a subset of such a cohort based on ethnicity/location to match the demographics of the population of interest.In our experiments, we tested the European subset of the 1000 Genome Project  and the EVS, and considered SNPs that differed from at least one of these at a significant level to be relevant. The 1000 Genome Project had the advantage of being a better ethnic match for our cohort (predominantly white Caucasian), while the EVS had data from a greater number of individuals . In the final program fSNPd produces results for both.The program calculates rare allele frequencies of SNPs in the population of interest. Individuals’ data is handled in vcf format  versus raw sequencing reads, so that each locus is assigned two possible alleles. Population allele frequencies are determined by tallying the counts of each allele at each SNP locus across all of the individuals in the population of interest and dividing by the total number of presumed alleles at each locus  or two  per individual for those on sex chromosomes).p-value of 2% or more, with Bonferroni and false discovery rate (FDR) corrections applied, according to the total number of SNPs compared  [Finally, allele frequencies per each SNP in the starting catalogue are compared between the population of interest and the general population using a two-tailed chi-squared test without Yates’ correction. SNPs with significantly higher or lower allele frequencies in the population of interest are identified. Significance is characterized by frequencies that differ by a  cohort) .p-values.Additionally, for SNPs that do not exist in particular individuals in the population of interest, raw sequencing data can be accessed to confirm coverage of the area. If no coverage exists, fSNPd can calculate upper and lower bounds of allele frequencies and subsequent The data input for the final fSNPd program are each individual’s vcf file, and the number of males and females. As an option the fSNPd program can check the actual read depth and quality of each SNP called, if this option is selected, each cohort individuals’ bam and bam.bai files are required. See Additional file We wanted to determine how many SNPs were accurately sequenced in an exome, and so analysed 40 exomes by fSNPd to determine base by base and exon by exon coverage. This analysis sought to determine whether particular genes or exons of genes would be predictably included in, or missed from, our results.We assess the performance of fSNPd for allele frequency determination by analysis of two patient cohorts . From the results we selected a sample of SNPs from each where allele frequencies were found to be statistically altered and checked the results “by hand” by use of the Integrated Genome Viewer and by Sanger sequencing of all of the cohort individuals.After generating results for all SNPs encompassed by an exome we chose to examine only those SNPs that would unequivocally alter proteins . This was for two reasons; such mutations are easier to assess by bio-informatics analysis, and are more easily amenable to functional testing to determine their pathogenicity. Others, however, may seek to examine all of the SNPs identified by the pipeline.We simulated the performance of fSNPd under a variety of conditions, see Additional file fSNPd is freely downloadable, instructions are in the Supplement .


NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) , or are located in genes preferentially expressed in the sinoatrial node . Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (−0.74<rg<−0.55) and blood pressure (−0.35<rg<−0.20). These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants  that influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization.Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in  Heart rate variability (HRV) describes the individual variation in cardiac cycle duration and is a measure of vagal control of heart rate. Here, the authors identify seventeen single-nucleotide polymorphisms associated with HRV, lending new insight into the vagal regulation of heart rhythm. Heart rate variability (HRV) is a physiological variation in cardiac cycle duration. When measured under supine or sitting conditions, resting HRV is most prominently centred around the frequency of respiration ∼0.25 Hz) and the intrinsic blood pressure rhythm ∼0.1 Hz). This reflects modulation of tonic activity in the cardiac vagal nerves originating in cortical and subcortical nuclei Hz. This15 Hz and Loss of cardiac vagal control as indexed by low HRV is associated with mortality in patients with cardiovascular disease11In silico post-GWAS analyses were performed to test for association with cardiac disease risk factors and disease outcomes and to provide insights into the biological mechanisms by which the identified loci influence cardiac vagal control and its effect on HRV.Large inter-individual differences in HRV exist in the basal resting state. Family and twin studies have uniformly confirmed a substantial genetic contribution to resting HRV with heritability estimates between 25 and 71% ref. . CandidaWe detect 17 SNPs in eight loci harbouring several genes preferentially expressed in the sinoatrial node and significant negative genetic correlations of HRV with heart rate and blood pressure. These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants in proteins  known to influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization.−6, 23 single-nucleotide polymorphism (SNPs) in 14 loci that were associated with one or more of these HRV traits were taken forward for wet-lab genotyping or in silico replication in 11 cohorts including up to 24,474 additional individuals of European ancestry, followed by a second stage meta-analysis  performed by 20 cohorts of European ancestry in up to 28,700 individuals . Using aanalysis .P<5 × 10−8). The loci on chromosomes 14 and 15 contained three and two independent signals, respectively, (r2<0.90)  in eight loci  that reactively, . Conditictively, . In totactively, . In fourr2<0.90) . Forest r2<0.90) . Sex-strr2<0.90) . Separatr2<0.90) . Resultsr2<0.90) .n=12,101) and NESDA  cohorts, adolescents from the TRAILS-Pop cohort , and children from the ABCD cohort   . The mulTo test the contribution of SNPs that did not reach genome-wide significance, we performed polygenic risk score analyses using increasingly more lenient significance thresholds and determined the percentages of explained HRV in the same four cohorts ; Table 3The total variance explained by common SNPs (SNP-based heritability) estimated by Genomic Restricted Maximum Likelihood or LD score regression analysis varied between 10.8 and 13.2%, with only small differences in estimates across methods and HRV traits .In data from up to 11,234 Hispanic/Latino individuals, five SNPs in five of the eight loci identified for RMSSD, seven SNPs in six of the seven loci for SDNN and three SNPs in three of the five loci for pvRSA/HF showed a statistically significant association that was consistent in direction with the association in individuals of European ancestry . In dataThe strong inverse association between HRV and heart rate reflects the well-established simultaneous biological effect of cardiac vagal activity on heart rateNDUFA11: p RMSSD=4.6 × 10−46; p heart rate=0.18). Analysis of summary statistics of the HRV and heart rate meta-analyses as implemented in the gtx R package showed that multi-SNP genetic risk scores for HRV were significantly associated with heart rate  and that the lead SNPs for RMSSD (rs12974991) and pvRSA/HF (rs12974440) were in perfect LD with this SNP  that are predicted to yield tolerated, benign amino acid changes  and lost significance after conditioning on the corresponding top eQTL. The eQTLs for NEO1 and RGS6 were replicated in at least one other whole blood eQTL study (GNG11 was replicated in the medulla (P=2.8 × 10−4) and the anterior tibialis artery (P=8.1 × 10−9). None of the 17 SNPs reached significance in a smaller heart eQTL database.We examined if the 17 HRV SNPs were eQTLs in a large whole blood database. Four of the HRV SNPs were significantly  associated with gene expression in blood : rs18128TL study . The eQTr2>0.70) with SNPs associated with methylation level of one or multiple CpG sites (methylation quantitative trait loci [mQTLs]) in whole blood  that was associated with methylation level of cg19493789, which is located in a CpG island shelf near RGS6.Nine of the 17 HRV SNPs were in high LD  mQTLs but were not themselves eQTLs. For example, HRV SNPs rs12974991, rs12974440 and rs12980262 (chromosome 19) were associated with methylation level of multiple CpG sites  located in or nearby analysis  showed NThis meta-analysis of GWAS for HRV yielded 17 lead SNPs (11 independent) in eight loci that were genome-wide significantly associated, six of which generalized to individuals of African-American and Hispanic/Latino ethnicity. Various ways that correct HRV for its mathematical dependency on resting heart rate attenuated the SNP effects, but largely left the associations intact. Together, the hits in the eight loci explained 0.9–2.6% of the variance in resting HRV in four independent cohorts of European ancestry. Details of known biological functions of the genes closest to these loci are given in the LINC00477 (C12orf67), SYT10, GNG11, HCN4 and KIAA1755) were associated with HRV at genome-wide significance level and six more attained nominal significance, with associations always in the expected direction. Genetic risk scores for HRV traits were also significantly associated with heart rate and LD score regression confirmed that the allelic variants that decrease HRV in parallel increase heart rate. This suggests to us that part of the HRV SNPs exert their effect on heart rate through oscillatory modulation of pacemaker activity by the vagal nerves.We noted a strong enrichment of our HRV loci in a previously conducted meta-analysis of GWAS for resting heart rate21GNG11, RGS6 and HCN4. By binding the muscarinic type 2 receptor (M2R) and dissociating the G-protein heterotrimer (Gαβγ) into a Gαi/o subunit and a Gβγ component, acetylcholine inhibits the ongoing depolarization of the pacemaker cells by β1/β2-adenylatecyclase activation of funny (If) channels and calcium channels2R-GIRK effects on the sinus rate is much shorter than for the M2R-HCN2/4 or the β1/β2-adenylatecyclase signalling pathways. Only signalling through the Gβγ component is fast enough (∼0.3 s) to rapidly track changes in vagal outflow to the sinoatrial node, for example, as they occur within the duration of a single respiration (∼4.5 s), whereas signalling through the α subunit is too slow (>3 s) to track such phasic changes in acetylcholine release1NDUFA11), that may act on the individual differences in these processes as they had no discernible effect on heart rate, in spite of their significant impact on HRV.The above leads to HRV only partially capturing the vagal effects on heart rate. Additional reasons for the imperfect relation between HRV and vagal effects on heart rate1GNG11, RGS6 and NEO1 were eQTLs and in strong LD with the top mQTLs and eQTLs for the corresponding genes. Two of these  readily provide a biological hypothesis to account for the associations detected in the meta-analysis. The C alleles of rs4262 and rs180238 of GNG11 coding for the γ11 subunit of the heterotrimeric G-protein complex Gαβγ cause decreased expression of this subunit and were associated with lower HRV. The effects of the GNG11 eQTLs associated with lower HRV are likely to lower the availability of the γ11 subunit, thereby reducing Gβγ component-induced GIRK activation. This potentially blunts the heart rate change in response to the oscillatory changes in cardiac vagal activity.The genome-wide significant SNPs in RGS6) gene on chromosome 14 was found to be linked to three independent signals for SDNN and RMSSD. RGS6 acts as a critical negative regulator of M2R signalling in the sinoatrial node of the heart rapidly terminating Gβγ signalling and thus curtailing vagal lowering of the heart rate25RGS6 in decreasing HRV previously hinted at by animal experimentation2327RGS6 SNP (rs4899412) causes increased expression of RGS6. By increasing RGS6 expression, the T allele acts as a gain-of-function mutation that gives rise to a decrease in GIRK-channel signalling and the observed decrease in HRV. Of note, Rgs6−/− mice, that show the expected increase in HRV, are characterized by a strong bradycardia and an increased susceptibility to AV block and atrial fibrillation which is attributed to an enhancement of GIRK-induced sinoatrial and atrioventricular node hyperpolarization by removing the negative regulation of Gβγ by RGS6 cortical, brainstem and medullary transmission of tonic vagal activity to the sinoatrial node, not just the impact of that activity on heart rate. Subcortical generation of tonic vagal activity is an important biomarker for cardiovascular health and potentially modifiable by interventions on psychosocial stressIn conclusion, this meta-analysis detects a critical role for genetic variation in Gβγ and HCN signalling in explaining individual differences in HRV. The HRV variants detected can help guide further investigations of the functional consequences and potential therapeutic implications of individual differences in sinoatrial Gβγ signalling.Appropriate IRB approval and informed consent from participants in all participating cohorts was obtained. Full information on consent procedures and details of the IRB boards are provided in the In this study, we investigated three HRV traits: SDNN, the RMSSD and pvRSA or HF. SDNN and RMSSD were derived from the IBI time series obtained from the R waves in the ECGHRV traits were extracted from the IBI time series preferably based on 2–10 min periods of ECG in a standardized setting, at rest and in a sitting/supine position. If ambulatory data were available, we advised cohorts to extract a period of sitting still in the evening, when this proved feasible. r>0.80) across a wide range of values for respiration and heart ratesSDNN and RMSSD have prevailed in epidemiological studies because they are more easily assessed in large cohorts and, as noted above, can be obtained even from short ECG recordings. HF and pvRSA were available in fewer cohorts, but they better reflect the cardiorespiratory coupling that drives the oscillatory modulation of vagal effects in the sinoatrial node. In the typical resting respiratory frequency range, these time- and frequency-domain measures of RSA are much less contaminated by oscillations in cardiac sympathetic control than SDNN (and other measures of HRV that span a broader frequency range). This is due to the temporal dynamics of the sinoatrial node signalling pathway that acts as a low pass filter allowing only oscillations in vagal effects to translate into HRV, whereas for sympathetic effects or vagal effects at progressively higher respiratory frequencies the node acts as a leaky integrator causing more tonic changes in heart rategHRV) consortium. The stage 1 discovery analysis was performed in up to 28,700 individuals of European ancestry from a maximum of 20 cohorts. Independent cohorts with either genome-wide or gene-centred array data or with the ability to perform wet-lab genotyping on the single-nucleotide polymorphism (SNPs) taken forward from the first stage were included in the second (replication) stage. This stage included additional data from up to 24,474 individuals from 11 cohorts of European ancestry (see Cohorts that had data on at least one of the three HRV traits and genome-wide data were invited to participate in the first (discovery) stage of the Genetic Variance in Heart Rate Variability (VThe following exclusion criteria were applied a priori: (1) individuals with heart disease  and (2) individuals known to use antidepressants (particularly tricyclic antidepressants) and all anticholinergic agents  because of the strong effects that these drugs have on HRV. Individuals reporting over the counter use of anticholinergic agents were not excluded.Imputation of SNPs was done to extend and create similar SNP databases between cohorts using different genotyping platforms. Most of the cohorts used the HapMap Phase II release 22 CEU panel as reference, but later releases  or other reference data sets  were also used .Each cohort performed linear regression analyses on all available HRV traits using an additive SNP model adjusting for age at the time of ECG recording, sex, principal components—to adjust for population stratification—and other study-specific parameters; all HRV traits were log-transformed because of the skewness of their distributions. Only autosomal associations were examined. Analyses were performed for all individuals as well as for men and women separately.Before meta-analysis, quality control of all uploaded cohort files was performed using the QCGWAS packagez-scores with METALP<1 × 10−6 were clumped for LD using pairwise LD checking in SNAPr2<0.1). A total of 23 lead SNPs in 14 loci were selected for follow-up in the second (replication) stage.An inverse-variance, fixed-effects meta-analysis was performed for RMSSD and SDNN for which SNPs of the different cohorts were merged based on rs-id. For pvRSA/HF, we performed a sample size weighted meta-analysis using r<0) was found, the cohort/trait pair was excluded from stage 2 analysis. For this reason results from one cohort for SDNN were excluded. The replication results were then meta-analysed per trait using an inverse-variance fixed-effects meta-analysis for RMSSD and SDNN and a sample size P weighted meta-analysis using z-scores in METALP<1 × 10−6 in stage 1, (2) it had a one-sided P<0.05 in the stage 2 meta-analysis congruent with the direction of effect in the stage 1 meta-analysis and (3) it had a genome-wide significant P<5 × 10−8/3 (two-sided) in the combined meta-analysis of stage 1 and 2 results, correcting for the testing of three separate traits.Stage 2 cohorts applied the same exclusion criteria and performed the same association analysis as in the discovery stage, but analyses were restricted to the 23 lead SNPs. If a SNP was not available in a cohort, the best available proxy was used instead based on strongest LD according to the 1000 Genomes database. To verify homogeneity of the results in the stage 2 cohorts with those in the stage 1 cohorts, the stage 1 meta-analysis effect sizes of the 23 SNPs were correlated to the effect sizes obtained in each cohort for each of the HRV traits. If a negative correlation (r2<0.1). To confirm independence between these SNPs within the loci on chromosome 14 and 15, we applied the conditional-and-joint analysis as implemented in the Genome-wide Complex Trait Analysis software packageIn the discovery stage independent SNPs were selected for follow-up based on LD clumping  was considered as the threshold for significance.We performed gene-based testing with the full set of ∼2.5 M HapMap SNPs from GWAS results of all three phenotypes, using VEGAS . This soThe Lifelines and NESDA cohorts were used for genetic risk score and polygenic risk score analyses to determine the percentage of variance explained by independent HRV SNPs that were genome-wide significant, and by SNPs meeting increasingly lenient significance thresholds, respectively. Lifelines and NESDA represent examples of a population-based cohort and a cohort ascertained on case-control status (for major depressive disorder). Both recruited adult participants. To test the stability of explained variance across the life span, we repeated this analysis in two other Dutch cohorts, the adolescent TRAILS-Pop cohort2 from a model with and without the genetic risk score, while adjusting both for age, sex and principal components.For the genetic risk score, stage 1+2 summary statistics were used for the selection of HRV SNPs. No correction was needed for ABCD as genotyping in this cohort had finished only after completion of the meta-analyses. However, the NESDA cohort had been included in both stage 1 and 2, TRAILS-Pop in stage 1, and Lifelines in stage 2, so the effect sizes and s.e.’s of the HRV SNPs were corrected to subtract the effects of those cohorts to obtain independent validation cohortshttp://prioritypruner.sourceforge.net/) to select independent SNPs, taking the significance of the SNP in the discovery meta-analysis of each of the HRV traits into account. This provided three LD-pruned SNP sets. Polygenic risk scores were then calculated in PLINK−8, 5 × 10−7, 5 × 10−6, 5 × 10−5, 5 × 10−4, 0.005, 0.05, 0.5 and 1 and associated with the three HRV traits and resting heart rate in the Lifelines, NESDA, TRAILS-Pop and ABCD cohorts. For NESDA and TRAILS-Pop pruning and polygenic risk score analysis was based on analytically corrected results, since these cohorts were part of stage 1 of our studyTo compute the polygenic risk scores, the imputed genotypes were first converted to best-guess genotypes. This was done regardless of the imputation quality, since it was previously shown that even low-quality SNPs might contribute to the variance explained by SNPs ref. . The SNPh2SNP). For this analysis, SNPs from the HapMap Phase 3 project were selected to obtain a set of independent SNPs. We further used LD score regression to estimate the heritabilities of the three HRV traits and the genetic correlation among HRV traits and with heart rateWe applied genomic restricted maximum likelihood analysis implemented in the Genomic Complex Trait Analysis software packageIn addition, we used the Oman Family StudyP<1 × 10−6 in stage 1 meta-analysis in European individuals, (2) it had a one-sided P<0.05 in the new ethnicity specific meta-analysis congruent with the direction of effect in the stage 1 meta-analysis in European individuals and (3) it had a genome-wide significant P<5 × 10−8/3 (two-sided) in the combined meta-analysis.We further examined the generalization of loci identified after meta-analysis of stage 1 and 2 results to other ethnicities using data from 11,234 individuals of two Hispanic/Latino cohorts, and 6,899 individuals from five African-American cohorts . Stage 3The well-known inverse association between HRV and heart rate in part reflects a dependency of the variance in IBI on the mean IBI that is unrelated to cardiac vagal activity15et al.https://walker05.u.hpc.mssm.edu/ and used the GWIS procedureet al.First, we corrected SDNN and RMSSD for their dependency on mean IBI by using the coefficient of variation, which is a more parsimonious solutionWe used the delta method to approximate a s.e. for the effect of the SNP given that we know the s.d.’s for the SNP effects on SDNN and HR, and their dependence. We obtain the dependence from analysis with LD score regressionSecond, we performed association analyses for our 17 top SNPs on the actual log-transformed coefficients of variation of SDNN and RMSSD computed in the Lifelines, NESDA and TRAILS-Pop cohorts and then meta-analysed these results. Because pvRSA and HF are expressed on different scales, such a meta-analysis was not feasible for pvRSA/HF.t-value was determined using a bootstrap procedure . The mediation P values of the three cohorts for SDNN and RMSSD and two for pvRSA/HF (as this was not available in Lifelines) were next meta-analysed to determine the significance of mediation and to compute the percentage of the SNP effect on HRV that was mediated through its effects on heart rate. We note that this is likely an overcorrection because the HRV SNPs are expected to influence heart rate through a common biological mechanism, that is, changes in cardiac vagal activity.Third, we repeated the association analysis for our 17 top SNPs on SDNN, RMSSD and pvRSA/HF in the Lifelines, NESDA and TRAILS-Pop cohorts with and without adjusting for heart rate as a covariate and performed mediation tests with the Sobel test to assess the mediation effect of heart rate on the HRV SNP association. Significance of the Sobel https://cran.r-project.org/web/packages/gtx), which approximated α by (Σω × β × seβ−2)/(Σω2 × seβ−2) with seα≅✓(1/Σω2 × seβ−2), where ω is the effect of the SNP on HRV, β is the effect of the SNP on heart rate and seβ is the s.e. of β. This approximation requires only single SNP association summary statistics extracted from GWAS resultsP was less than 0.0045 .We conducted a lookup of the 17 (11 independent) HRV lead SNPs identified in this study using the results of a recent GWAS meta-analysis for heart rateIn addition to these lookups that were restricted to genome-wide significant SNPs, we employed LD score regressionWe further examined the variance in resting heart rate explained by multi-SNP genetic risk scores (based on the lead SNPs only) and of the full polygenic risk scores for HRV in the four Dutch cohorts Lifelines, NESDA, TRAILS-Pop and ABCD. The identical approach was used as done previously for the computation of variance explained in the HRV traits themselves.et al.P was <0.05/21=0.0024. The 21 heart rate SNPs were tested in a multi-SNP risk score for their effect on the HRV traits using the gtx approach as described above.We also performed reverse analyses to detect the effects of heart rate SNPs on the HRV traits. In our GWAS meta-analysis results for SDNN, RMSSD and pvRSA/HF, we performed a lookup for the 21 previously identified heart rate SNPs by den Hoed r2=0.15) in Lifelines with a more significant heart rate SNP (rs7980799).To examine the variance explained in the HRV traits by the 21 heart rate SNPs, multi-SNP genetic risk scores and polygenetic risk scores based on the heart rate SNPs were computed in the Lifelines, NESDA, TRAILS-Pop and ABCD cohorts and these were tested for association with the available HRV traits. For the multi-SNP genetic risk scores weights were either the original SNP effect sizes on heart rate  or corrected because of participation of the cohort in the GWAS meta-analysis (Lifelines). Only 15 of the 21 SNPs were used in the Lifelines cohort because five SNPs lost genome-wide significance after subtracting the SNP effects of the Lifelines cohort. One other SNP (rs826838) was removed because it was in LD  in the cardiometabolic trait or disease GWAS meta-analysis results. Second, three separate HRV weighted genetic risk scores were calculated from 11 (RMSSD), 10 (SDNN) and five (pvRSA/HF) HRV SNPs, respectively . These were tested for their effect on the clinical outcomes using a regression model in the gtx package in R as described above for the association of the HRV SNPs with heart rate. The effects of the genetic risk scores were considered as statistically significant when the P was less than 0.0045 .The association analyses consisted of the same three steps as used for heart rate. First, we checked the In addition to these lookups that were restricted to genome-wide significant SNPs, we employed LD score regressionin silico bioinformatics-based approachhttp://www.ncbi.nlm.nih.gov/genome/tools/remap). For ±1 Mb regions surrounding the SNPs, we downloaded the according variance call format file from the 1000 Genomes Project. We used data of 503 European ancestry individuals from 1000 Genomes Project Phase 3 (version 5.a.) to calculate LD between the HRV SNP and all other SNPs within the area. SNPs in moderate to high LD (r2≥0.5) were subsequently selected and annotated by ANNOVAR softwareWe followed an We used RegulomeDB to integrate results from the RoadMap Epigenomics and ENCODE projects to identify variants that are likely to have functional consequences using the lead SNPs identified for the three HRV traits. We distilled information on transcription factor binding and chromatin states for SNPs that showed most evidence of being functional, that is, for SNPs with a RegulomeDB score <4.r2≥0.8) with them were looked up in the National Human Genome Research Institute GWAS catalogue to check for association with other complex traits or diseases identified in previous GWAS studiesFinally, all the HRV SNPs and those that were in high LD (We performed expression quantitative trait locus (eQTL) analysis in whole blood to identify regulatory variants that were associated with the HRV SNPs using the gene-expression database from NESDAcis effects for our HRV SNPs, meaning that the probe was at a distance <1 Mb from the SNP on the genome according to GRCh37/hg19. For each probe set that displayed a statistically significant association with at least one SNP in the cis region, we identified the most significantly associated SNP (top eQTL). Conditional eQTL analysis was carried out by first residualizing probe set expression using the corresponding top eQTL and then repeating the eQTL analysis using the residualized data.eQTL effects were tested with a linear model approach using MatrixeQTLAll HRV SNPs with significant results in the NESDA/NTR eQTL data were looked up in two other independent whole blood eQTL databases, eQTLs in lymphoblastoid cell lines, eQTLs in 10 different brain regions, and a heart eQTL database.cis-mQTLs) a Spearman rank correlation and corresponding P value was computed for each CpG-SNP pair, in which the CpG and SNP location were no further than 250 kb apart. To control for multiple testing, we used a permutation procedure to empirically control the false discovery rate at 5%. The distribution of observed P values was compared to the P value distribution obtained from the analyses on permuted data. For a permutation the sample identifiers of the genotype data set were shuffled, breaking the link between the genotype data set and the methylation data set. This was repeated 10 times to obtain a stable distribution of P values under the null hypothesis. To determine the false discovery rate only the strongest effect per CpG in both the real analysis and in the permutations were selected.We obtained mQTL results from a previously published studyHCN4) and synaptic signal transmission (ACHE) that served as training genes. For MetaRanker, we first combined results of the stage 1+2 meta-analyses of GWAS for the three HRV traits, retained the association with the lowest P for lead SNPs that were identified for multiple traits, and subsequently provided SNPs, P values, and the same two test genes (HCN4 and ACHE) as input. For DEPICT—arguably the most powerful and informative of the four methods—we used results from the stage 1 meta-analysis for all SNPs that reached a P for association <10−5 as input, for each of the three HRV outcomes separately. In order for genes to be prioritized by the combined four approaches, they needed to be either: (1) selected by DEPICT for at least one of the three HRV outcomes; or (2) identified by at least two of the three remaining tools .Potentially causal genes for the associations identified by GWAS were identified using four previously described bioinformatics tools: ToppGene, Endeavour, MetaRanker and DEPICT . To thisr2>0.50) non-synonymous SNPs mapped, (d) genes to which other linked (r2>0.80) SNPs mapped, (e) genes identified by VEGAS, and (f) expression probe gene names significantly associated with HRV eQTLs . The input gene list was extended to 100 by their most strongly interacting genes and a weighted composite functional association network was constructedWe performed gene network and enrichment analysis using the GeneMANIA algorithm, which uses data resources on genetic interactions, protein–protein, co-expression, shared protein domains and co-localization networks. To build a functional interaction network, we selected genes as input for this analysis using the following criteria: (a) genes implicated by gene prioritization using the four bioinformatics approaches described above, (b) the genes closest to our 17 HRV SNPs, (c) genes to which linked  to create reconstituted gene sets. It then determines which of these reconstituted gene sets are enriched for the HRV genes. A graphical representation of DEPICT’s reconstituted gene-set enrichment analysis (P<0.05 after Bonferroni correction for examining three HRV traits) was generated using a script that is based on an affinity propagation clustering algorithm by Frey et al.We used DEPICT for a tissue enrichment analysis to tabulate tissues that are enriched for expression of genes located within ±40 kb of SNPs with a Summary statistics of the meta-analyses are available on request from the corresponding authors after a formal data access request procedure and approval by the VgHRV consortium.How to cite this article: Nolte, I. M. et al. Genetic loci associated with heart rate variability and their effects on cardiac disease risk. Nat. Commun.8, 15805 doi: 10.1038/ncomms15805 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Agropyron Gaertn. (P genome) is a wild relative of wheat that harbours many genetic variations that could be used to increase the genetic diversity of wheat. To agronomically transfer important genes from the P genome to a wheat chromosome by induced homoeologous pairing and recombination, it is necessary to determine the chromosomal relationships between Agropyron and wheat. Here, we report using the wheat 660K single nucleotide polymorphism (SNP) array to genotype a segregating Agropyron F1 population derived from an interspecific cross between two cross‐pollinated diploid collections ‘Z1842’ [A. cristatum (L.) Beauv.]  and ‘Z2098’ [A. mongolicum Keng]  and 35 wheat–A. cristatum addition/substitution lines. Genetic linkage maps were constructed using 913 SNP markers distributed among seven linkage groups spanning 839.7 cM. The average distance between adjacent markers was 1.8 cM. The maps identified the homoeologous relationship between the P genome and wheat and revealed that the P and wheat genomes are collinear and relatively conserved. In addition, obvious rearrangements and introgression spread were observed throughout the P genome compared with the wheat genome. Combined with genotyping data, the complete set of wheat–A. cristatum addition/substitution lines was characterized according to their homoeologous relationships. In this study, the homoeologous relationship between the P genome and wheat was identified using genetic linkage maps, and the detection mean for wheat–A. cristatum introgressions might significantly accelerate the introgression of genetic variation from Agropyron into wheat for exploitation in wheat improvement programmes. Triticum aestivum L., 2n = 6x = 42, genomes AABBDD) is widely planted worldwide and is an important food source. However, the diversity of modern cultivated wheat has been restricted by selection for specific agronomically important traits during wheat domestication and improvement  encodes potentially valuable traits for wheat improvement, such as disease resistance, abiotic and biotic stress tolerance and high yield  was achieved by intergeneric hybridization  from specific‐locus amplified fragment sequencing (SLAF‐seq). However, because these SNPs were essentially P genome‐specific markers from noncoding DNA sequences which were highly differentiated among species, the homoeologous relationship between the P genome and hexaploid wheat chromosomes could not be determined.Although there have been some notable successes transferring valuable genetic variation from , et al.  construcet al., et al., et al. . Nevertheless, because there is high sequence similarity between wheat and wild relatives for transcriptome sequences  population to construct a genetic linkage map. The wheat 660K SNP array generated genotype data to reveal the homoeologous relationships, chromosome rearrangements and degree of collinearity between the P genome and wheat. In addition, we provided the first report on the production of a complete set of wheat–A. cristatum addition lines. Combined with the wheat 660K SNP genotyping array and the analysis of homoeologous relationships, an efficient high‐throughput screening method was developed to identify and characterize addition/substitution lines.In this study, a segregating Agropyron F1 CP population (two parents and 119 progenies). The most basic postgenotyping filter is based on the sample quality control call rate. The minimum, maximum and mean of the call rate for the 121 individuals were 74.6%, 80.6% and 77.4%, respectively  were appropriate for the CP population. Of the 18 192 Poly High Resolution and No Minor Homozygote SNP markers, 2 694 SNP markers were categorized into these three types. After removing markers with high segregation distortion, a total of 1 544 SNP markers, which included 735, 390 and 419 SNP markers for hk × hk, lm × ll and nn × np segregation patterns, respectively, remained for subsequent genetic mapping Table .Agropyron.After linkage analysis, 559 SNP markers for the male map Table , 521 SNPThe basic characteristics of the seven integrated genetic maps are shown in Table The parental map and integrated map make it possible to perform a comparison of collinearity between the two parents. The genetic maps of the two parents showed good collinearity with the integrated map, except for a few obvious rearrangements on chromosomes 2P, 4P and 7P Figure . TherefoA. cristatum  from derivatives of hybridizations between wheat and um Table . To idend Figure . Then, ad Figure . Furtherd Figure . The gend Figure . Subsequd Figure . We obtaA. cristatum (P genome) were successfully introduced into the common wheat variety Fukuho using intergeneric hybridization  linkage map of Agropyron and common wheat based on the wheat 660K array platform. In the seven LGs constructed for Agropyron in this study, 78.5% of SNPs on LG1, 65.1% of SNPs on LG2, 78.2% of SNPs on LG3, 78.0% of SNPs on LG5, 73.6% of SNPs on LG6 and 66.5% of SNPs on LG7 were assigned to 1, 2, 3, 5, 6 and 7 homoeologous groups (A/B/D) in the wheat genome, respectively  diploid collections ‘Z1842’   and ‘Z2098’     and electrophoresis on 0.8% agarose gels with a DNA marker.A set of 119 individuals were obtained as the FA. cristatum alien addition/substitution lines) using the Affymetrix GeneTitan System according to the manufacturer's instructions . Allele designation was performed with the Affymetrix proprietary software packages Affymetrix Power Tools (APT) and SNPolisher™ (http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/devnettools.affx), and for genotyping ‘Z1842’, ‘Z2098’ and their 119 progeny, a custom software pipeline ADAP  was written in Linux bash to simplify data analysis according to the Axiom Best Practices Genotyping Workflow . A variant call rate threshold of 80% was used instead of the default value (97%) to account for the lower call rates typically obtained from hybridizing wheat wild relatives and progenitors to the array. The apt‐probeset‐genotype program within Affymetrix Power Tools was used to determine genotype calls from Affymetrix SNP microarrays. Subsequently, the SNPolisher R package was used to calculate SNP performance metrics, such as call rate, cluster separation and deviation from expected cluster position, followed by the classification of the SNPs into performance categories. The following categories were used: (i) Poly High Resolution, representing codominant and polymorphic SNPs, with at least two examples of the minor allele; (ii) No Minor Homozygote, representing polymorphic and dominant SNPs, where with two clusters were observed; (iii) Mono High Resolution, representing monomorphic SNPs; (iv) Call Rate Below Threshold, where the SNP call rate was below the threshold but other cluster properties were above the threshold; (v) Off‐Target Variant, which included four clusters, one of which represented a null allele; and (vi) other, where one or more cluster properties were below the threshold : ab × cd, ef × eg, hk × hk, lm × ll, nn × np, aa × bb, ab × cc and cc × ab for the CP population. Among these patterns, only hk × hk, lm × ll and nn × np were suitable for constructing a genetic map for the FAgropyron Gaertn. was compared using BLAST  against the wheat genome sequence TGACv1  with hexaploid wheat .Click here for additional data file.Figure S6 Synteny of the female map (genetic position in cM) with hexaploid wheat .Click here for additional data file.Figure S7 GISH analysis of partial derivatives.Click here for additional data file.Figure S8 Distribution of heterozygosity in wheat genome.Click here for additional data file.Table S1 Summary of six SNP categoriesClick here for additional data file.Table S2 Summary of the three segregation patterns appropriate for the CP populationClick here for additional data file.Table S3 Basic characteristics of the male and female mapsClick here for additional data file.Table S4 Basic characteristics of the seven LGs from the integrated mapClick here for additional data file.Table S5 Distribution of SNP markers in LGs in the wheat genomeClick here for additional data file.Table S6 Summary of the SNP marker distribution for LGs in the wheat genome.Click here for additional data file.Table S7 Statistical analysis of missing genotypes of each wheat chromosome in each substitution line to identify missing wheat chromosomesClick here for additional data file.Table S8 An example of the segregation patterns of a SNP with 4 alleles (A/T/C/G)Click here for additional data file.


Mullus surmuletus) populations distributed across a wide salinity gradient. We then blasted these outlier sequences on published fish genomic resources in order to identify relevant potential candidate genes for salinity adaptation in this species.Adaptive genomics may help predicting how a species will respond to future environmental changes. Genomic signatures of local adaptation in marine organisms are often driven by environmental selective agents impacting the physiology of organisms. With one of the highest salinity level, the Mediterranean Sea provides an excellent model to investigate adaptive genomic divergence underlying salinity adaptation. In the present study, we combined six genome scan methods to detect potential genomic signal of selection in the striped red mullet  contains supplementary material, which is available to authorized users. Adaptive genomics aims to understand the molecular basis of local adaptation in species experiencing a wide range of environmental gradients. This emerging research field may help predicting how a species will respond to future environmental changes  as well The field of adaptive genomics has extensively studied terrestrial organisms comparatively to marine ones, potentially owing to the lack of available reference genomes for marine species . Yet, maDetecting signals of selection linked to salinity variation may benefit the recent advances in the field. Indeed, adaptive genomics is still a fast evolving study field, with the recent development of numerous analytical methods . These mMullus surmuletus) along the Mediterranean Sea, using 1153 SNP markers on 47 locations. Due to its enclosed geography and high evaporation rate, the Eastern Mediterranean basin displays high levels of salinity compared to the Western basin and the Northeastern Atlantic [M. surmuletus populations. A previous seascape genetics study using the same extensive marine spatial database showed no genetic differentiation in separated populations, but a longitudinal pattern of isolation by distance [M. surmuletus populations are locally adapted to this environmental variable. In the view of increasing our capacity to detect genomic signatures of selection, we combined six commonly used genome scan methods. Unsurprisingly, we observed inconsistency among the six genome scan methods and we further interpreted their outcomes regarding the algorithms and statistical model behind each of them. Then, we blasted the totality of outlier sequences found  on published available fish genomic resources. We were then able to identify four candidate genes potentially involved in salinity tolerance of the striped red mullet.In the present study, we investigated the potential for local adaptation to salinity in an exploited marine fish species, the striped red mullet Mullus sudistance . In thisMullus surmuletus) is a demersal fish species distributed in the Northeastern Atlantic Ocean, from the British Isles in the North to Senegal in the South. This species inhabits coastal areas from 0 to 100 m depth, and has high commercial value in the Mediterranean Sea. The study area covers the whole Mediterranean coastline, including islands. Our sampling design consisted of 47 sites distributed along the whole range of this species across the Mediterranean Sea  according to the manufacturer’s protocol. DNA quality was assessed by running 3 μL of each DNA sample on 1% agarose gels. DNA concentration was determined using NanoDrop 8000. We individually genotyped 541 fish samples from these 47 sites using a genotyping by sequencing approach . Six 96-Raw read sequences were filtered according to quality base calling, removing sequences with average Phred quality below 25. Trimming was performed to remove low quality bases at the extremities of the reads . Sequences shorter than 60 bp were discarded from the dataset. For each sequenced library, the number of raw reads and filtered data are provided in Additional file ST, calculated using the R package ‘polysat’ [ST, computed from allele frequencies and marine geographical distances, computed as least-cost path distances with infinite resistance values assigned to landmasses. We also performed a Principal coordinates analysis (PCoA) of the 47 sites using the Nei genetic distance calculated from SNPs allele frequencies to analyse the population structure. In order to test for isolation by resistance, we used Mantel tests between genetic differentiation (pairwise FST) and environmental distances . For both environmental variables separately, we calculated pairwise environmental distances as the difference in temperature/salinity between every pair of locations.Genetic differentiation between the pairs of 47 sites was quantified by the Wright’s pairwise Fpolysat’ . To testmax) may be a potential agent of divergent selection in M. surmuletus. This environmental variable has been identified as a driver of the wide neutral genetic variation of M. surmuletus suggesting isolation by adaptation [2 = 0.55; pvalue < 0.001), which makes it difficult to disentangle the adaptive effect of one or the other. Because M. surmuletus has a wide distribution in the North-East Atlantic, which encompasses a large temperature gradient compared to the gradient within the Mediterranean Sea, we expect salinity to be the main driver of adaptation here. Hence, we focused our analyses on salinity.The Mediterranean Sea is an evaporation basin where the evaporation rate exceeds the precipitations . The infaptation . In the max was computed by NEMOMED8 [max.SSSNEMOMED8 , which hNEMOMED8  from the, SSSmax). We describe below the five EA methods that we used to detect candidate SNPs  approaches detect SNPs that are significantly correlated to environmental variables. The null hypothesis (H0) is that genetic variation is only due to limited dispersal and genetic drift , thus thPs Table .Table 1Dmax was considered when both the chi-squared test and the t-test  were significant as recommended by Joost et al. [Linear regression (LM) is the simplest way to test for significant correlations among SNP allele frequencies and environmental variables assuming that allele frequencies follow a normal error model, and vary linearly with the environmental variables . Here sit et al. ., allele frequencies of multiple SNPs). First, linear regressions are computed between allele frequencies in each site and explanatory environmental variables at each SNP. Then fitted values of those regressions are analysed simultaneously using a principal component analysis (PCA) to produce ordination axes that are linear combinations of the original explanatory variables. This allows accounting for the multivariate properties of SNPs. We performed RDA to test the effect of SSSmax on allele frequencies in each pool (site). We also used latitude and longitude of the sampling sites as explanatory variables to control for the effect of potential neutral spatial structure. Outlier SNPs were identified on each of the first three ordination axes as SNPs with a ‘locus score’ that was ±3 SD from the mean score for that axis [max. We considered the correlation to be significant at a p-value lower than 0.05. The RDA was performed using the R package ‘vegan’ [RDA is an ordination method extending linear regression to multivariate response data (i.e.hat axis . We then ‘vegan’ .i) First, the power spectrum of each SNP is compared to the average power spectrum of all of the SNPs in order to identify outliers that show an unusual power spectrum. The power spectrum of a SNP is the squared correlation coefficient of that SNP with the MEM eigenvectors. ii) The second step uses Moran spectral randomization [max), while accounting for spatial autocorrelation between sites.MSOD uses Moran eigenvector maps (MEM) to quantify the distribution of allele frequencies across a range of spatial scales represented by MEM spatial eigenvectors . This prmization  to test PCNM’ version 2.1–4 [MEM provides a spectral decomposition of the spatial relationships among the study sites . A set oon 2.1–4 . The powmax and allele frequencies at each SNP detected as an outlier at step 1, given the power spectra of the candidate outliers. We performed MSR using the R package ‘adepsatial’ version 0.0–7 with 199 permutations [max under a threshold p-value of 0.05.At step 2, Moran Spectral Randomization (MSR) uses a randomization approach to build a null hypothesis of no correlation between SSSutations . The finThe software gINLAnd aims to quantify the correlation between allele counts at each site and the environmental variation using a logistic regression accounting for spatial autocorrelation between samples sites due to population history. For each SNP, gINLAnd fits a generalized linear spatial mixed model using an estimation of spatial covariance between sites as random factor, and the environmental variable as fixed explanatory variable. Spatial covariance is estimated according to the Integrated Nested Laplace Approximation (INLA) method proposed by Rue et al. .τ and κ, were calculated on a random subset of 500 SNPs and then taking the average values. For each SNP, we then fitted a full model including the environmental (SSSmax) and the random (τ and κ) variables, and a reduced model including the random variables only. Analyses were performed using the function ginland.inferences of the gINLAnd R package [For computational optimization, the parameters of the spatial covariance,  package . The SNPmax), accounting for the neutral structure through latent factors [p-values using λ. These adjusted p-values were used to decide on the significance of the association. We obtained lists of candidate SNPs using the Benjamini–Hochberg procedure under false discovery rates ranging from 0.05 to 0.2.Latent factor mixed models (LFMM) are mixed linear models that test for the correlations between allele counts and one environmental variable  has to be specified by the user for the analysis. Here, we used K = 1 and K = 2 to correct for population structure since multivariate analyses showed no clustering but isolation by distance  method, which investigates the existence of highly differentiated SNPs across the genome without assuming the effect of a particular environmental variable. PCAdapt estimates population differentiation at each SNP and compares the value with an expected estimation, calculated from the genome wide background. The null assumption tested is that each SNP is not under selection.When using pooled data, PCAdapt first generates individual genotypes at every SNPs based on the allele frequencies of the pools, using a binomial model. For each pool, one hundred individuals were generated, as advised in the tutorial. We checked the adequacy between allele frequencies estimated from these individuals and the allele frequencies of the pools, and as expected, both were strongly correlated .PCAdapt’ [PCAdapt function. We then retained K = 2 and calculated the FDR of the pvalues associated with Mahalanobis distance estimated by PCAdapt, using the qvalue function of the R package ‘qvalue’ [PCAdapt uses a PCA, a multivariate analysis able to detect population structure from the genetic data. The software constructs a set of axes from the previously generated individual genotypes, which are linear combination of the initial variables . The PCs are produced on a criterion of maximal variance. PCAdapt extends this analysis by calculating correlations between SNPs and a set of retained PCs . CandidaPCAdapt’ . We firs‘qvalue’ . Finally− 4. We then investigated the function of these genes according to the UNIPROT database (http://www.uniprot.org). To ascertain whether a given mutation was synonymous or non-synonymous, the codon containing the SNP variants was translated into an amino acid according to the location of the start codon. From the subset of candidate genes found in the NCBI’s nr database, we selected the genes that were already known to have an influence in salinity adaptation in other organisms.We assessed whether any of the 173 candidate SNPs identified by the six EA and PD approaches belong to known annotated genes in the NCBI’s nr database . We blasrda function available in the ‘vegan’ package and we tested the significance of the relationship using 1000 permutations.Longitude was previously suggested to have a wide influence on all the genomic variation of this species , and is Individual genotyping produced 626,867 SNPs. Filtering for an individual average minimal coverage of 5X and an average maximal coverage of 10× kept 1491 SNPs. Thirteen individuals with less than 3000 reads were discarded. Pooling individuals sampled in the same site allowed producing 1153 SNPs for 47 sites, with coverage of 10X and 100% of the sites genotyped at each marker (i.e. no missing data). On average over every loci, 11 individuals contribute to each pool, with a median of 10 and standard deviation of 2.91. The distribution of the MAF per SNPs was computed to verify the validity of SNPs calling and filtering. This distribution is shown in Additional file ST ranged from 0.018 to 0.065, with a mean of 0.033, showing weak genetic differentiation between sites, as expected for a mobile marine species. The PCoA showed differentiation between Gibraltar (site 20) and all the other sites , suggesting a pattern of isolation-by-distance in the data. Concerning isolation by resistance, the Mantel test between FST and pairwise distances in salinity was significant , whereas it was not significant for temperature . This suggests that salinity may influence M. surmuletus genetic structure in the Mediterranean Sea.Pairwise FOverall, the five EA and one PD approaches detected a total of 173 SNPs (15%) putatively under divergent selection across all locations, whereas the intersection of all methods did not found any. To identify SNPs detected as outliers by several methods, we delineated subsets of unique and shared number of candidate SNPs (hereafter outliers) detected by this set of approaches Fig. . ResultsAmong the methods detecting the largest sets of outliers, LM and PCAdapt, shared 45 common SNPs (35% and 51% of their respective sets), and they both also identified large unique subsets of outliers, with respectively 78 (60%) and 43 (49%) outliers that were not uncovered in any other methods Fig. . Except TP106031, TP221669, TP346795 and TP61263 were located into these four genes  and by Komoroske et al. [Hypomesus transpacificus).The BLAST search of these 173 outlier sequences against the NCBI’nr yielded to a set of 131 outlier sequences successfully identified as belonging to known genes in all NR database, using permissive criteria. Yet, only 30 outlier sequences were kept according to their homology, alignment length and their E-value (see criterion described in Methods). Among these 30 sequences, all were found in teleost and bony fishes, and 11 in cartilaginous fishes, as expected. From these 30 successfully annotated genes, four were already known to potentially play a role in adaptation to salinity. The sequences of the SNP  vulgare . The loc a lipid . This matabolism . Finallytebrates . The tratebrates  in the dTP61263, was still significantly correlated to SSSmax when spatial distribution was taken into account . Indeed, we found that longitude and SSSmax were both significant but the variance explained by SSSmax (17.1%) was twice the one explained by longitude (8.9%). In addition, SSSmax still explained 6.8% of the variation at this SNP after longitude influence was removed  confirmed that the SNP TP61263, may be a potential candidate for the local adaptation of the striped red mullet to salinity in the Mediterranean Sea , but none of the previous ones identified as belonging to known genes by the BLAST. We can then conclude that salinity seems to be the main driver influencing genetic variation observed at the candidate SNP detected.Considering that salinity and temperature are correlated in the Mediterranean Sea, we assumed that the candidate SNPs we identified could also be associated with variations in temperature. So, we tested the correlation between the allele frequencies of candidate SNPs (173 SNPs identified as outliers) and maximum temperature computed by NEMOMED8 . The abs, we underlined that using a various set of genome scan methods may facilitate uncovering potential genomic footprints of selection, since this strategy help to reduce the number of false negatives. In the following, we described how our work might enhance our understanding of salinity adaptation processes in marine species and its importance for evolution of such species facing climate change. Furthermore, we discussed methodological issues when environment is correlated to spatial variation.The most salient findings of this study are that, combining the six genome scan methods and the blast of the sequences containing the candidate SNPs against fish genomes, lead to the identification of four potential candidate genes. Those four genes harbor metabolism functions that may be involved in adaptation to salinity in the striped red mullet populations, and other marine species since there were also previously described as potential gene candidates. AdditionallyGasterosteus aculeatus) using common garden experiments [Spatial variations in salinity are expected to induce local adaptation of marine populations , 62, anderiments . Genes periments  and the eriments , 20 usineriments  combininSymphodus tinca) using genome scan, although it was not clearly associated with any environmental factor [Engraulis encrasicolus). This pattern was then further investigated by Catanese et al. [CYP7A1, which encodes the cholesterol 7 alpha-monooxygenase, an enzyme initiating the classic and alternative bile salt pathways [MSRA gene, as being a potential gene candidate for salinity adaptation since its repair function has been shown to protect cells from oxydative damage [Hordeum vulgare), suggesting that MSRA may be involved in plant adaptation to salt stress [SOCS2 gene that is part of the suppressors of cytokine signaling (SOCS) gene family. SOCS genes are implicated in regulation of a variety of signal transduction pathways, which are involved in immunity, growth and development of organisms [SOCS genes have been identified, among which SOCS2 has been described as a multifunctional protein that playing roles in signaling pathways and development of central nervous system [SOCS2 was expressed under osmotic stress conditions in a marine fish species, the delta smelt; and Martinez Barrio et al. [Haliotis discus discus).The Mediterranean mean annual salinity varies between about 32 to 40 PSU, which makes it one of the saltiest seas on earth . Thus, al factor . Anotherl factor  demonstre et al.  who usede et al.  on our ee et al. . Yet, fopathways . More papathways . On the e damage . Indeed,t stress . Yet, thrganisms . Eight Ss system , 71. Koms system  have shoo et al.  identifio et al.  highlighM. surmuletus populations in the Mediterranean Sea. Nevertheless, the entire list of candidate genes found here may serve future studies as a useful database to investigate local adaptation process in species experiencing changes in salinity.Detecting outlier markers showing minor allele frequencies linked to environmental variables is a first step towards the identification of adaptive processes and demonstrating the existence of such correlation does not prove that local adaptation occurs in the wild. Then, outcomes from population genomic studies should be further investigated at a functional level, to test the functional importance of the candidate SNP in a specific ecological context. For that purpose, additional genomic data such as sequencing of a full-genome and transcriptome analyses of gene expression would be required to provide new insights on the possibility that this gene is really involved in local adaptation process of The outcomes from the six genome scan methods varied widely in term of the number and identity of outliers detected in this study Fig. . Such inmax;r = 0.85, p-value = 5.14 × 10− 14; Fig. Correcting for confounding effects, such as spatial structure or allele surfing, was recently at the heart of genome scan methods development . TherefoThe relatively low number of markers (1153 SNPs) used in our study is expected to only represent a small fraction of the genome. Thus, our adaptive genomic study based on these SNPs most likely leaves out genes possibly involved in salinity adaptation but which do not contain - or are not linked to - any SNPs in this dataset . In addiM. surmuletus to the osmotic conditions in the Mediterranean Sea. Furthermore, confounding environmental variables such as habitat, pH, water quality or selective harvesting may have induced selective pressure similar to salinity and testing the influence of such variables would also be relevant in order to accurately define local adaptation to salinity [Candidate SNPs detected by genome scan are not direct evidence of local adaptation, as explicitly described in Benestan et al. . Therefosalinity . Yet, thSeveral studies investigated fish species response to salinity in the Baltic and North Sea , 73–75, In the current context of climate changes, adaptation of marine organisms to temperature is of greatest concern . Yet, saAdditional file 1:Appendix S1. Supplementary methods: sequence filtering and SNPs calling. Fig. S1. PCoA of the 47 sites computed using the Nei genetic distance. Fig. S2. Histogram showing the distribution of MAF per SNPs for the 47 sites. Table S1. Number of raw reads and filtered data for each sequenced library. Table S2. Parameters used in SNPs calling with UNEAK and Stacks. Table S3. Additional information on the sampling sites. (DOCX 284 kb)


Multi-parent advanced generation inter-cross (MAGIC) lines have broader genetic variation than bi-parental recombinant inbred lines. Genome-wide association study (GWAS) using high number of DNA polymorphisms such as single-nucleotide polymorphisms (SNPs) is a popular tool for allele mining in MAGIC populations, in which the associations of phenotypes with SNPs are investigated; however, the effects of haplotypes from multiple founders on phenotypes are not considered. Here, we describe an improved method of allele mining using the newly developed Japan-MAGIC (JAM) population, which is derived from eight high-yielding rice cultivars in Japan. To obtain information on the haplotypes in the JAM lines, we predicted the haplotype blocks in the whole chromosomes using 16,345 SNPs identified via genotyping-by-sequencing analysis. Using haplotype-based GWAS, we clearly detected the loci controlling the glutinous endosperm and culm length traits. Information on the alleles of the eight founders, which was based on the effects of mutations revealed by the analysis of next-generation sequencing data, was used to narrow down the candidate genes and reveal the associations between alleles and phenotypes. The haplotype-based allele mining (HAM) proposed in this study is a promising approach to the detection of allelic variation in genes controlling agronomic traits in MAGIC populations. Multi-parental intermated populations, known as multi-parent advanced generation inter-cross (MAGIC) lines have also been developed in plants, such as Arabidopsis thaliana2, tomato3, faba4, wheat6, barley7, maize8 and rice11. Single-nucleotide polymorphism (SNP) array and genotyping-by-sequencing (GBS) analysis based on next-generation sequencing (NGS) can assist in characterizing the genomic constitution and genetic architecture of these lines13. Linkage disequilibrium (LD) is reduced in MAGIC populations compared with that in bi-parental populations11. Genetic studies on MAGIC populations have revealed quantitative trait loci (QTLs) that control the transition from the vegetative to reproductive stage11, yield14, grain quality9, morphological development16, and responses to abiotic and biotic stresses17.Phenotypes differ according to the different alleles of a gene and gene-gene interactions. In genetic studies, bi-parental populations, such as recombinant inbred lines (RILs) and backcrossed inbred lines, are useful because of their simple inheritance patterns. However, the richness of their allelic and phenotypic variations is limited. To overcome this limitation, the collaborative cross (CC) of a multi-parental inter-mated population descending from eight mouse strains has been proposed11. However, the causative genes of a phenotype have rarely been identified. Therefore, improvements in GWAS methods are needed. Deep resequencing analysis can detect nucleotide variations in all genes of founders and can reveal the putative functions of the alleles. This information should facilitate the identification of causative genes. In the traditional GWAS methodology, the associations of a phenotype with SNPs are investigated using statistical models, such as mixed linear models19. SNPs include founder-specific and founder-shared randomly aligned SNPs, and genetically linked SNPs on a chromosome are integrated as haplotypes. The haplotypes in a MAGIC population might be informative for GWAS because they represent recombined chromosomal segments derived from founders.Genome-wide association studies (GWAS) using high-density SNPs have been applied to MAGIC populationsIn this study, we developed and characterized the Japan-MAGIC (JAM) population, a new MAGIC population descended from eight high-yielding rice cultivars in Japan. Genome-wide SNPs obtained by GBS analysis in the JAM population were used to translate the SNP data into haplotype blocks. We also determined the full allelic diversity from high-depth sequencing data of the eight founders. Finally, we evaluated the proposed allele mining method using the haplotype data of the JAM lines and the allelic information of the eight founders.indica and the latter four as japonica , RUTC (RU × TC), SUAK (SU × AK) and HOMI (HO × MI) , Hokuriku 193 (HO), Takanari (TK), Suweon 258 (SU), Mizuhochikara (MI), Bekogonomi (BE), Tachiaoba (TC) and Akidawara (AK)  and culm length (CL). The values of both RILs and JAM lines  were distributed beyond the parental ranges, indicating transgressive segregation , no third allele and less than 5% of missing values in all lines; the missing values were imputed with the BEAGLE program (http://faculty.washington.edu/browning/beagle/b3.html). We obtained the data for 16,345 SNP alleles distributed on all chromosomes with high density . In this work, the haplotypes at each SNP position are defined as types of genomes derived from the 8 founders. The proportion of haplotypes from the eight founders in the JAM population differed depending on genomic position  and indica  ranged from 0.15 to 0.96 and from 0.03 to 0.84, respectively, the median values in both japonica and indica founders were approximately 0.5, and the proportion of japonica or indica founders ranged from 0.3 to 0.7 in 350 (93%) of the 376 JAM lines. Next, we checked the rate of one qualitative trait, glutinous endosperm, derived from RU  supported the null hypothesis, meaning that the appearance of the trait was not due to distorted segregation at the locus for glutinous endosperm. These results confirmed that the JAM population has almost equal proportions of the backgrounds of the founders.To examine the genotypes of the JAM lines from another perspective, we investigated the proportions of haplotypes in each JAM line Fig. . Althougsee Fig. . The glu20 and categorized genes into allele types by considering the effects and the number of variations. A japonica Nipponbare allele and similar alleles with fewer than two variations were always placed into Cluster 1. The number of clusters can be as high as 8 because of the presence of eight founders, and the presence of a given allele in a higher number of clusters indicates a greater difference from the Nipponbare allele. The distribution of gene allele types differed among the founders  plots in the GWAS using the haplotype data for glutinous endosperm and CL showed more continuous, compared to the SNP data, and the deflection from each null line started earlier  or Class C (AK). Sd1 controls gibberellin biosynthesis25, although there is no information on the role of Cytochrome c in CL. Sequence data revealed four types of Sd1 alleles in the three classes: Class A was indica type (amino acid substitution Q273R), Class B was japonica Nipponbare type, and Class C included the IR8-type allele  and the Reimei allele (MU: D282H)27 . Previously, to distinguish the genomes of the eight founders, we used 4,653 markers28; here, we obtained three times as many markers. In an indica 8-way MAGIC population9, GBS analysis provided 17,387 SNPs, but the criteria  were less strict. In a subsequent report10, an Illumina Infinium rice 6 K SNP chip was used to genotype the same population. Given the imputation inaccuracy in haplotype estimation, SNPs with high amounts of missing data should be eliminated. Therefore, it was appropriate to apply strict criteria to the selection of SNPs to ensure the reliability of GWAS with the JAM population. The SNPs were used for haplotype prediction of JAM lines. The rate of glutinous endosperm in the JAM lines with the SNP with the lowest P-value in GWAS was close to 1  SNPs are different in the loci for the glutinous endosperm and CL phenotypes. In the loci and the surrounding region, both indica cultivar-shared SNPs and RU-specific SNPs were aligned. In the cases of glutinous endosperm and CL, the allelic effects of indica SNPs were cancelled due to different phenotypes, indicating no contribution of indica SNPs for the detection of the loci for the phenotypes. However, with an analysis based on haplotypes instead of SNPs, all SNP information has a potential contribution. We suggest that the conversion of SNP data to haplotype data is a key step to accurately define the QTL position in GWAS.For the qualitative and quantitative traits, GWAS based on the haplotype data showed smoother japonica rice varieties reportedly worked well to identify candidate genes for the traits of interest30, indicating that such analysis is effective for mapping candidate genes. Genome resequencing of japonica rice varieties enabled gene-based association analysis, the best method using currently available techniques. However, in the present study, we used resequencing data for only the eight founders and performed GBS analysis of the JAM population. This approach was suitable and cost effective because the genomes of the JAM lines are derived from those of the founders and resequencing all of the JAM lines would be redundant.We showed that the allele information for all annotated rice genes was useful for narrowing down the causative genes of glutinous endosperm and CL. Gene-based association analysis of 176 Sd1, and three classes of Sd1 alleles. These results indicate that the JAM population is useful for revealing the effects of multiple gene alleles. The allelic effect of a QTL is usually determined by evaluating near-isogenic lines, but establishing these lines requires several backcrosses. Phenotypic and genotypic analyses of MAGIC populations provide an alternative method to investigate gene allele functions. We confirmed two advantages of MAGIC lines—greater phenotypic diversity and a higher number of alleles—in comparison with RILs. The high number of alleles is particularly important for gene detection in genetic studies. Our QTL analysis using bi-parental RILs detected no QTL for CL in SUAK-RILs and detected the QTL for glutinous endosperm only in RUTC-RILs. These results emphasize that QTL analysis can miss QTLs in bi-parental RILs in which allelic functions of causal genes are similar or identical. In fact, the Wx alleles of all founders (except RU) and the Sd1 alleles of SU and AK belong to the same allele types. These results indicate that the genetic approach using MAGIC populations identifies more allele types and reduces the omission of highly effective genes.In GWAS for CL, we detected one of the candidate genes, The concept of allele mining in the JAM population using the haplotype-based allele mining (HAM) method would be applicable to the other MAGIC populations and nested association mapping populations. Our next target in research on the JAM population is to identify the alleles involved in grain yield. We will examine whether the HAM method is useful for elucidating this complex trait in the JAM population. Searching for genes regulating complex traits such as biomass and grain yield remains problematic because of the involvement of many weak alleles, the identification of which is hindered by high LD in local contexts. To further decrease LD in the JAM population, we are producing a concurrent crossed population for use in additional trials.To emasculate unpollinated spikelets, they were kept in a water bath at 43 °C for 7 min; the top third or half of the hull was then cut by scissors, and all anthers were removed with a pipette tip under vacuum. The pollen of vibrant yellow dehiscent anthers was used to pollinate the emasculated spikelets directly or with forceps. To prevent further pollination, panicles with pollinated spikelets were covered with envelopes for approximately 5 days, and mature seeds were collected 30 to 40 days later.Germinated seeds were sown in trays filled with soil on 9 May 2016 and incubated at 30 °C in the dark for 2 days. Seedlings were grown in a Tsukuba Kannondai paddy field covered with thin plastic film to protect the plants from the cold. Individual seedlings were transferred to another paddy field on 8 June 2016 and cultivated according to standard procedures in NARO in Tsukuba, Japan. DTH was scored as the number of days from sowing to the appearance of the first panicle. CL was assessed as the length of the longest culm in each plant, which was measured with a ruler more than 10 days after heading. DTH values were binned into 5 categories and CL values into 6 categories; these categories were then used to generate heat maps. Glutinous endosperm was evaluated based on the colour of dehulled rice. We used 376 JAM lines, which were selected with consideration of kinship. As explained above, we attempted to produce 1,000 JAM lines by developing 10 lines from each eight-way recombinant. We picked up 3 to 4 lines (F5) derived from each eight-way recombinant to select the 376 JAM lines. We used 144 HOMI-RILs, 138 TKBE-RILs, 81 RUTC-RILs and 132 SUAK-RILs.29. Low-quality bases and the adapters in each read were trimmed using Trimmomatic software31. Trimmed reads were mapped to the Nipponbare International Rice Genome Sequencing Project (IRGSP) v.1 reference genome using BWA software with default settings32. Only uniquely mapped reads with a mapping quality score of ≥20 were sorted and indexed using SAMtools software33. To improve the raw alignments around insertion and deletion mutations (indels), local re-alignments were performed using GATK software34 (https://software.broadinstitute.org/gatk/). PCR duplicates were removed using Picard software (http://picard.sourceforge.net). Indels and SNPs were identified individually for each sample using both SAMtools and GATK software. Homozygous SNPs found in the eight founders were used as reference SNPs to predict haplotypes in the JAM population.We performed short-read Illumina resequencing for AK, BE and RU and deposited the data in the DDBJ Sequence Read Archive under the accession number DRA005784. For the other five founders, we used the resequencing data we reported previously20. This software provides the number of variations that belong to one of the four categories  according to their putative effect. We classified all annotated gene loci  into allele types according to the number of alleles in each SnpEff category. Nipponbare alleles and similar alleles were in Cluster 1. The maximum number of clusters was eight because of the eight founders; a higher cluster number indicated a greater difference from the Nipponbare allele. The data on the clusters of all annotated genes in the eight founders are summarized in Supplementary Dataset 35.Non-homozygous and low-depth (<3-fold) variants were removed, and the remaining variants were used to analyse their effects on gene function in SnpEff software36. The outline of GBS procedure is described as follows. After check of sample integrity and purification detected by agarose gel electrophoresis, GBS library was constructed. 100 ng DNA was digested by restriction enzyme, ApeKI , then added common adapter and barcode adapter in ligation reaction mix. Ligate products were pooled and purified with QIAquick PCR Purification Kit . Several rounds of PCR amplification with PCR Primer Cocktail and PCR Master Mix were performed to enrich the Adapter-ligated DNA fragments. Then, the PCR products were selected by running another 2% agarose gel to recover the target fragments. Sample gels were purified with QIAquick Gel Extraction kit . The final library was quantitated in two ways: Determine the average molecule length using the Agilent 2100 bioanalyzer instrument  and quantify the library by real-time quantitative PCR . The qualified libraries were amplified to generate the cluster on the flowcell and the amplified flowcell was sequenced pair end on the Illumina HiSeq. 4000 System. To obtain the clean GBS data, preprocessing was performed as next four steps. 1) Remove reads with adaptors; 2) Remove low quality reads ; 3) Remove reads in which unknown bases are more than 10%; 4) Remove reads which do not contain barcode, trim the barcode, and then remove reads lacking key sequence of the enzymes.Total DNA was extracted from leaf blades of the 376 JAM lines and 8 founders. The genotyping-by-sequencing (GBS) of the 376 JAM lines and founders was undertaken at Beijing Genome Institute  using previously described methodsThe clean GBS data (an average of 667 Mb of 100-bp short reads per sample) obtained were analysed in a similar manner to the NGS analysis of the founders’ genomes. We extracted only high-quality homozygous SNPs with two or more reads . Heterozygous genotypes were considered null. The imputation produced the SNP set for LD and haplotype analysis of the JAM population.http://www.stats.ox.ac.uk/~marchini/software.html). A total of 2,355 randomly selected alleles  were mapped on the physical map due to the reduction in calculation time. Physical positions are indicated as the cumulative positions (Mb) from chromosome 1 to 12.LD heatmaps and LD decay plots were produced in the R package popgen (2 (http://mtweb.cs.ucl.ac.uk/mus/www/19genomes/magic.html). All parameters were set to their defaults, except “-p”, which was set to 2. Proportions of haplotypes from the eight founders were calculated in the JAM population and each JAM line.We estimated the parental haplotypes in the JAM lines using the method proposed for haplotype prediction in an Arabidopsis MAGIC population37. PCR products ranged from 80 to 150 bp.To determine the parental origins of variations in RILs, indel markers were designed based on large indel regions (≥10 bp) with high sequencing depth . Primer pairs for the selected indel regions were automatically designed using a Perl script to control the Primer3 core program38. The SNPs of RUTC-RILs were checked using the Golden Gate detection system  with the Fcore SNP set39. We checked all SNPs with GenomeStudio (Illumina) and chose 437 SNPs for the QTL analysis of DTH and CL; the SNP selection criteria have been described previously40.TKBE-, SUAK- and HOMI-RILs were analysed using 177, 176 and 167 indel markers, respectively. PCR with a KAPA2G Fast PCR kit  and DNA electrophoresis in 3% agarose gel were performed as previously described41 to calculate the P-values using the “rrBLUP” package. Spots of P-values were illustrated in Manhattan plots using the “qqman” package. Q-values were calculated from the P-values corrected with Bonferroni’s adjustment and were used to produce Q-Q plots.GWAS was performed using R software. Associations of the imputed genotype data with phenotype data for glutinous endosperm and CL were analysed using the population structure (Q)+ Kinship (K) model42. The QTL analysis was performed by interval composite mapping using version 2.5 of QTL Cartographer software, and the threshold was calculated from 1,000 permutations43.For QTL analysis for glutinous endosperm and CL in the four types of RILs, we constructed genetic maps using version 3.0b of MAPMAKER/EXP softwareSupplementary InformationDataset 1