P=3.86 × 10−8) and memory performance in 533 healthy young adults. The epigenetic effect on cortical thickness was replicated in a sample comprising 596 participants with major depressive disorder and healthy controls. The epigenetic signature mediates partially the effect of age on cortical thickness (P<0.001). A multilocus genetic score reflecting genetic variability of this signature is associated with memory performance (P=0.0003) in 3,346 young and elderly healthy adults. The genomic location of the contributing methylation sites points to the involvement of specific immune system genes. The decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related traits.Increasing age is tightly linked to decreased thickness of the human neocortex. The biological mechanisms that mediate this effect are hitherto unknown. The DNA methylome, as part of the epigenome, contributes significantly to age-related phenotypic changes. Here, we identify an epigenetic signature that is associated with cortical thickness ( Cortical thickness has high heritability estimates and is known to be influenced by genetic factors. Here, Freytag and colleagues show that DNA methylation patterns of peripheral blood monocytes are also correlated with cortical thickness and memory performance in human. Human cortical thickness, a brain morphometric measure that is linked to cognitive functioning, reflects the amount of neurons and neuropil in the horizontal layers of the cortical columns that are responsible for the organization of cortical connectivity1256Studies in twins and in unrelated individuals provide consistently high heritability estimates for cortical thickness ∼80%), demonstrating the importance of naturally occurring genetic variation for this physiological trait0%, demonDNA methylation, the most extensively studied epigenetic modification to date, regulates important processes such as imprinting, chromosomal inactivation and gene expression1113141519Thus, the existing data suggest that peripherally measured DNA methylation patterns might contribute to the identification of molecular underpinnings of age-related complex traits relevant to health and disease.Here, we investigated the relation between peripherally measured DNA methylation and cortical thickness in healthy young adults. In a first step, we performed Independent Component Analysis (ICA)-based decomposition of whole-blood methylomic profiles to identify independent signatures of physiological variation of global DNA methylation. ICA is a decomposition method, which provides a representation of complex relationships arising from high-dimensional data, such as genome-wide expression21N=533 healthy young individuals (We performed methylomic profiling (Illumina 450K Human Methylation array) of blood samples collected from ividuals . After qNext, we performed ICA to achieve a low-dimensional representation of genome-wide methylation profiles. Following the ICA paradigm introduced first for gene expression data analysisk=126 independent components (see Methods). The majority of the inferred components (n=111) were driven by single individuals contributing to more than 10% of the pattern's variability. Given that such components represent rather singular modes of variationUsing ICA decomposition, we obtained a total of N=514 participants. Consistent with previous findings in healthy young adults4r=−0.27, P=3.12 × 10−10).Participants from the methylomic profiling study underwent brain magnetic resonance imaging (MRI). Global ICA1 and ICA2) were significantly correlated with age, after Bonferroni correction for 15 comparisons , suggesting that the corresponding independent components capture distinct methylomic processes.Two out of 15 ICA methylomic patterns . After this adjustment, a significant correlation was exclusively detected between ICA2 and cortical thickness  also after correcting for individual white blood cell count (see Methods). We also examined which other available variables  were significantly associated with ICA2 in addition to age. Smoking frequency was also significantly associated with ICA2  but not with cortical thickness . After adjusting ICA2 for both age and smoking frequency, its association with cortical thickness remained nearly unchanged (r=−0.17). No significant correlations were detected between ICA2 and alcohol consumption (P=0.97), cannabis use (P=0.1) or body mass index (P=0.25).To test whether the significant correlations between F-test analysis to compare the fit of a model predicting cortical thickness from a fifth degree polynomial of age (age+agê2+…+agê5) to the fit of the same model augmented by ICA2. We observed a highly significant increase in adjusted R2 with the addition of ICA2 to the model =15.6, P=8.8 × 10−5). Thus, the association between ICA2 and cortical thickness is not driven by non-linear age effects.In order to capture possible non-linear age effects, we also performed an in silico annotation of blood cell types as described by Jaffe and IrizarryICA2 associations with both chronological age and cortical thickness remained highly significant . We also examined the association between ICA1 and ICA2 and chronological age in two publicly available data sets of purified blood cells ICA1 and ICA2 were estimated as the linear combinations between ICA1 and ICA2 loadings, respectively (as inferred from the Swiss DNAm sample), and blood samples' DNAm values, adjusted for main confounders , suggesting that the ICA–age correlations identified in whole-blood are also detectable in individual cell types. Altogether these results substantiate the lack of influence of blood cell counts on the reported associations. The correlation between cortical thickness and ICA1, that showed the strongest correlation with age, was not significant after adjusting for chronological age . Neither predictor was associated with cortical thickness after adjustment for chronological age , suggesting that these predictors  do not mediate the effect of age on cortical thickness.In addition to chronological age, we also calculated epigenetic cross-tissue- and whole-blood-based predictors in our sample as described by HorvathICA2 with age and age-adjusted cortical thickness after covarying for 111 individuals who contributed more than 10% to 111 inferred components not further studied herein. Both associations remained highly significant .Finally, we examined the association of ICA2 and cortical thickness, we studied an independent sample (termed herein the Munich sample) comprising 596 participants with major depressive disorder (MDD) and healthy controls (see Methods). The ICA2 pattern was estimated as the linear combination between ICA2 loadings (as inferred from the Swiss DNAm sample) and individual DNAm values of the Munich sample. In this independent sample, we observed a significant positive correlation between ICA2 and chronological age  and a negative correlation with global cortical thickness . After adjustment for chronological age and controlling for potential confounders , the association between ICA2 and cortical thickness remained significant . The same analysis in a sub-sample of N=163 participants younger than 40 years  revealed an almost identical effect size  compared to that observed in the Swiss sample.To test the generalizability of the association between ICA2 showed significant positive correlation with age and negative correlation with global cortical thickness. To investigate whether ICA2 mediates the negative correlation between age and global cortical thickness, we conducted a mediation analysisk2=5.1% of the maximum possible mediation effect) and significantly mediated by the methylomic pattern ICA2  (P<0.001) .ICA2 and global cortical thickness we next explored possible links between this methylomic pattern and regional variations in cortical thickness. Inter-individual variations in delineated brain regions often coincide with latent structural covariance patterns3132ICA2 pattern. After correction for multiple testing, we identified one factor score, F6, that showed significant correlation with ICA2 (r=0.78); importantly, F6 remained stable across the two solutions with an r=0.89 (P=6.9 × 10−24) between loadings before/after adjustment for mean thickness. In addition, F6 scores obtained from the mean thickness-adjusted EFA solution were still significantly associated with ICA2 , suggesting that the results presented herein were not driven solely by global mean thickness.Having detected an association between onducted). This faonducted). We alsoICA2 are related to thinning of a circumscribed cortical pattern that harbours neuroanatomical correlates of episodic memory (EM). Therefore, we investigated the relationship between this methylomic pattern and EM. Behavioural assessment was obtained for a total of N=531 subjects from the methylomic profiling study   .Thus, higher values of 0.00147) . This as0.00491)  and afteN=512 participants from the methylomic profiling sample , the correlation between F6 and EM performance was significant  (Complete MRI and EM assessments were obtained for g sample . In this P=0.283). In an a=0.00617). Importa0.00574) .nσ standard deviations from the mean of the loadings' distributionnσ=4, which led to the selection of 970 CpGs for ICA2 (ICA2 CpGs mapped to 593 genes (see Methods). Among the 970 CpGs constituting ICA2, one marker (cg18055007) was part of the 353 Horvath age-predicting markersICA2 CpGs (N=970) overlapped with those reported as being differentially methylated  in smokers3638ICA2 CpGs.Contributions to a given ICA component are commonly identified by selecting features (here: CpG sites) whose loadings, in absolute value, exceed a cut-off threshold of for ICA2 . The selted N=2,0 in smokewww.broadinstitute.org/gsea/msigdb/index.jsp), was performed using the GOseq algorithmICA2 revealed significant enrichment for 76 highly overlapping gene sets, which mainly encompassed genes related to immune system function, inflammatory response and hematopoietic system (ICA2 CpGs to that of blood cell subtypes and their progenitors using public data sets on 19 cell types (see Methods)N=970 CpG sites, P<10−60 for all correlations) between average whole-blood DNAm values of the ICA2 CpGs and all various cell subtypes examined (P<10−60) were observed for regulator and memory CD4+ T-cells  terms, molecular pathways and gene expression patterns, as catalogued in the Molecular Signatures Database  and to their relative location to gene transcripts. Given that a CpG site may map to multiple transcripts, each site was uniquely characterized according to following rulesχ2 test P=2.7 × 10−53) with 50% of all CpGs annotated as Open Sea, while Islands CpGs were clearly under-represented (10%)(χ2 test P=1.65 × 10−6), with an increased fraction of ‘Genic' CpGs and a decreased fraction of ‘TSS' CpGs. We also observed a lower fraction of intergenic CpGs as compared to the background distribution (We next characterized the ted (10%). The disribution .et al.N=656, P=0.000043 – P<10−60). We then examined the overlap between ICA1 and ICA2 CpGs identified in our sample and CpGs contributing to each of the Hannum age-associated IC pattern. A significant overlap with ICA1 was observed for one pattern . For ICA2, we observed a significant overlap with three age-associated Hannum patterns (P=1.9 × 10−6 – P<10−60), with said overlap being particularly strong for one pattern . The correlation of loadings between CpGs contributing to ICA2 and CpGs contributing to the HICb pattern was positive and of substantial magnitude . Thus, we observed highly significant between-sample overlap of ICA patterns despite the differences in age structure of the two populations.Finally, to test between-sample comparability of the identified ICA patterns, we performed ICA of the study population reported in Hannum ICA2 pattern. As for any genetically complex trait, several genetic variants are likely to contribute jointly to inter-individual variability of DNAm variation as represented by ICA2. Therefore, we employed gene set enrichment analysis (GSEA)4445ICA2.Given that DNA methylation patterns per se represent complex traits18DNA from all individuals participating in the methylomic profiling study was processed on the Affymetrix Genome-wide Human SNP Array 6.0. After standard QC, correction for minor allele frequency and deviation from Hardy–Weinberg equilibrium, a total of 733,370 autosomal SNPs were used for association analysis (see Methods).P values for association with ICA2, under an additive model, were tested for gene set enrichment using MAGENTAAge-adjusted single-marker ICA2 variability. The scores comprised 39 and 33 significant SNPs mapping to an equal number of genes from the ‘GO:0048534' and ‘Pyk2/Keratinocyte' groups respectively (ICA2 pattern (see Methods). As expected, both scores correlated significantly with ICA2 variability .For each of the gene groups we calculated multilocus genetic scores to capture their contributions to individual ectively  and 5. Gr=−0.10, P=0.01). No significant correlation was detected for the ‘Pyk2/Keratinocyte'-derived genetic score .The genetic score derived from the Haemopoietic and Lymphoid Organ development set (GO:0048534) was significantly correlated with EM performance : three samples, including subjects from the Basel imaging sample, comprised a total of N=2,603 healthy young subjects who performed either a picture free recall or a word free recall task; an additional sample included N=743 elderly healthy individuals who performed a word free recall task  of gene score SNPs in cis  to ICA2 CpGs as compared to a genome-wide random distribution (see Methods). Next, we performed mQTL analysis for each of the score SNP–ICA2 CpG pairs. We observed significant deviation from the null uniform distribution with particular over-representation of genetic associations with effect sizes ranging from small to moderate . No significant correlation was observed with cortical thickness .We also studied the association between the genetic score and cortical thickness in the methylomic sample (ICA2) that was associated with cortical thinning and decreased EM performance. We also observed that a significant part of the well-known negative correlation between age and cortical thickness was partially mediated by ICA2. CpG sites that contributed to this methylation pattern mapped to genes involved in immune system regulation and inflammatory response.In the present study we applied ICA decomposition of whole-blood genome-wide methylomic profiles in healthy young adults  and detected a specific pattern of DNAm , ZC3H12D (encoding zinc finger CCCH-type containing 12D) and CD4 (encoding CD4 molecule) are listed among the top ten ICA2 contributing CpGs  and chronological age in the examined blood cell-specific data sets. Thus, it is unlikely that the detected associations are driven by inter-individual variability in composition of blood cell types.Inter-individual variability in blood cell composition is known to influence whole-blood DNAm measurementsICA2. The reasons for this analysis were two-fold: (1) Given the fact that genetic variation is related to DNA methylation, we tested whether ICA2-related genetic variation can be used as a proxy for DNA methylation in larger samples, where such epigenetic measures were unavailable. (2) We hypothesized that the biological processes revealed through gene set enrichment would be similar regardless of the nature of the data input . Interestingly, the SNP-based analysis of ICA2 revealed a robust association between variants of genes involved in the regulation of the immune system and EM in independent cohorts of young and elderly healthy adults. This suggests that the association between ICA2, which reflects epigenetic variation, and EM performance is, at least partially, genetically driven.In addition to studying methylation patterns, we also performed a genome-wide SNP-based analysis of In conclusion, we adopted an ICA approach to achieve a tractable and biologically meaningful representation of genome-wide methylation profiles that are amenable to association testing. To this end we searched for methylomic profiles that arise from putatively independent biological processes, each reflected by a restricted number of CpG sites. By decomposing genome-wide DNAm profiles we identified an epigenetic mark of immune system genes linked to cortical thickness and to human memory. The well-known effect of age on cortical thinning is partially mediated by this epigenetic mark, and its genetic underpinnings also point to genes involved in immune system regulation. Thus, the decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related physiological traits. For example, peripheral markers of systemic inflammation are associated with reduced grey matter volume, both in midlife adultsMethylomic profiling sample. This sample is part of an ongoing, continuously recruiting imaging genetics study of healthy young adults in the city of Basel, Switzerland. Aim of the study is to recruit large samples of healthy young adults for assessing cognitive performance measurements, personality traits, functional and anatomical MRI and genetics  at the time-point of the main investigation. Advertising for the main investigation was done mainly in the University of Basel. Subjects were re-invited via email or at the time-point of the main investigation to an additional blood and saliva sampling. The time point of this second investigation was on average 348 days  after the main investigation. For the purpose of this study, a total of N=568 subjects underwent blood methylomic profiling (Data lock Apr. 2014). After pre-processing of methylomic data and genetic outliers exclusion, a total of N=533 subjects were included in the methylomic profiling sample .Zurich sample. This sample included a total of N=706 healthy young subjects recruited in Zurich, who underwent EM assessment and genotyping .All participants were free of any neurological or psychiatric illness, and did not take any medication at the time of the experiment . The ethics committee of the Cantons of Zurich, Basel-Stadt and Basel-Landschaft approved the experiments. All participants received general information about the study and gave their written informed consent for participation.AgeCode sample. This sample consisted of elderly participants of the German Study on Ageing, Cognition and Dementia in primary care patients (AgeCoDe). The AgeCoDe study is an ongoing primary care-based prospective longitudinal study on early detection of mild cognitive impairment and dementia established by the German Competence Network Dementia. The sampling frame and sample selection process of the AgeCoDe study have been described in detail previouslyMunich sample. The Munich sample consisted of patients with first episode and recurrent unipolar depression treated as in-patients at the Max Planck Institute of Psychiatry, Munich, and healthy control subjects  years; control subjects age 49.5 (s.d. 13.3) years), for the most part overlapping with imaging genetic and MDD association studies reported in collaboration with the ENIGMA consortium58N=596 subjects was available for statistical analysis. Description of methylomic profiling and structural imaging of the Munich sample are provided in P value≥0.05 were considered as missing. Individual probes were excluded based on the following criteria: (1) non-CpG context probes, polymorphic probes, probes harbouring three or more SNPs in their 50mer extension (MAF≥0.01), and cross-hybridizing probes, based on the annotation provided with the RnBeads packageBlood samples were collected from all the subjects using BD Vaccutainer Push Button blood collection set and 10.0 ml BD Vacutainer Plus plastic whole blood tube, BD Hemogard closure with spray-coated K2EDTA . DNA was isolated from the remaining fraction, upon plasma removal. The isolation was performed with QIAmp Blood Maxi Kit , using the recommended spin protocol. Subject's DNA was extracted between midday and evening . Microarray-based DNA methylomic profiling from whole-blood samples was performed at ServiceXS . In brief, the bisulfite conversion was performed with 500 ng genomic DNA input using the EZ DNA Methylation Gold Kit . A bisulfite conversion quality control on the samples was performed with DNA qPCR reaction and subsequent melting curve analysisr>0.99) and variance (r>0.95) of DNA methylation values across samples. We also observed high average correlation between DNAm values before and after normalization per-CpG site (average r=0.87), and per-sample .To rule out systematic shift in DNA methylation values induced by SWAN normalization, we compared the correlation between summary statistics of CpG sites before and after normalization. We observed high correlation for both average  between age at blood sampling and age at MRI assessment from the methylomic pattern. The association remained significant  indicating that Δage did not affect the results of the study.DNA methylation profiles were obtained on average 1 year after imaging acquisition. We performed a sensitivity analysis examining the association between P>0.04).Primary phenotypes  were not confounded with methylomic processing covariates  .ntrix ID . After aHaematological analysis, including blood cell counts, was performed at the collection time point with Sysmex pocH-100i Automated Hematology Analyzer .N=527 participants from the methylomic profiling sample.Lymphocytes, neutrophils and overall count of basophils, monocytes and eosinophils (mixture) were available for Participants from the methylomic and Basel imaging samples underwent identical MRI assessment.Measurements were performed on a Siemens Magnetom Verio 3T wholebody MR unit equipped with a 12-channel head coil. A high-resolution T1-weighted anatomical image was acquired using a magnetization prepared gradient echo sequence (MPRAGE) sequence with the following parameter: TE (echo time)=3.37 ms, FOV (field of view)=25.6 cm, acquisition matrix=256 × 256 × 176, voxel size=1 mm × 1 mm × 1 mm. Using a midsaggital scout image, 176 contiguous axial slices were placed along the anterior−posterior commissure (AC−PC) plane covering the entire brain with a TR=2,000 ms (flip angle=8°).N=533 participants from the methylomic profiling sample and N=753 from the Basel imaging sample, a total of 50 participants were excluded due to excessive movement or scanner noise by visual inspection of T1-weighted images, or technical reasons. This yielded a total of N=514 from the methylomic profiling sample entering structural imaging analysis, and N=722 subjects from the Basel imaging sample.From the initial T1-weighted images were processed using the publicly available FreeSurfer software (v4.5) refs , 32, 33.After probes and samples quality control, missing Beta values were imputed using the R package impute. In order to adjust the methylation signals for technical confounders and preserve effects of chronological age on methylation sites, we applied the iteratively re-weighted surrogate variable analysis algorithm implemented in the SVAR packageA) were clustered using hierarchical clustering, with complete linkage agglomeration, based on Pearson's correlation similarity. Final components were determined as the centrotypes of the inferred clusters.ICA decomposition of the standardized residuals was performed using the R package fastICA. The number of components to extract was estimated using the Random Matrix Theory algorithma priori. In our case, the genome-wide methylomic data set was decomposed into 15 components that were amenable to downstream association testing. Hence, traits correlated with these 15 components were subjected to following α level adjustment: P=0.05/15=0.0033. After having identified ICA2 as the only pattern associated with cortical thickness, we further investigated its relationship with eight regional thickness factor scores. The α level was thus adjusted for eight tests conducted (P=0.05/8=0.00625).When using such decomposition methods as ICA, multiple-correction depends on the number of identified components, which in not known Swiss sample. The association between ICA patterns and imaging or behavioural phenotypes was assessed using Pearson's correlation, with two-sided association test. Given the delta between age at methylomic profiling and age at main investigation, chronological age adjustment was achieved by partialling out age effects: effect of age at methylomic profiling was regressed out from methylomic patterns and age at main investigation was regressed out from the phenotypic measure, using linear regression. Adjustment of methylomic patterns for blood cell counts was performed by regressing out effects of chronological age at blood sampling and effects of each of the three white-blood cell parameters using linear regression. The obtained residuals were subsequently tested for association with the relevant imaging or EM phenotypes.Self-reported smoking frequency was measured on a 4-point Likert scale . Self-reported alcohol consumption and cannabis use frequencies were measured on a 3-point Likert scale . Association testing for each indicator was performed using linear regression.Munich sample. ICA2 patterns were calculated separately for the whole Munich sample and a subsample of<40-year-old subjects (N=163). DNA methylation values were first adjusted for sex using linear regression. ICA2 patterns were then calculated as linear combination between the scaled residuals and the inverse ICA2 loadings inferred from the Swiss sample . Factor extraction was based on principal axis factoring method. The number of factors to extract was determined using the parallel method implemented in R package psych. The factor analysis solution was rotated using the varimax method. A variable was considered to load on a factor if its absolute loading on the factor was 0.3 or greater. Based on the factor solution inferred from the methylomic profiling sample, we extracted factor scores predictions for both the methylomic profiling sample and the independent Basel imaging sample, using regression method. For each sample, factor scores were tested for association with EM performance using Pearson's correlation, with a two-sided association test. The same association analysis was conducted combining factor scores and EM performance of the two samples .Average regional cortical thickness in 68 areas (34 per hemisphere) were obtained from FreeSurfer automated parcellation method based on Desikan-Atlas3132ICA2 methylomic pattern and global cortical thickness were entered in a mediation analysisa multiplied by b, see k2 square value which is interpreted as the proportion of the maximum possible indirect effect that could have occurredChronological age at main investigation, CpGs were mapped to transcripts based on Illumina's annotation; EntrezID gene identifiers were downloaded from the UCSC genome database. Enrichment testing was performed using the GOseq packageGenotyping. DNA was extracted from saliva or blood using standard protocols. All subjects were individually genotyped using the Affymetrix Human SNP Assay 6.0 according to the manufacturer's recommendation. In the methylomic profiling sample, subjects with unusual ancestry according to the majority of the sample were excluded using Bayesian clustering algorithm and Hapmap reference populations (see Methylomic profiling). Subjects were also checked for inconsistency between reported and genetically inferred sex. Individual call rate averaged to 98.3%. For the purpose of scoring analyses, subjects from the Basel imaging, Basel cognitive, Zurich and AgeCode samples were additionally excluded based on the following criteria: genome-wide call rate<95%; IBD sharing defined by PI_HAT>0.2 (one-sample of each detected pair was excluded); Bayesian ClusteringN=1,445 individuals entering the genetic scoring analysis for Basel cognitive sample, N=534 for Basel imaging sample, N=624 for Zurich sample and N=743 for AgeCode sample.Validation of the link between methylation and genotype data. A per-subject crosscheck between phenotypic data, methylation data and genetic data was performed using the reported sex and sex-predictions based on the array data, as well as matching of all SNPs represented on the Illumina 450 K array to the corresponding Affymetrix SNP 6.0 genotype calls. This crosscheck allowed an unambiguous assignment of each methylation data set to the corresponding genetic and phenotypic data set.Gene set enrichment analysis of ICA2 pattern. GSEA was performed using the MAGENTAP values, while controlling for potential confounders . Genome-wide single-SNP association analysis was conducted on ICA2 pattern adjusted for chronological age (at blood sampling), using an additive model. A total of 773,330 autosomal SNPs that passed individual SNP quality control in the methylomic profiling sample  entered the analysis.P values were adjusted for multiple testing using FDR. Gene sets were extracted from the MSigDB v3.1 database (http://www.broadinstitute.org/gsea/msigdb), including gene sets from different online databases . We used a gene set size ranging between 20 and 200 genes to avoid both overly narrow and broad gene set categories, resulting in 1,411 gene sets to be analysed. Genes from the extended major histocompatibility complex region were excluded from the analysis.In order to capture signals from potentially regulatory variants, MAGENTA-derived gene scores were based on SNPs lying within 20 kb upstream and downstream of the extreme transcript boundaries. The GSEA algorithm includes a built-in procedure controlling for physical proximity of SNPs within a given gene set . Gene set enrichment statistic was based on the 75th percentile cut-off of the observed genome-wide gene-score distribution, which has been proposed to show optimal power for weak genetic effects as expected for complex polygenetic traits. Empirical Genetic scoring association analyses. The scores comprised SNPs associated with ICA2 pattern (P<0.05) mapping to an equal number of genes . Genetic scores were computed using the PLINKICA2 pattern inferred from the methylomic profiling sample. Genetic score calculations were restricted to SNPs meeting the inclusion criteria: MAF≥0.01; HWE P>0.0001; call-rate≥90% within a sample. Resulting genetic profiles were adjusted by regressing out the effect of the number of missing SNPs per-subject included in the scoring procedure. Associations between the inferred scores and behavioural or imaging phenotypes were assessed using Pearson's correlation. Given the negative correlation observed between ICA2 methylomic pattern and EM performance in the methylomic sample, genetic score correlation tests with EM performance were one-sided (lower tail).Description of additional analyses of ICA methylomic patterns can be found in The data that support the findings of this study are available from the corresponding authors on request.How to cite this article: Freytag, V. et al. A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory. Nat. Commun.8, 15193 doi: 10.1038/ncomms15193 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Tables, Supplementary Notes and Supplementary ReferencesRegional cortical thickness loadings on eight factors extracted from factor analysis.CpG sites contributing to ICA2Gene sets enrichment analysis of 593Biocarta PYK2/Keratinocyte geneticHemopoietic or Lymphoid Organ Development genetic score SNPs.Association of 15 ICA pattern with known technical/biological factors.Gene sets enrichment analysis of ICA signatures.Inverse ICA2 Loading inferred from the Swiss sample


Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency. Feed represents up to 70 percent of the total cost of chicken production, and the selection of feed utilization traits can reduce feed cost and greenhouse gas emissions. Feed coGenomic selection is revolutionizing animal breeding due to the higher accuracy of selection early in life –13. GenoThe aims of this study were to investigate the accuracy of genomic prediction for FCR and RFI and to assess the impact of improving the two feed efficiency traits on carcass traits in meat-type chickens.The present study was approved by the Animal Care Committee of the Institute of Animal Science, Guangdong Academy of Agricultural Sciences  .0 generation, 51 breeding individuals (8♂ + 43♀) from the F1 generation and 511 individuals from eight half-sib families of the F2 generation. All birds were genotyped, and only F2 birds had phenotypic records. The birds were genotyped using the Illumina Chicken 60K SNP Beadchip [The chicken population was constructed by crossing the “High Quality chicken Line A” (HQLA) with the Huiyang Beard chicken (HB), as described in Sheng et al. . The HQLBeadchip  by DNA Lth week. Thereafter, all birds were housed in single cages and feed was provided in single containers. The chickens had ad libitum access to water and feed. The feed were gradually changed to a grower ration of 2,950 kcal ME/kg and 180 g/kg CP during the 6th week. Body weight and feed intake were measured during the period from the beginning of 7th to the end of 12th week (42 d). Individual body weight gain was calculated based on body weight between the start and end of the period and used to derive ADG. The total feed intake for each bird was calculated by summing the feed consumptions during the period and used to derive ADFI. At the end of the 13th week, the birds were slaughtered to measure carcass traits. Quality control was applied to ADG and ADFI, removing deviant data that were more than 3 standard deviations from mean [Seven traits in the analysis included feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG), average daily feed intake (ADFI), eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP). The mean and standard deviation of the seven traits are shown in rom mean . Finallyrom mean :ADFI=μ+μ is the intercept, sex and hatch are fixed effects, MBW is the average of the start and end of body weight, β1 and β2 are partial regression coefficients, and e is residual. The estimated e was taken as the measure of RFI.In the model, In this study, a conventional pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP) were used to predict breeding values for each trait, respectively.y is the vector of phenotypic records of the trait , b is the vector of the fixed effect (sex and hatch), X is the incidence matrix linking b to y, a is a vector of additive breeding values to be estimated, Z is the incidence matrices linking a to y and e is the vector of residuals. It was assumed that A is the pedigree-based genetic relationship matrix. For analysis of RFI, the fixed effects were not included in the model because these effects were already corrected when calculating RFI values.The pedigree-based BLUP model isy=Xb+y, b, X, Z and e are the same as those in the BLUP model. g is a vector of genomic breeding values to be estimated. It was assumed that g ∼ N, where G is the genomic relationship matrix based on SNP markers [Vg is the covariance matrix of genomic breeding values.The GBLUP model  isy=Xb+ markers , Vg is tVariance components were estimated with a univariate linear mixed model, the genetic correlations between feed efficiency and each of the carcass traits were estimated with a multivariate linear mixed model, using the average information restricted maximum likelihood (AIREML) approach . The linCross-validation is a robust, non-parametric technique for assessment of predictive ability , 26. Thec) divided by the square root of heritability [c was the phenotypic value corrected for sex and batch effects. For RFI, the yc was RFI itself, as RFI was already corrected for sex and batch effects. The other was the model-based theoretical accuracy of GEBV obtained by inverting the coefficient matrix of the mixed model equations [Accuracies of GEBV were assessed in two ways. One was the correlation between prediction and corrected phenotypic value . For RFI, the top 50 efficient birds were expected to have the same ADG as the bottom 50 inefficient birds because RFI was already adjusted for ADG. The small difference  was due to sampling error. The efficient individuals had lower feed intake than the inefficient individuals for RFI, which is not the same for FCR. The three carcass traits had significantly higher values in the efficient group of birds (P<0.05).P<0.05). The correlations of FCR with EP, BMP and LMP ranged from -0.427 to -0.156. The correlations between RFI and the three carcass traits ranged from -0.404 to -0.320.The genetic correlations between feed efficiency and carcass traits based on full data are presented in Several studies have shown that the heritabilities for FCR and RFI are moderate to high in chickens , 28, 29.Although the size of the dataset was small in the current study, the standard errors of heritability estimates were low. There are at least three possible reasons for the low standard errors. First, the data were collected from a single farm during the same period; thus, the model was rather simple and only a few parameters needed to be estimated. Second, the dataset had a good structure  with regard to estimating genetic parameters. Third, heritabilities in the current study were estimated based on the genomic relationship matrix built using the 60K SNP chip, which described the relationship between birds more accurately than the pedigree-based relationship. Previous studies , 31 repoIn the current study, the accuracies of GEBV were evaluated in two ways: correlation-based and model-based theoretical accuracy. The results showed that in both CVF and CVR scenarios, the model-based theoretical accuracy was higher than correlation-based validation accuracy for all traits, except for ADG and ADFI in the CVF scenario. This phenomenon of high model-based theoretical accuracy was also observed in previous studies, 32, 33,2 population, which was a newly established line with a small effective population size. In a population with small effective population size, the animals shared large chromosome segments; thus, it is expected that genomic predictions will be more accurate [Although the training data set was small, the accuracy of genomic prediction was relatively high in this study. This could be explained as the following. The data were collected from a Faccurate , 36.The accuracy of predicted genomic breeding values for feed efficiency traits has been studied in laying hens. Wolc et al.  investigIn the real breeding scheme of poultry, parents are usually ready to be included in training data to predict the breeding value of progenies, but not sibs. In other words, the relationship between test and training birds would be higher than that in CVF and lower than that in CVR. Therefore, it could be expected that the accuracies of genomic prediction in the real scenario of chicken breeding would be between the accuracies of the CVF and CVR scenarios. In line with the higher heritability for RFI than FCR, the trait had a higher accuracy of GEBV than FCR, indicating an advantage of selection for RFI to improve feed efficiency.The accuracy of genomic prediction for the animals without their own records of FCR and RFI was actually lower than the Previous studies have reported strong genetic correlations between FCR and ADG , 6, 29. Carcass traits are highly important in meat-type chicken breeding because they can greatly affect the economic value of the birds. However, carcass traits are difficult to measure, and direct measurements can be obtained only when the individuals are slaughtered. Due to the limitation of phenotypic records, there are very few studies on the relationship between feed efficiency and carcass traits. N'Dri et al.  investigRFI and FCR are moderately heritable with higher heritability for RFI than FCR. Furthermore, GEBV of RFI was more accurate than FCR. In addition, RFI has a favourable correlation with all three carcass traits and no correlation with ADG. Therefore, RFI could be a good alternative to selection for improving feed efficiency in chicken breeding programs.S1 TableThe accuracies of genomic prediction were evaluated in two ways: the correlation-based accuracy and model-based theoretical accuracy.(XLSX)Click here for additional data file.


Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth  and depth . A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore correlation information of genetic variants, effectively reduce data dimensions, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new statistic method referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the ten competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and ten other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the ten other statistics. Association analysis of multiple phenotypes will unravel the genetic pleiotropic structures of multiple phenotypes, provide a powerful tool for developing drug with fewer side effects. To increase the power of the tests for high dimensional association analysis of multiple phenotypes with next-generation sequencing data, a key issue is to develop novel statistics that can effectively extract informative internal representation and features from high dimensional data. However, the current paradigm of association analysis of multiple phenotypes does not efficiently utilize the rich correlation structure of the genotype and phenotype data. To shift the paradigm of association analysis from shallow multivariate analysis to comprehensive functional analysis, we proposed a new general statistical framework referred to as a quadratically regularized functional canonical correlation analysis (QRFCCA) for association test which explores rich correlation information in the genotype and phenotype data. Large-scale simulations demonstrate that the QRFCCA has a much higher power than that of the many existing statistics while retaining the appropriate type 1 errors. To further evaluate the new approach, the QRFCCA are also applied to the TwinsUK study with 46 traits and sequencing data. The results show that the QRFCCA substantially outperforms the other statistics. As of February 6th, 2017, a catalog of published Genome-Wide Association Studies (GWAS) had reported significant association of 26,791 SNPs with more than 1704 traits in 2,337 publications . Most geTwo types of approaches can be used for genetic pleiotropic analysis. One approach is to utilize summary statistics for estimating genetic correlations and testing association of genetic variants with multiple traits –22. An aStatistical methods for testing the association of common variants with multiple traits have been well developed and successfully applied . The methttps://sph.uth.edu/research/centers/hgc/xiong/software.htm and https://cran.r-project.org/web/packages/.To evaluate the performance of the developed QRFCCA for association analysis of multiple phenotypes, we conduct large-scale simulations comparing QRFCCA to ten statistics: Sparse CCA (SCCA) , MSKAT , GAMuT , FCCA 5, kernel http://www.uk10k.org.The UK10K data can be downloaded from The essential component of the QRFCCA is data dimension reduction. To efficiently reduce the data dimension while exploring the rich correlation information in the data, the data dimension reduction pipelines of proposed QRFCCA for pleiotropic genetic analysis consist of four steps: (1) FPCA, (2) matrix factorization, (3) quadratic regularization, and (4) CCA. The FPCA changes the raw data  to the functional principal component (FPC) representation that can capture the correlation features. The matrix factorization is to embed the FPC scores or multiple scale phenotypes into the low dimensional vector space. It compresses the FPC score or phenotype data to a few new features that are another level of representation of data. Quadratic regularization further compresses the data and changes the representation of FPC scores or phenotypes. Finally, CCA is used as an effective tool for two-view dimension reduction. QRFCCA combines dimension reduction in different levels of data representations.Genotype-phenotype association attempts to unravel the relationships between certain combinations of genetic variants from multiple loci and certain combinations of multiple phenotypes. The CCA measures the linear relationships between two multidimensional sets of variables and hence will be naturally used as a general framework for identifying the association between genotypes and phenotypes.t be the position of a genetic variant within a genomic region and T be the length of the genomic region being considered. For convenience, we rescale the region from  to . We can view t as a continuous variable in the interval  because the density of genetic variants is high. We define the genotype function of the i-th individual ast and n is the number of sampled individuals. Although the FPCA can also be applied to function-valued phenotypes, for example, RNA-seq data, this paper will focus on genotype function, assuming that the phenotypes are scale variables.We first briefly introduce the smooth functional principal component analysis (FPCA) for genetic variant data . We firsβj(t),j = 1,2,… be a set of FPCs which can be obtained from solving integral eigenequation (details are referred to the book  ∈ Rn×r, V =  ∈ Rq×r, UTU = Ir×r, VTV = Ir×r, and Λ = diag ∈ Rr×r with λ1 ≥ λ2 ≥ … ≥ λr > 0. The columns of U and V are referred to as the left and right singular vectors of A, respectively, and λ1,…, λr are referred to as the singular values of A.A solution to problem (4) can be found by truncating the singular value decomposition (SVD) of Aij . Let thel = diag, Ul =  and Vl = . Define l approximation to the matrix A or the l-rank matrix factorization of A is then given by  with k traits and genotype matrix X =  with p SNPs or FPC scores. We assume that p + k variables Z = T jointly have the covariance matrixConsider a phenotype matrix LetK isdiag and q = min is the smaller number of variables in the two genotype-phenotype datasets.The SVD of It is well known that the canonical vectors areA squared canonical correlation measures the proportion of variance linearly shared by the two sets of canonical variates derived from the input genotype-phenotype data sets.Canonical correlations between the genotype and phenotypes measure the strength of their association. The CCA produces multiple canonical correlations. But we wish to use a single number to measure the association of the genetic variation with the multiple traits. We propose to use the summation of the square of the singular values as a measure to quantify the association of the genetic variation within a gene or genomic region with the multiple traits:X and Y or to test the hypothesis that each variable in the set X is uncorrelated with each variable in the set Y. The null hypothesis of no association of the genotype data X with the phenotype dataset Y can be formulated asTo test the association of the genetic variation in a gene or genomic region is equivalent to test independence between the two genotype-phenotype datasets H0: ∑xy = 0 isThe likelihood ratio for testing This demonstrates that testing for association using multivariate linear regression can be treated as special case of CCA .We usually define the likelihood ratio test statistic for testing the association as:TCCA can be approximated by r is the measure of association of the genetic variation in the gene or genomic region with the multiple traits. The stronger the association, the higher the power that the test statistic can test the association.For small H0: ∑xy = 0, TCCA is asymptotically distributed as a central Under the null hypothesis R2. We wish to increase the power via changing distribution of the canonical correlations and data reduction. In matrix factorization, for fixed rank l, we want to approximate the matrix A by the product of two factor matrices G and H as accurately as possible. However, the Frobenius norm of the matrices G and H may be large. We need to balance the approximation accuracy and the Frobenius norm of the factor matrices. Specifically, we add the Frobenius norm of the factor matrices to the objective in Eq . The matrices G and H are determined by τj. Seeking the matrices G and H to optimize the objective function in Eq + = max and λj is defined in Eq (Using techniques in (55), we can show that the solution to optimization problem  isτj=.We can first apply the quadratically regularized matric factorization to both multiple phenotype data and FPC score data in a gene or genomic region and then use CCA for association analysis of multiple traits. This analysis will be referred to as quadratically regularized FCCA (QRFCCA) for multiple trait association analysis. If only a single trait is considered, the quadratically regularized matrix factorization is only applied to the FPC score in the gene.Continuous phenotypes and multiple SNPs.Quadratically regularized matrix factorization is first applied to both multiple phenotypes and SNPs for data reduction and then CCA is used for multiple trait association analysis. This procedure is referred to as QRMCCA.Continuous phenotypes and a single SNP.Quadratically regularized matrix factorization is first applied to multiple phenotypes and then CCA is used for multiple trait association analysis. This procedure is referred to as QRSCCA.Both functional phenotype such as RNA-seq data and functional genotype data such as NGS data. FPCA is first applied to both functional phenotype and genotype data to obtain FPC scores for both phenotypes and genotypes. Quadratically regularized matrix factorization is applied to FPC scores of both functional phenotype and genotype data. Finally, CCA is used for multiple trait association analysis. This procedure is referred to as QRBFCCA.K or R2 matrix in the CCA. The test statistics then use the singular values of the reduced K or R2 matrix to test association of genetic variation with a trait.Quadratically recognized matrix factorization can also be applied to the To adjust for covariates, we first regress phenotypes on the covariates. If the covariates are the same for all traits, then the multivariate regression will be used to simultaneously regress all the phenotypes to the covariates. Otherwise, we regress each trait to the covariates individually. The residuals are then taken as one set of variables (similar to phenotypes) for CCA.r of the genetic variation within a gene or genomic region with the multiple traits defined in Eq  as a general framework and the covariance operator as a general tool for unifying CCA, kernel CCA, functional CCA and other association analyses including GAMuT. Many multivariate and functional statistical methods such as regression, CCA, kernel regression, kernel CCA, functional regression and functional CCA can be used to test the association of genetic variants with the phenotypes. In supplemental note B, we develop a unified framework for association tests to reveal the relationships among various multivariate and functional association tests.Kx = )m×m, Ky = )m×m, In Supplemental Note B, we define two kernels MuT test . In SuppFinally, we consider the FCCA. In Supplemental note B, we unify multivariate association tests and functional association tests. Suppose that the FPC scores form a feature space. In supplemental note B, we define the feature maps from the original functional data to the FPC score feature space. We show that the dependence measure in the FPC score-based kernel analysis is asymptotically equal to the association measure of the FCCA. This implies that the FCCA is a specific kernel analysis that uses the FPC score to define the kernels instead of directly using the genotype data to define the kernels.Yi = , k is the number of traits, μ is a vector of overall means, and εi is distributed as N, where ∑ is a k × k residual correlation matrix. We similarly model the correlation matrix as in Broadaway et al , moderate correlation [0.2–0.4] and high correlation [0.4–0.7]. In summary, the genetic model for power evaluation is given byyi represented phenotypes, xj was an indicator variable for coding the genotype of the jth SNP in the gene, taking values 0, 1, 2 to represent the number of minor alleles at the SNP. αij denoted the genetic effect that followed a normal distribution with ith trait, MAFj denoted the frequency of the minor allele at the jth SNP, bij in the matrix represented the probability of the jth SNP being the causal variant for the ith trait and followed a binomial distribution B. Notation ∘ denoted an element-wise matrices multiplication, ti ∼ B,ti ∼ B,ti ∼ B represented the probability of the gene being tested contributing the genetic effect to the ith trait for scenarios 1, 2 and 3, respectively, and ith trait and followed a uniform distribution U, εi, i = 1,…,K denoted residuals and followed a multivariate normal distribution as defined above.The residual correlation was simulated from a multivariate distribution with mean zero and covariance matrixThe genotype data in type 1 error calculations were also used for power evaluation. A total of 10,000 simulations were repeated for the power calculations.We first compared the power of QRFCCA with ten other competing statistics that are described in type 1 error rate calculations for testing the association of rare variants with multiple continuous traits. Power was estimated as a function of sample sizes. Figs Next we investigated whether the power pattern of the eleven statistics for testing the association of the gene with only rare variants would still hold when testing the association of the gene with both rare and common variants. Figs To show that the dimension reduction of the QRFCCA for the phenotype will also improve the power of the test, we presented Fig S26 that showed the power of eleven statistics for testing the power of a single common variant with 15 low correlated traits as a function of sample sizes. We observed that the QRFCCA still had higher power than all other ten statistics for testing the association of the single common variant with multiple traits due to its efficient dimension reduction, followed by kernel CCA. Since the MSKAT and GAMuT did not provide tools for efficiently reducing the dimension of the phenotypes, the power of the MSKAT and GAMuT were much smaller than that of the QRFCCA, and even smaller than that of kernel CCA.α = 0.00001, respectively. Again, we still observed that the power of the QRFCCA was the highest among eleven statistics. However, when the significance level was reduced from α = 0.05 to α = 0.00001 the power of all statistics was reduced. We also observed that when the significance level was reduced, the simulations were unstable. In this case, we need to increase the number of simulations.Finally, we presented Fig S27-S32 showing the power curves of the eleven statistics for testing the association of the gene including rare variants only, and both rare and common variants with 15 highly correlated traits in scenarios 1, 2 and 3, at the significance level These figures demonstrated that the QRFCCA substantially outperformed the ten other statistics and the difference in power between the QRFCCA and other statistics for the both rare and common variants was much larger than that for the rare variants only. This demonstrated that the regularization in singular vectors plays a more important role in association analysis of both rare and common variants than that in association analysis of only rare variants.Investigation of the contribution of the entire allelic spectrum of genetic variation to multiple traits are still at its infancy. The systematic searching for both common and rare variants associated with large number of traits is essential for unraveling the genetic architecture of complex diseases. To further evaluate the performance, the QRFCCA and ten other statistics were applied to the UK-10K dataset. The UK-10K Cohorts project used a low read depth whole-genome sequencing (WGS) to assess the contribution of the genetic variants to the sixty-four different traits . However−6. To examine the behavior of the test statistics, we plotted the QQ plot of the QRFCCA with one FPC, FCCA with one FPC, PCA and GAMuT using a linear kernel in We first studied the association of genes with only rare variants (MAF ≤ 0.01). The total number of genes with only rare variants tested for association was 33,746. A p-value for declaring significant association after applying the Bonferroni correction for multiple tests was 1.48 × 10The total number of genes consisting of only rare variants significantly associated with the 46 traits with and without PC adjustment using QFCCA, FCCA, PCA, SCCA, KCCA, MSKAT, GAMuT, CCA, USAT and MANOVA, were shown in RNU6-1229P calculated by GAMuT was smaller than that calculated by QRFCCA, we did not find any significant SNPs within RNU6-1229P . This may imply that association was spurious. In Table S20, we listed p-values of all SNPs within gene ADAM19. We observed that QRFCCA identified significance of ADAM19 with a p-value less than 6.07 × 10−11 and at least 4 SNPs in ADAM19 had very small p-values and two additional SNPs had p-values that were close to the threshold p-value of genome-wide significance . However, the GAMuT missed to identify significance of ADAM19.To further assess the performance of the QFCCA and GAMuT, we presented Tables S19 and S20. Table S19 summarized the top ten genes ranked using GAMuT where p-values calculated by both GAMuT and QRFCCA were also listed. None of ten genes reached genome-wide significance levels by the GAMuT. However, we noticed that 7 of top ten genes ranked by GAMuT were significantly associated with the 46 traits identified by QFCCA. Although we observed that the p-value of To characterize the pleiotropic pattern, we presented the heat map showing the pattern of cross phenotype association of genes with rare variants only and the most important pleiotropic effects of the genes . Table SPNOC which was associated with the 46 traits , HOMA-IR  and triglycerides  was reported to be associated with insulin resistance [MIR409 which was associated with the 46 traits , weight , Hip  and total lean mass  was used as a weight loss biomarker [GAPDH which showed an association with the 46 traits  and specifically with creatinine  was reported to be associated with creatinine [MLN that demonstrated association with the 46 traits  and showed a strong association with glucose  played an important role in controlling the rise rate of glucose level [JUN, which was associated with the 46 traits  and showed a strong association with creatinine , was reported to be correlated with the serum creatinine level [COMP which showed significance with the 46 traits p-value ≤ 4.83 × 10−7) and a strong association with specific trait creatinine  was also reported an association with creatinine [Some results can be confirmed in the literature. Throughout this section, all p-values were calculated using QRFCCA. We found that 30 out of 79 genes (38.0%) with rare variants significantly associated with 46 traits were reported association with some of them in the literatures. For example, sistance  and trigsistance . MIR409 iomarker . GAPDH wse level . JUN, wheatinine .−6. To examine the behavior of the test statistics, we plotted the QQ plot of the QRFCCA, FCCA, PCA and GAMuT using a linear kernel in Next we studied the association of genes with only common variants (MAF ≥ 0.05). The total number of genes with only common variants tested for association was 33,166. The p-value to declare the significant association after applying Bonferroni correction for multiple tests was 1.51 × 10The total number of genes significantly associated with the 46 trait that were identified using ten statistics with and without PC adjustment were listed in REG1B. We observed in Fig S36 that more than 11 SNPs in REG1B with p-values ≤ 0.0001 jointly made contributions to the strong association of REG1B with the 46 traits with p-value ≤ 1.65 × 10−116. The QRFCCA can catch the features of genetic variation. In Fig S36 we listed the p-values of all SNPs within the gene XXbac-BPG154L12.4. From Fig S36, we also observed that although the GAMuT treated XXbac-BPG154L12.4 as associated with the 46 traits , none of SNPs were even weakly associated with the 46 traits. We should point out that the QRFCCA did not find any, even mild association . The Manhattan plot showing genome-wide p-values of association of genes consisting of only common variants with the 46 traits calculated using QRFCCA was presented in To assess whether the QRFCCA for testing the association of genes including common variants only with multiple traits was appropriate or not, we presented Fig S36 which shows the p-values of all SNPs within gene To unravel the genetic pleiotropic structure of common variants, we presented the gene/phenotype association heat map that demonstrated the most important pleiotropic relations between a single gene and multiple traits  and summWe also analyze the association of all common SNPs in one gene with one trait for all the 46 traits. The results were summarized in Table S25. We observed that 34 genes were significantly associated with creatinine, 29 genes with HsCRP, 23 genes with HOMA-IR, 23 genes with HOMA-B, 9 genes with glucose, 8 genes with insulin, 7 genes with GGT, and 6 genes with VLDL. The distributions of the number of genes consisting of only common variants associated with traits were similar to that of rare variants although the number of genes associated with common variants was smaller than the number of genes associated with rare variants.Finally, we reported computation times of whole genome association testing for 10 test statistics as in REG1B, which showed the most significant association with the 46 traits , has been reported to be associated with glucose [−21), the production of insulin [−24 and HOMA-IR with p-value ≤ 5.02 × 10−94), and triglyceride whose increment has deleterious effects on the function of islet beta cells [−24). LEF1, which was associated with the 46 traits , has been related to insulin resistance [−52) and heart failure [−122 and Homocysteine with p-value ≤ 2.24 × 10−14). DYNC1H1, which was associated with the 46 traits , HOMA-IR , insulin , glucose  and creatinine , has been associated with hyperinsulinemia, hyperglycemia, the progress of glucose intolerance [DOCK7, which presented associations with the 46 traits , HsCRP  and BMI , has been associated with heart disease and ischemic stroke [GBF1, which was associated with the 46 traits , HOMA-B , has been reported to be involved in insulin resistance and type 2 diabetes [METAP2, which showed strong association with the 46 traits , HOMA-B , HOMA-IR  and insulin , has demonstrated associations with insulin resistance and insulin levels [GRN, which presented associations with the 46 traits , HOMA-IR  and insulin , has been reported to associate with insulin resistance in type 2 diabetes patients [USP44, which showed associations with the 46 traits , HsCRP  and HOMA-IR , has been reported to associate with congenital heart disease [Throughout this section, all genes included common variants only in the analysis. The literature confirmed many gene-trait associations which were identified in this study. We found that 58.2% of identified genes (39 out of 67 genes) with common variants only were reported association with some of 46 traits in the literatures. For example,  insulin   FPCA, (2) matrix factorization, (3) quadratic regularization, and (4) CCA. The FPCA changes the raw genetic variants to the functional principal component representation that captures the linkage disequilibrium features. The matrix factorization is to embed the functional principal component scores into the low dimensional vector space. It compresses the functional principal component score data to a few new features that are another level of representation of genetic variants. Quadratic regularization further compresses the data and changes the representation of functional principal component scores. Finally, CCA is used as an effective tool for two-view dimension reduction. QRFCCA combines dimension reduction in different levels of data representations. Multiple levels of representation of genetic variants are effective at leveraging data structure. This can be evidenced from the analysis of less derived traits. Our results showed that the significance decreased when the derived traits were removed from the analysis. Utilizing the data structure is a key component of the proposed QRFCCA method. Using less derived traits will decrease the dependence among traits and hence the QRFCCA analysis will use less correlation information among the traits and hence slightly reduce the significance. Multiple levels of representation of genetic variants are a philosophy. We borrow this concept from deep learning methods that are representation methods with multiple levels of representations, each transferring the representation at one level into a representation at another level.Specifically, to fully utilize the linkage disequilibrium information of genetic variants across the genomic region and efficiently reduce the dimension of the data, we proposed a new paradigm of association analysis that consists of three steps to combine multilevel data reduction and CCA. The first step is to apply FPCA to the original data for dimension reduction. The FPCA decomposes the genetic data into several functional components. Each functional component contains functional information of genetic variants across the genomic region, preserves the orders of genetic variants along the genomic and returns all possible pair-wise and high order linkage disequilibrium. If the phenotypes are function-valued physiological traits or RNA-seq data, the FPCA can also be applied to the phenotype data. The second step is to use quadratically regularized matrix factorization for further compressing the FPC scores into low rank representation and removing noisy data points. As a result, the FPCA and matrix factorization extracted useful genetic and phenotype information and deeply learned the internal genetic and phenotype representation. The third step is to apply CCA to the extracted FPC scores. Large scale simulations and real data analysis demonstrated that QRFCCA substantially outperformed all ten other statistics and FCCA outperformed some of multivariate statistics.The second issue is to develop a general framework for unifying association analysis, which provides a theoretic basis to evaluate various statistical methods for association analysis and design the guidance for developing novel statistics for testing the association of genetic variants with phenotypes. We used reproducing kernel Hilbert spaces (RKHS) as a general framework and the covariance operator as a general tool for unifying CCA, kernel CCA, functional CCA, dependence measure-based independence tests and other association analyses including GAMuT. We showed that multivariate linear regression are equivalent to the classical CCA. Covariance is a key measure to assess linear association. Its extension to covariance operator provides a tool for quantifying the nonlinear association and derives kernel-based dependence measures and independence tests which form the basis for the GAMuT test. We also show that the KCCA is quite similar to the kernel independent test. Finally, we considered the FCCA. To unify multivariate association tests and functional association tests, we used RKHS as a general framework for the formulation of the functional CCA. We showed that the dependence measured in the FPC score-based kernel analysis is asymptotically equal to the association measure of the FCCA. The FCCA also use the kernel-based dependence measure to develop association tests. Unlike the KCCA and GAMuT test where the kernels are selected by users, the FCCA uses the FPC scores as the feature space and derives the kernels from the data. This was why large-scale simulations and real data showed that in general, the FCCA outperformed the GAMuT, MSKAT and KCCA. Our FPC scores were generated via two steps. The first step used Fourier or wavelet expansions to derive eigenfunctions. The second step was to generate the FPC scores from the eigenfunctions. In the literature, some authors used one step FPCA to directly derive FPC scores from the Fourier or wavelet expansions. Our experiences showed that the number of functional principal components using two step FPCA was, in general, smaller than that directly derived from the Fourier or wavelet expansions. Therefore, two step FPCA had higher power than the one step FPCA.The third issue is how to reveal pleiotropic structure of the genetic variants and quantify the degree of pleiotropy. Pleiotropy is a widely used word to indicate that a gene affects multiple traits. However, the nature and extent of pleiotropy is less precisely defined. Recently, Schaid et al, 2016  gave theThe fourth issue is the cross-phenotype association analysis with next-generation sequencing. The popular methods for cross-phenotype association analysis is to assess the influence of a single variant on multiple distinct phenotypes. These methods work very well for cross-phenotype association analysis of common variants, but are not suitable for testing the association of rare variants with multiple phenotypes. To illustrate the urgent need to develop gene-based statistical methods for cross-phenotype association analysis of rare variants, we searched the variants across the genome for significant associations with the multiple phenotypes. We found that 21,272 rare variants were significantly associated with the 46 traits at the genome-wide significance level after Bonferroni correction. It is highly unlikely that so many rare variants affected the 46 traits. To overcome this limitation, we developed the QRFCCA for gene-based cross-phenotype association analysis. The QRFCCA can be applied to both multivariate phenotypes, function-valued phenotypes and NGS genotype data. Since the genotype profiles of the common variants and rare variants have different patterns, to increase the power of the tests, we take the association tests of common variants and rare variants separately. We found that the significant genes with common variants only were not overlapped with the significant genes with rare variants only. In pleiotropic analysis, we should conduct cross-phenotype analysis for both common and rare variants and separately. The QRFCCA provides a powerful tool to accomplish this task.To provide a guidance for cross phenotype association studies, we comprehensively evaluated the current existing statistics for cross-phenotype association by using large-scale simulations and real data analysis. In all simulated cases, when the sample size reached 2,000, the power of the QRFCCA varies between 80% and 90%. We found that the proposed QRFCCA not only substantially outperformed all other widely used competing statistics, but also was very flexible. The QRFCCA can be used for association analysis of both common variants and rare variants, and any phenotypes including quantitative or qualitative, multivariate or function-valued phenotypes.We performed cross-phenotype association analysis of a largest number of traits with NGS data up to the present time. We identified 79 genes including rare variants only which were significantly associated with the 46 traits and 67 genes including common variants only which were significantly associated with the 46 traits. These two sets of genes were not overlapped. Some of gene-phenotype association can be confirmed in the literature. We found that the largest number of the traits which a gene significantly affected at the genome-wide significance level was six and three in the cross-phenotype association analysis of common and rare variants, respectively. We also discovered that the largest number of traits which a gene affected with the P-value < 0.05 was 18 and 16 in the cross-phenotype association analysis of common and rare variants, respectively. In the single trait association analysis, we found that a large number of genes significantly affected creatinine , HsCRP  and HOMA-IR .The results presented in this paper are preliminary. The greatest lengths of the genes that were significantly associated with the 46 traits for rare and common variants in the real data analysis were 131Kb and 42Kb, respectively. The proposed methods may not have power to detect the association of the genes with lengths longer than these numbers. The number of basis functions for genotype profile expansion is an important factor for the power of the FPCA-based tests. We have not performed theoretical analysis to determine the appropriate number of basic functions for genotype profile expansions. We resort to ad hoc approaches to select the number of basis function in the expansions. The current pleiotropic analysis cannot identify the global causal structure of pleiotropy, which will decrease our power to unravel mechanisms underlying complex traits. To overcome this limitation, causal inference tools should be explored for cross-phenotype association analysis. The purpose of this paper is to stimulate further discussions regarding the great challenges we are facing in the pleiotropy analysis of high dimensional phenotypic and genomic data produced by modern sensors and next-generation sequencing.S1 Text(PDF)Click here for additional data file.S1 FigFig S1–Fig S36.(PDF)Click here for additional data file.S1 TableTable S1–Table S25.(PDF)Click here for additional data file.


Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shell-to-fruit ratio , mesocarp-to-fruit ratio , kernel-to-fruit ratio , fruit per bunch , oil per bunch  and oil per palm . Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection.Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of  These markers are used to build a predictive model using individuals with known genotypic and phenotypic information. With this model, genomic estimated breeding values (GEBVs) for the desired trait can be calculated and used to rank the individuals with unknown phenotype for subsequent selection. This method was initially developed for the use in cattle breeding, and is revolutionizing the industry2. Since then, this method has been introduced in plant breeding, inclusive of wheat, maize3 and pines4. However, in most crops, GS approaches are still in the research phase and yet to be applied in a large scale breeding program5. With the high marker density required for GEBV estimation, genotyping cost increases and the economic viability of GS remains in question for certain applications. In commercial perennial crops breeding, such as oil palm, each generation or selection cycle involves multiple crosses which easily produce far larger progeny numbers than in cattle breeding. A reduction in marker density will lower the genotyping cost. Thus, in order to maximize the value of GS in commercial crops such as oil palm, marker density needs to be optimized.Genomic Selection (GS) is defined as marker assisted selection using markers representing all QTL in the genomeElaeis guineensis Jacq.) accounted for 35% of the world’s vegetable oil consumption in 2015, with a steady upward trend seen since the late 90’s6. However, the realized oil yield in Malaysia remains stagnant at the range between 3.0 to 4.0 t/ha/yr, for more than 25 years7. Breeding gain needs to be increased significantly to address new challenges such as land degradation, climate change and agricultural land constraints. To provide a solution while ensuring a sustainable future, marker assisted breeding/selection has been introduced into oil palm breeding programs. The implementation of these programs requires genetic markers, which are more readily discovered now through resequencing, after the reference genome of oil palm became available8.Being the major oil crop of the world, oil palm  , S/F was 0.64  and K/F was 0.75 . The prediction accuracy for O/M across all methods was 0.50. For the traits with medium heritability, the average accuracy for F/B was 0.43 , O/B was 0.43  and O/P was 0.46 .r = 0.98) between prediction accuracy and genomic heritability was observed, which indicates that the higher the heritability, the higher the accuracy. For subsequent analysis, GS accuracy assessments for both RR-BLUP-B and LD-based marker filter were carried out using RR-BLUP as the basis for comparison.Figure The result for marker reduction differed according to traits. For M/F Fig. , the higBased on Fig. Marker reduction by LD was carried out for M/F and O/P using the first 300 ranked markers for each trait. Figure tenera population derived from multiple UR x AVROS families was studied. The average kinship within this population was calculated to be 0.23. This result agreed with known pedigree records, which mainly comprised of half siblings. A few of the individuals were found to be unrelated, which might be due to uncommon ancestors used in the founding population. The UR background is generally chosen in oil palm breeding for high bunch number and a high ratio of female: male inflorescences12, with the AVROS pollen parent conferring the properties of growth uniformity, precocity and high mesocarp oil content. This combination results in a hybrid population suitable for commercial oil production, with improved oil yield traits such as high O/M and low S/F. With high oil yield being one of the key characteristics of this population, the traits selected for this study were traits closely related to it. These traits included M/F, S/F, K/F, F/B, O/M, O/B and O/P. The selection response of each trait corresponded to their heritability estimates.A commercial 13. To distinguish this from the conventional heritability, the heritability estimates are referred to as “genomic heritability”. This estimate of genomic heritability is based on whole genome regression, where the regression on the markers is used to explain the phenotypic variance15. This differs from conventional heritability calculation, which is usually calculated from the slope of parent-offspring regression16. The calculation of genomic heritability is affected by the number of markers used under a linear model. Selection of the whole marker set assumes that every QTL is represented by a marker which accounts for a certain amount of the total trait variance, whereas the subset assumes that only certain QTL are responsible for the majority of trait variance1. Since the latter models the true biology better, higher heritability was estimated for all traits using this approach in this study. From another perspective, the use of all the markers in linkage equilibrium with QTL introduces more inconsistencies into the estimates of genomic heritability15. The problem with the use of marker subsets, however, is that defining every single QTL controlling a trait is difficult, and setting an association analysis cut-off for complex traits is rather subjective. Although the absolute heritabilities calculated were different, there was an almost perfect correlation between the values calculated from both methods (r = 0.99). Since the role of heritability in GS is to provide an estimate of the response to artificial selection for a trait, the relative comparison of trait heritability within a single method will still provide important information. One method for determining the marker density is to select them based on the highest prediction accuracy achievable under RR-BLUP B. However, this could only be done if every single QTL in the genome is represented by at least one marker. Even so, this method of calculation still slightly underestimates the true heritability, since it assumes that the causal variant for each QTL has been captured, whereas in fact, many of the markers are merely in LD with them18. With this, genomic heritability should only be estimated by this method with the basic condition that the marker density is representative of the entire genome, a condition that is also a must in GS.In this study, we found that the heritability estimates correlated well with the conventional heritability of published phenotypes 1. This was followed by the Bayesian methods that allow for different marker variances, which invariably indicate a preference of certain markers over others in modeling for a trait20. Both methods have met with varying degrees of success22. From our study, the accuracies for all the traits under both methods were generally high, ranging from 0.40 to 0.70. The current accuracy can be further improved with a larger training population. The prediction accuracies achieved were almost in a perfect correlation with the trait genomic heritability, with the highly heritable traits having high accuracy and vice versa. Similar results have been reported in other crops24. As both genetics and environment play an important role in determining the phenotype, a high heritability indicates a high genetic effect for the trait. Since GS captures the additive genetic effect, this would explain the good correlation between heritability and prediction accuracy. It has been reported that Bayesian methods are better options than RR-BLUP for traits with a few large QTL effects25. We observed that there is only a slight advantage in using Bayesian methods as compared to RR-BLUP . Similarly, in traits controlled by many small QTL effects , there is also only a slight increase in accuracy using RR-BLUP as compared to Bayesian methods. This is probably due to the fact that all important QTL were in LD with at least one polymorphic marker and represented in the genotyping array, and that we have used sufficient iterations for the Bayesian modeling step.The earliest GS method, namely RR-BLUP did not detect QTL and assumed that all markers explain the same amount of phenotypic variancer2 26 threshold of more than 0.2 between a marker and a QTL was found to be important2. In practice, since the QTL is not known, LD could only be calculated between markers used in the study. LD could be used as a method to reduce the marker density required, since markers in the same LD essentially carry the same information regarding the QTL. This method is particularly useful for traits where the QTL are few and have long LD regions with many markers, such as for M/F. For traits controlled by more QTL, inclusive of O/P, this method might not be effective. A way to distinguish between these two cases is by identifying accuracy drags when the accuracy per marker graph shape is not a perfect half sigmoid. A note of caution when applying this filter is that there might be a slight reduction in the global maximum accuracy. This is because it is difficult to set a common cut-off as every LD has a different length. A case by case approach would be better than setting a global cut-off. Even though the filtering by the LD method provides an option to reduce the number of markers required, in order to maintain prediction accuracy, a method that selects markers best representing the QTL must precede this step. RR-BLUP B provides a solution to this problem.In order for GS to perform well, the LD 27. This method ranks and subsets the SNPs based on marker effect. The accuracy of RR-BLUP B is dependent on the markers selected, which in turn is dependent on the association score cut-off being set. In Fig. 28. Also, this method required shorter running time. Each of the diagrams in Fig. 27. The principle behind RR-BLUP B is to use the most informative/largest effect marker up-front, and is therefore dependent on the efficiency of marker selection. Given the fact that the population in this study was split into smaller sets before attempting association studies, there were inconsistencies in the QTL detection due to the relatively small sample size, and this best explains the result observed for M/F and O/B. With sufficient sample size, the accuracy of all the traits can be improved. Association studies, therefore, are not only useful in detecting large QTL effects, but also offer the possibility of ranking QTL with small effects.A modification of RR-BLUP, termed RR-BLUP B, was found to be as effective as the Bayesian methodWith the associated markers showing more importance than other markers in the RR-BLUP B model, and RR-BLUP-B outperforming RR-BLUP in most cases, it might be more apt to define genomic selection as marker assisted selection that considers all markers representing all QTL responsible for a trait, instead of the entire genome. The problem that remains is what cut-off needs to be set for association or marker effects for the clear definition and representation of a QTL. This decision directly affects the number of markers to be used. On the other hand, the greatest strength of RR-BLUP B is the ability to control for the number of markers to be used in the model. In this study, approximately 200–400 top-ranked SNP markers for each trait  were required to achieve the optimal prediction accuracy. In other words, only the top 1–2% of the whole-genome SNP markers need to be deployed in GS implementation. The increment in accuracy was minor or negligible when the number of markers exceeded 400, when used with these models. The higher the number of markers, the higher the cost of implementation. Therefore, the number of markers to be selected for GS is also an economical decision instead of a purely scientific decision.in silico breeding, through crossing simulation, to select the best parents. The GEBV calculated for the simulated progenies can be used as the means to rank and select the best parents. This will reduce the cost even further, making genomic selection an even more attractive option for commercial perennial crop breeding.Characterization of simple traits requires only a few markers. Important traits in plants, for example O/P in oil palm, are complex in nature and require markers representative of the whole genome. The implementation of GS consolidates the effects of these markers into a GEBV, enabling ranking of the best performing individuals. The calculation of genomic heritability allows us to know the selection response of the trait of interest, which also implied the GS accuracy. The practical application of GS, however, is dependent on the cost, which is influenced by the number of markers used. Our results with marker sub-setting and LD filtering showed that marker reduction is possible without much reduction in accuracy. While it is recognized that the subsets of effective markers may be different according to the targeted traits, with a crop such as oil palm with large progeny numbers from a long breeding cycle, it would be feasible and practical to minimize genotyping costs by selecting the best markers for each trait and modeling them independently. In this study, our focus has been on selecting the best performing progenies for planting. However, the selection of parents is of utmost importance for commercial plant breeding programs. Therefore, instead of selecting for progenies, the models built can also be used in combination with tenera palms derived from multiple UR x AVROS crosses13 was selected as the study population. The population is maintained at Sime Darby R&D Centre, Malaysia and was phenotyped for the traits of M/F, S/F, K/F, F/B, O/M, O/B and O/P according to the industry standard30. The phenotying of these traits, except O/P were conducted under bunch analysis to generate reliable mean values with at least 3 bunches per palm. The O/P trait was then calculated based on the multiplication between 4-year average fresh fruit bunch (FFB) and O/B.A total of 1,218 31 and 92,057 SNPs were found to be polymorphic and were used for GS purposes.The genomic DNA of each palm was extracted from 100 mg of dried leaf tissue and purified using the DNAeasy Plant Mini Kit . Genotyping of this population was done using the OP200K SNP arrayrelated package32 implemented in R. Lynch & Li method33 was selected to calculate the pairwise kinship. This was done using the command “coancestry” and setting “lynchli” as 1. The bootstrap replication parameter was set at 100 and the inbreeding was set as False.Kinship coefficients within the population were calculated based on pairwise comparison between all the individuals used in this study. A total of 15,000 SNPs were selected at random from the full SNP genotypes. The resulting file was reformatted using an in-house Perl script. Kinship was estimated using the 17. Another calculation of heritability was based on marker subsets with selection criteria as defined under the RR-BLUP B section below. However, in this case, we set the association scores ) cut-off to be 1.3.Heritability was calculated based on a linear model using all informative SNP markersrrBLUP package34 and all the Bayesian methods were implemented using the BGLR package35. For the Bayesian methods, the number of iterations was set to 20,000, with the first 2,500 discarded as burn-in. For BL, the additional parameter of lambda was set to 25, type as gamma, rate as 1e-4 and shape as 0.55. A 5-fold cross validation was carried out, where the data was divided into 5 subsets, 4 of which  were used for modeling, and the last subset  was used for validation. This process was repeated 5 times, until all subsets were used for both modeling and validation. Correlation between the GEBV and observed trait value was used as a measurement for prediction accuracy37. Selection response for each of the traits was estimated based on the top 25% best performing individual as compared to the overall mean of unselected individuals.The different methods studied were RR-BLUP, Bayes A (BA), Bayes Cπ (BC), Bayes Ridge-Regression (BRR) and Bayes Lasso (BL). RR-BLUP was implemented using the 38, was carried out independently for each iteration during the cross validation step. Markers were ranked according to the association score. Subsequently, a stepwise increase by 10 markers, up to 20,000 markers, was carried out for model training. Genomic selection was carried out as described in the previous section for RR-BLUP. The accuracy for each marker set was defined based on the mean accuracy achieved. In addition, we have also looked at the optimal number of markers by calculating the accuracy increment for every 100 markers. The optimal marker density was defined as the point where the increment in the number of markers causes an improvement in accuracy of less than 0.05.Due to the large number of iterations required, only two traits with high heritability (M/F and S/F) and another two with medium heritability (O/B and O/P) were selected. For each of the traits, a genome-wide association study (GWAS), using the same method as described in our previous publication39 on top of association-ranked SNP. Pairwise LD calculation was done for each of the selected markers using R-squared and a cutoff at 0.6. Only one SNP was selected from a LD. Prediction accuracy was determined by RR-BLUP using the parameters described above. To illustrate the result, the prediction accuracy for the first 600 markers selected was plotted for both cases, with and without the LD filter.To illustrate LD-based marker reduction, the traits M/F and O/P were selected. LD was estimated using PLINKSupplementary table 2Supplementary information