CYP1A1/2 (rs2472297) and AHR (rs6968865) was chosen as a proxy for habitual coffee consumption. Null associations were observed when examining the associations of the genetic score with global and memory cognition , with high consistency between studies (Pheterogeneity > 0.4 for both). Domain specific analyses using available cognitive measures in the UK Biobank also did not support effects by habitual coffee intake for reaction time, pairs matching, reasoning or prospective memory . Despite the power to detect very small effects, our meta-analysis provided no evidence for causal long-term effects of habitual coffee consumption on global cognition or memory.Coffee’s long-term effect on cognitive function remains unclear with studies suggesting both benefits and adverse effects. We used Mendelian randomization to investigate the causal relationship between habitual coffee consumption and cognitive function in mid- to later life. This included up to 415,530 participants and 300,760 coffee drinkers from 10 meta-analysed European ancestry cohorts. In each cohort, composite cognitive scores that capture global cognition and memory were computed using available tests. A genetic score derived using  Its health benefits and risks have long been debated, with some studies suggesting reductions in the risk of various diseases5 and mortality8, while other studies have suggested a potential increase in cardiovascular risk9.Coffee is one of the most widely consumed beverages worldwide1. Caffeine can readily cross the blood-brain barrier, and is believed to exert its neurocognitive effects by antagonizing adenosine receptors in the central nervous system1. Large-scale meta-analyses of prospective studies suggested that higher coffee consumption is associated with a lower risk for Alzheimer’s disease11, while a recent genetic study using summary data from large consortia did not support a causal association12. Studies on the effects of coffee consumption on specific cognitive domains have yielded mixed results for executive function and memory, with some suggesting benefits, and others showing null or adverse effects15. Inconsistent findings may reflect methodological problems commonly seen with dietary exposures including confounding and reverse causation, the latter being particularly important in this case given that caffeine intake is typically one of the first behaviors to be altered when the individual’s health status declines16.Comprised of over 1,000 chemical compounds, coffee gains its popularity through its acute stimulatory effects, which are attributed to the pharmacological activity of caffeine17. In Mendelian randomization, causality is inferred from the association between exposure-related genetic variants and the outcome of interest19. As genotypes are assigned (at random) at conception they will not be generally affected by disease or social and lifestyle factors, allowing us to overcome some of the common problems with observational epidemiology. For a commonly used addictive stimulant such as caffeine, which has been proposed to have both adverse and beneficial effects on cognitive outcomes20, Mendelian randomization is therefore an attractive approach to use as it allows us to address issues both in relation to safety and preventative potential.Randomised clinical trials are the gold standard for testing causal effects, however, these are expensive to undertake and it is difficult to examine influences of prolonged exposures. An alternative is Mendelian randomization, also called “nature’s randomised trial”, which is a non-invasive and cost-effective observational approach to identify possible causal associations22. Two common genetic variants have the strongest effects and known biological mechanisms, one located between the cytochrome P450 1A1 (CYP1A1) and CYP1A2 gene regions (rs2472297) and the other 51 kb upstream the aryl-hydrocarbon receptor (AHR) gene (rs6968865)23. CYP1A2 accounts for ~95% of hepatic caffeine clearance, and AHR plays a key role in metabolizing xenobiotics and inducing the transcription of CYP1A224. In this study, including individual level data for up to 415,530 European ancestry participants, we examined the causal association between genetically instrumented habitual coffee intake, and cognitive function in mid- to later-life, with the aim of establishing causal evidence for potential long-term cognitive effects of coffee consumption. Given the variants influencing patterns of coffee consumption also affect caffeine intake and clearance25, secondary analyses were conducted with respect to habitual tea and caffeine intakes.Genome-wide association studies to date have identified 8 loci influencing habitual coffee intakesAHR and CYP1A1/2 genetic variants, who also had information on habitual coffee intake and cognitive measures.Data came from 10 European cohorts including the 1958 British birth cohort (1958BC), UK Biobank, Mothers of Avon Longitudinal Study of Parents and Children (ALSPAC-M), Northern Finland Birth Cohorts 1966 (NFBC1966), Cardiovascular Risk in Young Finns Study (YFS), Helsinki Birth Cohort Study (HBCS), Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS), Uppsala Longitudinal Study of Adult Men (ULSAM), Swedish twin registry (STR) and TwinGene studies. Participants in each study have provided an informed consent, and each study is covered by ethical approvals from the relevant ethics committees. A more detailed description of each cohort is provided in the supplementary text. Data analysis in each cohort was restricted to participants with complete information on 22. For our primary analyses we chose the two strongest loci that have well-characterized biological functions affecting caffeine metabolism, CYP1A1/2 and AHR24. The remaining six loci  were used in secondary analyses. Our strategy for the selection of instrumental variables is expanded upon in Supplementary Fig. Genome-wide association studies to date have identified eight loci influencing habitual coffee intake patternsCYP1A1/2 was rs2472297, and for AHR rs6968865. In cohorts where rs6968865 was unavailable, its proxy, rs6968554 (r2 = 1.0) or rs4410790 (r2 = 0.97) was used  and AHR 23. Information on genotyping and related quality control is provided in the supplementary text  was not available for most of the studies, and hence was not included in the computation for habitual caffeine intake. Detailed descriptions for coffee and tea intake information in each cohort can be found in the supplementary text  in each cohort was computed using the available self-reported intake information, and in cohorts where the intake was indicated in an interval (e.g. 2 to 4 cups a day), the median was used. A categorical indicator for the intake level  was also created for the purpose of stratification in subgroup analyses. Habitual tea consumption (cups/day) was computed the same way as the habitual coffee consumption, and habitual caffeine intake (mg/day) was approximated by combining information on coffee and tea consumption and assuming that one cup of coffee and tea contained approximately 75 mg and 40 mg of caffeine, respectively26. Individual cognitive tests used in the global cognitive and memory scores were all coded to have higher scores as indicative of better cognitive function. Aside from the composite cognitive scores in the primary analyses, domain-specific cognitive measures in the UK Biobank, including reaction time , pairs matching , reasoning  and prospective memory  were used in the secondary analyses for domain-specific effects.Cognitive measures included in the cohorts ranged from tests of global cognition such as the Mini-Mental State Examination (MMSE) to domain specific tests such as the Trail Making Tests, Digit Symbol Coding and the Paired Associates Learning test  the phenotypic associations between the habitual coffee intake patterns and global cognition and memory function, (2) genetic associations between the genetic score indexing higher coffee intakes and global and memory cognition, and (3) instrument validation assessing the association between the genetic instruments and coffee intake patterns. For the genetic associations, since the coffee-cognition relationship may vary by intake level, we performed the association analyses under different levels of habitual coffee intake (1–4 and ≥4 cups/day). As a negative control, we also examined the genetic associations among non-coffee drinkers (<1 cup/day). For all three association analyses we used the linear regression and fitted two models, with model 1 including sex and age as covariates, and model 2 adjusted for sex, age, smoking (non-current and current smokers), education attainment and depression. In the genetic analyses we assumed an additive inheritance model, and additionally adjusted for principal components (PCs) to account for population stratification.25, we conducted secondary analyses for habitual tea and caffeine intakes , with analyses on tea consumption further restricting the data to participants who did not drink coffee. Further, in the UK Biobank with a sample size up to 290,574, we performed domain-specific analyses to complement our primary findings using composite cognitive scores. Additional sensitivity analyses were conducted using alternative genetic instruments (POR and EFCAB5) to index habitual coffee intakes. Furthermore, we conducted analyses using an alternative MR approach, MR-Egger regression, which is suggested to be robust to pleiotropy27 and which therefore allowed us to use all eight genome-wide significant variants associated with habitual coffee intake patterns 21.We performed secondary subgroup analyses to test if the genetic and phenotypic associations varied by age (<65 or ≥65 years) or sex. Since the genetic instruments indexing habitual coffee intake likely influence the intake patterns through affecting caffeine clearance28 were performed to determine the minimal effect size we were powered to detect in the primary and domain specific genetic association analyses. All data analyses were performed using Stata .Data analyses were conducted in each participating cohort, and the results were sent to the University of South Australia for quality control and meta-analyses. To ensure the uniformity in data analysis across cohorts, a detailed analysis plan and related statistical script was supplied to all cohorts. Random-effects models were used for the meta-analyses, with sensitivity analyses using fixed effects in the absence of heterogeneity. Meta-regression was used to assess variation by cohort characteristics, including age, sex or country. Further, power analysesAHR and CYP1A1/2 were consistent with those reported for the respective populations in the 1000 Genomes project. The genotype frequencies of both variants were in Hardy-Weinberg equilibrium in all cohorts (P > 0.07).Information for coffee intake was available for 415,530 participants from 10 European cohorts, including 300,760 coffee consumers. Basic characteristics for participants in different studies are shown in Table There was no evidence for an overall phenotypic association between habitual coffee intake and global cognition or memory function Table . Althoug2 ranging from 66% to 85% .There was considerable variability in the phenotypic associations across studies, with I29.Genetic associations were not affected by adjustment for smoking, education level and depression. Results are presented for the simpler model with adjustment for sex, age, and PCs to minimize the risk of collider bias potentially introduced by the statistical adjustmentCYP1A1/2, AHR and the genetic score showed the expected patterns with habitual coffee consumption .2 ≤ 4% for both, Table heterogeneity ≥ 0.56 for both global cognition and memory). As expected, in analyses restricted to participants who did not drink coffee as the negative control, the genetic score was not associated with global cognition or memory . Sensitivity analyses using two additional variants associated with coffee consumption, POR and EFCAB521, provided no consistent evidence for association with global or memory cognition , a variant which had previously been suggested to be “non-pleiotropic” indicator for coffee consumption12, but which is located near the serotonin transporter21. MR-Egger regression, using all eight genome-wide significant variants associated with habitual coffee intake21, also provided no evidence for association with global or memory cognition . Domain specific information was available in the UK Biobank for reaction time , pairs matching , reasoning  and prospective memory . The genetic score was not associated with reaction time, pairs matching, or reasoning among coffee drinkers  or sex . There were no significant imbalances in allele distribution of For the genetic association, with a sample size of 300,760  and a two-sided α level of 0.05, at 80% of power we would sufficiently detect a 0.0055 standard deviation (SD) change in the cognitive score for every coffee-intake-increase allele. Approximating from the distribution of coffee intake in the UK Biobank (SD = 2 cups/day), this translates into a causal effect of 0.045 SD by each cup of coffee. For the domain specific analyses in the UK Biobank, with the lowest sample size of 93,512 (the sample size for the reasoning test), the effect size that we would be sufficiently powered to detect is 0.0099 SD for a continuous outcome and 0.024 log odds ratio  for a binary outcome. This translates into a 0.081 SD and 0.20 log OR (OR 1.22) by each cup of coffee, respectively.12, but contrasts with earlier observational studies14, alleviating concerns about potentially adverse effects on memory function. Our null finding is convincing given the power to detect even very small effects, and as the results were remarkably consistent between cohorts, and also across different genetic variants used to index habitual coffee intake. While our analyses did not provide evidence for long-term benefits, these data suggest that there are no adverse effects on memory or cognitive function.Our large-scale genetic analyses including over 300,000 coffee drinkers did not find any evidence to support beneficial or adverse long-term effects of coffee intake on global cognition or memory. Our findings are in line with a recent Mendelian randomization study on Alzheimer’s disease31. For studies to investigate the effects of coffee intake, it has the obvious attraction in the ability to test the preventative potential before advancing to more costly intervention studies. However, as used in the present study, Mendelian randomization has great prospects in addressing safety concerns with exposures considered to be potentially harmful.Mendelian randomization has become an increasingly popular approach for examining causal relationships, and it has begun to show its potential by clarifying a number of previously misconceived associations32. It is possible that the effect of habitual coffee consumption may vary from one domain to another. If this is true, then composite cognitive scores capturing different cognitive domains may potentially dilute any domain-specific effects. Domain-specific analyses were conducted using smaller sub-samples of the UK Biobank with available information, with no evidence for either benefit or harm by coffee intake. Some uncertainty remains with prospective memory, where results were borderline, retaining the possibility of some nominal benefit. Given that this effect was not consistently observed across all instruments for habitual coffee intake it is most likely to be a chance finding.Global cognitive function encompasses several domains with each domain itself being a complex entity22, including CYP1A1/2, AHR, POR, EFCAB5, GCKR, ABCG2, MLXIPL, and BDNF. We chose to use the two strongest variants to construct our primary instrument, as both consistently associate with habitual coffee intake23 and have well-characterized biological mechanisms influencing caffeine metabolism which plausibly explain their association with coffee intake24. However, pleiotropic effects cannot be fully excluded given genome-wide association analyses on Parkinson’s disease, blood pressure and bladder cancer have shown signals both for AHR and CYP1A1/235, although these weak associations could reflect downstream causal effects of caffeine or coffee consumption36. To exclude the possibility that the null association with cognitive function which we observed in this study is driven by opposing patterns induced by pleiotropy in AHR and/or CYP1A1/2, we conducted sensitivity analyses in the UK Biobank using other variants to index habitual coffee consumption21 and replicated our main analyses including POR and EFCAB5, which have unknown effects on caffeine metabolism but which did not have previously reported pleiotropic effects. Also these analyses provided no support for causal effects of habitual coffee intake on cognition, although there appeared to be an association between memory function and rs9902453, located in the intron of the EFCAB5. There is very limited information on EFCAB5 in the literature, however, the variant is in close proximity to SLC6A4, the gene encoding serotonin transporter21. Serotonin affects cognitive abilities, in particular memory consolidation37, hence, the observed rs9902453–memory function association is likely a reflection of a pleiotropic effect than a true causal effect of coffee intake. Pleiotropy was also considered to be a problem with four other variants identified in the earlier GWAS on coffee consumption 21, which according to the GWAS catalogue, had shown primary associations with traits including BMI, smoking initiation, and gout12. In further sensitivity analyses we included all eight coffee intake associated variants21 in the MR-Egger regression which is robust to pleiotropy27. However, any change in our instrument selection strategy did not affect our conclusions, and also these analyses provided no support for any causal association between habitual coffee intake and cognition.In Mendelian randomization the ability to make causal inferences directly depends on the availability and quality of genetic instruments. Genome-wide association studies to date have identified eight loci influencing habitual coffee intakesCYP1A1/2 and AHR, confirm that influences on caffeine metabolism likely explains the association between the genetic instruments and habitual coffee intake. It can be argued as a limitation with our approach that the genetic instruments, which were used to index habitual coffee intake, can also index habitual tea intake. However, it is unlikely that a causal coffee-cognition relationship would have been confounded by habitual tea intakes in our study, given the notably weaker genetic influences on tea than on coffee consumption. Further, it needs to be noted that, any inferences relating to caffeine will need to be drawn with caution as the instruments indexing greater caffeine consumption may reflect a faster rate of caffeine clearance, and hence a lower (rather than higher) circulating levels of bioactive caffeine38.As secondary analyses, we examined the association of the genetic instruments with habitual tea and caffeine intakes. While people who consume more coffee tend to drink less tea, our analyses suggest that the genetic drivers for coffee and tea consumption are similar. This, together with the known functions of 39. However, sensitivity analyses in the UK Biobank study conducted in the coffee and non-coffee drinkers combined, also showed a null association (results not shown). Also, while the test for the association between the genetic score and the outcome is a valid test for a causal relationship40, it cannot estimate the magnitude of the causal effect if it exists. However, given that null associations were observed in our study, there would have been little gain in applying additional methods 41 to quantify these ‘null’ effects.As a further limitation, it can be argued that our genetic association results among coffee drinkers could have been affected by the collider bias as the genetic score indexing the extent of habitual coffee intake is also associated with the probability of being a coffee drinkerIn conclusion, we found no evidence that habitual coffee consumption is causally associated with global and memory cognition in mid- to later-life, despite the power to detect very small effects. This suggests that interventions to protect cognition or slow cognitive decline using coffee are unlikely to be successful and should not be prioritized in future trials. That said, there was no evidence for any adverse effect, contrary to some previous observational studies, and hence it appears safe to consume coffee at least with respect to preserving memory function.Supplementary Material


Genome-wide association studies have identified numerous genetic regions that influence cross-sectional lung function. Longitudinal decline in lung function also includes a heritable component but the genetic determinants have yet to be defined.We aimed to determine whether regions associated with cross-sectional lung function were also associated with longitudinal decline and to seek novel variants which influence decline.1 and FEV1/FVC. Potential additional regions of interest were identified and followed up in two independent cohorts.We analysed genome-wide data from 4167 individuals from the Busselton Health Study cohort, who had undergone spirometry (12 695 observations across eight time points). A mixed model was fitted and weighted risk scores were calculated for the joint effect of 26 known regions on baseline and longitudinal changes in FEV−16 for FEV1/FVC) but no effect on longitudinal decline (p=0.160 for FEV1/FVC). This was replicated in an independent cohort. 39 additional regions of interest (48 variants) were identified; these associations were not replicated in two further cohorts.The 26 regions previously associated with cross-sectional lung function jointly showed a strong effect on baseline lung function (p=4.44×10Previously identified genetic variants jointly have a strong effect on cross-sectional lung function in adults but little or no effect on the rate of decline of lung function. It is possible that they influence COPD risk through lung development. Although no genetic variants have yet been associated with lung function decline at stringent genome-wide significance, longitudinal change in lung function is heritable suggesting that there is scope for future discoveries. Do genetic regions associated with cross-sectional lung function also show association with rate of decline?Twenty-six regions associated with cross-sectional lung function in adults—many of them associated with COPD risk—were not associated with rate of decline in lung function.These findings provide an important insight into the possible pathways through which known genetic regions influence lung function and risk of COPD.1 relative to FVC defines COPD, one of the leading causes of death worldwide. Measures of lung function are also important predictors of morbidity and mortality in the general population.4Reduction of FEVGenome-wide association studies (GWAS) to date have identified numerous regions associated with lung function measured at a single point in time .1 or FEV1/FVC and (2) to seek novel variants which reach genome-wide significance for association with longitudinal change in lung function, using a cohort with multiple lung function measurements over an extended period of up to 40 years and an imputation panel which provides dense coverage of both common and low-frequency variants.The objectives of our study were as follows: (1) to examine the association with longitudinal change for those regions previously identified as significantly associated with cross-sectional FEV1 and FVC were assessed. From 1966 to 1978 (five surveys), FEV1 and FVC were measured using a McDermott dry spirometer  and recorded as the highest values from three measurements (provided that two recordings were within 10% of each other). Wedge spirometers  were used in the 1981 survey and pneumotachograph spirometers  were used in 1994/1995.1 and FVC were measured in 2005 using Medgraphics pneumotachograph spirometers and recorded with BreezeSuite 6.2 .15The Busselton Health Study (BHS) is a longitudinal health survey that began in 1966 in the town of Busselton in the southwestern region of Western Australia. In 1994/1995, a cross-sectional community follow-up study was undertaken where blood was taken for DNA extraction. A sample of 1468 individuals with European ancestry were genotyped using the Illumina 610-Quad BeadChip (BHS1) and subsequent genotyping was carried out on an independent group of 3407 individuals with European ancestry using Illumina 660W-Quad (BHS2). Spirometric measures of FEVDemographic details and mean lung function measurements for this and replication cohorts are shown in 16−6) or had a minor allele frequency (MAF) of <1% (100 240 variants). Individuals were excluded if their call rate was <95%, if their submitted gender and gender inferred by genotype were inconsistent, if they were a duplicate or if they were an outlier for heterozygosity . Principal components analysis was used to exclude individuals of non-European ancestry  . Analysis of identity by descent (IBD) was compared with the reported pedigree; any inconsistencies were reviewed and individuals were excluded as appropriate ,http://www.R-project.org).Genotype data from BHS1 and BHS2 samples were merged and quality control (QC) was undertaken for both variant quality and sample quality. Prior to imputation, variants were excluded if they had a call rate <95%, deviated from Hardy-Weinberg equilibrium (p<1010.1136/thoraxjnl-2016-208448.supp1supplementary data2) was <0.3 or MAF <1% were excluded.Imputation was undertaken using the 1000 Genomes Project reference panel (1000G Phase I Integrated Release V.3 March 2012)).12).20 Ch1 was greater than FVC were excluded from the analysis. After phenotype and genotype datasets were merged, and after samples failing genotype QC were excluded, 12 863 observations for 4170 individuals and up to eight time points were available prior to further phenotype QC.Data on age and smoking were checked for consistency over time. Where possible, missing data on height or smoking at one time point were imputed to be the same as that at the nearest time point . Otherwise, observations with missing data on lung function, age or height or where FEV2, height, height2, sex and time as fixed effects and an intercept varying per individual, an intercept varying per family and a slope for time varying per individual as random effects was fitted to the data for each trait . Additionally, another four models were fitted, excluding the random intercept varying per family, the random slope for time varying per individual and age2 and height2 one at a time and comparing with the full model using Akaike information criterion and Bayesian information criterion. The final model included all the terms in the full model, except for age2 as this did not improve the fit of the model  with at least one other person in the family. A full model with age, age1, FEV1/FVC and FVC, were then excluded from the analysis . This resulted in 12 695 observations for 4167 individuals and up to eight time points included in the final analysis. The mean number of measurements per participant was three  for any of the three traits analysed, FEV1 and/or FEV1/FVC.1 and FEV1/FVC as well as on change over time in both traits. The single-nucleotide polymorphisms (SNPs) included in this analysis are shown in the online Previously published studies identified variants in 26 regions significantly associated with cross-sectional FEVUnbiased (winner’s curse-free) effect sizes, as calculated previously but excluding any data from BHS,1, FEV1/FVC and FVC was tested by adding a variant and variant-by-time interaction into the final phenotypic model described above. Using this model, genome-wide association analyses were undertaken for 29 798 550 variants assuming an additive genetic effect. Genomic control was subsequently applied .23The effect of individual genetic variants (which included SNPs and indels) on the rate of decline in FEV−6 taken as suggestive of association) with longitudinal FEV1, FEV1/FVC or FVC. We defined regions of association around the most strongly associated variant (sentinel variant) ±500 kb. We examined region plots to assess support from neighbouring variants and cluster plots for the closest genotyped variant to the sentinel variant in order to rule out associations driven by genotyping errors. Regions were selected for follow-up if they had at least one variant (either the top variant or a proxy in linkage disequilibrium with r2 >0.2) with imputation quality >0.7 and p value <5×10−5.−5 where the sentinel variant had imputation quality <0.7 and (3) for regions in which variants showed nominal interaction with smoking (p <0.05 for interaction term based on a Z-test between ever-smokers and never-smokers), the most significant variant in the group (ever-smokers or never-smokers) where the sentinel variant's effect size estimate was largest.Within each region, we selected variants for follow-up analysis as follows: (1) the sentinel variant, (2) a second variant in the region with imputation quality >0.7 and p value <5×10Sensitivity analyses were also undertaken for the regions which met the criteria for follow-up analysis to assess whether their effect could be mediated through smoking behaviour. The same model was fitted, with an additional term for smoking status (ever-smoked or never-smoked), and effect sizes were compared.Estimations of power were also obtained for the discovery of new signals and for detection of associations with the 26 variants previously reported to be associated with cross-sectional lung function , a prospective study of a random sample of the Danish general population, aged ≥20 years, drawn using the Danish Civil Registration System (n=9016) who had lung function measurements at up to three time points between 1976 and 1994,et al; http://CRAN.R-project.org/package=lme4).All risk score and association analyses were undertaken in R using the package ‘lme4’  and FEV1/FVC (r=0.78). However, estimated SNP effects on change in FEV1 and FEV1/FVC were weakly correlated with published estimates for the respective cross-sectional trait  and FEV1/FVC (p=4.44×10−16) but no effect was observed on change over time for either FEV1 (p=0.409) or FEV1/FVC (p=0.160)  .−10) but no association with change over time in FEV1/FVC (p=0.302) and strong association with baseline measurement (p=8.92×10−7) but only borderline association with change over time for FEV1 (p=0.030) (In silico data were available for eight known variants in CCHS and genotyping was undertaken for the remaining 18 variants. All of these passed QC procedures. They showed a joint effect similar to that seen in BHS: a strong association with baseline measurement (p=4.45×10p=0.030) .1, FEV1/FVC and/or FVC at a significance threshold of p<5x10−6. Thirty-nine of these regions (48 variants) were selected for follow-up based on the criteria described under ‘Methods’: 11 regions (13 variants) for change in FEV1, 15 regions (19 variants) for change in FVC and 13 regions (16 variants) for change in FEV1/FVC. These are shown in 1 (p=3.23×10−9).Genome-wide analysis identified 56 independent regions which were associated with decline in FEV−3).Effect size estimates for these variants were not attenuated after adjusting for smoking status and no variant-by-smoking interaction met a Bonferroni-corrected threshold for the number of regions tested  and 14 failed. The analysis included 9016 individuals with up to four time points (25 796 observations). None of these 34 variants showed replicated association with change in lung function  (see online Thirty-one variants (in 26 of the 39 regions) for which in silico data were available were also followed up in LHS. None of these SNPs showed significant association with lung function decline in LHS   , we assessed association with FEV1 and/or FEV1/FVCTNS1, RARB, FAM13A, GSTCD, HHIP, HTR4, ADAM19, AGER, GPR126, C10orf11, THSD4),1 or FEV1/FVC over time in our cohorts. We identified novel variants associated with decline in lung function in BHS which did not replicate in either a general population cohort (CCHS) or a cohort of smokers with mild lung function impairment (LHS).We analysed the joint effect of regions previously associated with cross-sectional lung function on longitudinal change in the general population, and undertook a GWAS to identify new signals associated with longitudinal change. Regions previously identified as significantly associated with cross-sectional FEVet al,A key strength of our study design is the improved coverage of both common and low-frequency variants achieved through imputation in the 1000 Genomes Project reference panel,The biggest challenge we faced was the availability of large sample sizes for well-characterised longitudinal measures of lung function. The sample size, in combination with possible measurement error, may have limited our ability to identify individual variants which reach stringent genome-wide significant levels and replicate. However, our study will have had much greater power to detect longitudinal effects of aggregate risk scores comprised of the 26 variants previously reported to be associated with cross-sectional lung function. We did not examine risk scores for FVC, as at the time of the analyses there were no published associations with FVC, and our focus was on the determinants of obstructive lung disease. It is possible therefore that there are genetic associations with change in FVC over time which we did not identify. We did not undertake analyses stratified by sex, for reasons of power. However, effect estimates were adjusted for sex. We did not adjust for smoking in the primary analysis, since the analysis also had potential to highlight novel signals for smoking behaviour. We undertook sensitivity analyses to assess whether any top signals could be mediated by smoking behaviour. The signals were not attenuated after adjustment for smoking. However, this was based on binary smoking status and there remains potential for incomplete adjustment.et al1. However, these did not replicate in two further cohort studies (including LHS). The authors noted that the number of lung function measurements and duration of follow-up varied considerably between studies included in the discovery phase and may have affected their ability to detect associations. The failure to replicate novel associations may relate to lack of power or, given that the larger replication cohort in their study (LHS) was composed of smokers with mild COPD, may indicate that the genetic determinants of lung function decline in those with COPD differ from those in healthy individuals.34Our study—the first to our knowledge to examine risk scores for change in lung function over time—complements existing studies which have sought individual SNP associations,1% predicted (in a cohort of smokers with mild lung function impairment from LHS) identified two regions reaching genome-wide significance which did not replicate in four general population cohorts or in a cohort with moderate-to-severe COPD. The authors suggested that regions which modify the effect of cigarette smoke on lung function decline (or determine rate of decline at different stages of COPD) may be distinct from those which influence lung function decline in the general population.36Similarly, a GWAS examining change in FEVDLEU7) showed evidence of replication (at p<0.05) in largely population-based cohorts, but it did not reach genome-wide significance in discovery, replication or two-stage meta-analysis.The suggestion that variants which determine lung function decline may show heterogeneity across different groups is further supported by findings from an earlier GWAS which identified suggestive evidence that different regions were associated with lung function decline in asthmatic and non-asthmatic individuals. Only the signal in non-asthmatic individuals  and then showed rapid decline, whereas the remaining half started with low FEV1 in early adulthood followed by a relatively normal rate of decline. This suggests that rapid lung function decline in later life is not necessary for development of COPD. We hypothesise that the known genetic variants examined in this paper may exert much of their effect in earlier life. Of the 26 regions examined in this paper, 19 have previously been shown to have directions of effect on lung function in children (aged 7–9 years) consistent with that in adults.Our findings are also consistent with recent work by Lange These findings emphasise the continuing public health importance of focusing on the key environmental determinants of lung function decline, particularly smoking. Nevertheless, genetic determinants of decline may remain a therapeutic target in the half of people with COPD for whom accelerated decline is important in pathogenesis.1 and/or FEV1/FVC1 or FEV1/FVC over time. The present study and others to date have identified no regions associated with lung function decline which reach genome-wide significance and replicate in independent cohorts. Genetic variants identified to date that influence cross-sectional lung function, while still relevant in predicting the risk of COPD, appear to have little or no effect on the rate of change in adult lung function over time in our study.In summary, regions previously identified as significantly associated with cross-sectional FEV


Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method  that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases . We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer’s disease, and bidirectional associations with opposite effects . Genetic methods are useful to test whether risk factors are causal for or consequence of disease. Here, Zhu et al. develop a generalized summary-based Mendelian Randomization (GSMR) method which uses summary-level data from GWAS to test for causal associations of health risk factors with common diseases. These associations are usually derived from observational studies that cannot distinguish whether the risk factors are “upstream” causal factors, “downstream” consequences of the diseases, or confounding factors associated with both the exposures and outcomes. The randomized controlled trial (RCT) is considered to be the gold standard approach to test for causality. For instance, LDL-cholesterol (LDL-c) was initially found to be associated with coronary artery disease (CAD) in an observational study5, and the association was subsequently confirmed to be causal by RCTs7. However, RCTs are time-consuming, expensive, and sometimes impractical or even unethical8. It is not feasible to design RCTs that can test many different interventions simultaneously. Genetic methods are useful to infer causality because genetic variants are present from birth and therefore unlikely to be confounded with environmental factors. Mendelian randomization (MR) is an analysis that uses genetic variants, which are expected to be independent of confounding factors, as instrumental variables to test for causality11. MR can be used to infer credible causal associations when RCTs are not feasible or as a strategy to rank order candidate causal associations to be prioritized for follow-up in RCTs. MR is becoming increasingly efficient and cost-effective given the ever-growing data curated from recent genome-wide association studies (GWAS). The large amount of GWAS data available in the public domain provide a great opportunity for methods that are able to make inference about causality by integrating summary-level GWAS data from different studies16. We have previously shown that the power of an MR analysis could be greatly improved by exploiting GWAS summary data from two independent studies with large sample sizes, and have applied a summary data-based MR (SMR) approach to test whether the effects of genetic variants on a phenotype are mediated by gene expression17.Health risk factors such as body mass index (BMI), serum cholesterol, and blood pressure are associated with many human common diseases18. Separation of signals of causality from pleiotropy  and further separation of marginal effect from conditional effect  are recognized issues that require careful interpretation in MR analyses19. We develop a method (HEIDI-outlier) to detect and eliminate genetic instruments that have apparent pleiotropic effects on both risk factor and disease, and another method  to estimate the effect of a risk factor on disease conditioning on other risk factors. All methods developed in this study only require summary-level data , providing a great flexibility to integrate data from multiple studies. We apply the methods to publicly available data of very large sample sizes  to test the causal associations between health risk factors such as BMI, serum cholesterol levels and blood pressure levels and a range of human common diseases. Our study develops powerful tools to integrate summary data from large studies to infer causality, and provides important candidates to be prioritized for further studies in medical research and for drug discovery.In this study, we extend the SMR approach to a more general form  by leveraging power from multiple genetic variants accounting for linkage disequilibrium (LD) between the variants, and demonstrate by simulation that GSMR is more powerful than existing summary data-based MR methodsy be the liability of a disease on the logit scale, x be a risk factor in standard deviation (SD) units and z be the genotype of a SNP . The MR estimate of the causal effect of risk factor on disease9 is bzy is the effect of z on y on the logit scale , bzx is the effect of z on x, and bxy is the effect of x on y free of confounding from non-genetic factors . SMR is a flexible and powerful MR approach that is able to estimate and test the significance of bxy using the estimates of bzx and bzy from independent samples17. If there are multiple independent (or nearly independent) SNPs associated with x and the effect of x on y is causal, then all the x-associated SNPs will have an effect on y through x  approach (Methods). The GSMR method essentially implements SMR analysis for each SNP instrument individually, and then integrates the bxy estimates of all the SNP instruments by GLS, accounting for the sampling variance in both 9, only the SNPs that are strongly associated with the risk factor should be used as the instruments for MR analyses including GSMR. We demonstrate using simulations   to detect pleiotropic SNPs at which the estimates of bxy are significantly different from expected under a causal model, and remove them from the GSMR analysis (Methods). The power of detecting a pleiotropic SNP depends on the sample sizes of the GWAS data sets and the deviation of 13. Our simulation results also show that the GSMR estimate of bxy is not significantly different from zero under a pleiotropic model without causal effect in the presence or absence of LD  that only requires summary data to conduct a GWAS analysis for a phenotype conditional on multiple covariate phenotypes (Methods). The purpose of developing this method is to estimate the effect of a risk factor on disease adjusting for other risk factors  27 with genotyped data released in 2015. We selected SNPs at a genome-wide significance level (PGWAS < 5 × 10–8) using the clumping algorithm (r2 threshold = 0.05 and window size = 1 Mb) implemented in PLINK28 (Methods). Note that the GSMR method accounts for the remaining LD not removed by the clumping analysis. There were m = 84, 43, 159, 141, 101, 28, and 29 SNPs for BMI, WHRadjBMI, HDL-c, LDL-c, TG, SBP and DBP, respectively, after clumping. These SNP instruments are nearly independent as demonstrated by the distribution of LD scores computed from the instruments for each trait .We applied the methods to test for causal associations between seven health risk factors and common diseases using data from multiple large studies. The risk factors are BMI, waist-to-hip ratio adjusted for BMI (WHRadjBMI), HDL cholesterol (HDL-c), LDL-c, triglycerides (TG), systolic blood pressure (SBP), and diastolic blood pressure (DBP). We chose these risk factors because of the availability of summary-level GWAS data from large samples n = 108,09–322,15429 (GERA)  and the subgroup of UKB27 . We included in the analysis 22 common diseases as defined in the GERA data, and added an additional phenotype related to comorbidity by counting the number of diseases affecting each individual  as a crude index to measure the general health status of an individual (Supplementary Table rg) analysis using the bivariate LD score regression (LDSC) approach30. The estimates of rg across all diseases varied from 0.75 to 0.99 with a mean of 0.91  of a SNP on disease in the general population can be approximated by that from a case–control study assuming that disease in the case–control study is defined similarly as that in the general population. Therefore, GSMR can be applied to data with SNP effects on the risk factor from a population-based study and SNP effects on the disease from an ascertained case–control study, and the estimated causative effect of risk factor on disease should be interpreted as that in the general population. We therefore included in the analysis summary data for 11 diseases from published case–control studies   n = 3,991 and33  imputed to 1000 Genomes (1000G)34. Using the large data sets described above, we identified from GSMR analyses 45 significant causative associations between risk factors and diseases (Supplementary Data PGSMR threshold = 2.2 × 10−4). For method comparison, we have also performed the analyses with MR-Egger13 and the methods in Pickrell et al.16 . The LD correlations between pairwise SNPs were estimated from the Atherosclerosis Risk in Communities (ARIC) data35 (Supplementary Data (BMI→T2D) = 3.29 is that people whose BMI are 1 SD  that higher BMI increases the risk of childhood asthma36. Moreover, we identified a protective effect of BMI against osteoporosis (OR = 0.68), consistent with the observed associations in previous studies38. The estimated risk effect of BMI on T2D in the community data (OR = 3.29) was similar to that in the case–control data , hypertensive disease OR = 1.85), dermatophytosis  OR = 1.67), peripheral vascular diseases (PVD) (OR = 1.59), osteoarthritis (OR = 1.50), dyslipidemia (OR = 1.37), asthma (OR = 1.35), and CVD (OR = 1.30). The risk effects of BMI on T2D, CVD, and hypertensive disease have been confirmed by RCT.85, derm7, periphbxy for disease count should be interpreted. We have shown in Supplementary Fig. bxy for disease status  was very similar to that for disease count. Although disease status and disease count are two distinct phenotypes and the analysis of disease count is more powerful, for the ease of interpretation, bxy for disease count can be approximately interpreted as logOR for disease status. Hence, Being overweight is a risk factor for general health outcomes as indicated by its risk effect on disease count (7. We found that LDL-c had a significant risk effect on dyslipidemia (OR = 3.36) and CVD (OR = 1.22) in the community data, and CAD (OR = 1.50) in the case–control data . Both LDL and TG had significant risk effects on disease count in the community data   and all of which had highly significant effects on both LDL-c and AD. Excluding these SNPs makes a more conservative GSMR test because if there is a true causal relationship of increased LDL-c with AD, then the GSMR test should remain significant based on evidence from other LDL-c associated SNPs. In fact, after removing the 16 pleiotropic SNPs, the estimated effect of LDL-c on AD was not significant . Nevertheless, the multiple pleiotropic signals clustered at the APOE locus are worth further investigation  was highly significant without HEIDI-outlier filtering (OR = 1.35 and 16) Fig. . The HEIPGSMR = 1.1 × 10−4) in the case–control data, which might explain the observation from a previous study that lowering LDL-c using statin therapy is associated with a slightly increased risk of T2D39. The estimate was not significant in the community data (likely due to the lack of power) but in a consistent direction . Given the strong genetic correlation between the two T2D data sets  as estimated by the bivariate LDSC analysis30, we meta-analyzed the two data sets using the inverse-variance approach, and performed the GSMR analysis to re-estimate the effect of LDL-c on T2D using the T2D meta-analysis data. The effect size was highly significant .We identified a significant protective effect of LDL-c against T2D , hypertensive disease (OR = 0.88), CVD (OR = 0.88) and disease count (OR = 0.94) in the community data, and T2D (OR = 0.81) and CAD (OR = 0.84) in the case–control data. However, none of these effects remained significant conditioning on the other risk factors, suggesting that the marginal effects of HDL-c on diseases are dependent of the other risk factors . The effect of HDL-c on dyslipidemia is negative .We identified significant risk effects of SBP on hypertensive disease (OR = 4.38), dyslipidemia (OR = 1.50), CVD (OR = 1.40) and disease count (OR = 1.43) in the community data, and CAD (OR = 1.73) in the case–control data. The results for SBP and DBP were highly concordant Fig. . The risPGSMR < 1.2 × 10−3)  45 significant causal associations between health risk factors and diseases Fig. . As the bxy of the covariate risk factors on the target risk factor and disease, rg among the covariate risk factors, SNP-based heritability  to perform a GWAS analysis for a trait conditioning on other traits using GWAS summary data  and the effect size remained largely unchanged and highly significant conditioning on the covariates . We conclude that HDL-c is likely to be a direct risk factor for AMD and the effect size is independent of the covariate risk factors analyzed in this study.Results from conditional analyses were largely consistent with those from unconditional analyses Fig. , suggest−2 for BMI, ~1 mmol L−1 for LDL-c, ~1 mmol L−1 for TG and ~19 mm Hg for SBP), we would have an increased risk of ~2.3-fold to T2D (e1.01−0.17), and 4.5-fold to CAD (e0.41+0.47+0.14+0.48).Given the estimates from conditional GSMR analyses Fig. , we coul48 and years of schooling49 (EduYears), for which there were a large number of instruments owing to the large GWAS sample sizes. We selected 811 and 119 near-independent genome-wide significant(GWS) SNPs for height and EduYears, respectively, using the clumping analysis (Methods). The threshold PGSMR after Bonferroni correction was 7.6 × 10−4 correcting for 66 tests. The large number of instruments for height gave us sufficient power to detect a small effect   from a bivariate LD score regression analysis30.Our results also showed that EduYears had protective effects against almost all the diseases Fig. . It show22. Nevertheless, it is interesting to investigate the changes in risk factors after development of the diseases. To do this, we selected instruments for diseases from the disease GWAS data . The false positive rate of reverse-GSMR is well calibrated as demonstrated by simulation under the null that there is no reverse effect  . Secondly, if the associations presented above are driven by reverse causality, we would expect to see strong association signals of the instruments with the diseases, which is not the case as demonstrated in Supplementary Fig. bxy were highly consistent with the slopes from Egger regression that are considered to be free of confounding from pleiotropy13 (MR-Egger)  to test for causal association between a risk factor (or phenotype) with a disease using summary-level GWAS data from independent studies. We have implemented the method in an R package (URLs). The method and software tool are general and can be applied more broadly to test for causality in other fields such as behavioral sciences. We applied the method to summary data from GWAS of very large sample size, and identified a large number of causal associations between risk factors and common diseases. As the effect sizes of SNPs on risk factor and disease used in the GSMR analysis were from independent GWAS data sets, the effect of risk factor on disease estimated by GSMR was very unlikely to be confounded by environmental factors. The results remain unchanged when we removed SNPs in the major histocompatibility complex (MHC) region  that appeared to have pleiotropic effects on T2D and BMI  in the future, if practical. Second, the effect of a risk factor on disease can be non-linear  whereas we used a linear approximation to estimate the effect because of the limited information that we had access to from GWAS summary data. Therefore, the bxy estimates need to be interpreted with caution at extremes. Third, although we have identified a large number of associations, we would expect that associations of small effect size would be missed in our study . The power can be improved in the future with GWAS results based on larger sample sizes. Fourth, our analyses ignored age-specific and sex-specific effects because of the lack of data from age- and sex-stratified analyses. Lastly, we have shown in a previous study that the SMR test-statistic is slightly deflated due to the use of a Taylor series expansion to compute an approximated sampling variance based on summary data, especially if the association between the instrument and risk factor is not strong enough. We therefore strongly recommend that only SNPs that are associated with the exposure at a genome-wide significance level  should be used in GSMR analysis, and as a rule of thumb advise application only when there are 10 or more independent  genome-wide significant SNPs.Nevertheless, there are several caveats in interpreting the GSMR results. First, if the exposure is a composite trait that comprises multiple sub-phenotypes, we could not rule out the possibility that the effect of exposure on disease is driven by one of the sub-phenotypes. For instance, we have identified from the GSMR analysis that EduYears had effects on many diseases Fig. . A conseIn summary, we present here summary data-based MR analysis approaches that leverage the large amount of GWAS data from independent studies to detect the effect of a risk factor on disease and assess the effect size conditional on the other risk factors. All the data used in this study were from the public domain, which demonstrates the power of an integrative analysis of existing data to make novel discoveries. The causal associations identified in this study not only provided important candidates to be prioritized in RCTs in the future but also provided fundamental knowledge to understand the biology of the diseases. Our findings of the effects of risk factors on common diseases could have a significant influence on medical research, pharmaceutical industry and public health.9. Let z be a genetic variant , x be the exposure  and y be the outcome . If z is significantly associated with x, the effect of x on y can be estimated using a two-step least squares (2SLS) approach51n is the sample size, y explained by x, and x explained z. This analysis requires individual-level data so that the statistical power could be limited if bxy is small. We have previously proposed an approach that only requires summary-level data to estimate bxy so that the power can be greatly improved if bzx and bzy are estimated from independent studies of large sample size17, i.e., 17. We have also shown previously that a SMR analysis using a single genetic variant is unable to distinguish between causality (the effect of SNP on outcome is mediated by exposure) and pleiotropy (the SNP has distinct effects on exposure and outcome). Here, we extend the SMR method to use all the top associated SNPs at a genome-wide significance level for the exposure as instrumental variables to test for causality. We call this method a generalized SMR (GSMR) analysis. The basic idea of GSMR is that if x is causal for y, any SNP associated with x will have an effect on y, and the expected value of i will be identical in the absence of pleiotropy. Let m be the number of GWS top SNPs associated with x after clumping. We have 1 is an m × 1 vector of ones and V is the variance-covariance matrix of ij-th element of V is i and j represent SNP i and j, respectively, r is LD correlation between the two SNPs . The i-th diagonal element of V is bxy from all the instruments using the generalized least squares approach as 2 distribution with 1 degree of freedom. Note that because logOR is free of ascertainment bias , the method can be applied to disease data from case–control studies, and the estimate of bxy should be interpreted as that of the general population.Mendelian randomization is a method that uses genetic variants as instrumental variables to test for causative association between an exposure and an outcomex and y, 52. These methods, however, do not account for possible LD between SNPs nor the sampling errors in the estimated effect sizes of the instruments on the exposures. We previously proposed an approach  to test for heterogeneity in bxy estimated at multiple correlated instruments17. Here, we extend this approach to detect heterogeneity in bxy estimated at m near-independent instruments (note that the method accounts for remaining LD not removed by clumping). The basic idea is to test where there is a significant difference between bxy estimated at an instrument i ) and bxy estimated at a target SNP that shows a strong association with the exposure. The power of detecting heterogeneity increases with the strength of association between the target SNP and exposure. However, we cannot simply choose the top exposure-associated SNP because sometimes when a SNP has an extremely strong effect on the exposure, it is also likely to be a pleiotropic outlier  for r is the LD correlation between the two SNPs . We can test the deviation of each SNP from the causal model using the χ2-statistic P-values < 0.01. We call this approach HEIDI-outlier. We choose a relatively less stringent P-value threshold for the HEIDI-outlier analysis because even if a causal signal is detected as pleiotropy and eliminated from the analysis, it will only affect the power rather than the false positive rate or biasedness of the GSMR analysis. To retain as much power as possible to detect heterogeneity, we use a modest threshold 0.01. This means that even if there is no pleiotropic outlier, we will remove only ~1% of the instruments by chance, which is very unlikely to result in a substantial decrease in power of the subsequent GSMR analysis.We have shown above that under a causal model the expected value of x0) on a disease (y) depends on other risk factors , we usually perform a joint analysis based on the model belowb0 is the effect of x0 on y, t-length vector with xi on y, and e is the residual. Such an analysis is equivalent to a two-step analysis with the first step to adjust both x0 and y by x and the second step to estimate the effect of adjusted x0 on adjusted y. We therefore can estimate the effect size of x0 on y accounting for x by a GSMR analysis using SNP effects on x0 and y conditioning on x.To test whether the effect of a risk factor . We know from previous studies53 that the joint effects of gx on y (bxy) can be transformed from the marginal effects (βxy), i.e.,t×t matrix with i and j, D is a t×t diagonal matrix with the i-th diagonal element i-th covariate. We can estimate 54, and estimate The conditional GWAS analysis usually requires individual-level genotype and phenotype data, which are not always available. Here, we propose a method to perform an approximate multi-trait-based conditional GWAS analysis that only requires summary data. Since GWAS summary data for risk factors and disease are often from multiple independent studies, the analysis has to be performed conditioning on the genetic values of the covariate risk factors . Vzx is the sampling variance-covariance of ij-th element xi and xj. The multi-trait-based conditional GWAS test can be performed using the test-statistic 23, as confirmed by simulation  (see below for details of the UKB data). GWAS summary data for the other traits were from published studies (Supplementary Table z-statistics (zzx) using the method described in Zhu et al.17 so that p being the allele frequency and n being the sample size). We then applied the clumping algorithm in PLINK28 to select near-independent GWS SNPs for each trait  using the 1000G-imputed ARIC data33  as the reference for LD estimation. As the statistical power of the GSMR analysis increases as the number of instruments, we performed the clumping analysis repeatedly for the SNPs in common between each pair of risk factor and disease data sets to maximize the number of instruments.We used nine risk factors as exposures for the GSMR analysis. These include seven health risk factors, i.e., body mass index (BMI), waist-to-hip ratio adjusted by BMI (WHRadjBMI), HDL cholesterol (HDL-c), LDL-cholesterol (LDL-c), triglyceride (TG), systolic blood pressure (SBP) and diastolic blood pressure (DBP), and two additional phenotypes  that had a large number of instruments. We conducted GWAS analyses for SBP and DBP using data from the UK Biobank29 (GERA) and UKB pilot phase27. There were 60,586 individuals of European ancestry in the GERA data. We cleaned the GERA genotype data using the standard quality control (QC) filters , and imputed the genotype data to the 1000G using IMPUTE255. We used GCTA56 to estimate the genetic relationship matrix (GRM) of the individuals using a subset of the imputed SNPs , and computed the first 20 principal components (PCs) from the GRM. We removed one of each pair of individuals with estimated genetic relatedness ≥0.05 and retained 53,991 unrelated individuals for analysis. Individual-level ICD-9 codes were not available in dbGaP but had been classified into 22 common diseases . We included in the analysis only the individuals of European ancestry. Similarly as above, we computed the GRM and the first 20 PCs based on the HM3 SNPs with MAF ≥0.01 and imputation INFO score ≥0.3, and retained 108,039 unrelated individuals (GRM threshold of 0.05) for analysis. Individual-level ICD-10 codes were available in the UKB data. To match the diseases in GERA, we classified the phenotypes into 22 common diseases by projecting the ICD-10 codes to the classifications of ICD-9 codes in GERA taking into account self-reported disease status (Supplementary Table http://cnsgenomics.com/software/gsmr/GSMR R package: http://cnsgenomics.com/software/gcta/#mtCOJOmtCOJO: http://cnsgenomics.com/software/smrSMR: http://pngu.mgh.harvard.edu/~purcell/plink/PLINK: https://www.cog-genomics.org/plink2PLINK2: http://cnsgenomics.com/software/gctaGCTA: https://github.com/bulik/ldscLDSC: http://cnsgenomics.com/data.html. All the other data sets used in this study are from the public domain. The software tools are available at the URLs above.The summary-level GWAS data from the meta-analyses of GERA and UKB are available at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


The aim of this study was to estimate the heritability, genetic and phenotypic correlations, and the genetic trends for pre-weaning growth traits including the birth weight (BWT), weaning weight (WWT), pre-weaning daily gain (PWDG), and weaning age (WA) in Awassi lambs.A total of 5131 Awassi lambs from two Jordanian sheep breeding stations were used. A multitrait animal model and restricted maximum likelihood methods were used to estimate the covariances between the studied traits.The mean±standard deviation of BWT, WWT, PWDG, and WA was 4.48±0.8 kg, 17.13±0.7 kg, 0.2±0.07 g, and 65.5±0.7 days, respectively. Heritability estimates were 0.30±0.04 for BWT, 0.19±0.04 for WWT and PWDG, and 0.2±0.04 for WA. Positive genetic correlations were obtained between BWT and other traits, while negative correlations were obtained between WWT, PWDG, and WA (−0.50±0.12) and between WWT and WA (−0.67±0.14). The positive phenotypic correlation was obtained between WA and PWDG (0.63±0.01). The highest additive genetic variance was obtained for WA (34.58), while the lowest was estimated for PWDG (6.22E-04). The highest phenotypic variance was obtained for WA (175.5), while the lowest value obtained was for BWT (0.54). Maternal additive variance ranged between 0.13 and 0.03. The genetic trends were around zero for all studied traits.Selection should be conducted using animals with high estimated breeding values through controlled breeding. Genetic selection is one of the most important methods that has been used to maximize the expected genetic gain of livestock through crossbreeding programs and breed selection using specific gene markers or genomic selection methods . Awassi Heritability, genetic correlation, and breeding values are considered important factors that determine the lamb’s genetic gain and therefore performance and economic value of the lamb ,5. StudyIn Jordan, phenotypic selection of sheep has been applied in two breeding stations using the WWT as the sole selection criteria. To the best of our knowledge, the present investigation is the first one that studies the effects of various maternal and environmental factors on the growth traits of Awassi sheep in Jordan. Therefore, the aim of this study was to estimate the genetic and phenotypic parameters and genetic trends of the BWT, WWT, pre-weaning daily gain (PWDG), and WA in Awassi lambs.This study was reviewed and all experimental procedures were approved by the Jordan University of Science and Technology Animal Use and Care Committee (JUST-ACUC).The study was conducted at two major Jordanian sheep breeding stations (AL-Khanasry and Al-Fjaj stations) that belong to the Ministry of Agriculture. AL-Khanasry Research station is located about 65 km to the Northern east of Amman, at 32°30ʹ N, and an altitude of 860 m above sea level. The annual total rain precipitation ranges between 150 and 200 mm [Flocks are managed using a semi-intensive system. Grazing is allowed for 4 h in the morning and 3 h in the afternoon year-round, except during pregnancy period. Rangeland consisted of green fodder and natural vegetation (shrubs and herbage) mostly from mid-January to late April. Each ewe receives 0.5 kg of alfalfa and 1.5 kg of concentrate during the past 8 weeks of gestation. After lambing, the amount of concentrate offered increases to 1.8 kg. Concentrate is composed of barley (67.5%), soybean meal (12%), wheat bran (18%), limestone (1.4), salt (1%), and trace minerals (0.1) in both stations.During the breeding season, group mating strategy is applied in the two stations. Before lambing, each ewe is placed in an individual pen that allows the ewe to take care of her newborn and stays this pen for about 3 days. Through this period, the ewe and her lamb are weighed and the lamb is identified with a permanent ear tag. All information is recorded for the newborn .Each lamb is allowed to consume the recommended amount of colostrum immediately after lambing. The regular lactation period starts and continues when the lamb is allowed to suckle freely from his dam during the full day for up to 15 days. Creep feeding starts at 15 days of age. Lambs are weighted biweekly to monitor their body weights and to prepare them for weaning. Weaning strategy for both stations is based on lamb weight and age. Lambs are weaned when they reach 14 kg of body weight. On the day of weaning, the lamb’s weight and age are recorded.The data were collected through 13 years (1999-2011) from AL-Khanasry and AL-Fjaj stations. The same traits and measurements were taken using similar procedures in the two stations.Data were analyzed with a linear mixed model using average information- restricted maximum likelihood methodology. To estimate the variance of BWT, WWT, PWDG, and WA, a multitrait animal model was used. The fixed part of the models for analysis of the traits included birth type (single or twin), lamb’s sex, and location. The fixed effect of year was considered for WWT, PWDG, and WA. The model used the BWT as a covariate. Animal effect, maternal effect, and residual effects were included as random effects. The model used for each trait, expressed in matrix notation , was as Y=Xb+Zu+Wm+eWhere Y=Vector of observations for each traits; X, Z, and W are the incidence matrices of fixed, additive genetic effect of the animal, and maternal permanent environment effect, respectively; b=Vector of fixed effects for the traits analyzed; u=Vector of random effects of the additive genetic effect of the animal; m=Vector of the permanent environmental effect; e=Vector of random error.Genetic correlations were obtained through bivariate analysis including the same effects included in the univariate model mentioned above. The analysis was performed using the software ASReml 3.0 .Genetic trend for the traits was calculated by estimating the predicted breeding values on the year of birth. Phenotypic trend also calculated using the regression of phenotypic values of BWT, WWT, and PWDG on the year of birth.The descriptive statistics and probability values of the studied traits are presented in Tables-The effect of type of birth (single vs. twins) on BWT, WWT, PWDG, and WA was highly significant (p<0.001).The BWT, WWT, PWDG, and WA were affected significantly by geographical location. Lambs born in AL-Khanasry station were heavier (p<0.001) at birth and at weaning than lambs born in AL-Fjaj station. The PWDG of lambs in AL-Khanasry station was also significantly more than that of in AL-Fjaj. At Al-Khanasry station, lambs were weaned significantly earlier than those in Al-Fjaj.The year of birth was also found to have a significant effect (p<0.001) on all pre-weaning growth traits. The maximum BWT average was in 2006 (4.43±0.037 kg), while the lowest weight was in 2009 (4.14±0.068 kg). The WWT was highest (p<0.01) in 2003 (20.61±0.26 kg), while the lowest (p<0.01) WWT was in 2008 (15.82±0.19 kg). The highest PWDG was in 2003 (0.23±0.006 g/day), while the lowest was in 2008 (0.13±0.0043 g/day). The year of birth had a significant effect (p<0.05) on WA. In 2008, the WA was 79.54±0.76 days, while in 2003, the WA was 53.74±1.02 day.2a), phenotypic variance (σ2p), and maternal genetic effect (σ2mg) of pre-weaning growth traits in Awassi lambs are presented in 2 a) of growth traits was 0.17, 2.24, 6.22E-04, and 34.58 for BWT, WWT, PWDG, and WA, respectively. The highest phenotypic variance was estimated for WA and the lowest was observed for PWDG. Phenotypic variances (σ2 p) were 0.54, 12.0, 3.34E-03, and 175.5 for BWT, WWT, PWDG, and WA, respectively. The maternal genetic effect on BWT was 0.13, 0.06 on WWT and ADG, and 0.03 on WA.The additive genetic variance (σThe heritability, genetic and phenotypic correlations among pre-weaning growth traits in Awassi lambs are presented in Genetic trends for the studied traits are presented in In the present report, similar to the previous ones, the fixed effect of lamb’s sex on growth performance was found to have a significant effect -10. The In this study, there was a significant difference in various pre-weaning growth parameters between two different geographically located breeding stations in Jordan. Significant variations of body weights between different farms may reflect the level of feeding programs, general farm management, and environmental conditions under which the flocks are maintained. Similar results have been reported previously by many authors ,12. AnotAnimal growth rate is influenced not only by direct additive genetic effects but also by parental genetic effects . AdditivIn this study, the results of phenotypic variance for WWT were similar to those reported in Kurdish sheep . HoweverThe phenotypic variance for WA reported in Dormer sheep and Kurdish sheep was lower than the results obtained in this study ,19. The The estimated maternal variance for BWT reported in this study in Awassi lambs is higher than that reported in Dormer Kurdish and Malpura sheep -21, whilIn general, the heritability estimates for various pre-weaning growth traits in this study were low to medium. Interestingly, the heritability estimates appeared to decrease gradually over age. The declining heritability over age of the lamb can be explained by poor environmental conditions or poor management in the postpartum period . SimilarThe genetic correlation between any two traits may be attributed to the pleiotropic effect, while phenotypic correlations are explained by the common environmental effects that different traits may share . The genResults of this study indicate that selection should be conducted using animals with high estimated breeding values through controlled breeding.KJ: Designed the experiment, organized the experimental work, and performed the statistical analysis. ZBI: Scientific and language editing, manuscript writing. FI: Drafted the manuscript, conducted field visits and data collection and interpretation. VJC: Performed the genetic analysis. MVP: Read and revised the manuscript. All authors read and approved the final manuscript.


Oreochromis spp.). The growth and survival data were recorded on 19,916 individual fish from a pedigreed population undergoing three generations of selection for increased harvest weight in earthen ponds from 2010 to 2012 at the Aquaculture Extension Center, Department of Fisheries, Jitra in Kedah, Malaysia. The pedigree comprised a total of 224 sires and 262 dams, tracing back to the base population in 2009. A multivariate animal model was used to measure genetic response and estimate variance and covariance components. When the homologous body traits in freshwater pond and cage were treated as genetically distinct traits, the genetic correlations between the two environments were high (0.85–0.90) for harvest weight and square root of harvest weight but the estimates were of lower magnitudes for length, width and depth (0.63–0.79). The heritabilities estimated for the five traits studied differed between pond (0.02 to 0.22) and cage (0.07 to 0.68). The common full-sib effects were large, ranging from 0.23 to 0.59 in pond and 0.11 to 0.31 in cage across all traits. The direct and correlated responses for four body traits were generally greater in pond than in cage environments . Selection for increased harvest body weight resulted in positive genetic changes in survival rate in both pond and cage culture. In conclusion, the reduced selection response and the magnitude of the genetic parameter estimates in the production environment  relative to those achieved in the nucleus (pond) were a result of the genotype by environment interaction and this effect should be taken into consideration in the future breeding program for Red tilapia.The extent to which genetic gain achieved from selection programs under strictly controlled environments in the nucleus that can be expressed in commercial production systems is not well-documented in aquaculture species. The main aim of this paper was to assess the effects of genotype by environment interaction on genetic response and genetic parameters for four body traits  and survival in Red tilapia ( Genotype by environment (G × E) interaction refers to the difference in the response of genotypes to different environments , freshwater pond and cage are two prevailing culture environments in major producing countries, predominantly in Asia  when selection was conducted in a different environment  is still not estimated in red tilapia. Quantification of such differences in the genetic gain and genetic parameter estimates between the testing and selection environments in the nucleus is needed because the aquaculture sector particularly for red tilapia is characterized by a diverse array of production systems from backyard farming to semi-medium and intensive large scales , each represented by 20–50 fish produced from a complete diallel cross involving three founder stocks , stocked at a density of 200 fry per m3. At least three nursery hapa replicates for each family were maintained in the same pond to reduce environmental differences between families. When the fingerlings reached an average weight of 5–10 g, about 100–150 individuals per family were randomly sampled and physically identified, using PIT (passive integrated transponder) tags. At tagging, the identification number, body weight (BW), standard length (L), body depth (D), and body width (W) were also recorded. After conditioning for about 3 days in fiberglass tanks without feeding, the tagged fingerlings were pooled together and representatives of each family were sent to communal grow-out testing in freshwater earthen pond and cage. The same producers were repeated in all generations during the course of the selection program for Red tilapia.In each generation, production of full- and half-sib families was conducted in hapas installed in ponds, following the nested mating design  prepared from annual routine genetic evaluation and mate allocation analyses. However, in practice, the two females were not always successfully mated with a male, due to breeding failure or mortality loss. After 7 days of mating, fertilized eggs were collected from the mouth of the female and immediately transferred to hatching jars for artificial incubation. Fry were often hatched after about 5–7 days. As soon as after sac-yolk absorption, the hatched fry of each family were transferred from the incubators to the nursery hapas  were positioned adjacent to each other in an irrigation canal (water depth of about 1.5 m) at Kodiang, Kedah, 22 km away from Jitra. An equal number of sibs per family was randomly assigned to each cage with an initial stocking density of 5 fish per square meter of surface water. The feeding rate was from 3 to 5% of their body weight on a commercial dry pellet feed with 32% protein content twice a day. Contemporaneously with freshwater cage culture, siblings from all families were also tested in earthen ponds of 0.01 ha located at the Aquaculture Extension Center, Department of Fisheries, Jitra, Kedah. The density in each pond was 2–3 fish per square meter of surface water. The same feeding, culturing and management practices were applied as used for the cages. In both environments, water quality parameters  were monitored once a week.At the end of the grow-out period, fish were harvested after pond drainage to measure body traits and survival. Individual fish was weighted using a digital scale (nearest to 0.1 g). Standard length was also measured with a ruler. Body width and depth were measured at the mid-side of the fish, where they were largest, by an electronic digital caliper (0.01 mm). Survival was recorded as a binary response (coded as 1 for fish that were present at harvest and 0 for those were absent). Body weight data collected at harvest was then processed to determine estimated breeding values (EBVs) for all individuals in the pedigree. The best animals (highest EBV for body weight) were selected to become parents of the next generations. The same procedures regarding the family production, genetic evaluation and selection process were repeated in subsequent generations during the course of the study from 2009 to 2012. Table Statistical analyses were performed on 19,916 data records  collected over three generations of selection 2010–2012), tracing back to the base population in 2009. Basic statistics given in Table 010–2012,After all the significant fixed effects were identified; the full model with the family effects was applied to analyse all traits studied. In matrix notation the mixed model can be written as:y is the vector of observations for body traits and individual survival, b is the vector of the fixed effects including generation , sex  and the two-way interactions between generation and sex. A linear covariate (age from birth to harvest) was fitted within sex by generations subclass as well-stocking weight (P < 0.05). Vector a is the random animal additive genetic effects ~ . X, Z, and W are incidence matrices relating observations to fixed effects, additive genetic effect of the individual animal and common effects to full-sib included in the model, respectively.Where The mixed model equation for the best linear unbiased estimator (BLUE) of estimable functions of b and the best linear unbiased prediction of a and c are:where Under the linear mixed model [1], heritabilities for body traits and survival were calculated as x/(1 +ex)) was used where p is the probability of fish survival recorded at harvest. The fixed effects were the same as defined in Equation (1), but the random effects were sire (sk) and common full-sib group (cl). With this logit threshold model, heritability was calculated using the variance of the logit link function, which implies a correction of the residual variance by factor π2/3.In addition to the linear mixed model as described above (Equation 1), survival was analyzed by generalized linear mixed model (GLMM), assuming that the data followed a binomial and underlying normal distributions with a logit link function  scale can be transformed to the underlying liability scale (logit) using the formula of Robertson and Lerner  in the full pedigree. Since the traits were measured on animals in different environments, there is no environmental covariance between them. The phenotypic correlations also do not exist because any individual fish will only express the trait in one environment.Multivariate linear mixed model was applied to estimate the genetic correlation between homologous traits in pond and cage environments. In these analyses, body measurements in each environment were treated as different traits in order to examine whether there were differences associated with culture environment. The model was as described above, also note that the fixed effect of environment was not included. Genetic correlations between expression of body traits in pond and cage were estimated via numerator genetic relationships matrix Multivariate linear mixed model analyses were also used to estimate the genetic and phenotypic correlations separately for each environment. The correlations were calculated from a series of bivariate analysis as the covariance divided by the product of the standard deviations of traits: Kennedy, .All the statistical analyses were conducted in ASReml, including the full pedigree traced back to the base population.The direct response to selection for body weight in pond environment was measured as changes in estimated breeding value (EBVs) using the full animal model (Equation 1). This model was also used to estimate the correlated responses in standard length, body depth, body width, and survival in cage and pond environments. The same fixed effects as shown in Equation (1) were used in all analyses. The EBV estimates for body weight and other traits were expressed in an actual unit  and in genetic standard deviation units .h2 estimates also differed between pond (0.013) and cage (0.119). The common full-sib effects  for body traits and survival were relatively large and the estimates were higher in pond than in cage .Table Phenotypic and genetic correlations among traits studied in pond and cage environments are presented in Table rg = 0.90 vs. 0.62–0.79). The estimated genetic correlations for length and survival between pond and cage environments were high but they were associated with large standard error .The genetic correlations between trait expressions in pond and cage environments were high  is given in Table A). Selection for increased harvest body weight resulted in positive correlated genetic changes in survival rate with the gain of 0.567 and 1.561 genetic standard deviation units in cage and pond after three generations, respectively.Genetic gain measured as EBVs and expressed in an actual unit (g) and in a genetic standard deviation unit (σA) and cage (−0.033 to 0.567 σA) environments. The gain in body weight was greater in pond than in freshwater cage across generations of selection from 2010 to 2012. The reduced selection response in cage relative to pond was likely due to the G × E interaction, which consists of two forms: scaling (heterogeneity of the variances) or re-ranking effects (low genetic correlation estimate for the same trait between the two environments). The scaling effect on genetic gain was generally not important  and survival were also greater in pond than in cage culture. The correlated genetic changes achieved for body traits were all in desired directions. The changes in body traits suggest that the Red tilapia line grew faster in the pond compared with the cage, whereas the fish reared in the cage was thicker than those in pond. Trọng et al.  also obsGadus morhua)    between freshwater and salt water environments has been reported in Nile tilapia by Luan et al. (rg = 0.56) and specific growth rate (rg = 0.27) between intensive recirculation aquaculture system (RAS) and semi-natural environment (POND) in common sole (Solea solea), Arctic charr  by Chiasson et al. . Further, when analyses were conducted separately for each environment, the genetic correlation estimates among body traits were almost identical (close to one). The genetic correlations between harvest weight and survival, although not significant, had opposite signs between ponds and cages. Phenotypically, weight, width and depth had negative correlations with survival, likely because dead fish in cages are those which had “good” (thick and round) body shape. Overall our estimates of heritability and genetic correlations for body traits and survival are in line with the published results in Nile tilapia  obtained for body traits in the present study were greater than those reported for other fishes  were lower than those achieved in the selection environment . The genetic correlation between pond and cage environments for body traits including harvest weight (the selection criterion), albeit high, was not close to one. There were also differences in the heritability and common full-sib effects between the two environments. However, running separate breeding programs for each environment is hardly justified, due to our limited resources. In the future genetic improvement for this population of Red tilapia, selection index approach should be applied by treating the expression in each environment as a separate trait, and weighing the traits in proportion to the relative importance of pond and cage culture. Accounting for the genotype by environment effects in the selective breeding program of Red tilapia will contribute to the sustained development of the aquaculture sector in major tilapia producing countries world-wide.The study and the protocol were reviewed and approved by the animal ethics committee of the Department of Fisheries, Malaysia . They were standard commercial procedures.Conceived and designed the study: NN, and AH. Managed and oversaw the project: NN and AH. Analyzed the data and wrote the paper: NN, AH, and NT.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.


Autism spectrum disorders (ASD) are persistent and lifelong conditions. Despite this, almost all twin studies focus on childhood. This twin study investigated the stability of autistic traits from childhood to early adulthood and explored the degree to which any stability could be explained by genetic or environmental factors.Parents of over 2500 twin pairs completed questionnaires assessing autistic traits when twins were aged either 9 or 12 years and again when twins were aged 18. Bivariate twin analysis assessed the degree of phenotypic and etiological stability in autistic traits across this period. Genetic overlap in autistic traits across development was also tested in individuals displaying a broad ASD phenotype, defined as scoring within the highest 5% of the sample.r = .39). The heritability of autistic traits was 76–77% in childhood and 60–62% in adulthood. A moderate degree of genetic influences on childhood autistic traits were carried across into adulthood (genetic correlation = .49). The majority (85%) of the stability in autistic traits was attributable to genetic factors. Genetic influences on autistic traits were moderately stable from childhood to early adulthood at the extremes (genetic correlation = .64).Autistic traits displayed moderate phenotypic stability  contains supplementary material, which is available to authorized users. Autism spectrum disorders (ASD) are neurodevelopmental disorders characterized by atypical social communication and behavioral inflexibility . There iUnder present diagnostic criteria, symptoms of ASD are required to have presented from an early period of development ; indeed,r = .17) was reported between autistic traits assessed by parents using the Child Behavior Checklist at age 2 and the self-rated Autism Spectrum Quotient in adulthood [Reasonably little is known about whether broader autistic traits are similarly stable from childhood into adulthood. Such a question is of relevance, since similar stability of ASD and autistic traits is another strand of evidence regarding the continuous nature of ASD. Three studies have focused on the stability of autistic traits across childhood. The first focused on two US-based samples of twins aged 3–18. Across a 5-year follow-up period, autistic traits were stable, albeit small decreases were also observed . The secdulthood . The reaFurther, little is known about the stability of the etiology of autistic traits between childhood and adulthood. The few existing twin studies of adults report that autistic traits are slightly less heritable in adulthood than during childhood –28, yet This study thus aimed to investigate the stability of autistic traits from childhood to early adulthood. Specifically, we aimed to elucidate the extent of phenotypic and etiological stability in autistic traits across this period, as well as establish the degree to which any stability in autistic traits could be explained by genetic or environmental influences. ASD appears to be moderately stable into adulthood as a dichotomous diagnosis and highly stable across childhood as a continuous trait; thus, we hypothesized that there would be a significant degree of stability in autistic traits from childhood to early adulthood.Data were collected from participants in the Child and Adolescent Twin Study in Sweden (CATSS ). All twN = 31 pairs), chromosomal disorders (N = 4 pairs), and/or epilepsy (N = 30 pairs). Sixty-four pairs were removed, leaving a sample of 2637 pairs (386 monozygotic (MZ) male, 430 dizygotic (DZ) male, 539 MZ female, 447 DZ female, and 835 DZ opposite-sex). Sixty-four percent of same-sex pairs born up until 2001 had their zygosity ascertained using a panel of five single-nucleotide polymorphisms. The remainder was assessed using an algorithm of five questions about twin similarity; only twins with more than 95% probability of being correctly classified using this method were included.Exclusions were conducted for any medical condition that may have influenced the etiology and phenotypic expression of autistic traits, including known brain injuries . The A-us paper .N = 34) scored significantly higher  than controls (mean = 0.64), t33.07 = −6.97, p < .001. As one would expect from a valid measure of autistic traits, males (mean = 0.81) scored significantly higher than females (mean = 0.66) on the A-TAC at age 18, t2097.77 = 3.16, p < .01. The A-TAC also correlates to a moderate degree with other validated scales assessing behaviors characteristic of ASD, for example a correlation of .43 with the Child Behavior Checklist social problems subscale has been reported [As shown in Table To increase statistical power, data at the age of first assessment (age 9 or 12) were combined, rather than analyzing data separately for twins aged 9 and twins aged 12 at first assessment. There was also no significant difference in the magnitude of the correlation between A-TAC scores at the two ages when the sample was split into those who responded at age 9 and those who responded at age 12 (contact the first author for details).The A-TAC was skewed at both ages (see Table rph) between the A-TAC scales at each age, estimated from a constrained saturated model in which means, variances, and phenotypic correlations were equated across twin order.Phenotypic stability in autistic traits was ascertained from phenotypic correlations . In ascertaining these statistics by zygosity, one gains an indication of the extent of genetic and environmental influence on a trait. Owing to the genetic similarity of MZ twins relative to DZ twins, additive genetic influences (A) are indicated when the MZ correlation exceeds the DZ correlation. Any discrepancy between MZ twins is assumed to be due to nonshared environment (E), which denotes environmental factors that create differences between twins in a pair. The opposite, shared environment (C), is indicated by DZ correlations in excess of half the MZ correlations. If the DZ correlation is less than half the MZ correlation, then non-additive genetic influences (D) may be in operation.Cross-trait cross-twin correlations were then estimated. One twin’s A-TAC score at age 9/12 was correlated with their co-twin’s A-TAC score at age 18. These statistics denote the degree to which genetic or environmental factors account for the covariance between two traits, in this instance the phenotypic stability in autistic traits. They can be interpreted in a similar manner to cross-twin correlations, with the exception that E is denoted by an MZ cross-trait cross-twin correlation that is lower than the phenotypic correlation. All twin correlations were estimated from a constrained saturated model, as described above.etiological correlations between the A, C or D, and E variance components, which denote the degree of causal overlap between two traits . Estimates of 1 indicate total overlap, while estimates of 0 suggest no overlap. One can then calculate the proportion of the phenotypic correlation attributable to A, C or D, and E.Twin models were fitted to estimate the proportion of each trait due to A, D or C, and E (E encompasses measurement error). The association between two phenotypes can then be investigated using bivariate Cholesky decompositions. A transformation of the Cholesky, the correlated factor solution, was fitted to data in order to estimate s) between one twin’s phenotype and their co-twin’s phenotype. Cross-twin correlations could not be collapsed across same-sex twin pairs , ADEs/ACEs with all sex differences dropped (homogeneity model), and then further reduced models which removed individual variance components. The fit of these models was compared with the ACE/ADE model using the likelihood ratio test; each model was summarized by the -2LL fit statistic. Differences in -2LL between two models are χ2 distributed, with degrees of freedom equal to the discrepancy in number of parameters between two models. Significant p values indicated that a given model fit the data significantly more poorly than the unreduced ACE/ADE model.Since C and D are confounded in the classical twin design, ACE or ADE models were fitted based on the twin correlations. The twin design assumes that variance is equal across zygosity, yet this assumption can be violated when sibling contrast effects arise. These effects refer to the instance whereby one parent’s rating of one twin causes them to ascribe lower ratings to their co-twin. The impact of such effects is to increase DZ variance relative to MZ variance, which was the case for our data  were selected on the basis of scoring within the highest 5% of the A-TAC ASD scale at each age. This cutoff was used to maximize statistical power while also ensuring that probands displayed reasonably severe scores. This corresponded to a score of at least 2.5 at ages 9/12 (z-score = 2.46) and 3 or above at age 18 (z-score = 2.05).Extreme scorers . Collapsed across sexes, the phenotypic correlation was .39 (.36–.41).Twin correlations are given in Table p = .21), although variance components could not be collapsed across sexes (p < .001). Non-additive genetic parameters could all be dropped (p = .27), although no other components could be dropped , which is a likely source of some instability in this study. For example, some of the etiology of autistic traits at age 18 could be due to innovative causal influences that emerge at an age we did not cover, such as the adolescent period. Future studies should seek to bridge this gap. In addition, while we examined the stability of autistic traits in the highest scoring 5% of our sample, approximating a broader ASD phenotype, we did not have sufficient statistical power to analyze the highest scoring 1%  or clinical cases. Previous twin studies, however, demonstrate consistent heritability of autistic traits of differing degrees , 7, whilFuture studies should also seek to obtain ratings of autistic traits from multiple sources, such as teachers and self-reports, to obtain a more complete picture of the stability of autistic traits from childhood to adulthood. One may question whether our findings derived from twins extend to singletons, although autistic traits have not been shown to differ between twins and singletons . InterprTo our knowledge, this is the first twin study to investigate the stability of autistic traits from childhood to early adulthood. Phenotypic stability in autistic traits was moderate and largely attributable to enduring genetic factors across development. The overlap in genetic influences across development was not total, however. Thus, one could conclude that autistic traits are initially caused by a given set of genetic and environmental factors in childhood, which persist to a certain degree, yet variation in autistic traits in young adults may be better understood through a combination of enduring and innovative genetic influences across development. Such age-specific effects are potentially important to account for when designing future studies on the expression and etiology of autistic traits.