ZNF831/20q13 and FTO/16q12. These are previously established variants for blood pressure (BP) and the FTO variant has also been associated with body mass index (BMI). Further analysis of BP variants establishes that variants at MECOM/3q26, FGF5/4q21 and SH2B3/12q24 also associate with preeclampsia through the maternal genome. We further show that a polygenic risk score for hypertension associates with preeclampsia. However, comparison with gestational hypertension indicates that additional factors modify the risk of preeclampsia.Preeclampsia is a serious complication of pregnancy, affecting both maternal and fetal health. In genome-wide association meta-analysis of European and Central Asian mothers, we identify sequence variants that associate with preeclampsia in the maternal genome at  Studies to identify maternal variants associated with preeclampsia have been limited by sample size. Here, the authors meta-analyze eight GWAS of 9,515 preeclamptic women, identifying five variants associated with preeclampsia and showing that genetic predisposition to hypertension is a major risk factor for preeclampsia. Hypertension accompanied by proteinuria is known as preeclampsia, which can progress into a serious disorder that contributes worldwide to the death of an estimated 50,000 women and up to 1 million babies annually, making it one of the principal causes of maternal and perinatal mortality2. Current models attribute preeclampsia to a combination of maternal susceptibility, often associated with an exaggerated inflammatory response to pregnancy and altered placental function with the release of stress factors that trigger widespread activation of the maternal vascular endothelium3. A familial predisposition to preeclampsia is well documented5, and the evidence points to contributions from both maternal and fetal genomes. We recently showed that the fetal genome contains a preeclampsia susceptibility locus at FLT16. However, previous attempts to identify maternal sequence variants associated with preeclampsia through genome-wide association scans (GWAS) or targeted gene-centric analysis have been hampered by small sample size11.Between 10 and 15% of pregnant women develop new-onset hypertension after 20 weeks gestation6. Maternal and fetal mortality attributable to hypertensive pregnancy disorders is higher in countries with developing maternity care systems, including those in Central Asia, an area that has been largely neglected in genome-wide studies. We present whole-genome sequences, and GWAS of maternal and fetal genomes, in data sets from Uzbekistan and Kazakhstan, and find no evidence that the genetic architecture of preeclampsia differs between the populations from Central Asia and those in Europe.Here we report the results of a meta-analysis of eight previously unreported GWAS with 9515 preeclamptic women and 157,719 controls from Europe and Central Asia as well as an expansion of our previous meta-analysis of preeclampsia offspring12. We have therefore explored our data using polygenic score analysis of the correlation between preeclampsia and variants associated with related phenotypes, including high BP, T2D, CAD, and BMI.There is compelling epidemiological evidence that women with hypertension during pregnancy, including preeclampsia, are at increased risk of essential hypertension, coronary artery disease (CAD), chronic renal disease, and type 2 diabetes (T2D) later in lifeIn our discovery analysis, we find five variants associating with preeclampsia through the maternal genome. All have previously been associated with BP. We further show that genetic predisposition to hypertension is a major risk factor for preeclampsia. However, comparison with gestational hypertension implies that additional factors are involved in the risk of preeclampsia.FLT1 locus on 13q126 was strengthened in this analysis, with a P-value of 3.0 × 10−11 and OR = 1.17 (95% CI: 1.12–1.23) for the sentinel variant rs4769612 (Supplementary Table r2 = 0.99). No other loci were associated at genome-wide significance (defined here as P = 4 × 10−9 after adjusting for the number of variants tested) in this data set . None of them reached genome-wide significance in the combined analysis  and confirmed another previously reported6 independent signal at this locus rs71433277 (r2 = 0.97 with previously reported rs12050029) (P = 6.6 × 10−6 after conditioning on rs4769612 and rs9508092)  in our combined meta-analysis of 4630 European cases and 373,345 controls and 2145 Central Asian cases and 2027 controls  in the maternal meta-analysis   on 16q12 remained significant  (Table ZNF831 (zinc finger protein 831) on 20q13 also reached genome-wide significance   were among the variants we followed up but that did not reach genome-wide significance in the combined meta-analysis (P = 1.2 to 1.7 × 10−8) . We thus conclude that in addition to the variants near ZNF831 and FTO that exhibited genome-wide significance, BP variants at FGF5, SH3B2, and MECOM also associate with preeclampsia.In addition to the ZNF831 variants rs259983 and rs6015450, identified through the preeclampsia meta-analysis and thorough testing of BP variants, respectively, are in strong LD  but the FGF5 and SH3B2 variant pairs were in strong LD  data , Central Asian mothers  and in European and Central Asian mothers combined  . We performed EMIM analysis on the sentinel variants at each of the five maternal loci using genotypes of trios and mother-baby duos that were available in two of the contributing studies. We found no independent effect of the fetal genome for any of the variants after accounting for the maternal genome. With the exception of the ZNF831 variant rs259983, the association was significant for each maternal variant after accounting for the fetal effect   while the maternal preeclampsia variants were tested using the reported birth weight of the female participant’s first child . The fetal FLT1 variant was associated with lower birth weight . For Europe, we found the heritability for maternal preeclampsia to be 38.1% (95% CI: 29.3–46.8) and in fetal preeclampsia 21.3% (95% CI: 7.4–35.3); for Central Asia, the heritability was found to be 54.4% (95% CI: 29.6–79.3) and 42.5% (95% CI 17.3–67.7) for maternal and fetal preeclampsia, respectively (Supplementary Table 5.We made use of the genetic data to study the heritability of preeclampsia. We applied GCTA Genomic Relatedness Restricted Maximum Likelihood (GREML) analysis to the chip genotypes of European and Central Asian subjects to estimate the SNP heritability of preeclampsia on the liability scaleFLT1 variant did not associate with increased risk in gestational hypertension offspring (P = 0.21)  . We found a positive genetic correlation between preeclampsia and both systolic and diastolic BP, hypertension, CAD and T2D and a negative correlation with birth weight of first child ) = 0.18, 95% CI: 0.13–0.23) (Supplementary Table P = 2.1 × 10−35), but the effect on gestational hypertension was nearly double that on preeclampsia  (Supplementary Table We used a polygenic risk score (PRS) analysis to explore further the correlation between preeclampsia and hypertension, T2D, CAD, BMI, and birth weight. To avoid confounding by population structure, we extracted risk alleles, P = 1.6 × 10−8). The effects of HT-PRS on preeclampsia in two additional European preeclampsia data sets (FINRISK and MoBa) and in the two Central Asian data sets were comparable to the deCODE set  in the meta-analysis. An overview of the data sets is shown in Supplementary Table The combined fetal meta-analysis included eight data sets with a total of 6775 cases and 375,372 controls. The combined maternal meta-analysis included 9515 cases and 157,719 controls. Both studies were based on subjects of Northern European and Central Asian origin. A further 2300 maternal cases and 5325 controls were used to follow-up variants with a suggestive association 6. Briefly, singleton affected pregnancies were identified through the scrutiny of hospital records from 1970 to 2017 at Landspitali University Hospital in Reykjavik. Records from 1999 and earlier were examined and each affected pregnancy reclassified while pregnancies from 2000 to 2017 were identified based on ICD-10 codes O14; O15 for preeclampsia and O13 for gestational hypertension. Further data on each pregnancy, including gestational duration at diagnosis of preeclampsia and BMI, were obtained directly from maternity records. Genotyping and imputation methods in the Icelandic samples were essentially as described previously38. In short, we sequenced the whole genomes of 15,220 Icelanders using Illumina technology to a mean depth of at least 10× (median 32×). SNPs and insertions and deletions (indels) were identified and their genotypes called using joint calling with the Genome Analysis Toolkit HaplotypeCaller (GATK version 3.4.07)39. Information about haplotype sharing was used to improve genotype calls, taking advantage of the fact that all of the sequenced individuals had also been chip-typed and long-range phased. The 33.4 million variants that passed the high-quality threshold were then imputed into 151,677 Icelanders who had been genotyped with various Illumina SNP chips and their genotypes phased using long-range phasing40. We further increased the sample size for association analysis and thus the power to detect associations by imputing the sequence variants into 282,894 un-typed relatives of the chip-typed individuals using genealogic information. This data set includes the maternal preeclampsia cases and their offspring as well as the age-, sex-, and county-of-origin-matched controls. The control group comprised individuals recruited through different genetic research projects at deCODE. Out of 2389 maternal cases included in the analysis, 1662 were chip-typed and 435 of the 2221 offspring included in the analysis were chip-typed. All of the variants that were tested had imputation information above 0.8. To account for inflation in test statistics due to cryptic relatedness and stratification, we applied the method of LD-score regression24. With a set of 1.1 million variants, we regressed the χ2 statistics from our GWAS scan against the LD-score and used the intercept as a correction factor. LD scores were downloaded from an LD-score database . The estimated inflation factors based on LD-score regression were 1.08 for maternal preeclampsia and 1.14 for the offspring analysis. The study was approved by the Icelandic National Bioethics Committee (VSN-14-174). Written informed consent was obtained from all genotyped subjects.The deCODE preeclampsia cohort is a part of an ongoing sample collection including a large fraction of the Icelandic population42. Obstetric data, including all measurements of BP  and of proteinuria  were extracted from obstetric records by one of six trained midwives. Gestational hypertension was defined as systolic BP ≥ 140 mmHg OR diastolic BP ≥ 90 mmHg on at least two occasions after 20 weeks of gestation in women who had not previously been diagnosed with hypertension outside of pregnancy. Preeclampsia was defined as hypertension as described for gestational hypertension accompanied by at least 1+ proteinuria on dipstick testing  occurring at the same time as the episodes of raised BP. The comparison (control) group were all other included women or offspring with GWAS data who were not affected by hypertension during pregnancy.ALSPAC  is a prospective birth cohort study, which recruited 15,454 pregnant women with expected delivery dates between 1st April 1991 and 31st December 1992 living in and around the city of Bristol in the South West of England. There was a total of 15,589 fetuses. Of these 14,901 were alive at 1 year of age. The study has been described in full elsewhere6. ALSPAC mothers were genotyped using the Illumina human660W-quad array at Centre National de Génotypage (CNG) and genotypes were called with Illumina GenomeStudio. Details of genotyping and quality control can be found elsewhere43. Haplotype phasing was performed using ShapeIT (v2.r644) and known autosomal variants were imputed with Impute V2.2.2 using the 1000 genomes reference panel  consisting of 2186 reference haplotypes (including non-Europeans). All imputed dosages converted to best guess genotypes in binary plink format, using a hard call threshold of 0.1. Cases and controls for gestational hypertension analysis were imputed with phased haplotypes from the Haplotype Reference Consortium (HRC) panel. Ethical approval was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Please note that the study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool .A total of 9912 ALSPAC children were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform44. DNA was extracted manually from whole blood samples obtained at recruitment  using the FlexiGene kit . Birth outcome information for all MoBa participants is obtained through linkage with the Medical Birth Registry of Norway (MBRN)45. In the MoBa Preeclampsia case–control study, all preeclampsia cases identified through linkage with the MBRN, and a subset of pregnancies unaffected by preeclampsia, were validated using antenatal records and hospital discharge codes46. Preeclampsia was defined using American College of Obstetrics and Gynecologists criteria47. We included, from among these validated records, women with a singleton pregnancy who conceived spontaneously, were verified cases or controls, returned both early and late pregnancy study questionnaires, had blood stored in the MoBa biobank, and had no history of chronic hypertension: 1564 validated preeclampsia cases  and 999 controls . The date of onset was defined as the first gestational week when both blood pressure and proteinuria criteria were simultaneously noted in the antenatal chart. Mothers were genotyped by the UNC Mammalian Genotyping Core using the HumanCoreExome-12 Bead Chip from Illumina . Samples and SNPs were examined using PLINK 1.07 for quality control. SNPs were excluded if the missing rate exceeded 5%, there was a substantial deviation from HWE (P < 1×10−3) or the MAF was <0.125. For each pair of related mothers, we preferentially included the one with the most complete genetic data, or in the case of equivalence, randomly sampled between them. Quantile-quantile plots and calculation of genomic control lambda (λGC = 1.01) indicated no systematic test statistic inflation, unidentified relationships, or cryptic admixture. Outliers for any of the first three 1000 Genomes axes of variation  > 3 standard deviations from the mean were excluded. The post-QC data set was imputed using PBWT and pre-phased using SHAPEIT2 against the 1000 Genomes Phase 3 reference panel. Imputation for GWAS analysis was conducted by the Sanger Imputation Service provided by the Wellcome Trust Sanger Institute. Data used for the analysis of disease onset and PRS was imputed at deCODE genetics using the same reference panel. The study was approved by the Regional Committee for Medical Research, South East Norway.The Norwegian MoBa  is a longitudinal study of over 110,000 pregnant women, their children and partners, recruited between 1999 and 2008 from maternity units throughout Norway, and has been described previously48, using the ICD-8 code 637.04 and the ICD-10 codes O14.1, O14.2, and O15.0–15.9. Timing of onset was defined based on gestational age at delivery. Women were classified as having had early preterm preeclampsia if they delivered before 34 completed weeks of pregnancy and late preterm preeclampsia if they delivered between 34 and 36 completed weeks of pregnancy. Controls for the maternal analysis were randomly selected from among parous women without cardiovascular disease, diabetes or kidney disease and with a history of only healthy pregnancies ending in live births at 40 weeks’ gestation, and were matched 1:1 to the case women on maternal birth year and age at first delivery. Fetal cases were singletons from preeclamptic pregnancies of a subset of cases from the maternal analysis and controls were individuals genotyped with the same array . Biological samples were drawn from the Danish Neonatal Screening Biobank49 and the biobank of the Danish National Birth Cohort50, both of which are part of the Danish National Biobank. This study was approved by the Scientific Ethics Committee of the Danish Capital City Region (Copenhagen)  and the Danish Data Protection Agency . The Danish Scientific Ethics Committee granted an exemption from obtaining informed consent from study participants as this research project was based on existing biological samples already held in a biobank. All SSI samples were genotyped using the Illumina Multi-Ethnic Global v2 A2 array. Data cleaning and quality control were performed using a sequential procedure. After the quality control steps, we ended up with 872 cases and 815 controls for the maternal analysis and 213 cases and 963 controls for the fetal analysis. After genotyping QC, we imputed unobserved genotypes with phased haplotypes from the HRC panel (version r1.0). Association analysis of imputed autosomal SNPs with preeclampsia was performed using logistic regression on imputed SNP dosages that had an imputation info score ≥ 0.8 and MAF > 1%, under an additive genetic model using PLINK51.SSI (Statens Serum Institut) Danish study subjects were drawn from a case–control study of severe, early-onset preeclampsia and were all of Scandinavian  ancestry. Severe preeclampsia cases were identified through the Danish National Patient Register, which includes all hospital diagnoses assigned since 197752. The surveys are based on random population samples from five (six in 2002) geographical regions of Finland. The age-range was 25 to 64 years until 1992 and 25 to 74 years since 1997. The survey includes a self-administered questionnaire, a standardized clinical examination carried out by specifically trained study nurses and the collection of a blood sample for laboratory measurements and DNA extraction53. We chose 402 women with a history of preeclampsia and 7924 female controls with normal delivery from the 1992, 1997, 2002, 2007, and 2012 FINRISK surveys. For identifying preeclamptic and eclamptic women, we used the following Finnish International Classification of Disease (ICD) codes in the comprehensive National Hospital Discharge Register covering years 1992 to 2007: ICD-10 (in use since 1996): O14.0, O14.1, O14.9, O15.0, O15.1, O15.2, O15.9; ICD-9 (in use from 1987 to 1996): 6424 to 6426, 6427A; and ICD-8 (in use from 1968 to 1986): 637.03, 637.04, 637.09, 637.10, 637.99. The FINRISK samples were genotyped using multiple different genotyping chips, for which the QC, phasing, and imputation were done in multiple chip-wise batches. Principal component (PC) analysis was applied to detect population structures and outliers removed. Imputation was done utilizing a population-specific reference panel (http://www.sisuproject.fi) of 2690 high-coverage whole-genome and 5,093 high-coverage whole-exome sequences with IMPUTE254. The FINRISK study was approved by the Helsinki University Hospital ethical committee.FINRISK is a series of health examination surveys carried out by the National Institute for Health and Welfare of Finland every 5 years in 1972–2012 and has been described previouslyCentral Asian subjects of Uzbek and Kazakh ancestry, determined by grandparental ethnicity, were recruited between 2012 and 2015 from six maternity units in Uzbekistan and seven in Kazakhstan. Women with singleton pregnancies affected by preeclampsia were recruited at the time of diagnosis; healthy pregnant controls were recruited from the same maternity centers. The diagnostic criteria for preeclampsia were: systolic BP ≥ 140 mmHg and diastolic BP ≥ 90 mmHg on at least two occasions within 24 h after the 20th week of pregnancy in a previously normotensive woman, accompanied by proteinuria ≥300 mg/L. Women below the age of 18, those with a prior history of hypertension, non-infective renal disease, type 1 or type 2 diabetes, or 3 or more consecutive miscarriages, were excluded. Volunteers provided a sample of venous blood for DNA extraction. DNA from the offspring of preeclamptic pregnancies was extracted from a sample of umbilical venous blood or umbilical cord obtained at delivery. All volunteers gave informed consent for participation in the study; mothers gave consent on behalf of their offspring.http://zzz.bwh.harvard.edu/plink/), SMARTPCA  and Admixture .Three batches of Kazakh preeclamptic mothers, babies, and control sets were genotyped at the deCODE genotyping facility using Illumina SNP genotyping platforms. Kazakh 1, including 1003 sets was genotyped on Illumina 2.5-8, Kazakh 2, including 761 set, on OmniExpress, and finally Kazakh 3, including 772 sets, on Infinium GSA. We carried out quality control analysis using PLINK  merged with 1000 genomes Phase 3 haplotype reference panel. The Ministry of Health, Republic of Kazakhstan, Central Ethics Committee gave approval for the conduct of the study.www.cog-genomics.org/plink2): violation of Hardy–Weinberg Equilibrium; haplotype-based test for non-random missing data, Bonferroni corrected P < 0.05; call rate <98%. A total of 2,292,786 autosomal variants were genotyped. The quality control procedure identified 50,793 poorly genotyped SNPs leaving 2,241,993 SNPs available for further analysis.2869 samples from Uzbek preeclamptic mothers, their offspring, and controls were genotyped on the Omni 2.5-8 chip. The genotypes were called using Gencall genotype calling algorithm. Quality control measures included gender and relatedness checks, and samples with call rates <95% or heterozygosity >± 3 SD were excluded, resulting in the retention of 2742 samples. The quality of the calls for individual variants was assessed by looking at founder individuals (mothers and controls). The following QC filters were applied to autosomal SNPs using the plink software ). For the FINRISK and Kazakh cohorts, the top twenty (FINRISK) and ten (Kazakhstan) ancestry principal components were included as covariates (SNPTEST (v2.5))55.Logistic regression was used to test for association between variants and disease, assuming an additive model, treating disease status as the response and expected genotype counts from imputation as covariates. For the deCODE cohort, information on the county of origin within Iceland were included as covariates to adjust for possible population stratification. This was done using software developed at deCODE geneticsFINNPEC (Finnish Genetics of Preeclampsia Consortium): The FINNPEC collection was assembled in Finland between 2008 and 2011 from two recruitment arms and has been described previously56. Samples were collected at the time of diagnosis of preeclampsia from 879 mothers and during pregnancy from 922 non-preeclamptic mothers from antenatal and labor wards. Their children and partners were also enrolled. A further 525 pregnancies affected by preeclampsia were identified by examination of hospital records, and women and offspring were invited to participate by letter. After exclusion of pregnancies that did not meet the entry criteria for this study, 678 preeclamptic mothers and 580 offspring were included as cases, and 700 non-hypertensive mothers and 760 offspring provided the control group. Offspring genotype data included in this study were generated in our previous study6. Samples were genotyped at the BHF Glasgow Centre for Cardiovascular Research using an OpenArray platform, and at the Wellcome Trust Sanger Institute using Sequenom technology. The Hospital District of Helsinki and Uusimaa Co-ordinating Ethics Committee approved the study.DNBC : The Danish follow-up samples were drawn from the DNBC, a population-based cohort of more than 100,000 pregnancies, recruited in the years 1996–2002, and have been described previously50. Extensive phenotype information is available for the DNBC mothers and children based on computer-assisted telephone interviews, questionnaire-based follow-up surveys and data from the Danish population and health registers. DNBC women with preeclampsia were identified from the Danish National Patient Register48 using ICD-8 codes 63700-63719 and ICD-10 code groups O13.9, O14 and O15. Only singleton pregnancies were included. As controls, we used mothers without any preeclampsia-related diagnosis codes in any pregnancy. The Scientific Ethics Committee for the Capital City Region (Copenhagen) and the Danish Data Protection Agency approved the study. The Scientific Ethics Committee also granted exemption from obtaining informed consent from participants (H-B-2007-124) as the study was based on biobank material. Follow-up variants were genotyped using the Centaurus (Nanogen)57 or KASP (LGC Genomics) platform.The HUNT study (HUNT) is an ongoing longitudinal health survey of the population of Nord-Trøndelag County in Norway, including ~120,000 individuals58. DNA samples are available from ~70,000 subjects recruited to HUNT2 and HUNT3, in 1995–1997 and 2006–2008, respectively. Women with a history of preeclampsia were identified retrospectively by linking the HUNT database to the Medical Birth Registry of Norway (MBRN), using diagnosis codes ICD-8 (before 1998) and ICD-10 (after 1998). The MBRN defines preeclampsia as an increase in BP to at least 140 systolic or 90 mmHg diastolic , combined with proteinuria (protein excretion of ≥0.3 g per 24 h or ≥1+ on dipstick). These criteria are in accordance with the diagnostic criteria used clinically in Norway until 2006. Of the HUNT participants, 1134 women with a history of preeclampsia in one or more pregnancies were identified, and for 2/3 of the women, the diagnosis had been validated by scrutiny of hospital records59. A total of 2212 women with healthy, non-hypertensive pregnancies and a further 4000 women from the HUNT population provided the control group.P < 0.0001). Variants were excluded if the cluster separation score was <0.3, Gentrain score was >0.15, genotyping fail rate >1% or they deviated from Hardy–Weinberg equilibrium (P < 1×10−4). Samples were phased with Eagle2 v2.360, and genotype imputation was conducted with Minimac3 61 and a merged reference panel that was constructed by combining the Haplotype Reference Consortium panel (release version 1.1)62 and a local reference panel based on 2202 whole-genome sequenced HUNT study participants.The HumanCoreExome-12 v1.0 (Illumina), HumanCoreExome-12 v1.1 (Illumina) and UM HUNT Biobank v1.0 was used to genotype ~600,000 variants. Genotype calling was performed with Genome Studio (Illumina). Samples with genotype call rates <99% were excluded, as were duplicates, samples with contamination >2.5% as estimated with BAF Regress, and those with gender mismatches or evidence of non-European ancestry from principal components analysis  and alleles. We included variants that were well-imputed (info > 0.8), with a minor allele frequency >0.5% and present in at least two data sets. This left 11,796,347 and 12,130,433 autosomal variants for the maternal and fetal analysis, respectively.65. Genomic control correction was applied prior to meta-analysis, where appropriate, using the genomic control option in METAL.The meta-analyses were conducted using the fixed-effects inverse-variance method based on effect estimates and standard errors implemented in METAL19, to the meta-analysis summary statistics to look for additional association signals at each of the genome-wide significant loci. We analyzed the European and Asian data sets separately, estimating LD between variants using sets of 8700 whole-genome sequenced Icelandic individuals for the European analysis and 1787 chip-typed Kazakh individuals for the Central Asian analysis. The European and Central Asian results were then meta-analyzed. The analysis was restricted to variants present in both the European and Asian data sets and within 1 Mb from the index variants. We tested 7098 variants on one locus in the fetal analysis and report two variants with conditional P-value < 7.0 ×10−6, and 11,844 variants at two loci in the maternal analysis with no variant reaching the conditional P-value < 4.2 ×10−6.We applied approximate conditional analyses, implemented in the GCTA softwareP = 4.02 × 10−8) , where na, nc are the number of cases and controls used. For the Icelandic cohort, the effective sample size was further divided by the estimated genomic inflation factor, λg, to adjust for relatedness of the cohort. The effective sample size for the study was then calculated as the sum of neff for individual cohorts.We estimated OR for a given allelic frequency of a variant for which we have 80% power to detect association using the estimated effective number of cases and controls for the study. The effective number of cases, or controls, was estimated for each cohort as r2 > 0.8. These variants were then annotated by the intersection with chromatin immunoprecipitation (ChIP-seq) and DNase hypersensitivity (DHS) data derived from the ENCODE project (www.encodeproject.org). ChIP-seq data were downloaded in pre-processed  bigWig format representing analysis of acetylation of lysine K27 of histone H3 (H3K27ac) and mono-methylation of lysine K4 of histone H3. The accession numbers used are listed out in Supplementary Data P-values were adjusted by the Benjamini-Hochberg procedure to account for multiple hypotheses and thresholded at the 1% FDR significance level. DNase hypersensitivity data (DHS) were downloaded in a pre-processed format . We then made use of the Joint Effect of Multiple Enhancers (JEME) resource to find enhancer-gene targets66.Variants in LD with the lead variants were identified based on whole-genome sequenced Icelandic individuals using N = 236,507) and the effect of the maternal variants on reported weight of the first child . Birth weight data were inverse normal transformed and adjusted for year of birth and the first 10 principal components. For the 6 variants tested, the Bonferroni corrected level of significance is P = 0.05/6 = 0.0083.The association between fetal and maternal discovery variants and disease onset was tested in combined GOPEC, deCODE, MoBa, and SSI offspring and maternal data sets, respectively, where data on time of diagnosis was available , may also exhibit a positive correlation of the known higher BP allele and preeclampsia  risk allele identified in our meta-analyses. Under the null hypothesis that such positive correlation does not exist at any of the 892 BP variants, the probability of concordance of the preeclampsia  risk allele and higher BP allele at each variant is random and equals 0.5; and since the 892 variants are not in LD and hence are independent, the null hypothesis probability of total concordances at the 892 variants can be calculated with a binomial distribution based on 892 binomial trials with 0.5 probability of trial success. To calculate the tail probabilities of the binomial distribution, we used the pbinom function implemented in R statistical software (www.r-project.org). The p-value of this two-tail binomial test is extremely low for each meta-analysis (P < 2.9×10−5 to P < 1.1×10−22)  was also the same allele known to be associated with higher BP at sentinal SNPs of all five known BP loci. Therefore, we hypothesized that some or all of the other 892 known BP variants listed in Supplementary Table 16 and the preeclampsia or gestational hypertension risk allele was the higher frequency allele in cases compared to controls at each variant as observed in the GWAS meta-analyses of European mothers, Central Asian mothers, or European-Central Asian mothers combined. In Supplementary Table For these analyses, the high BP allele at each of the 892 variants was taken from the European results section of Supplementary Table S1 or S2) or the offspring (R1 or R2). We considered a multiplicative effects model ℒ where R1,S1) vs the models where the association is explained purely by the fetal effect only; ℒ and by the maternal effect only ℒ. Both the likelihood ratio tests and confidence intervals show . EMIM models the multiplicative increase in disease risk relative to the homozygous reference genotype conferred by the presence of 1 or 2 risk alleles in the mother , well-imputed in both data sets, and for LD information we used pre-computed LD scores for European populations . To avoid bias due to overlapping samples, we calculated the genetic correlation between a meta-analysis of GOPEC, ALSPAC, and MoBa preeclampsia cohorts and the Icelandic GWAS summary statistic for each secondary trait, and also between a meta-analysis of deCODE, SSI and FINRISK preeclampsia cohorts and UKBB GWAS summary statistic for each secondary trait. The results of the two analyses were subsequently meta-analyzed. The birth weight analysis included included all European non-UK preeclampsia data sets and UKBB birth weight data. The sample size for each secondary trait is reported in Supplementary Table We estimated the genetic correlation between pairs of traits using the cross-trait LD-score regression methodhttps://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2) for an effective sample size of 10,255 maternal and 7259 fetal cases in the European meta-analysis.We estimated observed scale SNP heritability with LD-score regression using pre-computed LD scores for about 1.2 million variants found in European populations (downloaded from: Phet > 0.1).For each cohort, we applied GCTA (version 1.93.2) separately to the maternal and fetal case–control post-QC and pre-imputation genotypes. The genetic relatedness matrix was calculated on autosomal SNPs with –grm-cutoff=0.05 and the first 10 principal components were included as fixed-effects in the linear mixed model. The disease prevalence was set to 4%. The per-region heritability was calculated by combining the per cohort estimated heritability and standard error using fixed-effect inverse-variance meta-analysis. No significant heterogeneity was observed , and selected the PRSs that best predicted the trait itself. The fraction of causal markers used was: 30% for the PRSs for hypertension and birth weight, 3% for the PRS for BMI, and 1% for the PRSs for T2D and CAD. The correlation between the PRS and traits was calculated using logistic regression in R (v3.5) (http://www.R-project.org) adjusting for principal components by including them as covariates in the analysis.The risk scores were calculated using genotypes for about 600,000 well-imputed autosomal markers. We estimated LD between markers using 4000 phased Icelandic samples and used this LD information to calculate adjusted effect estimates using LDpredWhen analyzing the association of PRS for different traits on deCODE sets of preeclampsia and gestational hypertension the controls were the same as included in the GWAS analysis . For comparison of hypertension risk scores between traits in Iceland, and between cohorts, for consistency we excluded from the deCODE control group any individuals that were cases in any of the three traits tested , using the same, essentially hypertension-free, female control group for all three traits. Similarly, we only used female hypertension cases in this analysis.r2, using the method of Nagelkerke71. The reported variance explained is the estimated r2 value for the model including the PRS and covariates, minus the r2 for the model only including covariates.We estimated the variance explained, Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Capsicum spp.) fruit-related traits are critical determinants of quality. These traits are controlled by quantitatively inherited genes for which marker-assisted selection (MAS) has proven insufficiently effective. Here, we evaluated the potential of genomic selection, in which genotype and phenotype data for a training population are used to predict phenotypes of a test population with only genotype data, for predicting fruit-related traits in pepper. We measured five fruit traits  in 351 accessions from the pepper core collection, including 229 Capsicum annuum, 48 Capsicum baccatum, 48 Capsicum chinense, 25 Capsicum frutescens, and 1 Capsicum chacoense in 4 years at two different locations and genotyped these accessions using genotyping-by-sequencing. Among the whole core collection, considering its genetic distance and sexual incompatibility, we only included 302 C. annum complex  into further analysis. We used phenotypic and genotypic data to investigate genomic prediction models, marker density, and effects of population structure. Among 10 genomic prediction methods tested, Reproducing Kernel Hilbert Space (RKHS) produced the highest prediction accuracies  across the traits, with accuracies of 0.75, 0.73, 0.84, 0.83, and 0.82 for fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness, respectively. Overall, prediction accuracies were positively correlated with the number of markers for fruit traits. We tested our genomic selection models in a separate population of recombinant inbred lines derived from two parental lines from the core collection. Despite the large difference in genetic diversity between the training population and the test population, we obtained moderate prediction accuracies of 0.32, 0.34, 0.50, and 0.48 for fruit length, fruit shape, fruit width, and fruit weight, respectively. This use of genomic selection for fruit-related traits demonstrates the potential use of core collections and genomic selection as tools for crop improvement.Pepper ( Capsicum spp.) is an important vegetable crop, consumed as a spice and as a fresh vegetable around the world. It is an important source of nutrients such as vitamins C, E, and provitamin A , wheat (Triticum aestivum), barley (Hordeum vulgare), and potato (Solanum tuberosum). Investigation of GS performance for crop improvement was first conducted in maize (Phytophthora infestans) and common scab (Streptomyces scabies) in tetraploid potato (Solanum lycopersicum L.). However, the potential of GS in pepper remains to be demonstrated.GS has become a promising method in plant breeding as well as in animal breeding. However, GS has been mainly focused on staple crops such as maize  in 3 years at two different locations. Among whole accessions, considering its crossability, we selected C. annuum complex  for training the models. Through cross-validations, we evaluated the effects of trait architecture and heritability of fruit-related traits, population structure of the training population, and the number of markers on prediction accuracies. Finally, we tested our GS models in a population of recombinant inbred lines (RILs) derived from two parental lines from the core collection.In this study, we tested the potential of GS for the fruit-related traits: fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness in pepper. We cultivated 351 accessions from the pepper core collection .Plant materials were sourced from the previously constructed llection  of the HPepper seeds from the core collection were sown in early March, and seedlings were transplanted in early May. Since the maturation time of fruits varied among the accessions, fruits were harvested through mid to late August. Five fruit-related traits were measured.Recombinant inbred lines (RILs) derived from a cross between “Perennial” (P for PD) and “Dempsey” (D for PD) were used as a test population to evaluate genomic prediction models . The PD PstI/MseI and EcoRI/MseI. Library adapters were then ligated to digested gDNA, and the libraries were amplified using selective primers containing TA. Constructed libraries were pooled into five tubes and sequenced in separate lanes on a HiSeq 2000  at Macrogen . Whole-genome re-sequencing of the PD RILs population was conducted as previously described (Genotyping-by-sequencing was conducted for accessions from the pepper core collection as previously described . Librariescribed .C. annuum “Dempsey” (unpublished) using the Burrows-Wheeler Aligner  with a minimum read length of 80 bp and minimum quality score of Q20. Filtered raw reads of core collection accessions and PD RILs were aligned against the newly constructed high-quality reference genome of ner BWA; . Alignedner BWA; . Sorted ner BWA; . Missingner BWA; .To validate population structure information for the core collection from a previous study , principADMIXTURE v1.3  from 1 to 12, and the results were validated with fivefold cross-validation.Genetic clustering analysis was conducted using URE v1.3  to estimHierarchical clustering was conducted using the unweighted pair group method with arithmetic mean (UPGMA). Genetic distance was estimated based on Euclidean distance using the R package “poppr” . All ploC. annuum and other accessions after analysis of variance (ANOVA). Four fruit-related traits  were measured for the PD RILs used as a test population.Among the various agronomic traits of pepper, five fruit-related quantitative traits  were selected for testing traits of genomic prediction. Three plants  of each line were planted, and three randomly selected fruits from each plant were measured to generate raw phenotypic data. Fruit length of each fruit was measured by ruler, and fruit width and pericarp thickness were measured by caliper. Each fruit was weighed on a digital weighing scale to measure fruit weight. Fruit shape was defined as the ratio of fruit length to fruit width. To confirm the difference among accessions in the core collection, we conducted pairwise T-test between In this study, the experimental design was highly imbalanced. To control imbalanced phenotypic data in the core collection, best linear unbiased predictor (BLUP) values for each core collection line (genotype) were calculated using the R package “lme4” . The ranTen different genomic prediction models were used to investigate the best models for fruit-related traits in the pepper core collection: linear methods gblupRR, Ridge regression, LASSO, Elastic net, Bayesian LASSO (BL), extended Bayesian LASSO (EBL), Bayes-B, Bayes-C, and nonlinear methods reproducing kernel Hilbert space (RKHS) and random forest. gblupRR and RKHS are kernel methods; gblupRR estimates the variance of genetic effects of markers based on a single linear kernel, whereas RKHS is based on multiple Gaussian kernels. Each genomic prediction method was implemented by various R packages: “rrBLUP” for gblupRR and RKHS, “glmnet” for Ridge regression, LASSO, and Elastic-Net, “VIGoR” for BL, EBL, Bayes-B, and Bayes-C, and “randomForest” for random forest .The accuracy of genomic prediction across the training population was estimated using a 10-fold cross-validation methodology. The whole training population was divided equally into 10 groups. Among the subgroups, nine groups were used as training sets, and one group was randomly assigned as the validation set. This procedure was iterated in 10 different patterns for each trait. The prediction accuracy was recorded by Pearson correlation between predicted values and observed values in every pattern, and the mean of 10 iterations was recorded as the result for the specific trait.To investigate the effect of marker density, various marker sets were generated. SNPs were pruned from the all-marker sets based on linkage disequilibrium (LD) cutoff using the software Plink v1.9 . DiffereC. annuum complex, including C. annuum, C. chinense, and C. frutescens accessions (302 lines), was used to train the models and predict the GEBVs of PD RILs. Second, only C. annuum lines (229 lines) were used for training.To estimate the effect of population structure on genomic prediction, the core collection was dissected into its different species. First, the With each training population, a combination of three genomic prediction methods  and two marker sets  were tested on another population, PD RILs. Selected methods and marker sets were used to train the final genomic prediction model for each trait with whole genotypic datasets. Genotypic datasets of PD RILs were used as test data for the trained model. The prediction accuracies were confirmed by Pearson correlation between GEBVs of the PD RILs and mean of phenotypic values observed for 2 years. Only four traits  were tested because data were not available for PD RILs.C. annuum complex). The phenotypic variation ranged from 6.67 to 298.67 mm in fruit length, 0.44 to 43.14 in fruit shape, 3.38 to 101.33 mm in fruit width, 0.09 to 242.50 g in fruit weight, and 0.09 to 10.80 mm in pericarp thickness, respectively , indicating that population structure exists within the core collection  of the four traits was similar. All traits showed high heritability  less than 5% were removed, giving a total of 18,663 remaining SNPs.All 18,663 SNPs were pruned based on LD such that the remaining SNPs represented the high LD region. We selected seven marker sets from the total marker set, generated using different LD cutoff values: all SNPs , 2nd set , 3rd set , 4th set , 5th set , and 6th set (711 SNPs). Each pruned marker set showed well-distributed SNPs over 12 chromosomes . DecreasCapsicum species classification , phylogenetic tree reconstruction, and admixture analysis using 18,663 SNPs obtained from 350 accessions from the pepper core collection. The pepper core collection showed a significant population structure due to the presence of different species with various origins. Although some of the accessions showed a slight admixture, PCA results showed four genetic clusters according to fication . The firC. baccatum and the other three species. The four-species group was divided into two clusters, of which the major group mainly consisted of C. annuum accessions. We observed a clear genetic relationship between C. chinense and C. frutescens, which are known as the C. annuum-complex species. Finally, we conducted an ADMIXTURE analysis with the number of ancestral populations (K) ranging from 1 to 12. The cross-validation error was nearly saturated from K = 7, with a value of 0.3, indicating the high divergence level within the core collection  differed for the fruit-related traits evaluated and genomic prediction methods used, ranging from 0.66 to 0.84 . The pheThe average prediction accuracy of FL across 10 genomic prediction methods was 0.70, with RKHS having the highest prediction accuracy of 0.75 and Lasso, Bayes B, and Bayes C showing the lowest accuracy of 0.67. The mean of prediction model accuracy was 0.71 in fruit shape. The average prediction accuracy of fruit width across various methods was 0.83, which was higher than the average prediction accuracy of the other traits. The two kernel-type methods (RKHS and gblupRR) and random forest were the most effective genomic prediction methods for fruit width, showing prediction accuracy of 0.84. The mean of prediction accuracy for fruit weight and pericarp thickness were 0.79 and 0.81, respectively. RKHS and random forest resulted in the highest prediction accuracy of 0.84. Bayes C showed highly variable prediction accuracies ranging from 0.48 to 0.83. gblupRR, RKHS, elastic net, and random forest showed similar prediction accuracies for pericarp thickness of approximately 0.82. Among the 10 genomic prediction models, RKHS showed stable prediction accuracies across the four traits .We used only the RKHS model that showed relatively high prediction accuracies to investigate the effect of marker density on genomic prediction. Overall results indicated that prediction accuracy decreased as marker number decreased . AveragePrediction accuracy for fruit length was increased by 10.2% from 0.69 with 431 SNPs to 0.76 with all SNPs . HoweverFor the practical application of a GS method, the GS model should be validated across the population. To test our genomic prediction models, we applied three genomic prediction methods to a different population. The RIL population (PD RILs) was derived from two parental lines included in the pepper core collection. Since pericarp thickness data were not available for the PD RILs, we only investigated fruit length, fruit shape, fruit width, and fruit weight.When the model was trained with all SNPs set, the RKHS model showed the highest prediction accuracy for fruit shape (0.487), followed by fruit width (0.468), fruit weight (0.430), and fruit length 0.318; . OverallC. annuum accessions for training the models, prediction accuracies for fruit length predicted by gblupRR, RKHS, and random forest were slightly increased to 0.363, 0.345, and 0.328, respectively, compared with 0.352, 0.336, and 0.385 using C. annuum complex lines. However, when models were trained with the population, which were genetically far from the test population, all models could not predict most traits. In conclusion, the models trained with C. annuum complex showed stable prediction accuracies than the case of models trained with only C. annuum lines , when we used only um lines ; Table 4Pepper fruit-related traits are not only critical determinants for marketable quality but also directly affect its yield and postharvest quality. Therefore, fruit-related traits are one of the most important factors during selection in pepper breeding programs. Here, we investigated the effect of the number of markers, prediction methods, and population structure for prediction of fruit-related traits from genotype data.We calculated the best linear unbiased predictor (BLUP) of each core collection line by fitting a linear mixed effect model for several phenotypic data. BLUP values calculated for each trait were used for further analysis. The BLUP values for five fruit-related traits  showed a slightly skewed distribution. To investigate the effect of heritability on genomic prediction, we used the variance components from linear mixed effect models to estimate heritability of the traits. Some previous studies have reported high values of heritability in fruit-related traits . HoweverADMIXTURE showed similar results to PCA and hierarchical clustering. Although the error was saturated at K = 7, the accessions was clustered by species at K = 5. When the error was saturated, a strong substructure was not found additionally, and additional structures were clustered in only C. annuum accessions. It could be explained that the previous study showed that C. annuum accessions were clustered according to its origin  and C. baccatum except for some admixed lines. Especially, C. baccatum accessions were genetically distinct from the other three species (C. annuum complex). There was one C. chacoense accession, which was recently reported as C. baccatum complex  do not enable reliability since two populations share a small proportion of the genome . AlthougC. annuum complex accessions, C. annuum only accessions, and the rest of the accessions except C. annuum (C. chinense and C. frutescens). When the C. annuum complex accessions were used as the training population in RKHS method, higher prediction accuracies were obtained in most traits except fruit length. However, in the random forest model, prediction accuracies using the C. annuum only accessions were higher for fruit width and fruit weight than those using C. annuum complex. The differences in prediction accuracies between two training populations were not so high. However, when only the C. chinense and C. frutescens accessions were used for training population, prediction accuracies were drastically decreased. This may be due to the genetic distance between the training population and the testing population. Since GS was introduced, population structure has been a frequent issue for improving prediction accuracy , and preIn this study, we investigated the potential of GS in pepper for fruit-related traits having a high level (>0.90) of heritability. By using the core collection as a training population, with cross-validation, we found effective conditions for GS such as the type of genomic prediction model and the number of markers. The cross-validation results explained about 80% of genetic variation for fruit-related traits in the pepper core collection. We used genomic prediction models trained using a core collection and tested in a different population (RIL population) with narrow genetic diversity and weak population structure. Although the difference in genetic diversity was high between the training population and the test population, we obtained a moderate prediction accuracy. This study provides a simulation of the commercial pepper pre-breeding procedure and deep knowledge of the genetic architecture of pepper fruit-related traits, showing the potential of GS in pepper. This is, to our knowledge, the first genomic prediction study reported in pepper. To improve the prediction accuracy of genomic prediction, integration with larger-scale genomics and phenomics is needed.The datasets generated for this study can be found in The National Agricultural Biotechnology Information Center (http://nabic.rda.go.kr/).J-PH and NR contributed equally as the first authors of this work. J-PH conducted the phenotyping in the core collection, modeling of genomic selection, and genomic prediction, while NR conducted the phenotyping in the core collection and PD RILs. H-YL constructed the core collection of pepper and conducted genotyping in the core collection. GK wrote the manuscript and made the figures. J-KK and EY supervised the genomic prediction. B-CK supervised the overall processes and revised the manuscript. J-PH, GK, and B-CK contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Oncorhynchus mykiss) using three different phenotyping technologies , (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits , and quality-related traits ; genotypic data were then obtained for all fish using the 57K SNP Axiom® Trout Genotyping array. Heritability estimates for most of the 14 traits examined were moderate to strong, varying from 0.12 to 0.67. Most traits were clearly polygenic, but our genome-wide association studies (GWASs) identified two genomic regions on chromosome 8 that explained up to 10% of the genetic variance (cumulative effects of two QTLs) for several traits . For flesh colour traits, six QTLs explained 1–4% of the genetic variance. Within these regions, we identified several genes  that have been implicated in adipogenesis or carotenoid metabolism, and thus represent good candidates for further functional validation. Finally, of the three techniques used for phenotyping, MRI demonstrated particular promise for measurements of fat content and distribution, while the digital image analysis-based approach was very useful in quantifying colour-related traits. This work provides new insights that may aid the development of commercial breeding programmes in rainbow trout, specifically with regard to the genetic improvement of yield and flesh-quality traits as well as the use of invasive and/or non-invasive technologies to predict such traits.One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and quality-related traits in rainbow trout ( Oncorhynchus mykiss; 848 100 t in 2018, Aquaculture produces high-quality animal protein that is low in saturated fat, ideal for satisfying increased global demand. Worldwide, one of the most commonly farmed salmonid species is rainbow trout , but these approaches have been less commonly applied to the composition of meat .∗a∗b∗ international system using a Minolta Chroma Meter, but advances in computer vision have offered intriguing possibilities for automation and for capturing heterogeneity in trout , which have provided the genomic tools necessary for genome-wide association studies (GWASs) aimed at the detection of quantitative trait loci (QTLs) for traits of interest. In rainbow trout, GWAS has been used to identify significant QTLs associated with such traits of interest as growth or disease resistance , and not just whiteness; the potential to increase fillet fat content; and the ability to limit trimming losses of dorsal and ventral flesh . A more-thorough understanding of the genetic architecture of these traits could prove useful for the refinement of breeding objectives, particularly in balancing the economic trade-off between different traits.In addition to major quality traits, other traits that merit further investigation include fillet colour with respect to individual L® Trout Genotyping array, and (3) identify candidate genes present within these QTL regions.Within this context, the main objectives of this study were to (1) estimate and compare the genetic parameters of growth-, yield-  and quality-related traits  in a commercially selected population of rainbow trout using three different phenotyping methods , (2) detect QTLs associated with these traits through GWAS, using the 57K SNP AxiomThis study used fin clips collected by the breeding company “Les sources de l’Avance” as part of their commercial breeding programme. Rearing was carried out in compliance with Directive 98/58/CE on the protection of animals kept for farming purposes and Directive 2010-63-EU on the protection of animals used for scientific purposes. Data were collected from sacrificed animals and thus the experiment did not require approval from an Ethics Committee, in accordance with Article 1.5 of Directive 2010-63-EU.1. The fish came from a commercially selected line from the Sources de l’Avance breeding company , a subsidiary of Aqualande Group. In December 2016, 831 rainbow trout families were created using a partly factorial mating design. Through 10 factorial crosses, 84 dams were crossed with 99 neomales . A piece of fin was sampled for each parent for the purpose of DNA extraction. Half-sib families from each dam were incubated in eleven troughs containing five trays divided into two compartments. At 15 days post-fertilisation (dpf) corresponding to eyed stage, similar numbers of eggs (N = 700) from each dam were mixed together after fecundation assessment and were reared in 12 separate fiberglass tanks . At 94 dpf (fish about 0.5–1 g), the 12 groups were moved to 4 concrete raceways divided into three compartments (1 group by compartment) . At 163 dpf, juvenile fish (33 g mean weight) were vaccinated against Yersinia ruckeri and Aeromonas salmonicida, and transferred to the “Viviers de la Hountine” trout farm , located 12 km downstream of the hatchery, for further growth. The 12 groups were mixed and fish were reared in concrete raceways until the end of experiment (density 40–100 kg/m3 adjusting by water volume variation). Fish were fed to satiation (EOL_0001740) using extruded commercial feed: Neo start (17% lipids) and Neo CDC (23% lipids)  during the first stage, then Extra CDC AQL P/F/G25 (25–30% lipids)  and Viva Pro 9F NAT29 (30% lipids)  until the end of the experiment. In both farms, rearing units were supplied with flow through river water (EOL_0001571). The water temperature varied from 3 to 20°C  and oxygen concentration was not limited (>80% saturation: EOL_0000186).All measured traits and parameters of the rearing environment were defined according to the ATOL   and EOL Fish rearing followed classical practices. Because measurements were performed only on slaughtered fish from a commercial population, there was no need to consult an ethics committee. At 469 dpf, fish were individually tagged with RFID transponders and their DNA was collected through a fin sample that was preserved in 95% ethanol for parentage assignment.At the end of the growing period in April 2018, 1,510 fish were randomly sampled and divided into four sub-groups. Each sub-group was treated the same way: three days of fasting (EOL_0001739), followed by live transportation by truck to the Aqualande processing plant , where they were slaughtered and processed for analysis. Because RNA was to be sampled for future expression analyses, fish were humanely killed by a blow to the head and bled by cutting the gills in ice water, in accordance with good animal slaughtering practices. Post-mortem data collection and processing were accomplished as quickly as possible to ensure data accuracy.K = BW(g) × 100/BL3 (cm) (ATOL_0001653); headless gutted carcass yield ; head yield ; and gutted carcass yield . The total fat content in muscle  was recorded using a Fish Torry Fat-meter® as described in ∗a∗b∗ and HLS colour space, then, using Visilog 7.3 for Windows©, colour image segmentation was performed to quantify the area of the steak (ATOL_0005553), peripheral fat tissue , and myosepta  to obtain the following yields , fork length , head weight , headless gutted carcass weight , and viscera weight . These traits were then combined to create the following synthetic traits and yields see : Fulton’myosepta . Brieflymyosepta , the colelds see : myoseptTwo different protocols were used for the acquisition of MRI images; each corresponded to virtual slices of 5-mm thickness acquired in the middle of the steak. To quantify subcutaneous fat, a “spin-echo” image was used, which had a resolution of 0.75 mm × 0.75 mm in the image plane and provided a good contrast between flesh and lipid . SubcutaIn total, 14 variables were evaluated in this study see : body we® Trout Genotyping array from Thermo Fisher  for DNA extraction and genotyping. Following DNA quality control, 1,680 of the 1,693 samples  remained; these were genotyped for 57,501 SNPs using the 57K SNP Axiomo Fisher . The nump-value > 0.0001, and a minor allele frequency (MAF) higher than 0.05. In total, 29,652 SNPs were retained for the analysis. Samples in which less than 90% of SNPs were genotyped were removed from the dataset. All missing SNP genotypes of the remaining individuals were imputed using FImpute software 2.0  which were subjected to GWAS analysis.h2) and phenotypic and genetic correlations  were estimated for all traits using the restricted maximum likelihood method (AIREML) in BLUPF90 software  is the additive genetic covariance between trait 1 and trait 2, and where cov are thought to have a non-zero effect on the phenotype. The marker effects are estimated through an MCMC algorithm that considers a mixture of markers, of which proportion π have effects that follow a normal distribution Y the phenotype observed for the ith individual, μ the overall mean in the population, b the regression coefficient of Y on BW; n the total number of SNPs in the analysis, aj the additive effect of the reference allele for the jth SNP, with genotype gij for individual i ; and εikthe residual effect for the ith individual in the kth iteration. The vector of residual effects is normally and independently distributed, with k, the decision to include SNP j in the model depended on the indicator variable δjk: if δjk was equal to 1, the effect of SNP j was estimated as aj, while if δjk was equal to 0, no effect was estimated. This indicator variable was sampled from a binomial distribution with a probability π that δjk was equal to 1  and a probability 1 − π that δjk was equal to 0. The proportion 1 − π was sampled from a beta distribution, B, in which α was set as the total number of markers  and β was set at 300, so that approximately 1% of the SNPs were included in the model at each cycle. A total of 200,000 cycles were used, with a burn-in period of 5,000 cycles; Gibbs sampling was performed every 20 cycles and saved for further analysis. In order to check the convergence, the MCMC algorithm was initiated three times with three different seeds for the random number generator. Convergence was assessed by visual inspection of plots of the posterior density of genetic and residual variances and by high correlations (r > 0.99) between the genomic estimated breeding values (GEBVs) estimated from the different seeds of the MCMC algorithm.At each cycle Pi is the probability that the ith SNP has a non-zero effect.The degree of association between each SNP and a given phenotype was assessed using the Bayes Factor (BF): Following O. mykiss genome assembly release 100 (GCF_002163495.1), and gene symbols from All candidate genes that were located within the confidence or credibility intervals estimated using the BayesCπ approach are listed in In this study, 1,510 rainbow trout were phenotyped for multiple traits, which were then investigated using descriptive statistics and the estimation of genetic parameters. Cross-sectional steaks of 1,351 fish were subjected to MRI analysis. Finally, a GWAS analysis was carried out that included genotypes from 1,379 individuals. Descriptive statistics  for phenotypic traits are given in The heritability estimates of yield and quality traits, and their phenotypic and genetic correlations, are given in h2 MRI_F_F%)( = 0.67 ± 0.07), while the lowest was calculated for the percentage of myosepta in flesh as determined by digital image analysis (h2 Myo%)( = 0.12 ± 0.04). However, we identified a strong genetic correlation between these two phenotypes (rg = 0.76 ± 0.19), which are both associated with the lipid content of flesh. Similarly, the heritability of subcutaneous adipose tissue was higher when it was quantified using MRI technology than with digital image analysis (h2 MRI_F_sc%)( = 0.36 ± 0.06 and h2 Adip%)( = 0.27 ± 0.05), and both traits were strongly genetically correlated (rg = 0.80 ± 0.07). The genetic correlation between the amount of lipids in flesh (MRI_F_F%) and both measurements of subcutaneous adipose tissue (MRI_F_sc% and Adip%) was low . The estimated heritability of fat content as measured with a Fatmeter was the same as the heritability estimate for total fat in the steak as quantified using MRI technology, h2 = 0.53 ± 0.07, with a strong genetic correlation between the two traits (rg = 0.77 ± 0.05 with MRI_F%). Stronger genetic correlations were detected between Fat (measured by Fatmeter) and both measurements of subcutaneous adipose tissue (rg = 0.71 ± 0.07 with MRI_F_sc% and rg = 0.55 ± 0.11 with Adip%) than between Fat (using Fatmeter) and lipids in the flesh  or myosepta .In our study, heritability estimates of all traits were moderate to high, which suggests that these traits can be improved through selective breeding . Among srg = −0.03 ± 0.12 with MRI_F_F%).Headless gutted carcass yield (HGCarc%), which is used as an indirect predictor of fillet yield , was higrg = 0.48 ± 0.11, and MRI_F%, rg = 0.56 ± 0.10). Our results indicated a stronger genetic correlation between colour traits (L∗a∗b flesh) and the quantity of lipids in flesh (MRI_F_F%) than with subcutaneous adipose tissue (MRI_F_sc% and Adip%), with the highest correlation estimated for a_flesh and the lowest for b_flesh. L_flesh was strongly correlated with Myo% (rg = 0.88 ± 0.19) but moderately with MRI_F_F% (rg = 0.51 ± 0.10), and was only slightly correlated with the total quantity of lipids (Fat or MRI_F%).Regarding colour traits, heritability estimates for L_flesh, a_flesh, and b_flesh were 0.46 ± 0.07, 0.45 ± 0.07, and 0.28 ± 0.06, respectively. We detected the strongest genetic correlations between a_flesh and both measurements of fat  was identified as another QTL for K, Fat, HGCarc%, Carc%, and Adip%. This region explained a higher percentage of genetic variance – 7.83, 5.7, 4.1, 3.27, and 1.36%, respectively – and had credibility intervals that ranged from 1,739 to 7,279 kb in size .Strong evidence was found for a QTL on Omy22 with effects on BW, which explained 2.06% of the genetic variance of this trait.For colour traits, one QTL was detected on Omy17, between 64.49 and 66.29 Mb, that explained 4.2% of the genetic variance in L_flesh. Three QTLs were found for b_flesh , of whicNo QTL was found that explained more than 2% of the genetic variance in a_flesh, Head%, or MRI_F_F%, which suggests that these traits have a very polygenic architecture affected by multiple loci with small effects in this population of rainbow trout.The candidate genes identified within the different QTL regions are detailed in bub1 and htr1e genes. Bub1 codes for the mitotic checkpoint serine/threonine protein kinase and htr1e for the 5-hydroxytryptamine receptor 1E (bach2b (BTB and CNC homolog 1) gene and the map3k7 (mitogen-activated protein kinase kinase kinase 7) gene, at 14.10 Mb.In total, 18 genes were positioned between two SNP peaks (13.32 and 14.10 Mb) on chromosome 8, which together corresponding to seven QTLs spanning from 11.62 to 15.10 Mb see . The peaeptor 1E . The peavgll2a  and rfx6 (regulatory factor X6), and gnpat (glyceronephosphate O-acyltransferase) and ext1c [exostoses (multiple) 1c], while the third peak SNP was located within the s22a2 (solute carrier family 22 member 2) gene. The two peaks for Carc% and HGCarc% on Omy8 (at 21.42 Mb) were detected in the intergenic region between the ephx1 (epoxide hydrolase 1) gene and the srp9  gene.The second QTL region on Omy8 spanned from 17.25 to 25.79 Mb. Between the three peaks for Adip%, Fat, and K, there were 17 genes with annotation and two uncharacterised proteins. Two of the peak SNPs were located in intergenic regions between, respectively, rplp2l  and adora2b (adenosine A2b receptor). For b_flesh, the peak SNP on Omy13 was located within the kif16ba (kinesin family member 16Ba) gene at 21.95 Mb. Two peak SNPs for QTLs on Omy6 were located in mical2a  and tcf25 (transcription factor 25), respectively. Other candidate genes were also identified within the QTL regions associated with these traits, but at more of a distance from the peaks. For example, bcmo1  was located in a QTL for b_flesh on Omy6, and is known to be involved in carotenoid metabolism. Likewise, dkk3a  and bola3 (bolA family member 3) were located in a QTL for b_flesh on Omy6; both are known to be involved in adipogenesis  was located within a QTL on Omy6 while cyp2x9 , coding for a vitamin D 25-hydroxylase-like enzyme , were close to values that had been previously estimated for another population from another breeding company . In this respect, MRI also has an advantage over X-ray computer tomography; in two recent reports, the maximum number of whole pigs that could be analysed per day using this method was 15 . The majh2 above 0.5 for percent lipid stripe in a different population reared in a very different environment). It thus appears that myosepta measurements using this technique were less accurate than those of subcutaneous fat. Despite this drawback, digital image analysis certainly represents a good compromise between the accuracy of measurement and the price for subcutaneous tissue and colour. The main disadvantage is that this method is destructive, and thus, information from siblings must be used to evaluate breeding candidates. Nevertheless, its use as part of a well-designed genomic selection programme may offer advantages that offset the lower efficacy of sib selection for such traits.The approach based on digital image analysis was able to differentiate subcutaneous adipose and myosepta, and was unique among the three methods we evaluated in the ability to simultaneously evaluate the colour of flesh, which is genetically correlated to the quantity of lipids in flesh . HoweverOur study mainly targeted the development of muscle and adipose tissues. However, processing yields can be strongly affected by bony tissues, for which alternative technologies such as X-ray tomography  or Dual-∗a∗b∗) in Atlantic salmon (0.05–0.20)  , but our05–0.20) . This co∗a∗b∗) are also correlated to the fat content of muscle (∗ (red) and b∗ (yellow) qualities are pure colour components whose values depend only on flesh pigmentation. Instead, L∗ (luminosity) is affected by the quantity of lipids: the fattier the flesh, the thicker the myosepta (corresponding to white striations in the flesh), which leads to higher luminosity (rg = 0.51) without the use of MRI. The high genetic correlation between Myo% and L_flesh (rg = 0.88 ± 0.19), and the higher heritability and accuracy of L_flesh compared to Myo%, suggests that selection on L_flesh is possible, and this approach could replace one based on MRI phenotypes for the selection of lipids in flesh. It thus seems that flesh colour can be effectively improved by selection; however, future studies should explore the stability of the genetic correlation between L_flesh and Myo% at different pigmentation levels.Because pigments are liposoluble, colour parameters  have the ability to inform decisions about the genetic improvement of quality based on tissue differentiation. Improvement in the digital image analysis especially for the segmentation of the myosepta could be a way to quantify more precisely muscle fat. Beside the classical segmentation techniques, the development of deep learning methods could allow a better segmentation on low contrast images . Howevern − 3 long chain polyunsaturated fatty acids in fillets). A previous study revealed the potential of selective breeding to increase the levels of n − 3 LC PUFA in salmon  were located near the peak SNP and may represent good functional candidate genes. Serotonin, or 5-hydroxytryptamine, is a biogenic monoamine that can function as both a neurotransmitter and a hormone. The 5-hydroxytryptamine (Htr) receptor is known to be involved in many important biological functions, including appetite, homeostasis, and gastrointestinal functions (htr1e) has been linked with feed intake in rainbow trout  are associated with obesity , two Htr receptors , a member of the serine/threonine protein kinase family, is an essential component of the MAP kinase signal transduction pathway and plays a role in cell proliferation by controlling diverse cell functions, including transcription, apoptosis, and immune response , which is known to play a part in carotenoid metabolism. Bcmo1 participates in an early step of retinoic acid synthesis, the conversion of carotenoids of vegetable origin into retinal [which is then converted into retinoic acid (RA) by Raldh]. There is abundant evidence in the literature that RA is able to block adipogenesis. In addition, RA triggers the activation of the retinoid X receptor RXR, which forms heterodimers with PPAR (a major regulator of lipid metabolism) in order to activate target genes (PyBCO-like 1 as the key gene for carotenoid colouration in muscle (bco1 (beta-carotene oxygenase 1) and bcol1 (beta-carotene oxygenase 1-like)  .dkk3 and bola3, known to be involved in adipogenesis. This finding was consistent with previous work reporting a correlation between colour parameters and the fat content of muscle (dkk3 was recently reported to induce adipogenesis and angiogenesis in 3 T3-L1 preadipocytes (bola3 encodes a protein that facilitates production of iron-sulfur (Fe-S) clusters, which then play a key role in the maturation of lipoate-containing 2-oxoacid dehydrogenases and the creation of respiratory chain complexes in the mitochondria. A recent study revealed that the fat-specific deletion of bola3 results in a defect in Fe-S cluster formation that significantly reduces mitochondrial lipoylation and fuel oxidation in brown adipose tissue, leading to glucose intolerance and obesity  that have been implicated in adipogenesis or carotenoid metabolism. These represent good candidates for further functional validation to uncover the biological mechanisms underlying variation in yield and flesh quality in rainbow trout.In summary, this work provides new insights into the genetics of traits associated with yield and flesh quality in rainbow trout which can be applied to commercial breeding. First, we confirmed the applicability of MRI technology for the selection of fat content in flesh and digital image analysis technology for the selection of flesh colour. Our results confirm that yield and quality traits are polygenic, and most are moderately to strongly heritable in rainbow trout. Despite this polygenic architecture, we were able to link these traits with several genomic regions that explained 1–8% of the genetic variance. A region on Omy8 was identified as having particularly strong effects on condition coefficient, fat content, and headless gutted carcass yield. When we investigated the genes within these regions, we identified several (mathilde.dupont-nivet@inrae.fr and vincent.petit2@wanadoo.fr.The datasets presented in this article are not readily available because the data that support the findings of this study are available from the breeding company “Aqualande” and restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The data can be made available for reproduction of the results on request via a material transfer agreement and with permission of the breeding company “Aqualande.” Requests to access the datasets should be directed to Ethical review and approval was not required for the animal study because this study used fin clips collected by the breeding company “Les sources de l’Avance” as part of their commercial breeding programme. Rearing was carried out in compliance with Directive 98/58/CE on the protection of animals kept for farming purposes and Directive 2010-63-EU on the protection of animals used for scientific purposes. The data were collected from sacrificed animals and thus the experiment did not require approval from an ethics committee, in accordance with Article 1.5 of Directive 2010-63-EU.PH, MD-N, GeC, and VP contributed to the conception and design of the study. CB, JB, ND, GeC, FE, and MD-N collected the data. GuC carried out the MRI data collection. CB and FE organised the database. CB, JB, JD’A, ND, and FE performed the data analysis. CB, ND, FP, and MD-N collaborated on the design of the statistical methodology and interpretation of the results. FP and MD-N supervised the data analysis. CB wrote the first draft of the manuscript, with additional sections written by JB and XC. MD-N, PH, and FP contributed to the manuscript revision. All authors read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.