Socioeconomic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. In a sample of 286,301 participants from UK Biobank, we identify 30 (29 previously unreported) independent-loci associated with income. Using a method to meta-analyze data from genetically-correlated traits, we identify an additional 120 income-associated loci. These loci show clear evidence of functionality, with transcriptional differences identified across multiple cortical tissues, and links to GABAergic and serotonergic neurotransmission. By combining our genome wide association study on income with data from eQTL studies and chromatin interactions, 24 genes are prioritized for follow up, 18 of which were previously associated with intelligence. We identify intelligence as one of the likely causal, partly-heritable phenotypes that might bridge the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. These results indicate that, in modern era Great Britain, genetic effects contribute towards some of the observed socioeconomic inequalities. Household income is used as a marker of socioeconomic position, a trait that is associated with better physical and mental health. Here, Hill et al. report a genome-wide association study for household income in the UK and explore its relationship with intelligence in post-GWAS analyses including Mendelian randomization. An understanding of the causes underlying the association between socioeconomic position (SEP) and health is likely to be helpful to minimize social disparities in health and well-being4.People with advantaged socioeconomic backgrounds, on average, live longer, and have better mental and physical health than those from more deprived environments5. However, genetic factors  can capture shared genetic associations between measures of health and SEP7. Potential pleiotropic effects are highlighted in the observed genetic correlations between SEP variables, such as years of education completed, household income and social deprivation, and physical and mental health traits including longevity8.The link between SEP and health is typically thought to be due to environmental factors, including, but not limited to access to resources, exposure to harmful or stressful environments, adverse health behaviours, such as smoking, poor diet and excessive alcohol consumption, and a lack of physical exercise11, but—consistent with other complex traits, such as height—these loci collectively account for only a small fraction of the total heritability of the traits in question. For household income, an analysis of a sample of 96,900 individuals from Great Britain found that additive genetic effects tagged by common single-nucleotide polymorphisms (SNPs) accounted for ~11% (SE = 0.7%) of differences in household income7. Two loci attained genome-wide significance in that study, but they collectively accounted for <0.005% of the total SNP heritability.Loci associated with two SEP phenotypes, education and household income, have been identified via GWASs12 to examine genetic associations with household income  in a contemporary British sample. We identify 30 independent genome-wide significant loci, 29 of which are unreported in previous work. Using a method that leverages power from genetically correlated traits, multi-trait analysis of GWAS (MTAG), an additional 120 loci are found to be associated with income. We identify neurogenesis and the components of the synapse as being associated with income. Furthermore, we link transcription differences across multiple cortical tissue types, as well as both GABAergic and serotonergic neurotransmission, to income differences. We also show that the genes linked to differences in income are predominantly those that have been previously linked with intelligence8, and that intelligence is one of the likely causal factors leading to differences in income. We compare the genetic correlations derived using income with those derived using another measure of SEP, educational attainment, to show that the genetic variants associated with income are related to better mental health than those related to education. Finally, we predict 2.5% of income differences using genetic data alone in an independent sample.Here, we use the UK Biobank data setA flow chart summarizing all statistical analyses conducted is displayed in Fig. P < 5 × 10−8), across 30 independent loci .Linkage disequilibrium score (LDSC) regression showed that the mean cis-eQTL SNPs to 186 genes, and chromatin interaction mapping linked the SNPs to 277 genes  tissue and IMR90  tissue, whereas HOXB7 showed associations in the tissues of hESC (human embryonic stem cell), mesenchymal  stem cell, IMR90, left ventricle, GM12878, and trophoblast-like cells.Three methods of mapping allelic variation to genes were used to better understand the functional consequences of the 30 independent loci linked to income  analysis and chromatin mapping). Using positional mapping, SNPs from the GWAS were aligned to 117 genes. eQTL mapping was used to match nes Fig. . Both HO13, 118 genes were associated with income (P < 2.662 × 10−6)  with intelligence from the GWAS conducted by Hill et al.8. This indicates that the genes with the most biological relevance to income were also linked to intelligence, again suggestive of the role that intelligence plays in SEP differences.These 24 genes were then examined to determine if gene-based statistics had implicated them in intelligence due to the previously reported strong genetic correlations between income and intelligenceP = 1.31 × 10−5) and the testis (P = 1.31 × 10−5) than genes that were less associated with income (Supplementary Table P = 5.61 × 10−6), the cerebellar hemisphere (P = 5.99 × 10−6), the frontal cortex BA9 (P = 9.68 × 10−5), the cortex (P = 1.05 × 10−4), the nucleus accumbens basal ganglia (P = 2.93 × 10−4) and the anterior cingulate cortex BA24 (P = 6.81 × 10−4) . Also enriched was the Conserved (GERP RS ≥ 4) annotation providing further evidence that conserved regions of the genome are enriched for the heritability of income. None of the other functional categories were significantly enriched for the heritability of income  as were SNPs in the upper two quintiles for greater level of background selection . The first three quintiles describing nucleotide diversity and the same quartiles describing the level of LD (LDD – AFR) were also significantly enriched for heritability   were associated with a greater proportion of the heritability of income , but no other regions were found to be enriched , but the two glia annotations of astrocyte and oligodendrocyte were not linked to income  (Supplementary Table P = 0.189) (Supplementary Table Mendelian randomization (MR) was performed using the genetic instrument derived using 19 SNPs associated with intelligence from a meta-analysis of a GWAS of intelligence from the INTERVAL study sources . Here weGenetic correlations were calculated between household income and a set of 27 data sets covering psychological traits, mental health, health and well-being, anthropometric traits, metabolic traits and reproduction.7 by using a larger, better-powered data set on income to show that the genetic variants associated with household income are linked with those that influence intelligence, rg = 0.69, SE = 0.02, P < 10 × 10−200. We also show that there are genetic correlations between income with health , mental health  and longevity . Furthermore, we replicated the finding of Hill et al. (2016)7 by showing in our current study that while a general factor of neuroticism shows a negative genetic correlation with household income , the two special factors of neuroticism named anxiety/tension and worry/vulnerability each show positive genetic correlations with income .First, we build on the findings of Hill et al. (2016)rg = 0.09, SE = 0.03, P = 9.53 × 10−3) and bipolar disorder   Fig. .Fig. 6Pa23, schizophrenia showed a small positive genetic correlation with educational attainment , whereas, in the present study, income showed a negative genetic correlation with schizophrenia . Autism was positively genetically correlated with educational attainment  as previously described24, whereas there was no detectable genetic correlation between income and autism . There was evidence of differences between the income and education genetic correlations and nine other traits   Fig. .rg = 0.58  to rg = 0.45 , and ADHD, which decreased from rg = −0.48  to rg = −0.36 . This means that genetic variation that is associated with income, but not intelligence, shows much of the same overlap with the 27 traits used here, as the genetic variation that is common to both income and intelligence.Third, the role of intelligence in mediating the effect of genetic variation on income was explored by estimating the genetic correlation of income with each of the traits after conditioning the income GWAS on a GWAS of intelligence. As can be seen in Fig. rg = 0.03, SE = 0.03, P = 0.31), as previously found;8 however, subjective well-being was negatively genetically correlated after adjusting for income  (Pdiff = 9.92 × 10−5). The genetic correlation between intelligence and social deprivation (as measured by Townsend scores) of rg = −0.42  attenuated to rg = 0.04  (Pdiff = 1.29 × 10−13). The genetic correlation between intelligence and neuroticism  also attenuated to close to zero after conditioning on income  (Pdiff = 7.26 × 10−3). This means that the genetic variation that is associated with intelligence, but not income, shows less overlap with the 27 traits used here, than the genetic variation that is common to both intelligence and income. The genetic correlations with intelligence once conditioning on income were different for the variables of self-rated health (Pdiff = 6.76 × 10−12), age at first birth (Pdiff = 1.33 × 10−8), fatigue or tiredness (Pdiff = 6.82 × 10−8), ADHD (Pdiff = 5.55 × 10−4), height (Pdiff = 2.59 × 10−4), BMI (Pdiff = 0.013), obesity (Pdiff = 1.60 × 10−2), longevity (Pdiff = 0.014) and smoking (Pdiff = 0.032)  were derived using the summary statistics from our GWAS of household income and GS:SFHS. When examining the PGRSs within each of the five income groups in GS:SFHS, we found that those in category 5  had the highest PGRSs Fig. . The preP value cutoff of 0.1.Those in the lowest quintile of the polygenic score for income were found on average to have the lowest predicted income Fig. , with th8. We used MTAG here to increase the power of our GWAS on income by meta-analyzing it with another measure of SEP, educational attainment10, as measured by the number of years of education a participant has completed. MTAG was conducted using the default settings and applied to increase the power in the GWAS of household income. Following the application of MTAG, the mean χ2 statistic increased from 1.45 to 1.73 and increased the effective sample size to 505,541 for income.MTAG has previously been used to conduct the first well-powered GWAS on intelligence25, indicating that the data set was capturing variance associated with income. We also found that the genetic correlation between our MTAG-income phenotype and a previous GWAS on income7 was rg = 0.97 (SE = 0.024), with a genetic correlation of rg = 0.94 (SE = 0.004) with educational attainment. This indicates that the polygenic signal in the MTAG-income analysis is virtually identical to that found in previous GWAS of income, but also that it captures more of the variance that is shared between income and education.The maxFDR derived was 0.003, over an order of magnitude lower than the commonly accepted standard of false discovery and comparable with that reported previouslyUsing this MTAG-income phenotype, we identify 144 independent genomic risk loci  associated with income levels in Great Britain today. This represents a considerable advance on the two loci previously identified by Hill et al.26, GTEx27 and others, along with chromatin interaction data29, we were able to prioritize which genes were likely to be causal based on the overlap of multiple lines of biological enquiry. Although income, as a biologically distal phenotype, will not be directly linked to genetic variation , winning participants did not have a longer life or fewer hospital admissions compared with those who did not win the lottery38. This indicates that whereas high earners may be in better health and have a greater level of education than low earners, a high income might not be causal in such differences in affluent countries that have strong social support systems. Furthermore, children born to lottery winners were not found to be advantaged in terms of their level of scholastic performance compared with the children of those who did not win the lottery38, a finding that argues against a dynastic effect mediated via wealth. Although any causal effect of wealth on health is likely to differ across countries and time periods, should the results of this Swedish study generalize to the United Kingdom today, they would complement our results and together would support a model whereby genetic differences that are linked with health might be linked to partly heritable intermediary phenotypes, such as intelligence.However, previous work in Sweden using lotteries as natural experiments to examine the causal effect of wealth on health differencesIn this scenario, the similarities and differences between the genetic correlations derived using education and income might be accounted for in part by the differences in the intermediary phenotypes that give rise to each measure of SEP. Under this model, the observed differences between genetic correlations with mental health Fig.  would beWe found that when the genetic associations that are shared between income and intelligence were removed, the genetic correlations with other traits were largely unchanged. The exceptions were with ADHD and with age of first birth, where the genetic correlations with income are both attenuated once conditioned on intelligence. However, 12 of the income–health genetic correlations were attenuated after adjusting the SNP–income associations for intelligence. These results indicate that the genetic variation associated with intelligence and income is also associated with many health and mental health traits, because, when this shared variance is removed, leaving only the variance that is unique to intelligence, the magnitude of the genetic link between intelligence and health is reduced. In the case of the genetic link between intelligence, social deprivation, neuroticism and height, this genetic association disappears entirely following adjustment for income. The exception is that subjective well-being shows a genetic correlation with intelligence only after the variance that is common to both income and intelligence is removed.39.One interpretation of this finding is that the residual variance left in income after conditioning on intelligence still contains the genetic contributions to other partly heritable traits (such as conscientiousness or resistance to disease). These traits also contribute towards individual differences in income, and so the association between income and health is, largely, intact following conditioning on intelligence. This would imply that intelligence is only one of a number of factors that contribute to variation in income, but income is a very important factor that mediates the associations between intelligence and health. Future work examining the genetic relationship between income and health, as well as intelligence and health, should focus on this genetic overlap between intelligence and income using tools such as genomic structural equation modelling (SEM) to partition the total variance of traits like income into the variance that is shared with intelligence and the variance that is separate from itSixth, we were able to predict up to 2% of income differences using PGRSs. This shows that even for phenotypes that are not impacted directly by genetic effects, but rather are more biologically distal as is the case with income, that the link between genotype and phenotype is sufficient to make predictions, based on DNA alone.Seventh, using MTAG, we increased our effective sample size from 286,301 participants to 505,541. With this increase in power, we were able to increase the number of loci found to be associated with income from 30 to 144. Of these 144 associations, 120 were not found to be genome-wide significant before the application of MTAG. These loci demonstrated the same patterns of functional enrichment as shown in the 30 loci identified using income alone. We also identified the same relationship between expression in the brain and across multiple cortical structures, using the better-powered MTAG-derived income phenotype  with educational attainment, as measured on an individual level, indicating that the household-level effects are likely to be generalizable to individual persons41. An example of this would could be that parents with a greater predisposition towards intelligence are also those that are more likely to provide opportunities for their children to enter higher-income occupations. In this instance, the second assumption of MR, that the instrument must only affect income via their effect on intelligence, would be violated. The association of the offspring’s SNPs and income would be partially due to the effects of the parents’ genotype on their parents’ intelligence, which subsequently affects offspring’s income. Whereas the current data cannot differentiate between causality and dynastic effects, it should be noted that for another measure of SEP, educational attainment, there is evidence of indirect genetic effects that account for ~30% of the variance of the direct genetic associations42. Future work in multigenerational samples should examine the role that such indirect genetic effects play in individual differences in income, as well as if their presence (if established) could result in an inflation of the estimate for a causal effect using MR43.A limitation of the MR analysis specifically are potential dynastic effects, which may violate the assumptions of MR. Dynastic effects are where genetic variants that the parent has but the child does not, are associated with parental behaviours, and these parenting behaviours are a causal factor in the SEP of the child44. However, to break the assumptions of MR, it is not sufficient for the genetic variants to have pleiotropic effects45. The genetic variants we use as instruments must have horizontally pleiotropic effects mediated via mechanisms other than intelligence. If the genetic variants have vertically pleiotropic effects, for example, SNP → neuron → intelligence → income → health, then our MR estimates will not be biased. Equally, if the SNPs affect other phenotypes, but these phenotypes do not affect outcome, then these effects will not result in bias in the MR estimates. It is possible that the genetic variants identified in intelligence GWAS have horizontally pleiotropic effects; however, it is unclear what mechanisms would mediate these effects. The genetic correlations between intelligence and personality traits are relatively low46. The genetic variants identified in the intelligence GWAS are likely to also affect a range of cognitive ability-related traits. However again, these pleiotropic effects via related phenotypes are unlikely to cause bias if the results are interpreted as a test of general cognitive function. It is possible to investigate potentially horizontal pleiotropic effects further using multivariable MR47. If SNPs have been identified, which explain sufficient independent variation in two or more potential pathways, for example, intelligence and education, it is possible to identify the direct effects of each exposure. Future research should use multivariable MR to investigate this further.Furthermore, genetic variants associated with intelligence are likely to have pleiotropic effects48, as well as examine SEP variables using whole-exome or whole-genome sequencing. In addition, the participants of UK Biobank are drawn from the more educated and healthier individuals in the United Kingdom, which might introduce collider bias49. Whereas a comparison of the level of SEP between the individuals in UK Biobank and the census conducted in the United Kingdom indicates that SEP, as measured using the Townsend Deprivation Index50, was very similar7, future work aiming to quantify or control for collider bias would be of value in addressing this potential issue.Another limitation is that the present study was restricted to examining common genetic effects. Should rare or less common genetic variation be associated with income, then these effects will be absent from this study. Future work should utilize methods that can capture these genetic effects51. Such misunderstandings include describing associated variants as, genes for income, or the misinterpretation that any associated variant, and indeed any nonzero heritability estimate, is evidence for genetic determinism or the immutable nature of these phenotypes via environmental intervention. We include a figure . This work was conducted under UK Biobank application 10279.The primary sample used involved participants from UK Biobank, an open-access resource established to examine the determinants of disease in middle-aged and older adults living in the United Kingdomn = 12,721), or “prefer not to answer” . This left a total number of 286,301 participants  aged 39–73 years  with genotype data who had reported, between 1 and 5, their level of household income.A total of 332,050 participants had genotype data and data on their level of household income. Self-reported household income was collected using a 5-point scale corresponding to the total household income before tax, 1 being less than £18,000, 2 being £18,000–£29,999, 3 being £30,000–£51,999, 4 being £52,000–£100,000 and 5 being greater than £100,000. This 5-point scale was analyzed by treating the categories of income as a continuous variable. Participants were removed from the analysis if they answered “do not know”  and 438,427 participants (UK Biobank Axiom Array)57. Genotype data on 805,426 markers were available for 488,377 of the individuals in UK Biobank. Imputation to the Haplotype Reference Consortium (HRC) reference panel leads to 39,131,578 autosomal SNPs being available for 487,442 participants56. Allele frequency checks58 against the HRC59 and 1000G60 site lists were performed, and variants with minor allele frequencies (MAFs) differing more than ±0.2 from the reference sets were removed.Full details of the UK Biobank genotyping procedure have been made available34. These included the removal of those with non-British ancestry based on self-report and a principal components analysis, as well as those with extreme scores based on heterozygosity and missingness. Individuals with neither XX nor XY chromosomes, along with those individuals whose reported sex was inconsistent with genetically inferred sex, were also removed. Finally, individuals with more than ten putative third-degree relatives  were also removed. Following these exclusions, a sample of 408,095 individuals remained. Using GCTA–GREML on 131,790 reportedly related participants62, related individuals were removed based on a genetic relationship threshold of 0.025. Following this quality control, household income data and genetic data were available on 286,301 participants. Following association analysis, SNPs with an MAF < 0.0005, and an imputation quality score <0.1 were removed. Finally, only biallelic SNPs were retained, resulting in 18,485,882 autosomal SNPs.Additional quality control steps were conducted and described previously7, and 40 genetic principal components (to control for population stratification), genotyping array, batch, age and sex as predictors. The residuals from this model were then used in a GWAS assuming an additive genetic model as implemented in BGENIE56.The level of household income as measured on the 5-point scale was subjected to a regression using income as the outcome, as has been conducted previously63. First, independent significant SNPs were defined using a P value cutoff of genome-wide significance (P < 5 × 10−8), as well as being independent from each other (r2 < 0.6) within a 1-mb window. Second, SNPs that were in LD with any independent SNP (r2 ≥ 0.6) and within a 1-mb window in addition to being in the HRC genomes reference panel with an MAF >0.001, were included for further annotation. Third, lead SNPs were identified using the independent significant SNPs as defined above. Lead SNPs were a subset of the independent significant SNPs that were in LD with each other at r2 <0.1, with a 1-mb window. Fourth, genomic risk loci were created by merging lead SNPs if they were closer than 250 kb apart. This means that a genomic risk locus could contain multiple independent significant SNPs and multiple lead SNPs. Finally, all SNPs in LD of r2 ≥0.6 with one of the independent significant SNPs formed the border, or edge, of the genomic risk loci.Genomic risk loci were derived using the summary data from the data set of household income derived in UK Biobank, using FUnctional Mapping and Annotation of genetic associations (FUMA)64 and the Ensemble genes build 85. Intergenic SNPs were annotated as the two closest flanking genes, which can result in them being assigned to multiple genes.The lead SNPs and those in LD with the lead SNPs were then mapped to genes based on their functional consequences, as described using ANNOVAR65 was used to map SNPs to genes based on physical distance. SNPs were mapped to genes if they were within 10 kb of a known protein gene found in the human reference assembly (hg19).Three strategies were used to link the income-associated independent genomic loci to genes. First, positional mappingcis-QTLs was used and SNPs were mapped to genes up to 1 Mb away. A false discovery rate (FDR) of 0.05 was used as a cutoff to define significant eQTL associations.Second, eQTL mapping was carried out by mapping SNPs to genes if allelic variation at the SNP is associated with expression levels of a gene. For eQTL mapping, information on 45 tissue types from three databases  based on 66. In order to reduce the total number of genes mapped using chromatin interactions and to increase the likelihood that those mapped are biologically relevant, an additional filter was added. We only retained interaction-mapped genes if one region involved with the interaction overlapped with a predicted enhancer region in any of the 111 tissue/cell types found in the Roadmap Epigenomics Project67, and the other region was located in a gene promoter region . An FDR of 1 × 10−5 was used to define a significant interaction.Finally, chromatin interaction mapping was carried out to map SNPs to genes when there is a three-dimensional DNA–DNA interaction between the SNP and gene. No distance boundary was applied as chromatin interactions can be long-ranging and span multiple genes. Hi-C data of 14 tissue types were used for chromatin interaction mapping68. Gene-based GWAS was conducted using MAGMA13. All SNPs located within protein-coding genes were used to derive a P value describing the association found with household income. The NCBI build 37 was used to determine the location and boundaries of 18,782 autosomal genes, and linkage disequilibrium within and between genes was gauged using the HRC panel. In order to control for multiple testing, a Bonferroni correction was applied using each gene as an independent statistical unit . The gene-based statistics derived using MAGMA were then used to conduct the gene-set analysis, the gene-property analyses and the cell-type enrichment analysis.Gene-based analyses have been shown to increase the power to detect association due to the multiple testing burden being reduced, in addition to the effects of multiple SNPs being combined13, examines if genes within the gene set are more strongly associated with the trait of interest than other genes, and differs from self-contained testing by controlling for type 1 error rate as well as being able to examine the biological relevance of the gene set under investigation69.In order to understand the biological systems vulnerable to perturbation by common genetic variation, a competitive gene-set analysis was performed. Competitive testing, conducted in MAGMA70, Reactome71 and MSigDB72) were examined for enrichment of household income. A Bonferroni correction was applied to control for the multiple tests performed on the 10,891 gene sets available for analysis.A total of 10,891 gene sets  database14, or across 29 different age groupings14, was associated with a gene’s link to household income. This RNA-Seq GEncode v10 summarized to gene data was accessed using the following link: http://www.brainspan.org/api/v2/well_known_file_download/267666525. The detailed descriptions of the normalization processes used can be found in the technical white paper at http://help.brain-map.org/download/attachments/3506181/Transcriptome_Profiling.pdf?version=1&modificationDate=1382036562736&api=v2, where a total of 524 samples were available for analysis. The developmental stages were assigned to each of the two groups  based on the age of the sample. The groupings of 25 post-conception weeks (pcw) and 35 pcw were excluded from the age groups as they contained fewer than three samples. Next, the 52,376 annotated genes were filtered so that the average reads per kilobase (RPKM) are >1. This was performed in the developmental group and in the age group separately. This resulted in 19,601 genes for the developmental- stage groupings and 21,001 genes for the age groupings. RPKM was then winsorized at 50 (RPKM >50 was replaced with 50). Then, the average of log-transformed RPKM with a pseudocount 1 (log2(RPKM + 1)) per group  was used as a covariate conditioning on the average across all the labels. To control for multiple tests, a Bonferroni correction was used to control for 11 and 29 tests separately.An additional gene-property analysis was conducted to determine if transcription in the brain at 11 developmental stages10, a gene-property analysis was also conducted to examine a broad array of brain-specific cell types. Enrichment of heritability was tested against 173 types of brain cells (24 broad categories of cell types), which were calculated following the method described in Skene et al.31. Briefly, brain cell-type expression data were drawn from single-cell RNA-Seq data from mouse brains. For each gene, a specificity value for each cell type was calculated by dividing the mean Unique Molecular Identifier (UMI) counts for the given cell type by the summed mean UMI counts across all cell types. MAGMA13 was used to calculate cell-type enrichments where specificity values were then divided into 40 equal-sized bins for each cell type for the MAGMA analysis. A linear model was fitted over the 40 specificity bins (with the least specific bin indexed as 1 and the most specific as 40). This was done by passing the bin values for each gene using the ‘--gene-covar onesided’ argument.As previous studies had indicated the importance of cortical tissues to differences in SEPUnivariate LDSC regression was performed on the summary statistics from the GWAS on household income in order to quantify the degree to which population stratification may have influenced these results.χ2) from each GWAS onto the LD score (the sum of squared correlations between the MAF count of a SNP with the MAF count of every other SNP) of each SNP. This regression allows for the estimation of heritability from the slope, and a means to detect residual confounders using the intercept.For the GWAS on household income, LD score regression was carried out by regressing the GWA test statistics . The continuous annotations were also analyzed by examining the enrichment of each quintile for the six continuous categories of predicted allele age, background selection, recombination rate, nucleotide diversity, low levels of linkage disequilibrium in African populations and CpG content. Here, control for multiple testing was performed using a Bonferroni correction within each of the six annotations (α = 0.05/5 = 0.01).Partitioned heritability was performed using stratified LDSC regression76. Here, four data sets were used and examined for enrichment of household income. The first data set (gene expression) contained gene expression data from across 205 tissue or cell types taken from the GTEx73 database and from Franke lab data set78 from Finucane et al.76. The second data set (chromatin) contained data on 489 tissue and cell types taken from Roadmap Epigenomics consortium67 and from EN-TEx, a subgroup of ENCODE79. Data pertaining to expression in 13 regions, the brain was taken from GTEx73 and gene expression specific to the neuron, the astrocytes and the oligodendrocytes were taken from mouse data from the work of Cahoy et al.80.Cell-type analysis was conducted using the method of Finucane et al.α = 0.05/13 = 0.004) and across the three types of neurons (α = 0.05/3 = 0.017). For the gene expression and chromatin groupings, an FDR81 was applied to the 205 tests performed to look at enrichment using gene expression (α = 0.006) and to the 489 tests examining chromatin-based annotations (α = 0.003).Multiple testing for the partitioning of the heritability by cell types was conducted using a Bonferroni correction across the 13 brain regions .The causal effects of intelligence  on income  were investigated using univariate MR analysis. Here, the total causal effect of intelligence on income was examined by combining summary GWAS test statistics for intelligence and for income using an inverse-variance-weighted (IVW) regression model83. MR analyses, which use multiple SNPs, are more likely to include invalid SNPs with horizontally pleiotropic effects84. By not constraining the intercept to zero (as done using IVW regression) MR–Egger relaxes the assumption that the effects of genetic variants on the outcome act solely through the exposure (in this case intelligence). The intercept parameter of the MR–Egger regression indicates the average directional pleiotropic effects of the SNPs on the outcome. As such, the direct pleiotropic effect that the SNPs have on the outcome, independent of the exposure, can be quantified, where a nonzero intercept provides evidence for bias due to directional pleiotropy and a violation of the MR IVW estimator assumptions. Of note is that the MR–Egger regression estimates only remain consistent if the magnitude of the gene-exposure associations, across all variants, are independent of their horizontally pleiotropic effects on the outcome (i.e. the InSIDE assumption holds)83. In addition, power is almost always lower for MR–Egger and it requires variation in the size of effect of the SNPs on the exposure .The results of the IVW regression model were compared with the results obtained using MR–Egger regressionn = 95,521 for intelligence and n = 271,732 for income) were created, whereby the GWAS on income was rerun using only those participants whose data were not included in the interim release of the UK Biobank genotype data. A GWAS data set on intelligence was created by meta-analyzing publicly available data on intelligence with a GWAS (conducted for this study) using data from the INTERVAL study17  in nature7. The magnitude of these genetic correlations might indicate the phenotypes that occur as potential mediators between molecular genetic inheritance and household income.Genetic correlations were derived using bivariate LDSC regression. A total of 27 GWAS data sets on health, anthropometric, psychiatric and metabolic traits were examined for a genetic correlation with income . mtCOJO is used to perform conditional GWAS whereby the genetic effects from one GWAS are controlled for in another GWAS. Importantly, the mtCOJO method avoids well-known issues of collider bias that can occur by including heritable covariates86. In the current study, the GWAS on income was conditioned on a GWAS on intelligence (and the intelligence GWAS was conditioned on the income GWAS) before the genetic correlations between income (and intelligence) and 27 variables mentioned above were reran.In addition, intelligence is known to be genetically correlated with many physical and mental health traits87 and the Generation Scotland:Scottish Family Health Study (GS:SFHS) cohort. The recruitment protocol and sample characteristics of GS:SFHS are described in full elsewhere89. In brief, 23,690 participants were recruited through their GP from across Scotland. Participants were all aged 18 years and over and were not ascertained based on the presence of any specific disease. Following the removal of individuals who preferred not to answer, income was assessed in GS:SFHS by 5-point scale . Individuals who preferred not to answer were excluded from the analysis. Individuals who had taken part in UK Biobank were also removed from the GS:SFHS data set (n = 174). SNPs were included in the data if they had an MAF of ≥0.01 and Hardy–Weinberg P value >0.000001. Finally, one from every pair of related individuals were removed from the data set by creating a genetic relationship matrix using GCTA90 and removing individuals who are related at ≥0.025. This yielded a final sample size of 6680 participants who had genotype data and income data.The summary statistics from our GWAS of household income PGRSs were derived using PRSice-2The participant’s level of income was then used as a predictor in a regression analysis with age, sex and 20 principal components included to control for population stratification. The standardized residuals from this model were then used as each participant’s income phenotype. PGRSs were created using the income phenotype derived using UK Biobank.P ≤ 0.01, P ≤ 0.05, P ≤ 0.1, P ≤ 0.5 and P ≤ 1) applied to the association statistics from the summary data. The polygenic risk scores for each threshold were then standardized and used in a regression model to predict the income phenotype in GS:SFHS.In each instance PRSice-2 was used to create five PGRSs corresponding to one of five P value cutoffs  sample overlap between the GWAS data sets25. The goal of this analysis was to increase the power to detect loci associated with income, and so our income GWAS was meta-analyzed with the GWAS on years of education by Okbay et al.91 using MTAG. Both the Okbay data set and the income data set from UK Biobank had a similar level of power  and they showed a genetic correlation of rg = 0.77 (SE = 0.02), confirming that both income and education, as measured using these data sets, have a highly similar genetic aetiology. Functional annotation and loci discovery, gene mapping, gene-based GWAS, gene set and gene-property analysis, were also performed using the MTAG-derived data set on income. In addition, following the removal of UK-based cohorts from the educational attainment summary statistics, genetic prediction was performed using the MTAG-derived income phenotype and the GS:SFHS as described above.MTAGPeer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20Supplementary Data 21Supplementary Data 22


LEP, FABP4,DGAT1, TG, IGF1, IGF1R, MYF5, LGB,CAPN1, CAST, GHR, and OLR1) were evaluated in296 purebred Holstein–Friesian bulls using PCR-RFLP (polymerase chain reaction – restriction fragment length polymorphism). Associations between each segregating SNP and genetic merit for fatteningperformance were quantified using linear mixed models. Traits included in the study werefattening period, final weight, dry matter intake, feed conversion rate, and averagedaily weight gain. Apart from the general determination of the above-mentioned traits,each trait was evaluated based on the fattening periods between five selectedtarget body weights  in selected candidate genes with fattening performance traits in acommercial cattle herd. Fifteen SNPs in 12 candidate genes ( In cattle fattening farms, techno-economic performance optimization isimperative for reaching an adequate profitability, whether incalf-to-finish or fattening farms. Prior knowledge regarding the commercial targetsallows for the determination of objectives such as slaughter weight, weight gain, feedconsumption, and meat quality. Improved breeding methods as well as the application ofbiotechnology have advanced the efficiency of cattle production .Achieving satisfactory fattening performance and profitability are affected by breed ofthe animals, season, initial weight, concentrate level, sex, penned cattle population,and housing type; and in addition to this, they are closely associated with optimalslaughter ages and final weights, which vary widely among cattle breeds . Apart from these environmental factors, the genotypicstructure of the animals is another decisive constituent of an efficient fatteningperformance evaluation in cattle production, which necessitates a long generationinterval.LEP; GenBank accession number: AF536174.1),which is located on chromosome 4, regulates food intake and whole-body energy metabolism. In addition, this gene is also involved inthe regulation of body weight and growth traits , drymatter intake , and therefore it may be considered as one of the mosteffective biological markers reflecting body fatness . Similar to the LEP, the bovine fattyacid binding protein 4 (FABP4) gene plays an important role in lipid metabolismassociated with changes in lipid hydrolysis and intracellular fatty acid . FABP4 is a functional and positional candidate gene for fat synthesis incattle . Bovine chromosome 14 (BTA14), whereFABP4 is located, is widely known to harbor quantitative trait loci (QTL)associated with fat-related traits such as milk fat percentage ,back fat thickness , and marbling . The otherimportant markers that have been mapped to BTA14 arediacylglycerol-O-acyltransferase 1 (DGAT1) and thyroglobulin (TG).DGAT1 (GenBank accession number: AY065621) has been shown to be commonlyassociated with milk components and intramuscular fat content . TG (GenBank accession number: X05380)is a glycoprotein precursor and the molecular regulator for the thyroid hormones. Thisgene has been demonstrated to be associated with lipid metabolism and meat productiontraits in various cattle breeds .Recently, many pieces of evidence have been presented that show that fatteningperformance and carcass traits are rather influenced by a number of candidate genes invarious cattle breeds . The bovine leptin gene (IGF1) and myogenic factor 5 (MYF5) genes that play fundamental rolesin regulation of growth and development .IGF1 (GenBank accession number: AF210383) has been shown to be a strongcandidate gene for growth rate and meat production traits  owing to its key role inregulation of cell proliferation . In addition to IGF1,its associated receptor IGF1R (GenBank accession number: U33122), plays animportant role in the regulation of the metabolism and physiology of mammalian growth. MYF5 (GenBank Accession number: M95684), which is a memberof the muscle regulatory factors (MRF) gene family, was determined as amuscle-specific factor and its expression was associated with myoblast lineage.Polymorphisms in this gene have been reported to be effective on growth traits in cattle. The bovine oxidized low-density lipoproteinreceptor 1 (OLR1) gene was mapped in the interval of 106 to 108 cM(centimorgan) on BTA 5 . Several studies with dairy cattle had shownsignificant effects of the OLR1 marker on milk production traits . On the other hand, the in vivo physiological role ofOLR1 in metabolism was reported by Murase et al. (2000) and Vinsky etal. (2013).Bovine chromosome 5 (BTA5) harbors QTLs that influence milk production , reproduction , and growth and carcass traits . In this genomic region, the location ofsome of the QTLs approaches the position of the insulin-like growth factor(LGB) gene, situated on bovine chromosome 11, encodes themain protein of whey . LGB (GenBank accession number:X14710) locus plays a key role in the evaluation of the milk production potential, and inaddition this gene can also be associated with other loci that have a direct influence ongrowth .The beta-lactoglobulin  and calpastatin genes, located on bovine chromosomes 29 and 7, respectively .The activity of the calpain–calpastatin proteolytic system is closely related to meatquality through postmortem tenderization and is characterized by three componentsincluding GHR), mapped to bovine chromosome 20, encodesgrowth hormone (GH), also known as somatotrophin, which stimulates importantphysiological processes involving growth and metabolism in cattle .Growth hormone receptor gene .However, the current production needs to be more efficient to supply internal demands.Increased demand and insufficient production have resulted in high meat prices. Thus,Turkish ministries have allowed for the importation of living animals and carcasses toregulate supply and demand but the importation has not been effective in lowering priceincreases . In Turkey, there is a strong need for novel andanalytical breeding strategies, including methods for genome-assisted prediction ofquantitative traits. These strategies may help with maintaining or increasing productionand help Turkey become a self-sufficient country with respect to beef production. In manycountries, meat production originates from two sources: production from herds that areexclusively based on specific beef breeds and production from a combination of dairy ormeat products. Cattle farms in Turkey generally produce dairy and dual-purpose breeds,and the number of specific beef breeds is rather limited. In this respect, the mostcommon cattle raised in Turkey (43.84 %) is the Holstein breed, with6 989 126 purebreds and crosses. Considering 15 943 586 total cattle count, theHolstein breed has a significant impact not only on the dairy cattle but also on Turkishbeef production . Therefore, evaluating thepotential of Holstein meat should be considered to increase beef production in thecountry.LEP, FABP4, DGAT1, TG, IGF1,IGF1R, MYF5, LGB, CAPN1, CAST, GHR,and OLR1 on fattening performance in Holstein bulls. In addition, the combinedeffects of these markers were evaluated with respect to genotypic interactions.Holstein cattle, which exhibit excellent dairy-type characteristics, carry a potentialfor covering the shortage in meat production by means of genetic variability for beeftraits and therefore the dual capacity of the Holstein breed is used in several countriesfor improvement of beef production . However, there is limitedinformation about the associations between the genotypic structure and growth offattening performance traits in Holsteins. Therefore, the main purpose of this paper isto obtain a detailed perspective on the effects of 15 polymorphisms at the selectedcandidate genes 22.1The animals used in this study were recorded for the Pedigree Project of the TurkishMinistry of Food, Agriculture and Livestock, and Cattle Breeders Association. Ethicalapproval for this study was granted by the Uludag University local Research EthicsCommittee . A total of 296 Holstein–Friesian bulls thatwere randomly selected from a commercial herd (with a herd size of 10 000 cattle) andraised on the same farm located in the south Marmara region of Turkey, and average daily weight gain (ADWG) for each animal were determined. Drymatter intake (DMI) was measured daily and the ADWG was calculated for the intervalbetween the two weight measurements. The feed conversion rate (FCR) was calculated,dividing the total amount of food consumed (based on DMI) between the weighing days tothe total weight gain . In this study, apart from the generaldetermination of the FP, DMI, FCR, and ADWG, each trait was evaluated based on thefattening periods between five selected target body weights with respect to fattening performance, lipidmetabolism, and appetite regulation; (b) SNPs in genes previously associated with meatproduction and quality or milk quality traits ; (c) Databases such as dbSNPand Ensembl  wereused for confirmation and information of each marker.The markers were selected from different sources as follows: (a) SNPs from genes thatbelonged to QTL mapping studies . The concentration range (ng Genotyping of the SNPs in the selected candidate genes was performed byPCR-RFLP (polymerase chain reaction – restriction fragment length polymorphism). Primersequences and PCR conditions for amplification are shown in Table 1. The primer sequenceswere checked for specificity by conducting BLAST searches of the NCBI GenBank database.PCR reaction mixtures consisted of 1 2.4The allelic and genotypic distribution presented in this paper were calculated for allSNPs by the standard procedure . The Hardy–Weinbergequilibrium (HWE) was tested for all alleles by using the POPGENE software v1.32 . The population genetic indexes including gene homozygosity (Ho), geneheterozygosity (He), effective allele numbers (Ne), and polymorphism informationcontent (PIC) were calculated, on the basis of allele frequencies, by using the followingformulas as described by Botstein et al. (1980):Statistical analyses were performed on records of fattening performance traits, includingFW, DMI, FCR, and ADWG in purebred Holstein bulls  was used to analyze therelationship between the individual or combined genotypes and performance traits incattle. All analyses were done in two steps: initially, markers were evaluated using thesignificance of genotype effects for each trait; and afterwards, the interactions betweenthe mentioned loci (two- and three-way interactions) were added to the model and testedfor significance. For the association studies, the traits of interest were analyzed usingthe general linear model (GLM) procedure and an appropriate statistical model wasselected according to coefficient of determination . Ininteraction analyses, genotypes with very low frequency were not included in the analysisin order to avoid unreliable results or confounding the influence of genotype effects ontraits of interest. Taken together, the least square mean (LSM) estimates with standarderrors (SE) for genotypes and fattening performance traits were used. For all statisticalcomparisons, a probability level of Additive effects and dominance deviation were also calculated using a reparameterizedmodel as demonstrated by Falconer and Mackay (1996). In this respect, the additive effectwas estimated as the difference between the means of two homozygous divided by two (a),whereas the dominance effect was calculated as the deviation of the heterozygote from themean of the two homozygotes (d). The degree of dominance was evaluated according to d/a.33.1OLR1 (CC genotype was not present). The minor allele frequencies (MAF) rangedfrom 0.12 to 0.49. With respect to the TG and CAPN1 530 polymorphisms,the frequencies of C (0.88) and G (0.83) alleles, respectively, were remarkably high inthe current study. The frequency of heterozygous genotypes was quite high  and A (0.17),respectively, resulting in low genetic variabilities in He, Ne, and PIC compared to theother SNPs showing relatively high values of population genetic indices. In this context,the highest He value (0.4998) was observed for the DGAT1 marker. The resultsindicated that FABP4 2834, FABP4 3533, DGAT1, IGF1,LGB, and CAST markers were characterized by high values of Ne(Hardy–Weinberg equilibrium (HWE) of SNPs is shown in Table 3. The distributions ofgenotypes were in agreement with HWE within 3.2LEP, FABP4, DGAT1, TG, IGF1, IGF1R,MYF5, LGB, CAPN1, CAST, GHR, andOLR1 genes on fattening performance traits in Holstein bulls. The associationanalysis revealed that all markers tested, with the exceptions of CAPN1 530,IGF1R, TG, and DGAT1, showed associations (at least onesignificant association) with respect to fattening performance, comprising 36 significantassociations  or meat quality were tested for fattening performance traitsbecause of their potential influence on growth and lipid metabolism as indicated byprevious studies . Impact of the selectedgenes seemed most likely to be related with animal body weight, weight gain, and feedefficiency, which are the crucial constituents of fattening performance evaluation. SNPs within the LEP, FABP4, DGAT1,TG, IGF1, IGF1R, MYF5, LGB, CAPN1,CAST, GHR, and OLR1 genes were examined for their effects onthese traits in Holstein cattle. In the current study, we hypothesized that individual orcombined effects of the genotypes at the selected genes may be effective on performancetraits at different periods of animal growth and fattening. In this respect, thedistinctive aspect of the evaluation presented in this paper, when compared to othercattle fattening performance studies, is the determination of genotype effects onreaching selected target body weights and the comparison of the traits among theintervals between them (see Sect. 2). Postnatal growth and fattening performance areunder the polygenic inheritance. Genetic background of the animals, the existence ofgenotype In the current study, several genes were selected based on their biological functions tobe evaluated for association between polymorphisms and fattening performance. These geneswere chosen because they have been shown to be involved in the regulation of appetite,lipid metabolism, or growth. Moreover, the effects of some functional candidate genemarkers for milk production . As pointed out by several authors, the markersthat have MAF below 5 % should be excluded from association analyses to draw reliableconclusions . Nevertheless, the highest frequency observed forthe most common allele was 0.88 (TG marker); and accordingly, all the markersused in the current study can be evaluated as polymorphic loci. In genetic studies,population genetic indices play an important role in the assessment of population structure defined by geneticvariation in a particular gene or genes. The levels of He may indicate clues in breedingcharacteristics of population; to give an example, the decrease in He can be caused as aresult of eventual inbreeding. Besides, Ne expresses the effectiveness of loci alleleimpact in populations . PIC values are the most common indicesto determine the extent of the polymorphism of a marker. In this sense, the usefulness ofa marker in segregation analysis is directly related to its level of polymorphism. PIC value classes are PIC TG and CAPN1 530 markers, low values ofindices were observed as follows: He FABP4 3533, DGAT1,IGF1, and CAST markers, He values approached 0.50, whereas Ne valuesapproached 2.00 in this study. The reason for the above-mentioned results is directlyrelated to allele frequency distributions, which are known to vary between breeds andeven between different populations of the same breed .The present results showed a deviation from HWE for all the markers, except4.3LEP A80V may be a potential marker for growthand fattening performance evaluation in cattle. In this context, animals with theTT genotype reached the target body weights of 200, 300, and 450 kg faster thanalternative genotypes. Further, and more important, these animals reached the mentionedbody weights with significantly lower TDMI and TDDMI . Moreover, this gene exhibits a potent regulationthrough central and peripheral pathways and might affect body weight. Kulig andKmiec (2009) reported that A80V polymorphism significantly affected the body weight at210 days of age  and improved FCRin Holstein cattle. Although LEP A80V is known as a famous candidate marker,knowledge about its associations with fattening performance is rather limited and thestudies were mostly conducted on milk production traits. Therefore, further research onassociations between the LEP and growth, or fattening traits in cattle, isnecessary and may provide valuable contributions to selection programs.Results of this study show that SNP associations with cattle fattening performancetraits, such as DMI, FCR, and ADWG, can be complex. This estimated complexity offattening performance illustrates how SNP associations could differ when the targetweights and stages of growth and fattening differ. To the knowledge of the authors, thisis the first detailed report of associations between genotypic properties and fatteningperformance of Holsteins. The raw phenotypic data used for this study included204 879 records .Genotypic analyses indicated that FABP4, DGAT1, and TG, are localizedon this genomic region. FABP4 is a leading candidate gene with an impact onlipid synthesis, feed intake, and growth . In the current study,polymorphisms in the FABP4 gene were significantly associated with performancetraits expressed at different stages of fattening in Holsteins. In this respect,FABP4 3691 was significantly associated with DTRW3 and animals with theAA genotype were characterized by decreased growth (FABP4 2834 marker affected the FP and ADWG in theW2–W3 period (200–300 kg target body weights). Animals with the GG genotype exhibitedhigher ADWG , it has not beensecurely suggested for the impact of variation on growth or fattening performance traits.Here, we present a probability of relationship between FABP4 gene polymorphismsand cattle growth. To the best of our knowledge, no previously reported studies havedetected such associations between FABP4 genotypes and the above-mentionedperformance traits, and hence the present results may provide novel aspects to thisgenomic region. Similar to the FABP4 gene, DGAT1 and TG werelocated on BTA14, responsible for large effects on several economically important traits. However, there was no association betweenthe polymorphisms of these genes and any of the phenotypic traits evaluated in thecurrent study. Ardicli et al. (2017a) have indicated the same result, given that theDGAT1 K232A is not an effective marker on fattening performance traits inSimmental bulls. The K232A and the C422T SNPs of the DGAT1 andTG genes, respectively, have been shown to be associated with adjusted fatthickness, meat tenderness , and marbling . Nevertheless, adequate studies on the association of these markerswith growth and fattening traits have not been widely performed, and thus furtheranalyses in various breeds of cattle are needed to test possible relationships and toobserve novel associations.BTA14 has been widely studied and the results indicated that it harbors many QTLsassociated with fat metabolism. Several genetic markers associated with economicallyimportant traits, such as IGF1 and its corresponding receptor IGF1R encode the hormones that areof basic constituents in the somatotrophic axis, and thus these genes play a key role inthe regulation of the mammalian metabolism . In this study,significant associations between IGF1 C472T polymorphism and feed intake wereobserved. The TT genotype was characterized by the highest TDMI and TDDMI but the lowestTFCR. In this context, animals with the TT genotype had IGF1 C472T polymorphism  hassignificant effects on live weight and weight gain. However, these associations were notsubstantiated in the present study. For IGF1R G404T polymorphism, also known asIGFIR/TaqI, there was no significant association between the SNP andany of the performance traits in this study. Moody et al. (1996) and Curi et al. (2005a)indicated that this polymorphism is not very useful in studies on the identification ofQTL in cattle, due to unbalanced allele frequencies. Moreover, Switonski (2002) suggestedthat BTA21, where the IGF1R is located, is not one of the most interesting forthe localization of loci related to growth and carcass composition in beef cattle. Thesestatements are in line with those obtained in the present study and are also confirmedfor Holstein bulls.MYF5 gene has been reported tobe associated with the various growth traits including weight at 12 months and ADWG; preweaning average daily gain and average daily gain on feed ; withers height and height at hip cross . Here, we onlyreport an association of the MYF5 genotypes with W4–W5 DDMI . The SNP studied hereis located in an intron region, and thus it does not alter the amino acid sequence of theprotein. However, introns have been shown to affect transcriptional efficiency  and play an important role in gene regulation. Accordingly, there is still aneed for further genetic studies about the MYF5 effects on growth and fatteningperformance.A mutation of A/G in the intron 2 region of the LGB gene (exon IV) showedsignificant associations with respect to FP and ADWG in the W2-W3 period and TDDMI. On the other hand,Tambasco et al. (2003) reported that, LGB GH interaction wassignificantly effective on ADWG from weaning to yearling .The effects of milk protein polymorphisms on growth or fattening traits have beenlimitedly investigated. To our knowledge, this is the first study indicating a possibleconnection between LGB/HaeIII polymorphism and performance traitsincluding DMI, and partially ADWG and FP. These findings may be useful to achieve novelaspects in evaluation of potential genetic markers with respect to livestock selectionprograms.The polymorphism located in a coding region of the CAPN1 and CAST markers showed significantassociations with performance traits. CAPN1 G316A affected the DTRW4, FW, TFCR,and TADWG, while CAST was effective on DMI and FCR in the W2–W3 period. Aspointed out by several authors, these genes are considered strong functional candidatesfor meat quality . Moreover, there is fair evidence for an associationbetween the telomeric end of BTA29, where the CAPN1 is located, and performancetraits including weaning weight, carcass weight, and feed efficiency . The present results demonstrated anassociation between the GG genotype of the CAPN1 G316A marker and FW and TADWG. It is worth noting that allele C of thismarker has been associated with more tender meat in several studies . It is well known that the genetic and phenotypiccorrelations among weights at different ages are positive, and thus it is possible thatmeat tenderness shows an association with FW, and that selection for this marker may leadto changes in both traits and also in ADWG . On the other hand, theobserved results can be the consequence of an effect of tightly linked genes throughcalpain–calpastatin system. Concerning also the effect of CAST marker, it seemsthat both markers may be involved in some way in growth regulation. If so, as suggestedby Pintos and Corva (2011), selection for growth rate and body size in many beef cattlebreeds could explain, at least partially, the reported allele frequencies  in markers such as CAPN1 G316A. Here, wepresent that a similar implementation may be considered for Holsteins.In the current study, GHR S555G polymorphism is astrong candidate marker that influences performance traits in Holstein breed. Ninephenotypic traits including DTRW3, W2–W3 FP, W2–W3 DMI, W2–W3 FCR, W2–W3 ADWG, W4–W5FP, W4–W5 DDMI, W4–W5 ADWG, and TDMI were significantly affected by this polymorphismat different levels of significance (Table 4). Results indicated that AA animals reachedthe 300 kg target body weight (DTRW3) faster than alternative genotype carriers. As suggested by Fonsecaet al. (2015), this effect is important for weaning weight adjusted to 210 days of agewith which they observed a significant association . Broad genetic diversity in livestockprovides the opportunity for the selection of animals with superior performance inspecific desirable traits . Thus, molecular marker information andevaluation of highly effective SNPs can be of great use for identification of animalswith high genetic value for complex traits, such as fattening performance, and theselection process can be conducted on younger animals, even before birth.4.4LEP GHR andIGF1 LEP interactions have significant effects onperformance traits. Regarding the LEP GHR interaction, CCAAwas characterized as an unfavorable genotype with the highest values of DTRW2, W1–W2FCR, W4–W5 FCR, and TFCR. Moreover, the CCCC genotype ofIGF1 LEP was also associated with the unfavorable effectsincluding the highest values of DTRW2, DTRW3, DTRW4, W3–W4 FP, and W4–W5 FCR, and thelowest TADWG (Table 9). Selecting animals with these genotypes may have an inadequateimpact to commercial farms. Animals with the AAGC genotype of theFABP4 3691 FABP4 2834 exhibited significantly lower means(FAP4 3533 LEP reached the 400 kgtarget body weight faster than the other genotypes (Table 10). The above-mentioned genesare known to be the regulators of lipid metabolism, and hence interactions between themmay provide useful clues for understanding the molecular mechanisms behind cattlefattening performance.Association analyses have routinely considered each locus separately in most QTL studies,and therefore these studies do not account for interaction between different loci. Assuggested by Tambasco et al. (2003), interactions between marker loci should be morecarefully investigated as such interactions may account for differences in genotyperesponses across populations and genetic backgrounds. Here, we present some novel geneticassociations with respect to combined effects of the markers selected. In this sense, thepresent study indicates that 5LEP, FABP4,IGF1, MYF5, LGB, CAPN1, CAST, GHR,and OLR1 genes influence economically important traits, such as final weight,dry matter intake, feed conversion rate, and average daily gains. The results alsodemonstrate that SNP associations may differ when the target weights and stages of growthor fattening differ. Here, it should also be noted that genotypic interactions mayprovide valuable information about the evaluation of fattening performance traits. Inthis respect, novel associations were observed for analyses carried out withLEP GHR, IGF1 LEP,FABP4 3691 FABP4 2834, andFAP4 3533 LEP interactions. Selecting animals with thefavorable SNP genotypes may result in production of animals with higher fatteningperformance and meat yield. Most of these selected polymorphisms are located in exonicregions and they cause amino acid alterations. FABP4 g.2834C An extensive genetic evaluation of fattening performance was performed in Holstein bulls.The results of this study suggest that polymorphisms at the 10.5194/aab-62-9-2019-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/aab-62-9-2019-supplement.


Citrus spp.), suitable fruit quality is essential for consumer acceptance of new cultivars. To identify parental combinations producing F1 progeny with fruit-quality traits exceeding certain selection criteria, we developed a simple and practical method for predicting multiple-trait segregation in an F1 progeny population. This method uses breeding values of parental genotypes and an additive genetic (co)variance matrix calculated by the best linear unbiased prediction method to construct a model for trait segregation in F1 progeny. To confirm the validity of our proposed method, we calculated the breeding values and additive genetic (co)variances based on phenotypic records on nine fruit-quality traits in 2122 genotypes, and constructed a trait segregation model. Subsequently, we applied the trait segregation model to all pairs of the 2122 genotypes , and predicted the most promising combinations and evaluated their probabilities of producing superior genotypes exceeding the nine fruit-quality traits of satsuma mandarin (Citrus unshiu Marcow.) or ‘Shiranuhi’ (‘Kiyomi’ × ‘Nakano No. 3’ ponkan), two popular citrus cultivars in Japan. We consider these results to be useful not only for selecting good parental combinations for fruit quality or other important traits but also for determining the scale of breeding programs required to achieve specific breeding goals.In the breeding of citrus ( Citrus spp.), suitable fruit quality is critical for consumer acceptance of new cultivars. As such, fruit-quality traits including high sugar content, easy peeling, seedlessness, soft pulp, and segment softness have been the major focus of citrus breeding programs in Japan, including that of the National Agriculture and Food Research Organization (NARO) Institute of Fruit Tree and Tea Science ′ with aj indicating a breeding value of the jth trait, amp is the vector of the midparental breeding values of the parental genotypes; and the element is calculated as (afj + amj)/2, where afj and amj are the breeding values of the jth trait in the seed and pollen parents, respectively and MVN indicates a multivariate normal distribution (nine variates in this case) with V being the covariance matrix. We used the predicted breeding values of seed and pollen parents as afj and amj, which were calculated using the BLUP method for each trait under the assumption that their F1 progeny was not yet produced. The covariance matrix V, which represents the genetic (co)variance matrix for Mendelian sampling in F1 progeny for the nine fruit-quality traits, is written asFf and Fm indicate the inbreeding coefficients of seed and pollen parents, respectively. Inbreeding coefficient refers to the fraction of homozygous loci in a genotype, which does not affect Mendelian sampling under the infinitesimal model [σajk are elements of the additive genetic (co)variance matrix for the jth and kth traits; we incorporated their estimates We constructed a segregation model for the nine fruit-quality traits in a two-generation family consisting of two parental cultivars and their Fal model . We calcal model  ‘nadiv’ 1 progeny (1) was evaluated in three F1 families: 93 progeny of ‘Tamami’ × ‘Shiranuhi’, 81 progeny of ‘Tsunonozomi’ × ‘Mihaya’, and 69 progeny of ‘Harehime’ × ‘Seinannohikari’, which were included in the 2122 genotypes described above. In each population, we compared the frequency distributions of breeding values of the nine fruit-quality traits obtained by the proposed method using (1) with sample distributions of breeding values of F1 progeny included in the dataset predicted by the BLUP method. The prediction of trait segregation in F1 progeny based on the distribution (1) was considered highly accurate if the two distributions were well coincident with one another. The coincidence between the two distributions was also evaluated by Q–Q (quantile–quantile) plots using the ‘qqplot’ function in R software.The accuracy of the proposed method using the distribution of breeding values of F2122H2 = 2,252,503 possibilities. We set our selection criteria as fruit quality higher than that of satsuma mandarin or ‘Shiranuhi’ in nine target traits—i.e., larger fruit size, fruit skin and flesh with a deeper orange color, a smoother fruit surface texture, easier peelability, softer pulp and segments, higher sugar content, and lower acidity. The actual values of these criteria were determined from their breeding values predicted by the BLUP method described above. The probabilities of obtaining superior progeny with characteristics fulfilling the selection criteria were computed as P values from the multivariate normal distribution based on our proposed model, using the R software ‘mvtnorm’ package [We applied the proposed method to all possible pairs of the 2122 genotypes examined, and calculated the probabilities of obtaining progeny with characteristics superior to satsuma mandarin and ‘Shiranuhi’ (‘Kiyomi’ × ‘Nakano No. 3’ ponkan) in terms of the nine fruit-quality traits. Since the reciprocal crosses returned the same probabilities in our method, we searched for good parental combinations, including selfing among  package .1 progeny. The accuracy of the constructed segregation model was evaluated in three F1 populations: 93 progeny of ‘Tamami’ × ‘Shiranuhi’, 81 progeny of ‘Tsunonozomi’ × ‘Mihaya’, and 69 progeny of ‘Harehime’ × ‘Seinannohikari’ , which The accuracy of our proposed method also depends on the mode of inheritance of target traits. Our method considers additive polygenic effects under the assumption of an infinitesimal model . However1 progeny in consideration of parental genotypes of the major gene. In fruit cross breeding, segregation analysis may be especially useful for a population in which molecular markers cannot be used—such as a formerly culled population—because segregation analysis requires only phenotypic records from a pedigreed population. In contrast, if molecular markers linked to QTLs are available, they can be used to predict trait segregation and to select prominent genotypes [In addition to non-additive effects, the mode of inheritance of target traits would involve major genes that have significant effects on the phenotype . To predenotypes , 29. WheIn a recent case study of Japanese pear, a novel method was proposed for predicting the segregation of target traits based on genome-wide markers . That meIn conclusion, concerning the difficult problem of selecting parental combinations for citrus breeding, we have proposed a practical solution for selecting prominent parental combinations by using accumulated phenotypic data in ongoing citrus breeding programs. In the near future, we intend to validate our proposed method in other fruit breeding programs.S1 FigFW fruit weight, FSC fruit skin color, FST fruit surface texture, PE peelability, FC flesh color, PF pulp firmness, SF segment firmness, SC sugar content, AC acid content.Frequency is shown on the vertical axis, and residuals are shown on the horizontal axis. (PDF)Click here for additional data file.S2 FigSquared Mahalanobis distance was calculated from breeding values of nine fruit-quality traits, and their distributions were compared.(PDF)Click here for additional data file.S3 FigSquared Mahalanobis distance was calculated from breeding values of nine fruit-quality traits, and their distributions were compared.(PDF)Click here for additional data file.S4 FigSquared Mahalanobis distance was calculated from breeding values of nine fruit-quality traits, and their distributions were compared.(PDF)Click here for additional data file.S5 FigFW fruit weight, FSC fruit skin color, FST fruit surface texture, PE peelability, FC flesh color, PF pulp firmness, SF segment firmness, SC sugar content, AC acid content.Correlation coefficients between these two predicted values are shown in each trait. The computational iteration procedure was not convergent in the multi-trait model when dominance effects were included, and thus we applied the single-trait model with dominance effect. (PDF)Click here for additional data file.


Microcebus murinus). We performed an animal model analysis on six traits measured in a large sample of captive mouse lemurs (N = 486). We chose two personality traits, two life-history traits and two morphological traits to (i) estimate the genetic and/or environmental contribution to their variance, and (ii) test for genetic correlations between these traits. We found modest narrow-sense heritability for personality traits, morphological traits and life-history traits. Other factors including maternal effects also influence the sources of variation in life-history and morphological traits. We found genetic correlations between emergence latency on the one hand and radius length and growth rate on the other hand. Emergence latency was also genetically correlated with birth weight and was influenced by maternal identity. These results provide insights into the influence of genes and maternal effects on the partitioning of sources of variation in personality, life-history and morphological traits in a captive primate model and suggest that the pace-of-life syndrome may be partly explained by genetic trait covariances.The recent interest in animal personality has sparked a number of studies on the heritability of personality traits. Yet, how the sources variance these traits can be decomposed remains unclear. Moreover, whether genetic correlations with life-history traits, personality traits and other phenotypic traits exist as predicted by the pace-of-life syndrome hypothesis remains poorly understood. Our aim was to compare the heritability of personality, life-history and morphological traits and their potential genetic correlations in a small primate ( Indeed,irection . Moreove changed . Another changed . The stu changed  advocatePersonality is generally defined as a statistically repeatable behaviour across context and over time between individuals . These cVa) is low as compared with the variability in the phenotype (Vp). For morphological traits, selection is typically lower and the variability of additive genetic effects is higher than phenotypic variability [The narrow-sense heritability of a phenotypic trait is dependent on the presence of additive genetic variation according to its definition , which iiability ,26. Studiability ,28.Microcebus murinus from a captive population [Microcebus murinus is a short-lived primate and thus ideal to explore variation in traits as predicted by the pace-of-life syndrome. The captive population used in this study is unique by its size  and by the information on life-history and other traits available for these individuals [M. murinus and shown to be repeatable in both wild and captive animals [The aim of our study was to describe and compare the heritability of behavioural, life-history and morphological traits and their potential genetic correlations in a small captive primate, pulation . Microceividuals  allowing animals ,31, maki animals ,32, raisThe aim of this study is to (i) estimate key variance components of morphological and personality traits and compare their heritability to better understand their evolutionary path . We pred2.2.1.M. murinus, We collected data for 486 different grey mouse lemurs  and amplified it (Qiagen REPLI-g Mini Kit). The genetic analysis was conducted to determine the paternity for 111 infants for which we disposed of DNA samples and their potential father. Thus, each infant has a known mother and three to four potential fathers, which were assigned thanks to microsatellite analysis .2.3.et al. [We used the two personality traits that were described in Zablocki-Thomas et al. . Both tret al. . We cond2.3.1.R = 0.33 ± 0.04 s.e. [We conducted emergence tests using a small wooden box (18 × 18 × 31 cm). We caught animals directly in their nest box between 13.00 and 17.00, identified animals and placed a single individual in the wooden box. Next, we placed the wooden box at the entrance of the home cage of the individual. We then waited at least two minutes so that the animal could habituate and calm down from the manipulation. The test consisted of opening the trap door and recording the latency for the animal to leave the box and to return to its home cage. The test lasted 5 min maximum. Individuals that never left the box within the allotted 5 min were given a score of 300 s. We conducted this test between 1 and 13 times per individual for a total of 1238 tests. Some individuals were tested only once as they died before we could test them twice. We waited at least three weeks before repeating the test with the same individual. Repeatability for this trait reached .04 s.e. .2.3.2.R = 0.28 ± 0.04 s.e. [We followed the protocol described in Verdolin & Harper  and eval.04 s.e. .2.3.3.We recorded the length of the lower arm (ulna/radius) and head width with a pair of digital callipers , as reported in previous studies . We extr2.3.4.We extracted body weight at birth, body weight at three months, litter size and mother identity from the colony database. We calculated growth rate as the weight gain in grams over the first three months of life, which is the period during which most of the growth occurs in this species .N = 486). Indeed, we could not collect all the phenotypic data at the same time, in particular, to avoid additional stress during the behavioural tests. Some individuals could not be tested for all traits as some died or were unavailable .Complete data were not available for all individuals, which explains why the sample size for each phenotypic trait varies and is different from the total number of individuals present in the study  with AsREML-R software (v. 3.0)  to obtai2.4.1.10-transformed, as it is typically done for latency data that do not present classical distributions [h2) of our phenotypic variables with univariate models. We tested, step-by-step, the fixed effects of age, body mass and sex in interaction  by running the same model without the effect and by testing it against a χ2 distribution with one degree of freedom. To test for the significance of additive genetic variance, we ran the same model with the pedigree component removed and tested it against a χ2 distribution with one degree of freedom and divided them by two . To estimate maternal environment, also called ‘maternal effect’, we added mother identity as a random factor. To account for pseudo-replication due to repeated measurements per individuals, we added individual identity as random factor in the models [Vp) is divided in three to four parameters, depending on whether there are several measurements or notVa is the additive genetic variance, Vm is the variance explained by the identity of the mother, Vce is the common environmental variance explained by the animal's housing environment (within shared environment consistency), Vpe is the permanent environmental variance explained by the identity of the individual  [We did not transform our variables except for emergence latency, which was logibutions . We treae models . We calce models . In our istency) ,46.2.4.2.Ia) [We report the amount of genetic variance relative to the trait mean (Ia) ,47,48:Ia2.4.3.m) is null [a) as previously described by comparing the general model with a model in which COVa is null [We scaled all variables using the ‘scale’ function in R. We tested for genetic correlation between emergence latency and agitation, emergence latency and radius length, emergence latency and birth weight, emergence latency and growth rate, and genetic correlation between agitation and radius length, agitation and birth weight, agitation and growth rate . The genetic correlation between head width and radius length was extremely high (0.73) and is possibly due to the allometric effect of overall body size.We found a significant and negative additive genetic covariance of emergence latency with both radius length and growth rate. We also detected a significant and positive covariance of emergence latency with growth rate when we took maternal effects into account . This su4.4.1.h2 = Va/Vp) was higher for one morphological trait compared with behavioural traits, but this was not the case for the other morphological trait. The agitation score during handling has previously been suggested to be associated with shyness and anxiety [Ia × 102 = 4.1–7.3) than that of other traits. Similar to a recent study in squirrels (Tamias striatus) [In this study, we decomposed the sources of variation of personality, morphological and life-history traits in order to compare their narrow-sense heritability. Personality traits showed significant additive genetic variance, but in contrast, only one morphological trait and none of the life-history traits showed significant additive genetic variance. As predicted, narrow-sense heritability  , we founh2 = 0.11 for males and 0.25 for females) [Ia × 102 = 0.35–0.47) for these traits compared with that for morphological traits (Ia × 102 = 0.013–0.047).Consistent with the literature, heritability estimates for life-history traits (growth rate and birth weight) were relatively low , when cofemales) . The amoMacaca mulatta) [We also detected significant maternal effects for life-history and morphological traits. Maternal effects have been shown to be important in primates such as macaques (mulatta) , but alsmulatta) . The impmulatta) , both ofWe also accounted for fixed effects including sex, age and body weight ,56 in ou4.2.We detected a genetic correlation between one personality trait, emergence latency and one morphological trait, radius length. This personality trait was also correlated with the two life-history traits; birth weight through the covariance with respect to the mother and with growth rate through additive genetic covariance. Previous studies have detected genetic links between personality traits and other traits, as for example in zebra fish , where sThe genetic correlation between personality and morphology and life history is in accordance with the phenotypic correlations previously reported for this population of mouse lemurs where individuals with lower birth weight had a shorter emergence latency . MoreoveTamias striatus) [Tamiasciurus hudsonicus) [However, we found no significant genetic correlation between the two personality traits in contrast to the predictions of the behavioural syndrome hypothesis . Our restriatus) ,62, betwsonicus) , and higsonicus)  have beesonicus) , consistsonicus) . Geneticsonicus)  comparinWe faced major difficulties with the convergence of the bivariate models. We could have obtained better estimates of maternal variance by a pedigree with more known paternities. A more resolved pedigree would also have resulted in an improvement of the precision of our models, but we do not expect changes to the principal patterns observed. Indeed, the ‘animal model’ approach was developed to conduct heritability estimations with missing paternities , and for4.3.M. murinus, our results are probably transposable to wild populations.We deliberately chose to study a captive population of primate species of interest to several fields of research, including personality research. It is important to acknowledge the potential influence of founder effects when dealing with genetic studies on captive populations. In this species, wild populations present a relatively high level of homozygosity, which can be interpreted by the presence of small breeding units, with breeding distances that are shorter than foraging distances . This poHowever, heritability is subject to variation across time and populations. In addition, heritability differences could arise between wild and captive populations ,70. Inde5.This study provides the first evidence of additive genetic variance of two personality traits in the grey mouse lemur. It also shows how variable the sources of variation are for different types of phenotypic traits. This study also brings the first evidence of a genetic correlation between a morphological trait and a personality trait in this small primate, and highlights the importance of maternal effects. Investigating the genes underlying these correlations and the selection operating on them would be of interest to better understand the evolution of phenotypic diversity in this primate model.


Plant breeding has been proposed as one of the most effective and environmentally safe methods to control fungal infection and to reduce fumonisin accumulation. However, conventional breeding can be hampered by the complex genetic architecture of resistance to fumonisin accumulation and marker-assisted selection is proposed as an efficient alternative. In the current study, GWAS has been performed for the first time for detecting high-resolution QTL for resistance to fumonisin accumulation in maize kernels complementing published GWAS results for Fusarium ear rot.Thirty-nine SNPs significantly associated with resistance to fumonisin accumulation in maize kernels were found and clustered into 17 QTL. Novel QTLs for fumonisin content would be at bins 3.02, 5.02, 7.05 and 8.07. Genes with annotated functions probably implicated in resistance to pathogens based on previous studies have been highlighted.Fusarium verticillioides.Breeding approaches to fix favorable functional variants for genes implicated in maize immune response signaling may be especially useful to reduce kernel contamination with fumonisins without significantly interfering in mycelia development and growth and, consequently, in the beneficial endophytic behavior of The online version of this article (10.1186/s12870-019-1759-1) contains supplementary material, which is available to authorized users. Aspergillus, Penicillium or Fusarium. Concern about kernel contamination with fumonisins is world-wide spread because these toxins are biosynthesized by species of the Gibberella fujikuroi complex, such as Fusarium proliferatum (Matsushima), F. subglutinans (Wollenw. & Reinking) and F. verticillioides (Sacc.) Nirenberg, which infect maize kernels all around the world  [patatin-like phospholipase gene at bin 4.02)]   and auxin 4.02)] . The posen shown . In geneen shown . Finallyen shown . TherefoZmLOX3, that suppress defense-related branches of the maize oxylipin pathway and favor Fusarium verticillioides virulence and fumonisin accumulation  for theFusarium verticillioides was characterized by a constitutive expression, and by a prompt and enhanced induction of some key genes [Fusarium verticillioides infection in the resistant genotype in the work by Lanubile et al. [Leaf rust 10 disease-resistance locus receptor-like protein kinase gene, could be involved in basal defense against fungi [Lanubille and coauthors observed that the response of a resistant genotype to kernel infection by ey genes . Therefoe et al.   in Ames, Iowa, and NCRPIS accession names are shown in Additional file A subset of 270 inbred lines from a maize diversity panel (composed of 302 inbred lines) that represents much of the diversity available in public breeding sector around the world  was eval− 1. In each row, between seven and 14 days after silking date, five primary ears were inoculated with two milliliters of a spore suspension of a local toxigenic isolate of Fusarium verticillioides using a tested kernel inoculation protocol [6 spores per milliliter and was injected into the center of the ear using a four-needle vaccinator. Inoculated ears from each row were collected 2 months after inoculation, dried at 35 °C for 1 week, and shelled. From each plot, a representative kernel sample of approximately 200 g was ground and stored at 4 °C until performing chemical analyses. Kernels were ground through a 0.75 mm screen in a Pulverisette 14 rotor mill .Evaluations were done at Pontevedra , Spain, using an 18 × 15 α-lattice design with two replications. Trials were hand-planted and each experimental plot consisted of one row spaced 0.8 m apart from the other row with 29 two-kernel hills spaced 0.18 m apart. Plots were overplanted and thinned, obtaining a final density of ~ 70,000 plant haprotocol . The spo1, B2, and B3) content using a commercial ELISA kit . This kit is a competitive enzyme immunoassay for quantification of fumonisin residues in maize. The recovery rate of the test was approximately 60% with a mean coefficient of variation of approximately 8%; specifities for B1, B2, and B3 were 100%, around 40%, and almost 100%, respectively, and the detection limit was 0.025 ppm (mg kg− 1). Extraction and preparation of samples, as well as test performance, were carried out as described in the commercial kits.Ground samples were sent to the Food Technology Department of the University of Lleida, Spain, for determination of total fumonisin  derived from a genotyping-by-sequencing (GBS) strategy  and uplifted to AGPv3  . SNPs inHeritabilities , β is a vector of fixed effects, including the SNP marker tested, u is a vector of random additive effects (inbred lines), X and Z represents matrices, and e is a vector of random residuals. The variance of random line effects was modeled as Var(u) = KK is the n × n matrix of pairwise kinship coefficient and Genome-wide association analysis based on mixed linear model (MLM) was performed in Tassel V5.2.25 . The MLMvariance . Restricvariance .p < 1 × 10− 4 and subsequently detected in ≥50 subsamples, i.e. resample model inclusion probability (RMIP) threshold of 0.50, were considered as significantly associated with the trait under study. Analysis of linkage disequilibrium (LD) among SNPs significantly associated with fumonisin content was performed in Tassel.To identify SNPs with the most robust associations with traits, a subsampling or subagging procedure was employed in GWAS analysis , 81. Eacr2 > 0.4). All genes contained in the supporting interval were considered as candidate genes and identified and characterized by the use of the MaizeGDB genome browser [We also examined the LD in the genomic region around each significant SNP to stablish a supporting interval for the significant association. That supporting interval would comprise the surrounding region in LD  and 2011 (right). (PNG 22 kb)Additional file 2:Table S1. Candidate Genes for each QTL. (XLSX 15 kb)Additional file 3:Table S2. Names of the panel inbreds along with their accession identifications at the North Central Regional Plant Introduction Station (NCRPIS). (XLS 40 kb)


There is evidence that education protects against cardiovascular disease. However, it is not known whether such an effect is independent of cognition.We performed two-sample Mendelian randomization (MR) analyses to investigate the effect of education and cognition, respectively, on risk of CHD and ischaemic stroke. Additionally, we used multivariable MR to adjust for the effects of cognition and education in the respective analyses to measure the effects of these traits independently of each other.In unadjusted MR, there was evidence that education is causally associated with both CHD and stroke risk . This effect persisted after adjusting for cognition in multivariable MR . Cognition had an apparent effect on CHD risk in unadjusted MR , however after adjusting for education this was no longer observed . Cognition did not have any notable effect on the risk of developing ischaemic stroke, with  or without adjustment for education .This study provides evidence to support that education protects against CHD and ischaemic stroke risk independently of cognition, but does not provide evidence to support that cognition protects against CHD and stroke risk independently of education. These findings could have implications for education and health policy. This work applied the multivariable Mendelian randomization technique to study the effects of educational attainment and cognitive function independently of each other on risk of coronary heart disease and ischaemic stroke, respectively.We found that educational attainment affects risk of coronary heart disease and ischaemic stroke independently of cognitive function, but did not identify an effect of cognitive function on these outcomes that was independent of educational attainment.These results are in keeping with previous work in this area and add to the body of evidence now available to inform public health and educational policy. ,,,Coronary heart disease (CHD) and stroke together make up the largest cause of morbidity and mortality worldwide, accounting for over 15 million deaths in 2016.Mendelian randomization (MR) is an instrumental variable method that can overcome some of the limitations of observational studies, by using genetic variants as instruments to study the effect of varying an exposure. The random allocation of such variants thus avoids the effect of confounding environmental factors to make causal inferences on an outcome of interest.,,An extension of the MR approach is multivariable MR (MVMR),P-value <5 × 10–8) and independence (linkage disequilibrium r2≤0.1). Educational attainment was measured as the number of years of schooling that individuals completed. Due to discrepancies in educational systems and qualifications between the cohorts for educational attainment, the International Standard Classification of Education (ISCED) system was used to match educational qualifications across countries into one of seven harmonized ISCED categories. IJE online, details how ISCED scores were mapped to years spent in education.Association estimates between single-nucleotide polymorphisms (SNPs) and educational attainment were derived from the publicly available summary data of a genome-wide association study (GWAS) meta-analysis (that excluded the 23andMe cohort) of 766 345 individuals of European ancestry.,,2 values were estimated for the instrument SNPs of both traits to evaluate their strength in conventional MR and proportion of phenotypic variance explained, respectively (with the specific formulae used detailed in IJE online).,For cognitive function, SNPs were taken from a GWAS meta-analysis performed in the UK Biobank and COGENT consortium in 257 841 participants of European ancestry.IJE online. Adjustment was made for genetic ancestry using the genomic control method, and the CHD definition was broad, including acute coronary syndrome and angina.The CARDIoGRAMplusC4D 1000 Genomes-based GWAS meta-analysis was used to obtain genetic association estimates for CHD.,IJE online. Adjustment was made for genetic ancestry using principal component analysis,For ischaemic stroke, instrument SNP genetic association estimates were extracted from a GWAS of 37 792 cases and 397 209 controls performed by the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN), and were downloaded from the Cerebrovascular Disease Knowledge Portal.IJE online. Overlap in the cohorts used for the exposure and outcome genetic association estimates is also detailed in Cohort details for all participating studies in these four GWAS meta-analyses are provided in http://cnsgenomics.com/shiny/mRnd/].We performed power calculations using the mRnd power calculator for conventional MR, available at  .http://www.cardiogramplusc4d.org/data-downloads/] .http://cerebrovascularportal.org/informational/downloads] [loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study].,Summary genetic data for both cognitive function and educational attainment can be downloaded from the Social Science Genetic Association Consortium (SSGAC) portal [D.G. is funded by the Wellcome 4i programme at Imperial College London.dyz200_Supplementary_DataClick here for additional data file.