DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 × 10−6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145  and rs17070072 . A third locus upstream CYLC2  when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 × 10−6). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 × 10−3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank ( Previous studies have shown that the effects of environmental factors on traits may be partially heritable3 and moderated by genetics5. Major depressive disorder (MDD) is the most common psychiatric disorder with a lifetime prevalence of approximately 14% globally6 and with a heritability of approximately 37%7. There is strong evidence for the role of stressful life events (SLEs) as risk factor and trigger for depression12. Genetic control of sensitivity to stress may vary between individuals, resulting in individual differences in the depressogenic effects of SLE, i.e., genotype-by-environment interaction (GxE)16. Significant evidence of GxE has been reported for common respiratory diseases and some forms of cancer22, and GxE studies have identified genetic risk variants not found by genome-wide association studies (GWAS)27.Mental illness results from the interplay between genetic susceptibility and environmental risk factors16; validating the implementation of genome-wide approaches to study GxE in depression. Most GxE studies for MDD have been conducted on candidate genes, or using polygenic approaches to a wide range of environmental risk factors, with some contradictory findings32. Incorporating knowledge about recent SLE into GWAS may improve our ability to detect risk variants in depression otherwise missed in GWAS33. To date, three studies have performed genome-wide by environment interaction studies (GWEIS) of MDD and SLE36, but this is the first study to perform GWEIS of depressive symptoms using adult SLE in cohorts of relatively homogeneous European ancestry.Interaction between polygenic risk of MDD and recent SLE are reported to increase liability to depressive symptoms37. Genetic factors predisposing to MDD may contribute to exposure and/or reporting of SLE38. To tackle this limitation, measures of SLE can be broken down into SLE likely to be independent of a respondent’s own behaviour and symptoms, or into dependent SLE, in which participants may play an active role exposure to SLE40. Different genetic influences, including a higher heritability, are reported for dependent SLE compared to independent SLE44, suggesting that whereas GxE driven by independent SLE is likely to reflect a genetic moderation of associations between SLE and depression, GxE driven by dependent SLE may result from a genetic mediation of the association through genetically driven personality or behavioural traits. To test this, we analysed dependent and independent SLE scores separately in Generation Scotland (GS).Interpretation of GxE effects may be hindered by gene–environment correlation. Gene–environment correlation denotes a genetic mediation of associations through genetic influences on exposure to, or reporting of, environments45. Therefore, genetic stress-response factors in MDD may also underlie the aetiology of other stress-linked disorders with which MDD is often comorbid47 . We tested the hypothesis that pleiotropy and shared aetiology between mental and physical health conditions may be due in part to genetic variants underlying SLE effects in depression.Stress contributes to many human conditions, with evidence of genetic vulnerability to the effect of SLEIn this study, we conduct GWEIS of depressive symptoms incorporating data on SLE in two independent UK-based cohorts. We aimed to: (i) identify loci associated with depressive symptoms through genetic response to SLE; (ii) study dependent and independent SLE to support a contribution of genetically mediated exposure to stress; (iii) assess whether GxE effects improve the proportion of phenotypic variance in depressive symptoms explained by genetic additive main effects alone; and (iv) test for a significant overlap in the genetic aetiology of the response to SLE and mental and physical stress-related phenotypes.The core workflow of this study is summarised in Fig. 52. At baseline, blood and salivary DNA samples were collected, stored and genotyped at the Wellcome Trust Clinical Research Facility, Edinburgh. Genome-wide genotype data were generated using the Illumina HumanOmniExpressExome-8 v1.0 DNA Analysis BeadChip  and Infinium chemistry53. The procedures and details for DNA extraction and genotyping have been extensively described elsewhere55. In total, 21,525 participants were re-contacted to participate in a follow-up mental health study , of which 8541 participants responded providing updated measures in psychiatric symptoms and SLE through self-reported mental health questionnaires56. Samples were excluded if: they were duplicate samples, had diagnoses of bipolar disorder, no SLE data (non-respondents), were population outliers , had sex mismatches or were missing >2% of genotypes. Single nucleotide polymorphisms (SNPs) were excluded if: missing >2% of genotypes, Hardy–Weinberg equilibrium test p < 1 × 10−6, or minor allele frequency <1%. Further details of the GS and STRADL cohort are available elsewhere58. All components of GS and STRADL obtained ethical approval from the Tayside Committee on Medical Research Ethics on behalf of the NHS (reference 05/s1401/89). After quality control, individuals were filtered by degree of relatedness (pi-hat < 0.05), maximising retention of those individuals reporting a higher number of SLE. The final dataset comprised data on 4919 unrelated individuals  and 560,351 SNPs.GS is a family-based population cohort representative of the Scottish populationN = 21,525) excluding respondents and their relatives (N = 5724). Following the same quality control criteria detailed above, we maximised unrelated non-respondents for retention of cases, or proxy cases (see below), to maximise the information available for each phenotype. This resulted in independent datasets with unrelated individuals for each trait. Differences between respondents and non-respondents are noted in the figure legend of Table Additional datasets for a range of stress-linked medical conditions and personality traits were created from GS N = 2,525 excl59. After quality control, 1,009,208 SNPs remained. UKB received ethical approval from the NHS National Research Ethics Service North West (reference: 11/NW/0382). Further details on UKB cohort description, genotyping, imputation and quality control are available elsewhere62.This study used data from 99,057 unrelated individuals  from the initial release of UKB genotyped data . Briefly, participants were removed based on UKB genomic analysis exclusion, non-white British ancestry, high missingness, genetic relatedness (kinship coefficient > 0.0442), QC failure in UK BiLEVE study and gender mismatch. GS participants and their relatives were excluded and GS SNPs imputed to a reference set combining the UK10K haplotype and 1000 Genomes Phase 3 reference panelsAll participants provided informed consent.64  were constructed for GS. UKB participants were screened for ‘illness, injury, bereavement and stress’’ (Supplementary Table UKB) was constructed by summing the number of ‘yes’ responses.GS participants reported SLE experienced over the preceding 6 months through a self-reported brief life events questionnaire based on the 12-item list of threatening experiences67. Participants rated the degree and severity of their current symptoms with a four-point Likert scale . A final log-transformed GHQ was used to detect altered psychopathology and thus, assess depressive symptoms as results of SLE. In UKB participants, current depressive symptoms over the preceding 2 weeks were evaluated using four psychometric screening items  validated to screen mental illness70. Each question was rated in a four-point Likert scale to assess impairment/severity of symptoms. Due to its skewed distribution, a four-point PHQ score was formed from PHQ  to create a more normal distribution.GS participants reported whether their current mental state over the preceding 2 weeks differed from their typical state using a self-administered 28-item scaled version of the General Health Questionnaire (GHQ)52. These conditions were selected from literature review based on previous evidence of a link with stress45  of depressive symptoms and self-reported SLE measures, and within samples bivariate genetic correlation between depressive symptoms and SLE measures using GCTA72.A restricted maximum likelihood approach was applied to estimate SNP-heritability  were fitted as covariates. In UKB, age, sex and 15 PCs recommended by UKB were fitted as covariates. The genome-wide significance threshold was p = 5 × 10–8.GWAS were conducted in PLINKUKB in UKB fitting the same covariates detailed above to reduce error variance. GWEIS were conducted using an R plugin for PLINK73 developed by Almli et al.74 (https://epstein-software.github.io/robust-joint-interaction). This method implements a robust test that jointly considers SNP and SNP–environment interaction effects from a full model (Y ~ β0 + βSNP + βSLE + βSNPxSLE + βCovariates) against a null model where both the SNP and SNP×SLE effects equal 0, to assess the joint effect (the combined additive main and GxE genetic effect at a SNP) using a nonlinear statistical approach that applies Huber–White estimates of variance to correct possible inflation due to heteroscedasticity . This robust test should reduce confounding due to differences in variance induced by covariate interaction effects if present75. Additional code was added  for TSLE, DSLE and ISLE in GS and on PHQ for TSLE76 including: gene-based tests, functional annotation, gene prioritisation and pathway enrichment  were generated using PRSice-277  independently. PRS were also weighted in both samples using either UKB-GWAS or GS-GWAS statistics (PRSD), and summary statistics from Psychiatric Genetic Consortium (PGC) MDD-GWAS  that excluded GS and UKB individuals when required . Furthermore, we calculated PRS weighted by the joint effects  from either the UKB-GWEIS or GS-GWEIS. PRS predictions of depressive symptoms were permuted 10,000 times. Multiple regression models fitting PRSGxE and PRSMDD, and both PRSGxE and PRSD were tested. All models were adjusted by same covariates used in GWAS/GWEIS. Null models were estimated from the direct effects of covariates alone. The predictive improvement of combining PRSGxE and PRSMDD/PRSD effects over PRSMDD/PRSD effects alone was tested for significance using the likelihood ratio test (LRT).Polygenic risk scores (PRSs) weighting by GxE effects . The predictive improvement of fitting PRSGxE combined with PRSD and covariates over prediction of a phenotype using the PRSD effect alone with covariates was assessed using LRT, and LRT-p-values adjusted by FDR (conservative threshold at LRT-p = 8.35 × 10–4).Prediction of PRSr2 = 0.22, p < 2.2 × 10–16) and GS . Significant bivariate genetic correlation between depression and SLE scores was identified in UKB , but not in GS . This estimate remained significant after adjusting by TSLEUKB effect , suggesting a genetic contribution unique to depressive symptoms. The h2SNP of TSLEUKB was also significant . However, in an ad hoc estimation from the baseline sample of 6751 unrelated GS participants , suggesting that the power to estimate h2SNP in GS may be limited by sample size. Estimates were not significant for either TSLE , but h2SNP was significant for DSLE , supporting a potential genetic mediation and gene–environment correlation.In UKB, a significant p < 1 × 10−5) are summarised in Supplementary Table 1000 < 1.01).No genome-wide significant SNPs were detected by GWAS in either cohort. Top findings p <  × 10−5 ap = 2.77 × 10−6; DCC, p = 7.53 × 10−8; ACSS3, p = 6.51 × 10−7; DRD2, p = 6.55 × 10−7; STAG1, p = 1.63 × 10−6; FOXP2, p = 2.09 × 10−6; KYNU, p = 2.24 × 10−6; Supplementary Figure biological process: positive regulation of long-term synaptic potentiation, and GWAS catalogue associations: brain structure, schizophrenia, response to amphetamines, age-related cataracts (age at onset), fibrinogen, acne (severe), fibrinogen levels and educational attainment; all adjusted-p < 0.01. There was no significant gene-set enrichment from GS-GWAS.Gene-based test identified six genes associated with PHQ using the UKB-GWAS statistics at genome-wide significance (Bonferroni-corrected 1000 < 1.01). No genome-wide significant GWEIS associations were detected for SLE in UKB. GS-GWEIS using TSLE identified a significant GxE effect (p < 2.97 × 10−8) at an intragenic SNP on chromosome 11  and rs17070072 p = 1.96 × 10−8) were significant. GWEIS for joint effect using DSLE identified two further significant SNPs on chromosome 9 , although the effect direction was consistent between cohorts for the SNP close to PIWL1 and SNPs at CYLC2. No SNP achieved genome-wide significant association in the GS-GWEIS using ISLE as exposure. Top GWEIS results (p < 1 × 10−5) are summarised in Supplementary Tables Manhattan and QQ plots are shown in Supplementary Figures t p < 2.9 × 10−8 at p < 2.9 × 10−8 aACSS3 p = 1.61 × 10−6; PHF2, p = 2.28 × 10−6; Supplementary Figure ACSS3 was previously identified using the additive main effects, whereas PHF2 was only significantly associated using the joint effects. Gene-based tests identified MTNR1B as significantly associated with GHQ on the GS-GWEIS using DSLE in both GxE (p = 1.53 × 10−6) and joint effects . Using joint effects, prioritised genes were enriched on upregulated differentially expressed genes from artery tibial (adjusted-p = 4.34 × 10−2). In GS, prioritised genes were enriched: in upregulated differentially expressed genes from artery coronary (adjusted-p = 4.55 × 10−2) using GxE effects with DSLE; in downregulated differentially expressed genes from artery aorta tissue (adjusted-p = 4.71 × 10−2) using GxE effects with ISLE; in upregulated differentially expressed genes from artery coronary  and artery tibial  tissues using joint effects with both TSLE and DSLE; and in downregulated differentially expressed genes from lung tissue (adjusted-p = 3.98 × 10−2) and in up- and downregulated differentially expressed genes from the spleen (adjusted-p = 4.71 × 10−2) using joint effects with ISLE. There was no enrichment using GxE effect with TSLE.We prioritised genes based on position, eQTL and chromatin interaction mapping in brain tissues and regions. In UKB, prioritised genes using GxE effects were enriched for upregulated differentially expressed genes from adrenal gland , similar to PRSMDD weighted by PGC MDD-GWAS . PRSGxE weighted by the UKB-GWEIS GxE effects explained 0.15% of GHQ variance  compared with PRSMDD alone. Similar to PRSGxE with PRSD . PRSJoint weighted by the UKB-GWEIS also predicted GHQ , although the variance explained was significantly reduced compared with the model fitting PRSGxE and PRSD together (LRT-p = 4.69 × 10−7), suggesting that additive and GxE effects should be modelled independently for polygenic approaches . Predictive improvements using the PRSGxE effect fitted jointly with PRSMDD or PRSD were not significant . PRSJoint significantly predicted PHQ , although the variance explained was significantly reduced compared with the models fitting PRSGxE and PRSD together .In UKB Fig. , both PRD, PRSGxE and PRSJoint are summarised in Fig. -p < 6.16 × 10−3, FDR-adjusted Empirical-p < 0.05) using both UKB and GS PRSD was seen for: depression status, neuroticism and schizotypal personality. PRSGxE weighted by the GS-GWEIS GxE effect using ISLE significantly predicted depression status mapped by proxy .Prediction of stress-related traits in independent samples using PRSLRT-p < 0.05) of fitting PRSGxE, over the PRSD effect alone, using summary statistics generated from both UKB and GS were detected for schizotypal personality, heart diseases and chronic obstructive pulmonary disease (COPD) by proxy .Nominally significant predictive improvements , were slightly higher than previous estimates from African-American populations34, and over a third larger than estimates in MDD from European samples78. h2SNP for PHQ in UKB (9.0%) remained significant after adjusting for SLE (7.9%). Thus, although some genetic contributions may be partially shared between depressive symptoms and reporting of SLE, there is still a relatively large genetic contribution unique to depressive symptoms. Significant h2SNP of DSLE in GS (13%) and TSLEUKB in UKB (4%), which is mainly composed of dependent SLE items, were detected similar to previous studies (8 and 29%)42. Conversely, there was no evidence for heritability of independent SLE. A significant bivariate genetic correlation between depressive symptoms and SLE (rG = 0.72) was detected in UKB after adjusting for covariates, suggesting that there are shared common variants underlying self-reported depressive symptoms and SLE. This bivariate genetic correlation was smaller than that estimated from African-American populations 34. Genetic correlations between SLE measures and GHQ were not significant in GS , perhaps due to a lack of power in this smaller sample.SNP-heritability of depressive symptoms . Previous studies have implicated these genes in liability to depression 79. This supports the implementation of quantitative measures such as PHQ to detect genes underlying lifetime depression status80. For example, significant gene ontology analysis of the UKB-GWAS identified enrichment for positive regulation of long-term synaptic potentiation, and for previous GWAS findings of brain structure81, schizophrenia82 and response to amphetamines83.Post-GWAS gene-based tests detected six genes significantly associated with PHQ (p > 0.05). The strongest association was using TSLE at 53-kb downstream of PIWIL4 (rs12789145). PIWIL4 is brain expressed and involved in chromatin modification84, suggesting it may moderate the effects of stress on depression. It encodes HIWI2, a protein thought to regulate OTX2, that is critical for the development of forebrain and for coordinating critical periods of plasticity disrupting the integration of cortical circuits86. Indeed, an intronic SNP in PIWIL4 was identified as the strongest GxE signal in attention deficit hyperactivity disorder using mother’s warmth as environmental exposure87. The other significant GxE identified in our study was in ZCCHC2 using DSLE. This zinc-finger protein is expressed in blood CD4+ T cells and is downregulated in individuals with MDD88 and in those resistant to treatment with citalopram89. No GxE effect was seen using ISLE as exposure.The key element of this study was to conduct GWEIS of depressive symptoms and recent SLE. We identified two loci with significant GxE effect in GS. However, none of these associations replicated in UKB  with obsessive-compulsive disorder and Tourette’s syndrome93. Gene-based test from the GS-GWEIS identified a significant association with MTNR1B, a melatonin receptor gene, using DSLE  identified two genes significantly associated with PHQ 95. Multiple regression models suggested that inclusion of PRSGxE weighted by GxE effects could improve prediction of an individual’s depressive symptoms over use of PRSMDD or PRSD weighted by additive effects alone. In GS, we detected a predictive gain of 19% over PRSMDD weighted by PGC MDD-GWAS, and a gain of 23% over PRSD weighted by UKB-GWAS , but not with dependent SLE. GxE effects using statistics generated from both discovery samples, despite the differences in measures, nominally improved the phenotypic prediction of schizotypal personality, heart disease and the proxy of COPD (LRT-p < 0.05). Other studies have also found evidence supporting a link between stress and depression in these phenotypes  interactions, or genotype-by-genotype-by-environment (GxGxE) interactions, contributing to depression99. In contrast, PRS prediction of the stress-related traits: schizotypal personality, heart disease and COPD, was primarily from derived weights using independent SLE, suggesting that a common set of variants moderate the effects of SLE across stress-related traits and that larger sample sizes will be required to detect the individual SNPs contributing to this. Thus, our findings support the inclusion of environmental information into GWAS to enhance the detection of relevant genes. The results of studying dependent and independent SLE support a contribution of genetically mediated exposure to and/or reporting of SLE, perhaps through sensitivity to stress as mediator.In this study, evidence for SNPs with significant GxE effects came primarily from the analyses of dependent SLE and not from independent SLE. This supports a genetic effect on probability of exposure to, or reporting of SLE, endorsing a gene–environment correlation. Chronic stress may influence cognition, decision making and behaviour eventually leading to higher risk takingThis study emphasises the relevance of GxE in depression and human health in general and provides the basis for future lines of research.Supplementary MaterialSupplementary Tables


Efficient production and use of doubled haploid lines can greatly accelerate genetic gains in maize breeding programs. One of the critical steps in standard doubled haploid line production is doubling the haploid genome using toxic and costly mitosis-inhibiting chemicals to achieve fertility in haploids. Alternatively, fertility may be spontaneously restored by natural chromosomal doubling, although generally at a rate too low for practical applications in most germplasm. This is the first large-scale genome-wise association study to analyze spontaneous chromosome doubling in haploids derived from tropical maize inbred lines. Induction crosses between tropicalized haploid inducers and 400 inbred lines were made, and the resulting haploid plants were assessed for haploid male fertility which refers to pollen production and haploid fertility which refers to seed production upon self-fertilization. A small number of genotypes were highly fertile and these fertility traits were highly heritable. Agronomic traits like plant height, ear height and tassel branch number were positively correlated with fertility traits. In contrast, haploid induction rate of the source germplasm and plant aspect were not correlated to fertility traits. Several genomic regions and candidate genes were identified that may control spontaneous fertility restoration. Overall, the study revealed the presence of large variation for both haploid male fertility and haploid fertility which can be potentially exploited for improving the efficiency of doubled haploid derivation in tropical maize germplasm.The online version of this article (10.1007/s10681-019-2459-5) contains supplementary material, which is available to authorized users. Due to misclassification or inhibition of markers used for classification of induction cross seed, the resulting D0 seed usually includes false positive (hybrid) seed, which are later removed in the DH nursery based on their phenotype  lines. Use of DH lines in maize breeding increases the genetic gain by shortening the breeding cycle time, enables cost savings and increases the efficiency and precision of selection  association mapping panel, and the rest were CIMMYT Maize Lines (CMLs) adapted to tropics/subtropics across Latin America, sub-Saharan Africa and Asia. Haploid induction and evaluation of haploid fertility traits were carried out in 2 years. Haploid induction crosses were produced at Agua Fria experimental station in Mexico . Inbred lines were grown in two replications with each plot consisting of two rows of 4.5 m with 19 plants/row. Two tropicalized haploid inducers, TAIL8 and TAIL9 were used as a pollen parents for haploid induction crosses  was calculated as the proportion of total number of haploids in the total number of survived plants and expressed in percentage. Non-germinated seed and seedlings/plants that died before evaluation of ploidy status were not considered in HIR determination. HMF was calculated as a proportion of the total number of pollen-producing haploid plants from the total number of haploid plants per plot and expressed in percentage. HF was calculated by dividing the total number of seed-producing haploids from the total number of haploid plants per plot and expressed in percentage. Analyses of phenotypic data revealed normality in the distribution of residuals for all traits except for HMF and HF, where data was skewed more towards zero. Therefore, HMF and HF data were transformed by applying a logit-transformation.ijklo is the observed phenotype in the jth environment at lth replication of the oth incomplete block for the haploid from the cross of the ith genotype with the kth inducer. μ is an intercept term, rl is the effect of the lth replication, bol is the effect of the oth incomplete block in the lth replication, Gi is the genetic effect of the ith genotype, Ej is the effect of the jth environment, and eijklo is the experimental error. Firstly, the replication, inducer and genotype effects were taken as fixed and genotype x environment interactions (GxE) and incomplete block effect as random to obtain Best linear unbiased estimates (BLUEs) for each line. Secondly, the effects of genotype, GxE and incomplete blocks were treated as random to estimate their variances and the residual error variance . Heritability (h2) for each trait was calculated as h2 = σG2/(σG2 + (σGxE/E2) + (σe2/Exr)) where E and r refers to the number of environments and replications, respectively. Phenotypic correlations among traits were calculated using R  option in SAS 9.2 (SAS Institute 2010). Variance components across environments were determined by following linear mixed model:ApeKI. GBS libraries were constructed in 96-plex and sequenced on Illumina HiSeq 2000  was drawn to visualize the possible population stratification among the inbred lines. A kinship matrix was also computed from identity-by-state (IBS) distances matrix (as executed in TASSEL). The extent of genome-wide LD was based on pairwise rn et al. .P < 0.05 using the Bonferroni-Holm procedure . Within the local LD block including associated SNPs, the filtered genes in MaizeGDB (http://www.maizegdb.org) containing directly or adjacent to each associated SNP were considered as possible candidate genes for HMF and/or HF.GWAS was conducted by regressing BLUEs of inbred lines on marker genotypes by using mixed linear model (MLM)  was performed by Ridge Regression-Best Linear Unbiased Prediction (rrBLUP) employing the R package ‘rrBLUP’ with five-fold cross-validation Endelman . From thAnalyses of phenotypic data revealed normality in the distribution of residuals for all agronomic traits except for HMF and HF, where data was skewed more towards zero. Therefore, HMF and HF data were transformed by applying a logit-transformation. After the estimation of variance components and BLUEs, the BLUEs data were back transformed and used for further analyses. HMF and HF were assessed in a final set of 315 inbred lines which revealed large genetic variation for both traits Table . The linHMF was significantly correlated with plant height, ear height and number of tassel branches Table . Howeverhttp://hdl.handle.net/11529/10431. Per chromosome, the average marker heterogeneity was approximately 0.037, the proportion of missing values was close to 0.05, and the minor allele frequency (MAF) was around 0.21, respectively .A total of 955,690 SNP markers were obtained from the GBS platform. SNPs were initially filtered to remove SNPs with missing rate > 10%, SNPs with minor allele frequency (MAF) < 5% and heterogeneity > 5%, resulting in 214,520 markers used for GWAS analyses, these genotypic data are available in CIMMYT’s data repository: 2) between adjacent pairs of markers versus distance in kb which showed that average LD decay was 27.31 kb at r2 = 0.1 at and 9.48 kb at r2 = 0.2 for the panel  was consistent with earlier GWAS study on testcross populations  and high co-localization of QTL for HF and HMF warrants further research to understand whether both traits are controlled by same genes or different genes and their interactions which pave the way for developing lines improved for both HMF and HF. Overall, HF is a complex polygenic trait governed by many moderate effect QTL.The QTL n et al.  ranged bn et al. . AnotherGWAS revealed a set of putative candidate genes identified on chromosome 1, 3, 4, 5 and 10 for HMF; these were primarily involved in cell development or cell-to-cell transport Table .Genomic predictions revealed moderate accuracy for HMF and HF which supported their quantitative nature. Compared to HMF, HF seems to be more complex as it showed lower prediction accuracy. This lower prediction accuracy is also possibly due to missing of all favorable alleles for HF in most of the lines in the present panel, as evident by > 40% of lines in the panel showing < 1% of HF. Interestingly, for both the traits, we found only 2 to 3% of the lines carrying most of the favorable alleles. The most significant markers which are also consistent with previous studies could act as potential sources for introgression into other elite lines lacking the trait.Even though mechanisms of spontaneous chromosomal doubling are not yet fully understood, similar mechanisms may be responsible for both artificial and spontaneous chromosomal doubling. Meiotic restitution is one of the mechanisms that can result in incomplete meiotic cell division, thereby leading to unreduced male gametes  derived from the induction cross of the same inbred were shown at 28 days after planting (JPEG 336 kb)Distribution of phenotypic performance of tropical inbred lines in association panel for haploid male fertility and haploid fertility across environments (JPEG 316 kb)Summary of the heterogeneity, minor allele frequency (MAF) and proportion of missing SNPs of 214,520 selected SNPs. Chromosome assignments are indicated. The heterogeneity, MAF, and percentage of missing value are shown in left on y-axis, and the number of markers for each chromosome was shown in right on y-axis (JPEG 70 kb)Below is the link to the electronic supplementary material.


The evaluation of prediction machines is an important step for a successful implementation of genomic-enabled selection in plant breeding. Computation time and predictive ability constitute key metrics to determine the methodology utilized for the consolidation of genomic prediction pipeline. This study introduces two methods designed to couple high prediction accuracy with efficient computational performance: 1) a non-MCMC method to estimate marker effects with a Laplace prior; and 2) an iterative framework that allows solving whole-genome regression within mixed models with replicated observations in a single-stage. The investigation provides insights on predictive ability and marker effect estimates. Various genomic prediction techniques are compared based on cross-validation, assessing predictions across and within family. Properties of quantitative trait loci detection and single-stage method were evaluated on simulated plot-level data from unbalanced data structures. Estimation of marker effects by the new model is compared to a genome-wide association analysis and whole-genome regression methods. The single-stage approach is compared to a GBLUP fitted via restricted maximum likelihood, and a two-stages approaches where genetic values fit a whole-genome regression. The proposed framework provided high computational efficiency, robust prediction across datasets, and accurate estimation of marker effects. Genome-wide markers are utilized in plant and animal breeding to capture quantitative trait loci (QTL) and relationship among individuals for prediction and selection . Most inEvaluating the predictive performance of these various methodologies has become an important step for a successful implementation of genomic-enabled selection . Hence the method of choice must have two desirable features: computational feasibility and accurate prediction across various scenarios  Estimate the genetic values from phenotypes of replicated trials; 2) Calibrate marker effects upon the genetic values to estimate the breeding values and enable prediction. This approach is referred to as a ”two-stage” approach. However, single-stage analysis can benefit genomic evaluation by jointly modeling genotypes and replicated phenotypes , an iterative method to fit a WGR using a Laplace prior. Laplace priors are popular in genetic analysis for QTL detection and genomic prediction  estimates of regression coefficients alongside their associated parameters, updating one parameter at a time . The term ”single-stage” has been used to define the joint modeling of replicated observations at the phenotypic level  represents genetic signal not captured by the markers. Genotypes may have variable number of observations and weights can be assigned to the genotypes to account for the unbalancedness. The WGR step can be solved assuming unweighted observations for computational convenience, or weighted according to the number of observations of each genotype, thus b, a, and β until convergence  must be updated conditional to g.Conditional to other model terms, the variance component associated to this random effect is estimated as :σg2=g′gnmated as :σg2≅ of grain yield collected in as many as 18 environments under a modified augmented design . BLUPs wThe BLUPs are not optimal for conducting second stage analyses  the predictive ability measured as the correlation between predicted and observed genetic values, computed within-family from fivefold cross-validations with individuals were sampled at random  in each round and this procedure was repeated 20 times, and leave-family-out cross-validation by using 39 families to predict the family left out, and repeating this procedure for all 40 families; and 2) the elapsed time for calibrating the model using the whole data.π and BayesDπ implemented in the bWGR; GBLUP with REML variance components implemented in rrBLUP (ν and ϵ support vector machines (SVM) implemented in kernlab  and reproducing kernel Hilbert spaces (RKHS) implemented in BGLR ; BayesCπn rrBLUP ; boostinn rrBLUP ; L1L2 man glmnet ; random n glmnet ; ν and ϵ kernlab ; and the kernlab . The latδ is the average value of Methods above were deployed with default settings. Tuning parameters for ridge, LASSO and elastic-net were computed through 10-fold cross validation in the training set. To mitigate the computational burden necessary to tune parameters, PLS used 5 components and the empirical Bayes Lasso hyperparameters a-b were set to 0.5. The Gaussian kernel employed for RKHS was computed as An experimental population was generated through simulation to evaluate FLM estimates of large effect parameters. From an F2 bi-parental cross with 1000 individuals, 250 individuals were randomly selected and randomly mated to generate a new population of 1000 individuals. This bottle-necking with subsequent random mating was repeated 5 times. The resulting allele frequency ranged from 0.32 to 0.63. The simulated genome had 10 chromosomes of length 100 cM. The genotyping density was 0.5 marker/cM. A causative marker was assigned to the center of each chromosome with alternating values of positive and negative one.The response variable was evaluated under heritability of 0.25 and 0.50. The ability of FLM to detect major genes was compared to the Bayesian ridge regression and Bayesian LASSO implemented in the R package BGLR , and a mBreeding data are inherently unbalanced. Genotypes are often unreplicated or not equally distributed across environments, and observations from different environments present a variable degree of noise. The single-stage approach was evaluated on simulated datasets that recreated such condition.The simulations were based on assigning the simulated individuals described in the previous section, a genetic pool with 1000 genotypes, to a random set of environments. Each simulated scenario was performed with a combination of number of observations across trials  and genetic architectures . The number of environments for each simulation was sampled from a uniform distribution between 4 and 10 with effect For the simulated scenarios with less than 1000 observations, most genotypes were unreplicated since the observed individuals were sampled from a pool of 1000 genotypes. Selection across unreplicated trials are not unusual when genomic prediction is deployed  Iterative single-stage with Laplace (FLM-SS) and Gaussian prior , implemented in RcppEigen , carryinThe criteria for comparison was the computation time necessary to fit the model as the elapsed time, and the prediction accuracy as the correlation between estimated breeding values and true breeding values.y). GBLUP and Single-Stage fit environment (u) as function of intercept (μ) and marker effects :Two-stage approach:GBLUP:github.com/alenxav/FLM). The FLM-SS code can be made available for research purposes.The soybean data are available in the R package SoyNAM. The R code for FLM is in the appendix. Cross-validation scripts are available on GitHub  is presented in The accuracy of Gaussian prior models (GBLUP and RR-SS) was sensitive to the number of observations when the trait was controlled by a small number of QTL. As the number of QTL increased, the predictive advantage of FLM-SS and two-stages over GBLUP and RR-SS decreased, and GBLUP outperformed the other methods under the scenario with the lowest number of observations.The computation time of GBLUP was considerably larger than iterative single-stage, and two-stage approach yielded intermediate computational performance. The discrepancy in computation time between iterative single-stage and two-stages can be attributed to the MCMC sampling in the second step and the estimation of variance components in the first step.The discussion section frames FLM as a potential method of choice for genomic prediction in plant breeding. The proposed methodology provided accurate prediction across datasets, as well as computational efficiency. Besides the predictive and computational performance, the FLM is an easy-to-implement regression method (Algorithm 1) without the need for tuning or matrix inversion.Most methods provide comparable predictive performance  build the kernel and 2) compute its inversion or Eigendecomposition. The time needed to build the kernel depends on the number of both individuals and parameters. Many kernels require the computation of distance matrices, which is more computationally demanding.For the prediction of new observations, kernels must be augmented with the genotypes of observed and unobserved individuals, making the inversion or spectral decomposition more challenging. This is particularly cumbersome in plant breeding where the size of the offspring being predicted and selected can be much larger than the training set, whereas the parameter estimates required for prediction from regression and tree models can be stored and easily employed for prediction of new observations.The two-stage and iterative single-stage approaches were faster than GBLUP by an order of magnitude. Such difference can be attributed to the sparse nature of the algorithm and the complexity associated to the estimation of variance components. GBLUP was fit using AI-REML, a general-purpose algorithm, whereas FLM-SS was specifically designed to provide efficient computation of breeding models. The two-stages model provided an intermediate outcome.The lower accuracy of the GBLUP and RR-SS in scenarios with few QTL can be attributed to the statistical nature based an infinitesimal model. Gaussian priors work by capturing the relationship among individuals  where thFrameworks where marker effects are estimate alongside all other parameters are not new, but rarely utilized (Multi-stage procedures have been proposed to address the computational burden of single-stage without compromising in the quality of the results . HoweverFuture directions for this research include the comparison of breeding values estimated from GBLUP, multi-stage and RR-SS to understand how the proposed algorithm reproduces standard procedures on real data. An evaluation of the estimated variance components is also a desirable target. Further studies must contrast scenarios with genotype-environment interaction, spatial trends, dominance and epistasis.A robust prediction methodology is a key component for a successful genomic-assisted breeding pipeline. This study introduced a fast and accurate algorithm for solving a WGR with Laplace prior, alongside a single-stage methodology that allows to connect WGR into mixed models with replicated observations.The proposed framework provided more accurate predictions and higher computational efficiency than other methods based on a cross-validation evaluation on real datasets. With a simulated dataset, it was shown that the fast Laplace model provided reasonably accurate estimation of QTL effects, being less biased than Bayesian LASSO and ridge regression, and proving less spurious signals than genome-wide association analysis. The algorithm extension to single-stage also presented promising properties, benefiting both computation and prediction.


Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near‐isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome‐wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL‐like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large‐effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population‐based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in  C. elegans. We used high‐throughput phenotyping to measure a genetically complex multivariate quantitative trait. We asked whether 0.001 of the genome is likely to carry alleles that detectably influence the trait. For nine of 15 such intervals, we found that allelic differences between two strains conferred significant phenotypic effects. Surprisingly, these effects were not small. If such effects were present across the genome, the total amount of variation would greatly exceed the variation we observe. Our findings point to large‐effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.Genetic association studies have suggested that nearly every region of genome harbors variants that affect typical complex traits, providing ample fuel for evolution. In this study, we test these findings using experimental genetics in A detailed understanding of the genetic architecture of complex traits is necessary to address questions about the origin and maintenance of heritable phenotypic variation and the mechanisms of adaptation. Many models explain heritable variation as the result of an equilibrium between the introduction of variation by mutation and its erosion by drift and stabilizing selection. For traits affected by enormous numbers of loci, particularly in species with low effective recombination rates, linkage between variants may also be important. Very tightly linked variants, inherited together, can act as coupling‐phased supergenes that increase phenotypic variance or repulsion‐phased linkage blocks that reduce phenotypic variance. Under stabilizing selection near an optimum, we may expect an excess of the latter case: tightly linked variants with opposite effects. Near a phenotypic optimum, mutations that counteract the effect of linked variants will be favored, and recombination between antagonistic‐effect loci will be disfavored Fisher .Caenorhabditis elegans. This species is well suited for this type of study, as it has a short generation time and is naturally inbred  from a cross of strains N2 and CB4856  for each trait for each of 237 independently passaged replicate populations . We used this 237‐observation dataset to test whether genotype accounts for phenotypic variation, as described below.P‐values for these comparisons, we used permutations, shuffling the strain labels among the observations for the pair of strains in each test. We used these permutations to derive null test statistic distributions for univariate trait comparisons as well.To test for the effect of each interval on the multivariate demography phenotype, we compared two strains at a time. For each pair of strains that differ by a single interval  for a nickel‐by‐genotype interaction. After placing three L4 hermaphrodites in each well of a microtiter plate and allowing them to mature and produce progeny over four days, we passed each resulting population through a COPAS BIOSORT large‐particle sorter (Union Biometrica). The sorter counts the number of animals in each well, and for each animal it measures time of flight, which serves as a measure of body length. The result of each assay is thus a histogram of the body lengths observed in the broods of three hermaphrodites at a fixed time , and the eight‐QTL model explains 29% and 16% of the variance in the small and medium size worm proportions, and 8% of the variance in progeny number.We found no evidence for pairwise or higher order epistasis among the detected QTL  spread along a 1.4 Mb region on the X chromosome , and they expose the variation within the interval that could be masked by tightly linked antagonistic QTL. In total, there are 1838 SNPs and 635 indels in the NIL interval . The differences between the parental NILs are limited to the size‐class distribution, as the number of progeny is indistinguishable  for each of the ∼15 replicate populations of each NIL , using the entire dataset to account for variation due to assay day, assay plate, and well position. We then applied a fixed‐effect multivariate model to estimate each strain's demography phenotype. As shown in Figure P‐value threshold of 0.003 . If we assume that these windows are typical samples of the 100 Mb genome, simple extrapolation implies that N2 and CB4856 differ in about 600 100 kb intervals with significant effects on the phenotype. The large effect sizes in the NILs raise the question of whether a genome full of such effects is consistent with the variation observed from genome‐wide segregation in the RIAILs. We therefore simulated a RIAIL phenotype (number of progeny) by assigning effects to a random 600 of the markers genotyped in the RIAILs, with effects drawn from a normal distribution with the inferred NIL effect‐size mean (−0.1) and variance (8.6). In a million simulated datasets, the simulated RIAIL variance was on average 11 times greater than the observed RIAIL variance, and never as low as the observed variance. Other approaches to simulating the effect sizes, including drawing from a uniform distribution or resampling directly from the NIL effects, yielded similar results Fig. .The simulations aimed to synthesize the RIAIL and NIL findings, and they reject the simplest such synthesis, wherein the NIL interval effects are simply assigned to random positions in the genome. An alternative model is that the causal variants of opposite sign tend to be tightly linked more than expected under a random distribution of effect signs . Under such a model, we predict (1) that the sequence of effects along the 15 intervals in the NILs should not be random but instead should roughly alternate in sign, and (2) simulations of coupled canceling QTL should recapitulate the observed RIAIL variance.P = 0.014  identified more than 8900 single nucleotide variants segregating in the region  are a common feature of complex‐trait architectures .Table S1. Permutation‐based NIL p‐values.Click here for additional data file.


Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding. Forest tree breeding encompasses a number of steps to increase the frequency of advantageous alleles for several traits concurrently in a target population. Recurrent cycles of selection ultimately result in genetically improved planting material by maximizing genetic gain per unit time in the most cost-effective way  and association mapping is first presented. It provides a quick historical perspective on how and why we reached the point of convergence between quantitative genetics and genomics. Furthermore, it also serves to substantiate the fact that reductionist “genetic dissection” approaches or attempts to use single candidate genes or diffuse, indirect information from transcriptomics, have not proven useful for breeding practice and are therefore not discussed further. We focus on the factors that affect and the challenges that remain to fully integrate genomic data in tree breeding in light of the recent promising results of whole-genome prediction. Although making predictions is difficult “especially about the future” as Niels Bohr and others once amusingly said, we attempt to look at the near future of tree breeding when genotyping, whole genome sequencing and computational prediction of genomic features for thousands of trees will not be limiting. We anticipate a future where the progressive advances made possible by routine genomic selection (GS) in multiple large populations will provide a more powerful platform to revisit the discovery of discrete genomic elements that may further enhance whole-genome phenotype prediction and eventually allow direct discrete interventions at the DNA sequence level.The prospects of MAS for forest trees was properly doubted early on, limiting its potential value to specific genetic backgrounds resulting from linkage equilibrium of forest tree populations, QTLs interacting with environments and changes of allele frequencies across generations , whose effects on the phenotype are estimated in a “training” population. In forest trees, such a training set is usually composed by sampling one to a few thousand individuals in progeny trials derived from mating a few dozen parents that constitute the target elite germplasm bred. SNPs are used to build prediction models to be later applied to “selection candidates” for which only genotypes are gathered and phenotypes are predicted by the genomic data. The prediction models are cross-validated against a “validation” population, a set of genetically related individuals to the training set but that did not participate in the estimation of marker effects. A prediction model that delivers a high correlation between the observed and predicted breeding values is subsequently used in the breeding phase to calculate the genomic estimated breeding or total genotypic values of the selection candidates Figure . GS fundirσA/L)  because the phenotypes of a much larger number of seedlings in the nursery can be predicted with marker data compared to the number of trees that can be tested in conventional field progeny trials. Additionally, the use of realized genomic relationships is associated with increased accuracy in estimating σA and breeding values (r) . Phenotypes of the selection candidates can be predicted at very early ages, for example, when the seedlings are a few weeks old. GS not only could preclude or at least enhance the efficiency of progeny testing but would also optimize clonal testing phases by advancing a smaller number of pre-selected trees to be assessed in multi-site expanded clonal trials  genetic covariance matrix that combines the pedigree-based average numerator relationship matrix (A) with the marker-based relationship matrix (G). HBLUP increases the precision of the genetic parameters generated from traditional pedigrees as shown in recent studies with forest trees , age-age correlations, the long term models performance and cost and quality of DNA marker data. All these have been the subject of research and reviewed in detail in the context of tree breeding by Grattapaglia , with marker effects treated as random, normally distributed with common variance, has been very efficient. RR-BLUP has been equivalent to Genomic BLUP (GBLUP), providing the best conciliation between prediction efficiency and fast computation, while also revealing that essentially all major traits in forest trees fit the infinitesimal model . Model updating strategies will therefore be crucial to counteract the decay of relatedness and LD between the original training set and selection candidates as generations of breeding advance, as shown by simulations for eucalypt breeding . The use of a common SNP genotyping array across breeding programs of different organizations will be a key issue to provide the necessary economy of scale to integrate genomics into breeding.In the past 2 years, a number of additional experimental GS studies have been reported (Cappa et al., Eucalyptus clones (Marcatti et al., With easy access to SNP genotyping and positive results in essentially all major forest trees, we are now at the brink of widespread adoption of genomic prediction data, thus realizing the early promises of MAS in forest tree breeding. In addition to the outstanding research challenges discussed above, a promising area to enhance the value of genomic data will involve the inclusion of environmental co-variables in GS models as already shown in crops (Jarquin et al., Another area that will demand research comes from the evolution of sequencing technologies in moving from sparse SNP data to sequence data for GS. Apart from the challenge and cost of managing massive next generation sequencing data sets for large numbers of individuals in a breeding program framework, in theory, if sequence data were used instead of dense SNPs, accuracy should increase because rare causal alleles would be better captured in predictive models. Until now, however, simulation and experimental studies in domestic animals have shown that whole-genome sequence data does not increase accuracy when LD has a slow decay pattern (Macleod et al., Eucalyptus (Müller et al., Eucalyptus (Hussey et al., Populus (Zinkgraf et al., The success of whole-genome prediction and the poor outcome of dissection approaches in identifying functional quantitative trait nucleotides, have contributed nevertheless to an exciting new perspective for the study of complex trait variation. A clear pattern has emerged in annual plants indicating that the association signal of common variants in large sample sizes, although spread across the entire genome, is heavily concentrated in regulatory DNA in open chromatin marked by deoxyribonuclease hypersensitive sites (Sullivan et al., DG drafted the first version of the manuscript and all co-authors subsequently contributed to it by editing and formatting the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Individual differences in executive functions (EF) are heritable and predictive of academic attainment (AA). However, little is known about genetic contributions to EFs or their genetic relationship with AA and intelligence. We conducted genome‐wide association analyses for processing speed (PS) and the latent EF measures of working memory (WM) and inhibitory control (IC) in 4,611 adolescents from the Avon Longitudinal Study of Parents and Children. While no loci reached genome‐wide significance, common genetic variants explained 30% of the variance in WM and 19% in PS. In contrast, we failed to find common genetic contributions to IC. Finally, we examined shared genetic effects between EFs and general intelligence, AA and ADHD. We identified significant genetic correlations between WM, intelligence, and AA. A more specific pattern was observed for PS, with modest genetic overlap with intelligence. Together these findings highlight diversity in the genetic contributions to specific cognitive functions and their genetic relationship with educational and psychiatric outcomes. Executive functions (EF) are cognitive processes controlling thoughts and actions; they allow us to pay attention, resist impulses and change our course of action. Although the structure of EF remains a topic of debate, including potential developmental changes in EF structure —independent of IQ—when measured both using individual tasks and latent factors  to 100% (WM), with lower estimates reported for individual EF tasks (29%–76%) ; the largest GWAS meta‐analysis of g  reported 18 independent SNPs and 30 gene‐based associations  using the U.K. Biobank sample of adults . It identified two significant SNP associations with PS, but none for short‐term memory  and Stroop tasks  failed to find specific SNP associations with EF, but found an association with PS   seek to identify specific genetic variants  that contribute to the heritability of cognitive abilities in order to identify the associated biological pathways and functions, and gain mechanistic insights  and correlations (rG) directly from DNA, even when no genome‐wide significant associations have been identified  and PS (h2SNP = 11%), although this might in‐part be due to the relatively coarse measures used and their low test–retest reliability. Another  GWAS obtained higher estimates for WM using the N‐back task (h2SNP = 24%–41%)  have been reported between general cognitive ability and educational attainment . Assessing the extent of generality and specificity of shared genetic influence between cognitive domains and educational outcomes has important implications for understanding why some children do better at school than others, and paves the way for studies centered on understanding causal relationships across development.Recently developed statistical approaches use summary statistics generated from GWASes to calculate heritability estimates ; namely (a) WM, (b) IC, (c) and PS  of each cognitive measure and (b) index genetic correlations (rG) between the cognitive measures and between these measures and publicly available GWAS data sets for general intelligence, years in education, and college completion as well as attention deficit hyperactivity disorder (ADHD). We include ADHD due to the large body of research linking it with EF deficits and poorer educational outcomes  of cognitive task data collected when participants from the Avon Longitudinal Study of Parents and Children (ALSPAC) were between 9 and 20 years of age  is an ongoing population‐based study investigating factors influencing development and health. Initial recruitment included 14,541 mothers with 13,988 children alive at age 1. Another round of recruitment at around age 7 left the total sample size for data collected after this age at 15,247  measures were also taken at age 13. Finally, a visuospatial N‐back task was used at 17 years to test WM and updating. The working memory component of the PCA comprised measures of performance on the N‐back, Digit Vigilance, Counting Span, and Dual tasks. The inhibitory control component comprised measures of performance on the Stop Signal task. Finally, the PS component comprised RT measures of the Digit Vigilance, Simple RT, Choice RT, Sky Search, and Stop Signal tasks. No shifting component was identified.The three latent cognitive measures used  were previously derived from a set of 10 cognitive tasks available in the ALSPAC data set during adolescence, broadly defined as between the ages of 10 and 17 years of age. PCA was used as different tasks had been used at different ages, and resulted in a three‐factor solution  stores the summary statistics from published GWASes. For the purpose of the present study we selected five educationally relevant traits of lifespan intelligence, child intelligence, years spent in education, college completion, and ADHD. The study on lifespan intelligence included 78,308 individuals from 13 different cohorts of European descent: eight cohorts of children <18 years , and five cohorts of adults 18 –78 years . The measure of intelligence for these cohorts was either g or a primary measure of fluid intelligence  also included in the Sniekers et al. study, again using a combination of g and fluid intelligence measures . The measure of educational attainment used in this study was number of years spent in education  from 42 different cohorts of Caucasian ancestry <30 years  included 12 cohorts from Europe, North America, and China. Cases had a clinical diagnosis of ADHD (ADHD Working Group of the Psychiatric Genomics Consortium (PGC) et al., Years in education is derived from the 2016 Social Science Genetic Association Consortium (SSGAC) GWAS that included adults (<30 years) of European descent  and the Laboratory Corporation of America . The raw genome‐wide data was subjected to standard quality control procedures to identify individuals and SNPs for exclusion. Samples that passed quality control stages were phased and imputed against the Haplotype Reference Consortium panel using Impute V3  and genetic correlations (rG) were estimated from GWAS summary statistics using LDSC regression and cross‐trait LDSC regression as implemented in LDhub  were located in or close to genes previously reported to be associated with neurocognitive decline, psychiatric disorders, and/or educational attainment .Univariate genome‐wide association analyses on the latent measures of working memory, inhibitory control, and processing speed failed to identify any genome‐wide significant SNP associations (p < .001) genetically correlated with all five phenotypes, with the lowest overlap found for ADHD (rG = −.54) which was also the only negative correlation. Genetic correlations for PS were 20%–55% lower than that seen for WM, with nominally significant (p < .05) correlations found only for lifespan intelligence (rG = .53) and ADHD (rG = −.30).Genetic correlations were estimated between WM and PS, and with five education‐related phenotypes using the LDhub database Table . GeneticFAM181B was associated with WM (p = 1.99 × 10−6) and TNNI2 was associated with PS , which is similar to other estimates that used well‐replicated tasks . Again, as expected this is smaller than twin estimates . Several explanations could account for this finding. It is possible that individual differences in IC are explained by environmental factors and DNA sequence variants not captured in the GWAS. However, it is also possible that inconsistencies in the parameter settings of the Stop Signal task at age 15 may have complicated the detection of common genetic effects. We attempted to correct for this by regressing out parameter differences from the scores, but this may have resulted in a less reliable measure. Alternatively, our finding could reflect other research showing that IC is not distinguishable from a common EF factor  of inhibitory control, and while this study did not estimate SNP heritability it failed to detect any significant genetic associations with the Stroop task  in or near genes that have previously been reported in genetic studies of psychiatric disorders such as schizophrenia and major depression, sleep disorder, and Alzheimer's disease. Interestingly, several of these SNPs have also been associated with cognitive or neurological measures, including performance on the anti‐saccade task and brain volume measures , as well as educational achievement . We also found suggestive associations in regions previously associated with subjective well‐being and Parkinson's disease.We failed to identify any robust genetic associations at a genome‐wide threshold with any of the three cognitive latent measures examined. This is not unexpected given the highly polygenic nature of higher‐level cognitive measures and the modest sample size. However, both WM and PS showed a number of suggestive hits (rG = .80 ± .16) and years in education (rG = .70 ± .15) were lowest, although the standard errors were large and overlapped with the estimated genetic correlation with lifespan intelligence. These results suggest that genetic variants captured in GWAS studies that affect WM are pleiotropic, and the measures share common underlying biological mechanisms. Further advances in this research will require the identification of genome‐wide significant associations and further dissection  to gain insight into the nature and direction of causal relationships between WM, intelligence, g, and academic outcomes across development. There was also a significant negative correlation between WM and ADHD (rG = −.54), which replicates previous genetic and phenotypic study findings . This is an intriguing result given that PS is thought to be integral to EF and academic achievement , and has been associated with Alzheimer's disease , is part of a collection of genes involved in governing muscle function, and mutations in this gene are associated with digestive system disease (Jostins et al., A gene‐based association was also found between PS and In conclusion, although individual and common EF components and PS have been found to be closely related (Gordon et al., Appendix S1. online Supporting InformationClick here for additional data file.


Follow-up MR analyses supported a causal relationship; genetic variants for nsCL/P homogeneously cause decreased philtrum width. In addition to the primary analysis, we also identified two novel risk loci for philtrum width at 5q22.2 and 7p15.2 in our Genome-wide Association Study (GWAS) of 6,136 individuals. Our results support a liability threshold model of inheritance for nsCL/P, related to abnormalities in development of the philtrum.There is increasing evidence that genetic risk variants for non-syndromic cleft lip/palate (nsCL/P) are also associated with normal-range variation in facial morphology. However, previous analyses are mostly limited to candidate SNPs and findings have not been consistently replicated. Here, we used polygenic risk scores (PRS) to test for genetic overlap between nsCL/P and seven biologically relevant facial phenotypes. Where evidence was found of genetic overlap, we used bidirectional Mendelian randomization (MR) to test the hypothesis that genetic liability to nsCL/P is causally related to implicated facial phenotypes. Across 5,804 individuals of European ancestry from two studies, we found strong evidence, using PRS, of genetic overlap between nsCL/P and philtrum width; a 1 S.D. increase in nsCL/P PRS was associated with a 0.10 mm decrease in philtrum width  is a birth defect, primarily affecting the upper lip and hard palate. Individuals with nsCL/P and their unaffected family members sometimes present with other minor craniofacial anomalies and may have differences in facial morphology compared to the general population. Here, we investigate the shared genetic relationship between nsCL/P and facial morphology in a sample of around 6,000 Europeans from two different studies. We demonstrate that genetic risk factors for nsCL/P are associated with decreased philtrum width in unaffected individuals from the general population and that the relationship is likely causal. This finding is important because it demonstrates that nsCL/P, which is often treated as a dichotomous trait, may have a continuous dimension and offers insight into potential biological mechanisms that result in nsCL/P. Orofacial clefts are malformations characterised by a failure of fusion between adjacent facial structures in the embryo . Cleft lMAFB) [IRF6 (a major nsCL/P-associated gene) strongly predicted lip-shape variation in females [Facial morphology in the general population is also likely to be highly polygenic; genome-wide significant loci have been found for multiple facial phenotypes across diverse ethnic populations –14. In sMAFB) . LikewisMAFB) , 15, 16.MAFB)  while in females .The use of individual markers to demonstrate genetic overlap between two phenotypes has notable limitations; a large number of statistical tests are introduced, and interpretation is difficult when some SNPs show an association and others do not. Polygenic risk scores (PRS) involve incorporating multiple markers, including those not reaching genome-wide significance, into a genetic score that serves as a proxy for a trait . PRS havFig 1). The inheritance of dichotomous traits can be modelled on the liability scale, where every individual has an underlying normally distributed liability to the trait determined by genes, environment and chance. Individuals above a liability threshold develop the trait, while increased liability may cause related phenotypic differences in individuals without the trait [Interpreting genetic overlap between nsCL/P and a facial phenotype is difficult because the development of the face and development of an orofacial cleft are largely synchronous. One possibility is that differences in the facial phenotype are a sub-phenotypic manifestation of genetic liability to nsCL/P . MR is an instrumental variable approach, testing causality of an “exposure” and an outcome by using genetic instruments to mimic a randomised controlled trial . MR reliFig 2.In the absence of large-scale publicly available GWAS summary data for nsCL/P, we used individual level genotype data from the International Cleft Consortium to Identify Genes and Interactions Controlling Oral Clefts (ICC) and GWAS summary statistics from the Bonn-II study  to repliS1 Table).We performed a GWAS of nsCL/P using the TDT on 638 parent-offspring trios and 178 offspring duos of European descent, and then meta-analysed our results with GWAS summary results previously published on 399 cases and 1,318 controls in the Bonn-II study [−5 was the most predictive of nsCL/P liability in both European and Asian trios (S2 Table). Therefore, this threshold was used for generating polygenic risk scores from the meta-analysis summary statistics. SNPs included in the selected score are listed in S3 Table.We also evaluated the predictive accuracy of nsCL/P that could be achieved using different PRS constructed from these summary data by comparing the strength of association at different inclusion thresholds of the PTDT. We determined that including independent SNPs that surpass a P-value threshold of 10S4 Table).Prior to testing the performance of our nsCL/P PRS on predicting facial morphology, we calculated the minimum genetic correlation required to detect an association between the PRS and the facial phenotypes. We found that the minimum genetic correlation required ranged from 0.17 to 0.28 with differences attributable to different heritability estimates across the facial phenotypes  (Table 1).We evaluated the performance of our nsCL/P PRS for prediction of seven facial morphological traits. Facial distances used in the analysis are shown in -4). Meta-analysing these results; indicated that a 1 S.D. increase in nsCL/P PRS is associated with a 0.10 mm decrease in philtrum width .We attempted to replicate this finding in the 3DFN study and found a consistent effect of 1 S.D. increase in nsCL/P PRS being associated with a 0.14 mm decrease in philtrum width , within the non-coding RNA intronic region of an uncategorised gene ENSG00000232633, and 7p15.2 , an intergenic SNP near HOXA1 (S5 Table). We found some evidence that the two lead SNPs may be eQTLs for nearby genes (S6 Table). The two lead SNPs of the genome-wide significant loci, rs255877 and rs2522825, were used as genetic variants associated with philtrum width in subsequent MR analyses. The GWAS summary statistics are available at the University of Bristol data repository, data.bris, at https://doi.org/10.5523/bris.1kz9y0moa8sgj2lxlk53mdmlbj [To generate SNP-philtrum width association information for MR analyses, we performed GWAS of philtrum width in both ALSPAC and 3DFN separately, before meta-analysing. The combined sample included 6,136 individuals of recent European descent. We identified two novel chromosomal regions associated with philtrum width with genome-wide significance at 5q22.2 .Firstly, we determined whether genetic variants contributing to liability of nsCL/P cause changes in philtrum width, by testing SNPs strongly associated with nsCL/P for association with philtrum width. A 1-unit log odd increase in liability to nsCL/P was associated with a 0.11mm  decrease in philtrum width. Sensitivity analyses suggested there was no strong evidence for pleiotropy or heterogeneity and validated the consistency of the instrument. Leave-one-SNP-out analysis showed consistent effect estimates after exclusion of each SNP (S7 Table), weak evidence was found of an association between philtrum width-associated variants and liability to nsCL/P . Sensitivity analyses for pleiotropy were limited, with only 2 SNPs.Secondly, we determined whether genetic variants associated with philtrum width also affect liability to nsCL/P, by testing two independent SNPs associated with philtrum width at genome-wide significance (derived in the ALSPAC and 3DFN cohorts) for association with nsCL/P. Utilising strong LD proxies (−10).Thirdly, we used the MR-Steiger test of directionality to test the direction of effect between philtrum width and liability to nsCL/P. The results suggested that the true direction of effect is that genetic variants contributing to liability to nsCL/P cause changes in philtrum width .The causal inference made in this study was achieved through the use of two independent cohorts as discovery and replication samples which greatly reduces the risk of false positives and demonstrates that results can be generalised to different populations. Detailed facial phenotyping data on a large number of individuals in our cohorts along with other detailed phenotype and genotype data enabled us to identify philtrum width as being the most relevant facial morphological feature from amongst seven biologically likely candidates. Statistical power does limit the detection of other features that may have mechanistic relationships with smaller effect sizes , however there is evidence suggesting that there are distinct aetiological differences between these traits, , 36, 37 We conclude that genetic liability to nsCL/P is causally related to variation in philtrum width and that this finding supports a polygenic threshold model of inheritance for nsCL/P, related to abnormalities in development of the philtrum. Further research looking at the relationship between genetic liability for nsCL/P and severity of cleft would provide further evidence for the polygenic threshold model.Data were used on parent-child cleft trios from the ICC (dbGaP Study Accession phs000094.v1.p1) , 39 whicAnalysis was restricted to trios with a proband diagnosed with nsCL/P. 654 of the parent-offspring trios and 164 of the parent-offspring pairs were of European descent and were used in the primary analyses. 759 parent-offspring trios and 159 parent-offspring pairs of Asian descent were included in secondary analyses.https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000094.v1.p1)GWAS genotypes and phenotypes are available at dbGaP .We used data on children from the Avon Longitudinal Study of Parents and Children (ALSPAC), a longitudinal study that recruited pregnant women living in the former county of Avon (UK) with expected delivery dates between 1 April 1991 and 31 December 1992. The initial number of enrolled pregnancies was 14,541, which resulted in 14,062 live births and 13,988 children alive at the age of 1. When the oldest children were approximately 7 years of age, the initial sample was boosted with eligible cases who had failed to join the study originally. For analyses of children after the age of 7, the total possible sample size is 15,247 pregnancies, resulting in 14,775 live births. Full details of the enrolment have been documented elsewhere , 41. DatThe 3D Facial Norms Database (3DFN) has been described in detail previously . In briehttps://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000949.v1.p1)).GWAS genotypes and phenotypes are available at dbGaP . The coordinates of 22 facial landmarks were derived from the scans. Further methodological details are contained in a previous publication ..Distances between facial landmarks were computed by calculating the Euclidean distance between the 3D coordinates. To alleviate multiple testing issues, this study chose to test 7 distances that were either tested previously or have biological relevance to nsCL/P  and were released on dbGAP (phs000094.v1.p1). Pre-dbGaP release, SNPs in sample-chromosome combinations with a chromosomal anomaly (e.g. aneuploidy) were excluded. Post dbGaP release, SNPs were excluded for missingness (>5%), MAF (<5%) and HWE (P < 0.05) leaving 490,493 SNPs using PLINK .A total of 9,912 ALSPAC children were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform. Individuals were excluded from further analysis based on having incorrect gender assignments; minimal or excessive heterozygosity (0.345 for the Sanger data and 0.330 for the LabCorp data); disproportionate levels of individual missingness (>3%); evidence of cryptic relatedness (>10% IBD) and being of non-European ancestry . The resulting post-quality control dataset contained 8,237 individuals. The post-quality control ALSPAC children were combined with the ALSPAC mothers cohort (described in detail previously ) and impIn collaboration with the Center for Inherited Disease Research (CIDR), 2,454 subjects in the 3DFN database were genotyped using a genome-wide association array including 964,193 SNPs from the Illumina OmniExpress+exome v1.2 array and an additional 4,322 SNPs from previous craniofacial genetic studies. Imputation was performed using the 1000 Genomes reference panel (phase 3) .The transmission disequilibrium test (TDT)  evaluateThe Bonn-II study  summary We started by estimating the most appropriate P-value inclusion threshold for the nsCL/P PRS. The Bonn-II study summary statistics were used to construct PRS with different P-value inclusion thresholds in nsCL/P trios from the ICC. Analysis was performed separately in the Asian and European trios. The Polygenic-Transmission Disequilibrium Test (PTDT)  was then2<0.1 and 250 kb) using the most predictive P-value threshold. The PRS were then constructed in the ALSPAC sample.Next, using ALSPAC as a reference panel for linkage disequilibrium, PLINK was used to prune and clump the nsCL/P meta-analysis summary statistics  [Power calculations for PRS analysis were performed using AVENGEME , 52. Asss (GCTA)  heritabiOf the 4,747 ALSPAC children with face-shape scans, 3,941 individuals had genotype data. GCTA  was usedDistances with some evidence of an association (P < 0.05) in the ALSPAC children were followed up for replication in an independent cohort (3DFN). 2,429 3DFN individuals had genotype and face-shape data. 332 individuals were removed due to missing SNPs in the PRS. The final sample consisted of 2,097 individuals. The association between implicated facial measurements and the nsCL/P PRS was measured using a linear regression adjusted for sex, age, height and the first 4 principal components. Effect sizes were reported per standard deviation increase in PRS.A bidirectional two-sample Mendelian randomization analysis was performed using the TwoSampleMR R package , testingMR analysis required relevant SNP association information with respect to both nsCL/P and implicated facial measurements. SNP information relevant to nsCL/P was extracted from the nsCL/P meta-analysis summary statistics, previously described.For implicated facial phenotypes, GWAS were performed using PLINK  in both The ALSPAC/3DFN meta-analysis GWAS summary statistics of implicated facial phenotypes were subsequently analysed and functionally annotated  with theFor the forward direction, genetic instruments for nsCL/P are SNPs that are strongly associated with nsCL/P. 6 well-characterised genome-wide significant nsCL/P SNPs in Europeans were taken from a previous study .S10 Table.Information on the 6 nsCL/P SNPs is contained in For the reverse direction, genetic instruments for implicated facial phenotypes are SNPs that are strongly associated with the implicated facial phenotypes. We LD clumped (r2<0.001 within 500KB) the ALSPAC/3DFN meta-analysis summary statistics to generate independent instruments for the MR analysis. LD proxies (r2>0.9) were used for SNPs unavailable in the nsCL/P summary statistics and were generated using LDlink and LDproxy  using thFig 4).The results of the bidirectional MR and relevant sensitivity analyses were used to infer the likelihood of the liability-related sub-phenotype model. Three distinct possibilities were considered to explain the association between nsCL/P PRS and implicated facial phenotypes (which were detailed previously in S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.