Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes  common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D  and headache and T2D . Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated  with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D ( Migraine is a chronic recurrent neurological condition, typically characterised by repeated attacks of headache lasting between 4 and 72 h, often accompanied by nausea, vomiting, photophobia, and phonophobia, that affects up to 14.7% of the world’s population and is the second leading cause of disability . The assGWAS has been successfully used to identify genetic risk variants for complex traits, in which hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) are genotyped and tested for association . SeveralIn addition, several epidemiological observational studies have shown that women have a substantially higher risk of migraine than men ,20; howeThe overall study design (summarised in https://www.diagram-consortium.org/ (accessed on 10 August 2019)) from a T2D GWAS of 74,124 cases and 824,006 controls [http://www.nealelab.is/uk-biobank/ (accessed on 15 March 2021) [https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=6159 (accessed on 15 March 2021)).The genetic overlap analyses utilised the largest available GWAS summary statistics. The migraine GWAS summary statistics were obtained from a GWAS of 102,084 cases and 771,257 controls of European descent [controls . The heaAccording to the International Headache Society, headaches are classified into primary and secondary headaches . Migrainhttp://www.headachegenetics.org/ (accessed on 15 March 2020)) [http://www.nealelab.is/uk-biobank/ (accessed on 15 March 2021)) we downloaded UKB sex-stratified GWAS summary statistics for T2D, of which 10,686 cases and 155,780 controls were male, and 6589 cases and 187,137 controls were female; and headache, of which 26,324 cases and 140,340 controls were male and 45,348 cases and 148,379 controls were female. UKB data field 2443 was used to determine doctor-diagnosed diabetes, and data field 6159_1 was used to determine headache pain experienced last month for sex-specific GWAS data. These GWAS studies were conducted on individuals of European ancestry.We analysed GWAS sex-stratified summary statistics for migraine from the study of Anttila et al. (2013), which analysed a total of 23,285 cases and 95,425 controls, of which 3083 cases and 31,832 controls were male; and 20,202 cases and 63,593 controls were female, obtained from the IHGC ). We dropped SNPs if multiple SNPs were assigned to the same chromosomal location in separate datasets. In addition, SNPs with conflicting alleles across datasets were eliminated. For each SNP, a Z-score for association was calculated by β/SE(β) or LN(OR)/SE(LN(OR)), where β, OR, and SE are the beta coefficient, odds ratio, and standard error of the effect estimate, respectively. After variant extraction, the numbers of remaining variants were 23,352,867 for T2D.Where available, we obtained missing rsIDs for SNPs using dbSNP151  to the same effect allele and extracted a subset of independent SNPs (r2 < 0.1) using ‘p-value informed’ linkage disequilibrium (LD) clumping. The LD-independent SNPs were then partitioned into 12 subsets based on their association p-values, where p ≤ {0.01, 0.05, 0.1, 0.2, 0.3. 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, yielding 144 subsets from all possible combinations of SNPs for each dataset. Fisher exact tests were carried out on the 144 SNP subsets to assess whether there was an excess of SNPs where the effect directions were concordant across two datasets. More details of the SECA approach are provided in the original publication [SNP effect concordance analysis (SECA)  of GWAS lication .p-values on which to perform LD-clumping and SNP subsetting.We first used migraine and headache GWAS (combined and sex-stratified) as dataset 1 and T2D GWAS (combined and sex-stratified) as dataset 2. We also reversed the datasets using T2D GWAS as dataset 1 and migraine and headache GWAS as dataset 2 to allow for power differences of each GWAS to produce robust https://github.com/bulik/ldsc (accessed on 12 October 2019)) [h2SNP. We utilised pre-computed LD scores derived from 1000 Genomes Project European reference genotype data provided by the LDSC developers. Genetic correlations (rg) were also estimated for combined and sex-specific studies using bivariate LDSC between migraine and headache with T2D. LDSC provides an unbiased estimate of gr, which may exceed the range (−1 to 1) when standard errors are large and genetic correlations between studies are highly significant. The SNP-based heritability estimates were transformed to the liability scale using the trait sample and population prevalence. Population prevalences of 16% for migraine  [We applied univariate LD-score regression (LDSC) (r 2019))  to calcufor men)  and 10% for men)  were useWe applied pairwise GWAS (GWAS-PW) to find genomic regions associated with a pair of traits. This program uses a Bayesian statistical model to assess the probability (posterior probabilities of association (PPA)) for four models: (1) the region contains a genetic variant only associated with trait 1 (PPA1); (2) the region contains a genetic variant only associated with trait 2 (PPA2); (3) the region contains a genetic variant that is associated with both traits (PPA3), and (4) the region contains different genetic variants that are separately associated with each trait (PPA4). The posterior probability of each independent genomic region for each pair of traits was calculated using the software GWAS-PW v0.21 . The GWAhttp://genetics.cs.ucla.edu/meta/ (accessed on 10 December 2020)). The FE model used the inverse variance weighted approach to estimate the SNP meta-analysis p-value, assuming the GWAS traits examined the same (fixed) effect. Therefore, when SNP effects are heterogeneous, the modified random effects (RE2) model may be more appropriate to estimate the SNP meta-analysis p-value. The RE2 model [Pmeta < 5 × 10−8) but were not genome-wide significant in any of the two individual trait GWAS datasets (5 × 10−8 < Psingle-trait < 0.05) were considered novel. We further evaluated SNPs and loci from the meta-analysis of migraine and T2D, and headache and T2D, using the METASOFT m-value approach [Methods for cross-trait meta-analysis are advantageous for identifying novel genetic variants or loci associated with multiple traits. We performed cross-trait GWAS meta-analyses using two models: the fixed effect (FE) and the modified random effects (RE2) models , which aE2 model  accountsapproach . The m-vapproach . M-valuehttp://fuma.ctglab.nl/ (accessed on 14 November 2021)) [p-value (p < 5 × 10−8) and independence from one another (r2 < 0.6) within a one Mb window. Next, lead SNPs were classified as significant independent SNPs independent from one another at r2 < 0.1 within a one Mb window. The 1000 Genomes Project European reference panel was used to calculate all LD information [r2 > 0.1) with an original GWAS trait SNP were deemed to be within a known locus and excluded. The genome-wide significant lead SNPs from our cross-trait meta-analyses that remained were designated novel lead SNPs. Novel lead SNPs were also examined using PhenoScanner (v2) [r2 > 0.5) were genome-wide significant in published GWAS studies for migraine, headache, or T2D. Finally, we report only novel lead SNPs from cross-trait meta-analyses that were also statistically significant in the original individual trait GWAS at a false discovery rate (FDR) adjusted p < 0.05.FUMA was used to identify independent lead SNPs from a cross-trait meta-analysis of migraine and T2D, and headache and T2D. FUMA is a web-based tool for functional mapping of genetic variants (r 2021)) . First, ormation . All genner (v2)  to see ip values for migraine and T2D, and headache and T2D, were calculated using the SNPs overlapping the migraine and T2D GWAS, and the SNPs overlapping the headache and T2D GWAS, in the combined and sex-stratified dataset. Gene boundaries and positions were taken from the NCBI build 37 , and the 1000 Genomes Project European phase 3 release was used to estimate LD. SNPs were mapped to 19,418 genes based on the NCBI 37 gene coordinate information, and SNPs found within 10 kb of each gene boundary were allocated to that gene. The gene-based test takes into account the non-independence (LD) between SNPs to adjust for testing multiple SNPs. The GATES test is advantageous because it requires only GWAS summary statistics and a suitable LD reference. It effectively controls the type 1 error rate regardless of gene size and LD pattern among SNPs, and it does not require permutation or simulation to assess empirical significance. The minSNP gene-based test was used, which adjusts the smallest p-value associated with the gene by the effective number of independent SNPs assigned to the gene.Compared with single SNP-based association analysis, advantages of gene-based analysis include multiple SNP effects being combined into a single test for a gene and a decreased multiple test burden . Combiner2 < 0.1). Analysis of gene-based associations using GEC controls type 1 errors, by correcting for multiple testing using an estimate of the effective number of independent genes [Due to LD between the most significant SNP (‘best SNP’) assigned to each gene, gene-based association results may be correlated across neighbouring genes; therefore, the genetic type I error calculator (GEC)  was usednt genes . We calcp-values thresholds . First, the raw number of overlapping genes was defined as the number of genes overlapping both traits in each of the three p-value levels. To assess whether the proportions of overlapping genes were more than expected by chance, we estimated the effective number of independent overlapping genes [p-value thresholds for GEC analysis. The observed number of overlapping genes was defined as the number of effective genes with p-values smaller than the threshold in the discovery and target datasets [p-value less than the threshold [p-values below the threshold in the target dataset was divided by the total effective number of genes in that dataset to calculate the expected proportion of genes overlapping the two traits [p-value thresholds to determine statistical significance. We examined whether the proportion of overlapping genes was larger than expected by chance [p-value (FCP) approach to combine gene-based association p-values across two traits. Several studies recently used this gene-based approach to demonstrate gene-based pleiotropy across different traits [We evaluated whether the proportion of associated genes overlapping migraine and T2D, and headache and T2D (combined and sex-stratified) was more than expected by chance at three different nominal ng genes ,40,41. Wdatasets ,40. The hreshold . The effo traits . Finallyy chance . Lastly,t traits ,40,42,43r2 < 0.001) SNPs with GWAS p < 5 × 10−8 were used as instrumental variables (IVs). To estimate causal effects, we employed the inverse-variance weighted (IVW) approach as the primary analysis. This method can yield a reliable estimate when horizontal pleiotropy is absent or balanced. However, the IVW model has limitations as one invalid IV can cause the total estimate to be biased. To address this limitation, several complementary MR approaches were used in sensitivity analyses, including MR-Egger [We investigated bidirectional causal relationships between migraine and headache with T2D in both combined and sex-stratified datasets using two-sample Mendelian randomisation (2SMR)  and geneMR-Egger , MR-weigMR-Egger , and MR-MR-Egger , which hMR-Egger  frameworp ≤ 5 × 10−8 and LD r2 < 0.05 was used to select independent IVs. The GSMR guidelines recommend using at least ten independent leading SNPs as genetic tools to provide robust results. Therefore, a threshold of p ≤ 1 × 10−5 was used when less than 10 SNPs exceeded the default threshold. In addition, HEIDI (heterogeneity in the dependent instrument) outlier detection was utilised to filter instruments showing clear pleiotropic impacts on exposure and outcome traits [p-value threshold of 0.01, which excludes 1% of SNPs by chance if no pleiotropic outlier effects are found. The method estimates a putative causal effect of the exposure on the outcome (bxy) as a function of the relationship between the SNP’s effects on the exposure (bzx) and the SNP’s effects on the outcome (bzy), given the assumption that the effect of non-pleiotropic SNPs on an exposure (x) should be related to their effect on the outcome (y) in an independent sample only via mediation through the phenotypic causal pathway (bxy) [xy) for the case–control trait are nearly equal to the natural log odds ratio (OR). An odds ratio of 2 is a doubling risk compared to the population prevalence of a binary trait for every standard deviation increase in the exposure trait. This approach can help distinguish the possible causal association between two traits but cannot explain the intermediary mechanisms involved in any possible causation process. This study used the ‘TwoSampleMR’ v0.5.6, ‘MR-PRESSO’ v1.0 packages in R version 4.0.2, and the GSMR analysis, integrated into the GCTA v1.93.2 software [We employed GSMR  to detere traits . For theay (bxy) . The calsoftware  to perfohttps://github.com/lukejoconnor/LCV (accessed on 17 March 2021)) [L mediates the genetic correlation between two traits and tests whether this latent variable has a more significant association with one trait than the other [A latent causal variable model (LCV) was also applied to examine whether an observed genetic correlation reflects a causal association. The genetic causality proportion of T2D on risk for migraine and headache was estimated using LCV (h 2021)) . Althoughe other . The GCPgeneP < 0.01 for the combined GWAS and geneP < 0.1 for the sex-stratified GWAS were used as input to the ‘g:GOSt’ tool to identify pathways with an adjusted enrichment p-value (Padj) < 0.05 [Padj, which controls for multiple testing. In addition, the term sizes of the functional category were limited to values between 5 and 350 [To help characterise the genes identified from cross-trait gene-based association analysis, we used the g:GOst webtool ,52 from p = 9.99 × 10−4) between migraine and T2D, and between headache and T2D, with an excess of SNPs where the effect directions are concordant, indicating that SNPs associated with an increase in migraine and headache risk are also associated with an increased risk of T2D (p < 0.05) across migraine (dataset 1) and T2D (dataset 2), while 141 SNP subsets showed significant concordant effects across headache (dataset 1) and T2D (dataset 2). SECA provided results that followed a similar pattern in reverse analyses using T2D GWAS data as dataset 1 and headache or migraine GWAS data as dataset 2. SNPs with lower (more significant) association p-values for migraine and headache exhibited a statistically significant trend towards lower p-values in T2D. In line with expectations for true genetic overlap, an increased concordance was observed in SNP subsets with more robust effect estimates  (p = 9.99 × 10−4). Our SECA results revealed an effect concordance across headache (dataset 1) and T2D (dataset 2) in males, with 102 SNP subsets displaying nominally significant concordant effects. Analogous results were obtained using the male T2D GWAS data as dataset 1 and the headache GWAS data as dataset 2. In contrast, no genetic overlap was detected between headache and T2D in females, nor between migraine and T2D in males and females. A summary of all SECA analyses is presented in Analysis of the combined and sex-specific GWAS data using SECA produced evidence for significant genetic overlap  . In addih2SNP) (without constrained intercept) of 11.08% (95% CI: 10.24–11.92%) for migraine, 9.03% (95% CI: 8.23–9.83%) for headache, and 17.23% (95% CI: 15.72–18.74%) for T2D. Sex-specific analyses produced similar heritability estimates for males  and females . Given the observed significant evidence for SNP-based heritability, we tested for genetic correlation between migraine and headache with T2D (combined and sex-stratified) using bivariate LDSC. Bivariate LDSC analyses of the combined GWAS data estimated a significant genetic correlation between T2D and migraine , and T2D and headache . Analogous to the SECA results, we also identified a significant genetic correlation between T2D and headache in males  but not in females and found no evidence for a genetic correlation between migraine and T2D in males and females. The SNP-based genetic correlation and heritability estimates for migraine and headache with T2D are summarised in Single-trait LDSC estimated a SNP-based heritability on a liability-scale  having PPA3 > 0.9, indicating that these regions are pleiotropic and likely harbour a single causal variant influencing both traits. For headache and T2D, significant pleiotropy (PPA3 > 0.9) was identified in five regions . Notably, two of these pleiotropic regions (chr5:73759326–75795407 and chr14:94325596–95750857) were common to both migraine and T2D, and headache and T2D. Additionally, we identified 35 and 13 pleiotropic genomic regions between migraine and T2D, and between headache and T2D at PPA3 > 0.5; seven regions were common to both . These fr2 ≥ 0.1) with these SNPs, our cross-trait GWAS meta-analysis identified 23 LD-independent novel lead SNPs for migraine and T2D. These lead SNPs achieved genome-wide significance in the cross-trait meta-analysis (Pmeta < 5 × 10−8) and nominal significance (FDR-adjusted p < 0.05) in the original single trait GWAS (Pmeta ≤ 3.11 × 10−12), located at the DENND1A locus. Cross-trait GWAS meta-analysis identified three novel lead SNPs significantly associated with headache and T2D . Notably, two of these lead SNPs, rs546738 and rs73050128, mapped to regions near the protein-coding genes NLGN1 and ELFN1, respectively. These two gene regions were also implicated in the cross-trait GWAS meta-analysis of migraine and T2D, by rs536445 and rs62442924, respectively; and SNPs rs546738 and rs536445, and SNPs rs73050128 and rs62442924, were in significant LD . Results for all novel lead SNPs found in the cross-trait meta-analysis between migraine and T2D (38 lead SNPs), and headache and T2D (17 lead SNPs), and an unadjusted p < 0.05 in their respective single-trait GWAS, are provided in Following the exclusion of genome-wide significant SNPs in the corresponding original single-trait GWAS, and SNPs in LD  identified from cross-trait meta-analysis of the 2016 migraine GWAS [p-value in the recent and more powerful Hautakangas et al. (2022) migraine GWAS [p < 1 × 10−5)  identified from cross-trait meta-analysis, five (83%) had a more significant p-value in the recent and more powerful migraine GWAS [p < 1 × 10−5)  . Of the ine GWAS  and T2D ine GWAS , 15 (60% × 10−5) . Further × 10−5) . These rp-value thresholds using a binomial test for an increase in genes associated across the traits  . Similarly, the observed proportion (0.412) of genes with a gene-based p value ≤ 0.05 for both headache and T2D was significantly larger than the expected proportion (0.287) . Significant gene-based genetic overlap was also observed for both the male- and female-specific analysis of headache and T2D, and the female-specific analysis of migraine and T2D  and Pgene < 3.65 × 10−6 for T2D  and identified 303 genome-wide significant genes associated with migraine  and geneP < 3.65 × 10−6 for T2D  and identified 161 genes associated with headache  were genome-wide significant for all three traits . Using the FCP approach, we combined evidence for a gene-based association for genes overlapping migraine and T2D, and headache and T2D, with an FDR-adjusted single-trait Pgene < 0.1. FCP results show that 440 genes overlapping migraine and T2D (PFisher’s-combined < 3.63 × 10−6), and 204 genes overlapping headache and T2D (PFisher’s-combined < 3.65 × 10−6) reached a gene-based genome-wide significant level (SNPP < 1 × 10−5). Of the 204 genes overlapping headache and T2D, 78 had their top SNP associated with both traits at a genome-wide suggestive significant level  between T2D (exposure variable) and migraine (outcome variable). The results of the weighted median  and MR-Egger  models were similar to the IVW result (Pivw = 4.59 × 10−50); however, the MR-Egger intercept (p = 0.48) showed that the observed heterogeneity was not the result of horizontal pleiotropy. The raw estimate from MR-PRESSO after removing eight outliers  also agreed with the IVW results, and the MR-PRESSO ‘global test’ showed significant evidence for horizontal pleiotropy . In contrast, GSMR found significant evidence for a causal association . Reverse 2SMR analyses also found no evidence for a causal association of migraine (exposure variable) on T2D (outcome variable) utilising the IVW , weighted median , and MR-Egger models . MR-PRESSO  and GSMR  both support this finding , weighted median , MR-Egger , and MR-PRESSO  models. While GSMR analysis found some evidence for a causal effect of T2D on headache . Reverse 2SMR found some evidence for a causal association of headache on T2D using the primary IVW model ; moreover, in line with the IVW model, the weighted median  and MR-Egger  sensitivity analyses also found significant evidence for causal association. There was evidence for heterogeneity (Cochran’s Q Pivw = 3.21 × 10−3), although the MR-Egger intercept (p = 0.17) indicated that this was not likely caused by horizontal pleiotropy. Similarly, MR-PRESSO produced a significant raw estimate after excluding two outliers  and a global test for horizontal pleiotropy . Furthermore, GSMR found significant evidence for a causal association   on headache (outcome variable) using the IVW  .p = 5.0 × 10−3). Reverse MR analyses found no significant evidence for a causal effect of migraine on T2D. In contrast, IVW, MR-PRESSO, and GSMR found significant evidence for a causal effect of T2D on headache in females, and no causal association in males, whereas reverse MR analyses found no significant evidence for a causal effect of headache on T2D in females, and IVW and MR-PRESSO found evidence for a causal effect of headache on T2D in males.Using the same MR methods, we tested for sex-specific causal association(s) between migraine and T2D, and headache with T2D . In malep = 0.32) and for headache  with T2D indicates that T2D is not genetically causal for migraine and headache, and vice versa . Similarly, LCV found no evidence  for genetic causality between T2D with headache in males , cellular processes, epigenetic mechanisms, oxidative stress, and immune system  were observed to be significantly (P(adjusted) < 0.05) enriched for both migraine and T2D, and headache and T2D overlapping genes (P(adjusted) < 0.05) enriched in females , and headache and T2D (353 genes) with a ng genes . Additio females  and male females , respectrg = 0.0589; 95% CI: 0.0324–0.0854) and headache and T2D , indicating shared genetic factors contribute to the co-occurrence of migraine and headache with T2D. These findings substantiate previous observational studies that found a higher co-occurrence of T2D with migraine [rg = 0.09, p = 0.004) [gr = 0.0922; 95% CI: 0.0226–0.1618) but not in females , and found no evidence for a genetic correlation between migraine and T2D in males or females. Considering the increased prevalence of migraine and headache in females compared to males [This deep investigation using large and well-powered GWAS datasets provides new and important insight into the genetic relationship between migraine, headache, and T2D. We observed similar significant genetic correlations between migraine and T2D (migraine . Additio= 0.004) , using ito males , and larp < 1.0 × 10−5   . ANKDD1Brisk SNP . Identifrisk SNP . For exaammation , which mammation . No signp < 5 × 10−8) associated with migraine, headache, and T2D. We found 23 novel lead SNPs associated with migraine and T2D, and three novel lead SNPs associated with headache and T2D, that were not at genome-wide significant loci in the respective single-trait GWAS. Notably, two of these novel lead SNP loci, near the protein-coding genes NLGN1 and ELFN1, overlapped. The NLGN1 (neuroligin 1) gene is abundant at excitatory synapses and is essential for synaptic function. In addition, NLGN1 recently revealed high expression in vascular endothelial cells and was reported to play a crucial function in vascular development [NLGN1 in endothelial cells may thus play a significant role in the pathogenesis of endothelial cell dysfunction in T2D. Furthermore, clinical investigations have shown that genetic variants of NLGN1 are associated with neuropsychiatric conditions such as autism spectrum disorder, memory loss and depression in Alzheimer’s disease, and post-traumatic stress disorder, indicating a role for NLGN1 in multiple neurological disorders [ELFN1 (extracellular-leucine-rich repeat fibronectin domain 1) is highly expressed in GABAergic (γ-aminobutyric acid-ergic) interneurons in the hippocampus and is involved in the recruitment of metabotropic glutamate receptors, such as mGluR7, to the presynaptic membrane [ELFN1 knocked out experience seizures [L3MBTL2 and DENND1A are also particularly interesting. For example, L3MBTL2 is a transcriptional repressor expressed in adipose, brain, heart, lung, and muscle; and was very recently implicated in migraine, schizophrenia, and depression via cross-trait genetic analysis [DENND1A is involved in endosomal membrane trafficking [DENND1A is associated with insulin resistance (IR) in women with polycystic ovarian syndrome, indicating that metabolic dysfunction may play a pathophysiologic role [We used genome-wide cross-trait meta-analysis to enhance our power to identify novel SNPs significantly  from the cross-trait meta-analysis of migraine and T2D was not novel  with migraine and T2D, and headache and T2D, respectively. Among the overlapping genes between migraine and T2D, seven genes  were the closest gene to a lead migraine SNP in Hautakangas et al. (2022) [MACF1, THADA, SLC9B1, POC5, PLEKHA1, CELF1, BCAR1, EYA2) were the closest gene to a lead T2D SNP in Mahajan et al. (2018) [ANKDD1B gene encodes a protein called ankyrin repeat and death domain-containing 1 B. Recent studies have shown that the ANKDD1B gene is involved in vascular and endothelial function and contributes to migraine and blood pressure risk [ANKDD1B, it may be relevant to the pathophysiology of migraine and T2D due to its role in vascular function. Five of the 10 genes overlapping headache and T2D  were previously associated with T2D [PLEKHA1 and BCAR1 overlapping headache and T2D were previously associated with headache [Our gene-based analyses identified 33 and 10 genes significantly associated (. (2018) . For exaure risk . Althougheadache . Hence, p levels in diabetic patients were proposed as a potential mechanism for this association [Our MR analyses found inconsistent evidence for a causal effect of genetic liability to T2D on migraine and headache, with only GSMR producing significant evidence for T2D having a negative causal  effect on both migraine and headache. This inconsistency may be due to the insufficient power of the T2D SNP IVs to detect a causal association using 2SMR. However, the GSMR finding is consistent with previous evidence from observational studies showing that T2D patients are less likely to develop migraine and headaches ,71,72; hociation ,78. TherReverse MR analyses using 2SMR and GSMR found no evidence for a causal effect of migraine on T2D. In contrast, reverse 2SMR and GSMR analyses produced significant evidence for headache having a negative causal  effect on T2D. These findings were surprising given the positive genetic correlation between migraine and T2D, and headache and T2D. A possible explanation for this result could be that MR relies on a subset of genetic variants, whereas genetic correlations quantify the average sharing of genetic effects between two traits across the entire genome. Therefore, MR may detect specific relationships that may be ‘washed out’ by other (including opposing) effects when estimating genome-wide correlation, which may explain why our LCV analyses (which are based on the genome-wide genetic correlation estimate) found no causal relationship between migraine and headache with T2D. Previous observational studies reported that migraine patients have a decreased risk of developing T2D ; howeverFinally, 90 and 118 biological pathways/processes were found to be enriched for genes associated with migraine and T2D, and headache and T2D , respectOur study has various strengths. This is the first comprehensive study to explore the genetic overlap and causal relationship between migraine and headache with T2D. The analysis utilised the latest and largest (most powerful) available GWAS summary statistic datasets. Second, we analysed people of European descent to minimise the impact of genetic heterogeneity related to ancestry. We have used the latest and largest summary data for migraine in this study, and there are no known replication samples exist. Furthermore, our findings are more robust since they were based on genetic data than prior observational research that may have produced unreliable or unclear results due to small sample numbers, or confounding influences from lifestyles or the environment. This study had some limitations. Firstly, the GWAS datasets only included people of European ancestry; hence we cannot determine how relevant these findings are to other non-European populations. However, we were restricted to performing our analyses using the available datasets. Unfortunately, adequate migraine and headache GWAS data are currently not available for population samples of non-European ancestries.In conclusion, our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically regulated biological mechanisms driving their co-occurrence, and evidence for a causal relationship between headache and T2D. We identified novel risk SNP and gene loci that provide new biological insight and intervention targets underlying migraine, headache, and T2D. Future functional investigations focusing on specific loci reported through this cross-trait genetic analysis may provide further insights into the biological mechanism underlying the risk of migraine and T2D. Our findings provide important motivation for designing novel treatment strategies to manage T2D in migraine and headache patients.


The relationship (genetic and phenotypic) between feed efficiency  and greenhouse gases [methane (CH4) and carbon dioxide (CO2)] traits in New Zealand (NZ) maternal sheep has not previously been investigated, nor has their relationship with detailed estimates of body composition. To investigate these relationships in NZ maternal sheep, a feed intake facility was established at AgResearch Invermay, Mosgiel, NZ in 2015, comprising automated feeders that record individual feeding events. Individual measures of feed intake, feeding behavior (length and duration of eating events), and gas emissions (estimated using portable accumulation chambers) were generated on 986 growing maternal ewe lambs sourced from three pedigree recorded flocks registered in the Sheep Improvement Limited database (www.sil.co.nz). Additional data were generated from a subset of 591 animals for body composition (estimated using ultrasound and computed tomography scanning). The heritability estimates for RFI, CH4, and CH4/(CH4+CO2) were 0.42 ± 0.09, 0.32 ± 0.08, and 0.29 ± 0.06, respectively. The heritability estimates for the body composition traits were high for carcass lean and fat traits; for example, the heritability for visceral fat (adjusted for body weight) was 0.93 ± 0.19. The relationship between RFI and CH4 emissions was complex, and although less feed eaten will lead to a lowered absolute amount of CH4 emitted, there was a negative phenotypic and genetic correlation between RFI and CH4/(CH4+CO2) of −0.13 ± 0.03 and −0.41 ± 0.15, respectively. There were also genetic correlations, that were different from zero, between both RFI and CH4 traits with body composition including a negative correlation between the proportion of visceral fat in the body and RFI (−0.52 ± 0.16) and a positive correlation between the proportion of lean in the body and CH4 (0.54 ± 0.12). Together the results provide the first accurate estimates of the genetic correlations between RFI, CH4 emissions, and the body composition (lean and fat) in sheep. These correlations will need to be accounted for in genetic improvement programs.There is simultaneous interest in improving the feed efficiency of ruminant livestock and reducing methane (CH Specifically, in New Zealand, farmed ruminants contribute 35% of the country’s greenhouse gas (GHG) emissions  is higher. However, the genetic correlation between RFI and CH4 is unknown in sheep. That one exists is, however, plausible given changes in short chain fatty acids ratios are associated with reduced CH4 production, specifically reduced acetate relative to propionate ] also produced carcasses with proportionately more lean, based on carcass composition prediction from a commercial abattoir grading system  and AgResearch Invermay  Animal Ethics committees. Specific approval numbers were AEC13563, AEC13892, and AEC14221.n = 400) which represents a variety of maternal breeds as described by n = 408)   in cohorts of approximately 200 lambs. In Year 1 (2015) a single cohort (Cohort1) of 200 ewe lambs was measured; in Year 2 (2016) and Year 3 (2017), two cohorts of 200 ewe lambs were measured back-to-back per year . The ewe lambs used were of New Zealand maternal genetics sourced from one of three genetically linked flocks: the Beef + Lamb New Zealand Genetics Central Progeny Test  , which hn = 192) . Each siFeed intake data were collected using 20 custom made automated feeders designed to allow delivery of pelleted feed. A ratio of 10 animals to each feeder was provided allowing the intake of up to 200 animals to be measured at one time. The feeders were designed by AgResearch Ltd. engineering staff and utilized a metal feed trough fitted on weighing load cells with an automated feed delivery of alfalfa pellets through an auger. Only one animal could access a feeder at a time, by placing its head through a keyhole-shaped opening providing access to a metal feed trough. At all times animals had unrestricted access to the feeders. Unrestricted access to water was also provided through reticulated water troughs. When full, the feed trough contained 2.5 kg of pelleted feed. If the feed available at the end of a feeding event was recorded to be lower than 1 kg, a door would close across the keyhole and the amount of feed would be increased to 2.5 kg. All animals were fitted with radio frequency identification (RFID) ear tags, which were read by panel readers fitted within the feeders. The entry and exit times, together with the weight of feed consumed were recorded in real-time against the animal RFID. An illustration of the feeders is provided in For the first 2 years , the feeders were fitted within an indoor facility, located near AgResearch Invermay, Mosgiel, NZ, consisting of five pens of equal size across a raised floor shed, with four feeders fitted in each pen. Each pen housed a maximum of 40 animals. Individual animals had access to up to four feeders. Due to the sale of the indoor facility, it could no longer be utilized and in the third and final year (Cohorts 3a and 3b), the feeders were re-fitted into a semi-open shed, at AgResearch Invermay, Mosgiel, NZ, divided into 2 pens with 10 feeders each. Each pen could house 100 animals, enabling individual animals to access up to 10 feeders. This facility is illustrated in The ewe lambs were introduced to alfalfa pellets in the facility over 14 days , carbon dioxide (CO2), and oxygen (O2) were made using PACs as described by 4, CO2, and O2 in the chambers were determined using an ENVCO Eagle 2 hand-held gas meter  at the start (0 min), middle (∼30 min), and end (∼60 min) of the PAC measurement period along with the exact time of measurement. Ambient temperature and atmospheric pressure were also recorded.Measures of methane  and repeated after 2 weeks with animals completely re-allocated to a time period (lot) and chamber (round 2). This re-allocation was only made for the PAC measurements, with the animals remaining within their RFI pen group whilst in the feed intake facility for the duration of the test period. Successful repeat PAC measurements were made on 986 animals.M. longissimus (C)  .For Cohorts 1, 2a, and 2b, body composition after the feed intake test period (42 days) was also estimated using computed tomography (CT) scanning. CT scanning was limited to 40 animals per day, and so was carried out in the 5 days after completion of the test period, but the animals were still offered access to the alfalfa pellets until they were scanned. Animals were randomly allocated to a day and order of scanning using a randomized incomplete block design. In preparation for scanning, sheep were sedated with Acetylpromazine (Ace10) given by intramuscular injection (1 ml/50 kg body weight). The animals were scanned in a prone position using a custom-built cradle  with hind legs extended caudally and forelegs under the chest. The animal was scanned using X-ray computed tomography  using the procedure described by Live weight data collected across the feed intake test period (42 days) was modeled to determine the live weight change profile of the animals from which the growth rate of the animals and the metabolic mid-weight (Day 21) were calculated. Two approaches were considered for modeling the data; 1) a linear regression model fitted through the data points where x = day of measurement and y = live weight as described in The average intake of the animals was calculated as the mean of the 42 daily intakes of the animals across the test period, measured in grams of pellets per day. This was converted to energy intake using the information on the feed as described in 0.75), ADG = average daily gain as estimated from using the bi-weekly liveweight measurements and the day of measurement (with the first measurement made on day 0), Flock = Flock A, B or C, Cohort = Cohort 1, 2a, 2b, 3a or 3b, Pen=, and ε = the residual which is taken as the trait of RFI.The trait of RFI was calculated based on the model of Additional feeding behavior traits were calculated from the feed intake data including the average number of feeding events an individual animal undertook per day, the average duration of each feeding event, the average amount consumed per feeding event, and the rate of feeding during each event (intake/duration).4 and CO2 produced per day. The gas traits were scaled to adjust for the variance within the contemporary PAC lot  due to varying waiting times after removal from pasture. The ratio of actual value/lot mean was multiplied by the overall trait mean. Absolute values of CH4 and CO2 are reported together with their sum and the derived trait of CH4/(CH4+ CO2) (mol/mol). This trait is a description of CH4 yield with CH4+ CO2 considered as a proxy of intake (4 yield (different denominators).The raw gas data were converted to appropriate measures as described by f intake  but it iSeveral body composition traits were derived from the CT images. Adipose traits generated were the absolute amounts of subcutaneous fat, visceral fat, and intermuscular fat. Carcass fat as would be observed on a carcass post-slaughter was calculated as the sum of subcutaneous and intermuscular fat, with total fat calculated as the sum of subcutaneous, visceral, and intermuscular fat. Carcass lean and bone were calculated as the sum of all estimates of lean and bone, respectively. Carcass weight was estimated as the sum of carcass fat, lean, and bone. Computed tomography weight was calculated as the sum of carcass weight, visceral fat, and non-fat visceral components and is the equivalent of a fasted body weight (no gut fill). Dressing-out-percent was calculated as the ratio of carcass weight to computed tomography weight.p < 0.05) are summarized in Variance components were estimated using restricted maximum likelihood (REML) procedures fitting an animal model in ASReml 3.0 . Pedigre2 of the model fitted to the energy intake data, from which the trait of RFI was estimated, was between 0.69 and 0.78.Full feed intake and growth data from which the trait of RFI could be calculated were available for 986 growing ewe lambs, measured when they were approximately 9–12 months of age. Summary statistics for traits measured are provided in The heritability estimates for individual traits measured through the feed intake facility and body composition traits, together with summary details for the traits, are in 4 and CO2 (0.32 ± 0.08) and the lowest heritability was for CH4/(CO2 + CH4) (0.29 ± 0.06). The repeatability of the gas traits was highest for CO2 and CO2 + CH4 (0.57 ± 0.02).The heritability estimates and the repeatability for the gas traits, together with summary details for the traits are provided in The genetic and phenotypic correlations between RFI and the other feed intake and body composition traits are in 4/(CO2 + CH4) and RFI . Phenotypic and genetic correlations were observed between CO2 and daily feed intake and mid-trial live weight (phenotypic correlations 0.45–0.47 with standard errors of 0.2 and genetic correlations of 0.59–0.62 with standard errors of 0.13–0.14) Positive phenotypic and genetic correlations, that were different from zero, were reported between CH4 and feeding behavior traits of average intake per feeding event and average feeding rate (phenotypic correlations greater than 0.23 with standard errors of 0.03 and genetic correlations greater than 0.41 with standard errors of 0.13–0.16). There were phenotypic and genetic correlations, that were different from zero, between absolute CH4 and carcass lean  and non-fat visceral components  estimated from CT scanning. Several other correlations, that were different from zero, between CO2 and body composition traits were also found, with strong negative phenotypic genetic correlations between CO2 and fat traits, and conversely strong positive correlations with lean traits.The genetic and phenotypic correlations between the gas traits and the feed intake and body composition traits are provided in When this program of work commenced, there was limited literature relating to feed intake in sheep. To calculate the base trait of residual feed intake as first described by The phenotypic variation in RFI was explored by The heritability estimates for the feeding behavior traits were all moderate to high. Between animal variation has been observed in feeding behavior traits by several authors  althoughM. longissimus muscle. Whilst this measurement is related to total body composition , variation in feed intake before entry into the facility and potential diurnal variation in behavior  may contribute to the repeatability being reduced to that observed from respiration chamber measurements  sheep measured in this current study showed a trend of smaller, more frequent meals which is consistent with these observations.There is evidence that low CHterparts . The low4 + CO2) and feed intake. Given that the gas measures were only taken for 1 hour at two time points and are being used to predict feed intake over 42 days, the results suggest that the use of CO2 as a low-cost predictor of feed intake warrants further investigation.It has been postulated that total gas emissions might be used as an indication of metabolic rate and a predictor of feed intake. The strongest correlation observed between RFI, and the body composition traits measured in this study was a genetic correlation between visceral fat as assessed at the end of the test period and RFI, a correlation never reported in the literature previously because of difficulties in estimating the trait (can only be assessed using CT scanning or carcass dissection). Other results in this study are not consistent with cattle literature, which is based on ultrasound data, in which it is generally concluded that more efficient animals are leaner . In cont4/(CH4 + CO2) which is consistent with the finding of 4 yield selection lines and reported lambs from the low CH4 yield line to be leaner than those from the high CH4 line. In this current study the genetic correlations were only low, but still different from zero, for starting C and visceral fat. There were strong negative correlations between CO2 and the fat traits and conversely positive with carcass lean, bone, and non-fat visceral components. Carbon dioxide is produced as a by-product of tissue metabolism. A biological interpretation of this relationship is that fat is metabolically inert in a growing animal  of the animal. There is evidence of a positive correlation between carcass fat traits and CHg animal . Hence, 4 cannot be inferred from this dataset but could be estimated through further processing of the CT images at a future date when resources allow.Rumen size was not explicitly estimated from the CT images as it was by 4 versus CH4/(CH4 + CO2). However, when armed with the knowledge that the genetic correlation between RFI and CH4/(CH4 + CO2) is only moderate, but both are moderately heritable traits, the use of selection indexes can ensure simultaneous genetic improvement if all traits in the index are regularly measured.This study has combined a number of difficult and/or expensive to measure traits into one study with the same animals being directly measured for all traits reported in the study (with the exception of a lower number that have CT estimated body composition data). The results of this study highlight the complex interactions between RFI, greenhouse gas, and body composition traits. A number of novel findings have resulted from the ability to generate detailed body composition data on the animals through the use of CT scanning. Whilst some of the relationships are consistent with the literature, a number have not previously been reported, or are inconsistent with the literature. This is particularly true for the relationship between body composition traits and RFI, which in cattle is assumed to be an unfavorable relationship, but in this study has been shown to be favorable. That said, basic biological understanding of the attributes of lean and adipose do support the findings of this study including that different adipose depots will likely have different relationships with different traits. Given the underlying relationships reported in this study it highlights the importance of extensive phenotyping when investigating novel traits to understand correlated responses, and that relationships will vary depending on how different traits are expressed for example, the different relationship observed between RFI and CH4, CO2, and body composition including their phenotypic and genetic correlations for New Zealand maternal sheep genetics, as estimated from data collected in a feed intake facility. All traits investigated exhibited moderate to very high heritabilities, indicating genetic variation exists for these traits. There are several phenotypic and genetic correlations, that are different zero, some of which are novel in the literature and highlight that correlated changes in traits that affect production systems, such as body composition, can occur unless all traits are measured. We have demonstrated that selection and breeding for improved RFI and reduced CH4 is possible independently, but our results do suggest that residual feed intake and CH4/(CH4 + CO2) may be moderately negatively correlated with each other and that there is a relationship between selecting for these traits and body composition traits. It is appropriate to account for these correlations using selection index approach to ensure no unintended consequences of genetic selection on individual traits.This study presents a comprehensive investigation into genetic parameters for RFI, CH


We further constructed latent causal variable (LCV) models to test for partial genetic causality and found evidence for a potential causal relationship between alcohol dependence and schizophrenia . This putative causal effect with schizophrenia was not seen using a continuous phenotype of drinks consumed per week, suggesting that distinct molecular mechanisms underlying dependence are involved in the relationship between alcohol and schizophrenia. To localise the specific genetic overlap between schizophrenia and substance use disorders (SUDs), we conducted a gene-based and gene-set pairwise meta-analysis between schizophrenia and each of the four individual substance dependence phenotypes in up to 790,806 individuals. These bivariate meta-analyses identified 44 associations not observed in the individual GWAS, including five shared genes that play a key role in early central nervous system development. The results from this study further supports the existence of underlying shared biology that drives the overlap in substance dependence in schizophrenia, including specific biological systems related to metabolism and neuronal function.Almost half of individuals diagnosed with schizophrenia also present with a substance use disorder, however, little is known about potential molecular mechanisms underlying this comorbidity. We used genetic analyses to enhance our understanding of the molecular overlap between these conditions. Our analyses revealed a positive genetic correlation between schizophrenia and the following dependence phenotypes: alcohol ( Schizophrenia is a complex and debilitating psychiatric disorder with a diverse array of symptoms and comorbidities including substance dependence . While tn = 165,811) found that the prevalence of any SUD in schizophrenia was 41.7%, with 26.2% for cannabis, 24.3% for alcohol and 7.3% for stimulants [While these SUDs arise independently of psychotic and affective disorders, they are frequently comorbid. A 2018 meta-analysis by Hunt et al. of 123 articles between 1990 and 2017 n = 16,811 founimulants .N = 144,609) to demonstrate the elevated SZ polygenic risk score was associated with increased risk of alcohol and substance use disorders [Despite the high heritability and prevalence of SUDs in schizophrenia, genome-wide association studies (GWAS) for SUDs lag those for other major psychiatric disorders. In comparison to GWAS mega-analyses like schizophrenia and major depressive disorder, there is still the need for larger cohorts for SUD phenotypes to boost discovery power, particularly for psychostimulants such as cocaine and amphetamines. Current GWAS on SUD phenotypes have not revealed any clear novel biological pathways involved in these phenotypes, with limited loci found in SUDs, thus more work in this area is needed. In terms of the genetic relationship between SUD phenotypes and schizophrenia, previous research has shown that the odds of substance use in individuals with schizophrenia is higher than the general population , Hartz eisorders . The larisorders . DespiteN = 130,644) and substance use and dependence from the Psychiatric Genomics Consortium (PGC), International Cannabis Consortium (ICC) and GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Substance use and dependence GWAS include Alcohol Dependence  with alcohol exposed controls  [Ncases = 10,287, Ncontrols = 234,603) [Ncases = 43,380, Ncontrols = 118,702), Cannabis Use Disorder  [Ncases = 14,080, Ncontrols = 343,726), Opioid Dependence (unexposed controls) [Nscase = 3272, Ncontrols = 25,437), which included individuals with no life-time OD diagnosis who were not exposed to illicit or prescription opioids as controls, Cigarettes per day [N = 337,334), and Drinks per week [N = 941,280). All AD, CUD, and OD cases met criteria for a life-time diagnosis of DSM-IV derived from clinician ratings or semi-structured interviews, ND had the ICD-10-CM Diagnosis Code F17 recorded from in their hospital records.GWAS Summary statistics of European ancestry were obtained for schizophrenia  20,272) , Nicotin234,603) , Life-ti234,603) ,    of the respective distributions to assess whether there is evidence for a causal effect of one trait on the other. As a result, the LCV framework can calculate a metric termed the posterior mean causality proportion (GCP), from which the sign can be used to infer a potential causal direction. In practice, GCP > 0 implies that trait one is partially genetically causal for the second trait, while GCP < 0 implies the reverse. Given GCP > 0, for example, marginal SNP-trait one effect sizes tend to be proportionally larger on trait two, but this is not observed in the opposite direction—hence, the direction of causal effect can be estimated as operating from trait one to trait two. We defined partial genetic causality using the recommended threshold of a significantly non-zero |GCP | > 0.6, as this was shown by O’Connor and Price in simulations to guard against false positives [Linkage disequilibrium score regression analysis  was utilised to estimate genetic correlation between schizophrenia and each SUD phenotype . Common ositives . It shouhttps://ctg.cncr.nl/software/magma). The MAGMA gene-based method utilises P-values as input, whereby the test-statistic is a linear combination of SNP-wise P-values. In comparison to univariate GWAS, the burden of multiple-testing correction is dramatically reduced in gene-based association analysis [P-values such that test-statistic inflation arising from SNP-wise dependency due to LD within genes can be suitably accounted for. The 1000 genomes phase 3 European reference panel is used as the LD reference for this purpose. Variants were mapped to 18,297 autosomal protein-coding genes from NCBI hg19 genome-assembly. Genes that arise from the major histocompatibility complex  on Chromosome 6 were removed. Statistical inference for a significantly associated gene for each disorder was set as P < 2.7 × 10−6 to adjust for the number of genes tested via the Bonferroni method.Gene-based association was performed on each disorder using MAGMA version 1.07 , which were probit transformation of the P-values, were meta-analysed individually using MAGMA—that is, schizophrenia was meta-analysed with AD, then with CUD, and so on. A weighted Z-test was utilised for this purpose, which is based off the inverse normal (Stouffer’s) method, whereby the weight (wi) was set as the respective GWAS sample sizes [P < 0.01), which may be driven by sample overlap, we compared our Stouffer’s method meta-analytic P-values for schizophrenia with AUD to those derived from the Cauchy combination test method [P-values, per gene, transformed to approximate a Cauchy distribution, which is also flexible to incorporate weights of the input study sample sizes. The test-statistic is insensitive to correlations among the P-values that arise due to these P-values being from the same sample due to the heavy tail of the Cauchy distribution, with the combined meta-analytic P-value approximated using the cumulative density function of the Cauchy distribution. We calculated the Pearson’s correlation estimate, with 95% confidence intervals (CI), comparing the negative logarithm base ten transformed P-values of both methods.The genic le sizes . Given tt method . In thisCompetitive gene-set analysis results were obtained using MAGMA. For this analysis, 14,969 hallmark, 2921 canonical and 2598 regulatory miRNA target gene ontology gene-sets from the molecular signatures database  were selected , 34. A lg = 0.368, SE = 0.076, P = 1.61 × 10−6), CUD , ND , and the substance use phenotypes CPD , DPW  and LCU . We also found nominally positive genetic correlation between schizophrenia and OD , however, this did not survive multiple-testing correction. LCV models were then constructed for the significant traits for which their schizophrenia genetic correlation estimate passed multiple-testing correction  to investigate whether any of the observed genetic correlations between SUD and schizophrenia may constitute a causal relationship . We note that while the SNP heritability estimate for AD was significantly non-zero, the Z-score for AD (h2/SE) was somewhat noisier (Zh2 = 5.98) than recommended by the authors of the LCV method (Zh2 > 7). As a result, this inference of partial genetic causality needs to be cautiously interpreted in light of this, with larger GWAS of AD diagnosed using DSM-IV/DSM-V or similar likely required to boost the precision of AD SNP heritability. DPW did not show any evidence for a causal relationship with schizophrenia like alcohol dependence. Interestingly, AD and DPW showed genetic correlation , as did CUD and LCU  but there was no evidence of partial genetic causality of DPW on AD  or CUD and LCU . No evidence of genetic correlation was observed among ND and CPD . These results suggest that the underlying mechanisms driving AD, CUD, and ND may not strongly present in substance use observed in a population sample, although this requires further investigation.LD score regression (LDSR) analysis revealed significant genetic correlations between schizophrenia and several of the SUD phenotypes after correcting for the number of tests performed Fig. a. SpecifP < 2.7 × 10−6, Supplementary Table PDE4B (P = 2.09 × 10−6) and FOXP2 , CADM2 (P = 1.14 × 10−13), AS3MT (P = 9.43 × 10−7), NCAM1 (P = 1.44 × 10−9), ATXN2L (P = 1.85 × 10−8), TUFM (P = 5.72 × 10−8), SH2B1 (P = 3.15 × 10−8), ATP2A1 (P = 1.68 × 10−8), RABEP2 (P = 1.64 × 10−6), and SRR (P = 1.61 × 10−6) (Supplementary Table Using aggregated gene-level association (MAGMA) on the individual traits, we observed that 534 genes were associated with schizophrenia alone after Bonferroni correction (HIST1H4 (P = 2.86 × 10−38), was also the most significant for the schizophrenia meta-analysis with AD (P = 2.70 × 10−31), and OD (P = 4.50 × 10−36). Whereas the most significant gene observed in the CUD and schizophrenia meta-analysis was ST3GAL3 (P = 9.68 × 10−16) and in the ND and schizophrenia meta-analysis the gene ZFYVE21 (P = 1.11 × 10−14). We then restricted these genes to those which were also at least nominally significant (P < 0.05) in the individual GWAS for schizophrenia and SUD but did not survive multiple-testing correction for either of the respective univariable GWAS  MED19 in the schizophrenia + AD, schizophrenia + CUD, and schizophrenia + ND meta-analyses; along with (3) a BDNF, (4) FUT2 and (5) IZUMO1 for both the schizophrenia + AD and schizophrenia + CUD meta-analysis .The genetic architecture shared between schizophrenia and substance dependence was further investigated using genic pairwise meta-analysis (MAGMA) to identify potentially pleiotropic signals that do not reach conventional significance thresholds in the individual GWAS Fig. . The num4 P = 2.8 × 10−38,We investigated the involvement of 14,969 gene-sets (MsigDB v7.4) in four categories including Biological Process (BP), Cellular Component (CC), Molecular Function (MF), and Human Phenotype Ontology (HPO), respectively. For the individual schizophrenia GWAS, 84 gene-sets remained statistically significant after multiple-testing correction (FDR < 0.05). No gene-sets passed false discovery rate (FDR) correction for the individual substance dependence phenotypes.long-term synaptic potentiation , exocytic vesicle , paroxysmal ventricular tachycardia , peptidyl serine dephosphorylation , hypoplasia of the olfactory bulb , regulation of heart contraction , and regulation of peptidyl serine dephosphorylation . The schizophrenia and CUD meta-analysis yielded 33 statistically significant sets, with 12 not observed in the individual SZ GWAS , aggressive behaviour , hypoplasia of the olfactory bulb , and obsessive compulsive behaviour . There were 60 gene-sets passed FDR correction in the schizophrenia meta-analysis with OD , histone deacetylase complex , and aggressive behaviour . Finally, schizophrenia meta-analysed with nicotine dependence yielded the fewest significant gene-sets , glutamatergic synapse , long-term synaptic potentiation , and type I pneumocyte differentiation . Interestingly, the human behaviour ontology set abnormal aggressive impulsive or violent behaviour and aggressive behaviour was common among schizophrenia meta-analysed with CUD, ND or OD, while long-term synaptic potentiation (LTP) was also common between schizophrenia and AD and ND.In the pairwise meta-analysis, 73 gene-sets were statistically significant (FDR < 0.05), for schizophrenia meta-analysed with AD, with 10 gene-sets not previously observed in the individual schizophrenia GWAS , and the schizophrenia and ND model  but was only nominally significant in the individual schizophrenia GWAS (P = 0.007), supporting how this meta-analysis approach can increase discovery power.We then considered the association of 2,598 microRNA (miRNA) regulatory target prediction gene-sets with each individual GWAS, followed by the bivariate meta-analyses (Supplementary Tables ngenes = 487) were significantly enriched with schizophrenia associated variation considering the individual schizophrenia GWAS . However, meta-analysis of schizophrenia with each of the substance dependence phenotypes did not increase this association, suggesting the signal was more localised to schizophrenia upon considering the currently available GWAS. We hypothesised that the biology associated with targets of miR-137 may be shared with that of the 17 novel miRNA target sets implicated by the meta-analysis. To test this, we evaluated which biological pathways and other ontological gene-sets the targets of miR-137 were overrepresented in relative to the 17 novel miRNA target sets uncovered. We found that there were several neuronal related processes enriched among the targets of miR-137 and at least one of the 17 miRNAs’, such as neurogenesis, nervous system development, and glutamatergic synapse and several implicated in metabolic processes.The miRNA miR-137 is one of the most well studied genes implicated by schizophrenia GWAS, the regulatory targets of miR-137 , the AUDIT is still limited to the past 12-months [In this study we used genetic approaches to explore common genes between schizophrenia and substance dependence using GWAS that met strict criteria for a life-time diagnoses. As most available substance use and misuse GWAS only measure a recent state instead of a stable trait, and substance dependence is likely neurodevelopmental in origin, most current SUD GWAS likely do not capture life-time substance dependence. A 2020 study by Zhou et al. on problematic alcohol use (PAU)  and a 202-months , 41. Tho2-months . FurtherIn this study, we further supported previously known evidence of genetic correlation between schizophrenia and these substance dependence phenotypes, while there was further evidence of a causal relationship between alcohol dependence and schizophrenia. Interestingly, there was no causal relationship between the consumption of alcohol (drinks per week) and schizophrenia, or between AD and DPW. This was consistent with the trans-ancestral GWAS of alcohol dependence , which sTRAF3IP2, MED19, BDNF, FUT2, and IZUMO1) were common hits in more than one of the paired disorders, with the 3’ untranslated region (UTR) of FUT2 previously reported by Jang et al. to be associated with continuous alcohol and psychiatric disorder phenotypes [FUT2 as a nearby gene to one of these loci [TRAF3IP2, encodes nuclear factor-kappa-B (NF-κB) activator 1 (Act1), an IL-17 receptor adaptor protein [TRAF3IP2 plays a critical role in the activation of multiple pro-inflammatory signalling pathways [MED19) is a physical and functional target of RE1 silencing transcription factor (REST), with combined depletion of MED19/MED26 shown to result in de-repression of REST targets in vivo [Strikingly, we also observed 44 genes associated with substance dependence in schizophrenia that were not seen in the individual GWAS. Five of these genes  transcriptional activity [spherical high-density lipoprotein particle was enriched. In humans, activation of PPARγ is generally associated with an increase in plasma HDL-cholesterol [activity , which iactivity . Intereslesterol . Adiposelesterol . The dyslesterol , with Ollesterol . Cocainelesterol . Moreovelesterol , 64.BDNF) has long been linked to neurodegenerative diseases and psychiatric disorders such as substance dependence [BDNF plays an important role in the regulation of synaptic strength and plasticity in the brain [BDNF [BDNF upon meta-analysis of schizophrenia with alcohol dependence and cannabis use disorder. In the gene-set analyses, neurotrophin receptor binding was also a novel ontological pathway significant for schizophrenia and ND. Additionally, several of the novel 44 genes have also been found to play important roles in metabolism. In mouse studies, protein tyrosine kinase 2 beta (PTK2B) was found to play a critical role in the differentiation of beige adipocytes [Spherical high-density lipoprotein particle was also a significant pathway in the Schizophrenia and OD meta-analysis.Brain-derived neurotrophic factor , it does not account for potential behavioural differences in subjects who have been exposed to prescription opioids as compared to those which are illicit. Future GWAS of polysubstance dependence would also be invaluable to disentangle some of the complex biological drivers of these disorders. Lastly, the findings in this study are limited to European/caucasion populations only, and give an incomplete picture of the genetic underpinnings of SUDs comorbid with schizophrenia.Despite the significant disease comorbidity in schizophrenia and substance dependence, previous GWAS for these disorders failed to reveal large overlap between genome-wide significant hits. We demonstrate that the increase in power afforded through our pairwise meta-analysis approach was able to identify shared genetic signals, including new genes and biological pathways relevant to both the neurobiology of addiction and psychosis. These novel genes and biological systems should be used in future analyses to refine whether causal variation is mapped to these genes, and whether any of these signals could warrant therapeutic intervention.Related Manuscript File


Fertility and hatchability are economically important traits due to their effect on poult output coming from the turkey hatchery. Traditionally, fertility is recorded as the number of fertile eggs set in the incubator (FERT), defined at a time point during incubation by the identification of a developing embryo. Hatchability is recorded as either the number of fertile eggs that hatched  or the number hatched from all the eggs set . These traits are collected throughout the productive life of the bird and are conventionally cumulated, resulting in each bird having a single record per trait. Genetic evaluations of these traits have been estimated using pedigree relationships. However, the longitudinal nature of the traits and the availability of genomic information have renewed interest in using random regression (RR) to capture the differences in repeatedly recorded traits, as well as in the incorporation of genomic relationships. Therefore, the objectives of this study were: 1) to compare the applicability of a RR model with a cumulative model (CUM) using both pedigree and genomic information for genetic evaluation of FERT, HOF, and HOS and 2) to estimate and compare predictability from the models. For this study, a total of 63,935 biweekly FERT, HOF, and HOS records from 7,211 hens mated to 1,524 toms were available for a maternal turkey line. In total, 4,832 animals had genotypic records, and pedigree information on 11,191 animals was available. Estimated heritability from the CUM model using pedigree information was 0.11  Turkey meat continues to be a popular meat for consumption with a total production of approximately 6 million tonnes in 2019 . The conTraditionally, genetic evaluation of these traits has been performed using cumulative (CUM) records collected over the productive life of the bird . HoweverThe availability of genomic information in many species has allowed for a better estimation of the relationships between individuals. The combination of this information with pedigree information simultaneously in a single-step genomic best linear unbiased prediction (ssGBLUP)  has beenPhenotypic data for this study were provided by Hybrid Turkeys, Kitchener, Canada. In total, 63,935 egg production records collected on a biweekly basis from a purebred turkey female line were available from 2010 to 2019 , 2. Thesr2, which is less dependent on allele frequencies and was greater than 98%. For quality control measures, non-autosomal SNP markers and autosomal SNP markers with MAF less than 0.05 and significantly deviating from Hardy–Weinberg equilibrium (P < 1 × 10−8) were excluded. After editing, there were a total of 35,751 SNP markers retained for further analysis.Of the animals with phenotypic records, a total of 4,832 animals were genotyped using a proprietary 65K SNP panel . The genotype call rate was 94%, and missing genotypes were imputed using AlphaImpute version 2 . For impTo investigate the influence of different parameters on reproductive traits, a CUM animal mixed model using only a single record per animal and a RR animal mixed model using all available records per animal were applied to estimate genetic parameters based only on pedigree relationships.jth animal belonging to the ith hatch week, ith hatch week, and With the CUM model, the following mixed model equation is used to estimate genetic parameters for reproductive traits:nth hen mated to the oth tom at the kth age belonging to the ith hatch week, µ is the overall mean, ith hatch week, jth egg hatch week, lth degree Legendre polynomial fitted for the effect of kth age, nth hen mated to the oth tom, oth tom, oth tom, oth tom, lth degree Legendre polynomial fitted for the additive genetic and permanent environment effects at kth age, and For the RR model, the following mixed model equation is used to estimate genetic parameters for reproductive trait biweekly records:A matrix of only the genotyped animals, and Following the presentation of creating a relationship matrix that included pedigree and genomic information , an H reith animal, ith animal, and W is a matrix of age covariate ranging from 38 to 62 weeks and associated with the degree of Legendre polynomials used.Estimates of the RR coefficients from BLUP and ssGBLUP RR models were used to derive the pedigree-estimated breeding value (EBV) and genomic-estimated breeding value (GEBV), respectively.All variance components and genetic parameters used in this study were estimated using the WOMBAT software program . To adeq.Predictive ability of BLUP and ssGBLUP for all models was assessed using the following technique. Initially, observed phenotypes were corrected for all fixed effects fitted in the full model based on traditional BLUP (adjusted phenotype). Next, phenotypic records were removed for approximately 10%  defined by hatch between the year 2018 and 2019 (reduced data set). These young animals were assigned to the validation population, and the remaining 90% animals were assigned to the training population. Thereafter, EBV and GEBV for the validation population were estimated. The predictive ability for each trait was calculated as the Pearson correlation coefficient between the EBV or GEBV estimated based on the reduced data set and the adjusted phenotype estimated from the full data set from the CUM model, while considering only animals in the validation population. Likewise, Pearson correlation coefficients between biweekly adjusted phenotypes from the full model and the biweekly EBV or GEBV estimated from the reduced data set and only considering animals in the validation population were used for the RR modelThe observed mean for FERT, HOF, and HOS was 81.27, 80.86, and 67.94%, respectively, for single records used for the CUM model as shown in Estimates of the variance components for all the models implemented are presented in An important reason for the implementation of RR is the ability to appropriately model the trajectory of longitudinal traits. This accounts for both the additive and permanent environment effect for traits with repeated records. The trends observed in the variance components estimated with the RR model are shown in Heritability estimates from the CUM model for the traits ranged from 0.11 to 0.24 for all traits with pedigree information , 9, 10. Phenotypic and genetic correlations from the RR model are shown in As shown in The present study sought to estimate genetic parameters for reproductive traits in a maternal turkey line using CUM and RR models with pedigree and genomic information. Estimated average HOF and FERT were approximately 80%, which is close to the estimates reported by The present study shows that the additive genetic effect of both the hen and sire plays a significant role in the observed variation in FERT, which is in line with the study published by Predictive ability estimated based on ssGBLUP had higher estimates than BLUP for most traits. This is expected as many studies have shown lower predictivity from pedigree-based EBV than marker-based EBV . SimilarIn this study, the applicability of RR ssGBLUP was investigated and compared to the traditional CUM model used in estimating the reproductive trait in turkeys. Our findings suggest that genomic relationships result in higher heritability estimates over traditional pedigree relationships, consequently causing higher predictive ability. In addition, the RR model captured the covariance and correlation that exist between different ages throughout the productive life of the animal. Therefore, the use of RR with the incorporation of genomic information is a feasible endeavor for analyzing longitudinal traits like FERT, HOF, and HOS in turkeys.


This systematic review and meta-analysis investigates the correlation of genetic and shared and nonshared environment factors with infant developmental milestones. What are the overall genetic and shared and nonshared environment estimates for psychological traits and developmental milestones in infancy?In this systematic review and meta-analysis of 139 infant twin studies involving almost 80 000 twins globally, moderate to high genetic and shared and nonshared environment estimates were found across a range of important psychological traits and developmental milestones in infancy.These results offer insight into the degree to which genes and environments estimate outcomes in key domains of infant functioning and suggest highly heritable traits that may be particularly suitable candidates for gene discovery. Although infancy is the most rapid period of postnatal growth and development, factors associated with variation in infant traits are not well understood.To synthesize the large twin study literature partitioning phenotypic variance in psychological traits and developmental milestones in infancy into estimates of heritability and shared and nonshared environment.PubMed, PsycINFO, and references of included publications were searched up to February 11, 2021.Peer-reviewed publications using the classical twin design to study psychological traits and developmental milestones from birth to 2 years old were included.International Classification of Functioning, Disability and Health: Children and Youth Version. Data were pooled in 3-level random effects models, incorporating within-cohort variance in outcome measurement and between-cohort variance. Data were analyzed from March 2021 through September 2021.Data were extracted in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses and categorized using the The primary outcomes were monozygotic and dizygotic twin correlations. These were used to calculate genetic and shared and nonshared environment estimates.2h, 0.59; 95% CI, 0.25-0.79; P < .001). Several categories of traits had substantial shared environment estimates, the highest being mental functions of language . All examined categories of traits had moderate or high nonshared environment estimates, the highest of which were emotional functions  and family relationships .Among 139 publications that were systematically retrieved, data were available on 79 044 twin pairs (31 053 monozygotic and 47 991 dizygotic pairs), 52 independent samples, and 21 countries. Meta-analyses were conducted on psychological traits and developmental milestones from 106 publications organized into 10 categories of functioning, disability, and health. Moderate to high genetic estimates for 8 categories were found, the highest of which was psychomotor functions (pooled These findings may be an important source of information to guide future gene discovery research, public perspectives on nature and nurture, and clinical insights into the degree to which family history and environments may estimate major domains of infant functioning, disability, and health in psychological traits and developmental milestones. The quantitative genetic method most widely and comprehensively performed in infancy is the classical twin design, which has been used for more than a century to partition phenotypic variance into additive genetic variance (heritability) and variance in the shared and nonshared environment. Family studies comparing biologically related siblings or parent-offspring pairs are typically unable to separate genetics from shared environment. In contrast, the classical twin design can provide separate estimates of heritability (the proportion of trait variation explained by genetic differences) and shared and nonshared environment. Twin studies are more feasible than adoption studies (which compare degree of resemblance between adoptees and their birth parents with resemblance between adoptees and their adoptive parents) to conduct at scale during infancy because adoption often occurs later in childhood. This has resulted in a far smaller and less comprehensive body of evidence in infancy from adoption studies than twin studies. The molecular genetic literature on infant traits is also small; the first genome-wide association study of infant traits was only recently conducted,8 and most molecular genetic studies in infancy have used candidate gene association methods, which in general have failed to yield replicable findings.9Evidence suggests that complex traits are substantially but not entirely, heritable.7 synthesizing virtually all twin studies of complex traits  found a heritability of 49% across the lifespan when all traits and age groups were combined. The analysis combined data from infants and older children, calculating pooled estimates for children aged 0 to 11 years. Infancy is a rapid and sensitive period of development that deserves special focus. To address this, we conducted the first, to our knowledge, meta-analysis of twin studies of psychological and developmental functioning, disability, and health in infancy (birth to age 2 years), calculating pooled estimates of heritability and shared and nonshared environment.A landmark meta-analysis,PRISMA) reporting guideline 2020 statement and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline proposal for reporting. Given that the review involved the synthesis of anonymized information available in the public domain, it was exempt, according to University College London Research Ethics Committee (UCL REC) regulations, from requirements for ethics review by the UCL REC and the need for informed consent.This study protocol was registered with PROSPERO , and the systematic review and meta-analysis were performed in line with the Preferred Reporting Items for Systematic Reviews and Meta-analyses .11 Uncertainty about which ICF-CY category to use for a phenotype was resolved through discussion with M.M. and senior researchers P.F. and A.R. Phenotypes were excluded from the meta-analysis if they could not be categorized or were in categories containing data from fewer than 5 independent samples.Phenotypes were classified by C.A. using the World Health Organization 12 we conducted two 3-level multilevel random-effects models  on twin correlations weighted by sample size from 10 categories of the ICF-CY containing data from 5 or more twin cohorts (eMethods in the r) and standard error. The second model was identical but reparameterized with the MZ group as the reference category, producing a pooled DZ twin correlation (DZ r) and standard error. To allow for differences in variability in MZ and DZ subsets, models had a random error structure creating separate study-level and outcome error terms for MZ and DZ twins. As detailed in the eMethods in the 13 We used 95% CIs around the pooled estimates in the twin study meta-analysis. Forest plots for analyses were produced using the R package metafor version 2.4-0 for R statistical software version 4.0.2 .12 We calculated 2I for each of 3 levels in multilevel models. According to Cochrane guidelines, I2 ≤ 40% suggests low heterogeneity, while I2 = 30%-60% suggests moderate heterogeneity and I2 ≥ 50% indicates substantial or considerable heterogeneity.14 To reduce heterogeneity, analysis steps were repeated in 10 ICF-CY subcategories (with data from ≥5 samples) and 3 ICF-CY categories (with separate data from parents and observers from ≥5 samples) by parent and observer subgroup  given that differences in rater have been found to be associated with differences in heritability estimates.16In the R package metafor,17 In line with Cochrane recommendations, publication bias tests were run only on estimates in trait categories containing at least 10 estimates.14 Egger tests of publication bias were 2-sided and were considered significant at P < .05. Data analysis was conducted March through September 2021.We ran Egger tests of publication bias using the standard error as the estimator, and created funnel plots, plotting effect sizes against standard errors.156 containing data on 79 044 twin pairs (31 053 MZ and 47 991 DZ twins), 52 twin cohorts, 21 countries, and 6 continents between 1972 and 2020. The sample included 66 407 twin pairs from Western, educated, industrialized, rich, and democratic countries  and 12 637 twin pairs from Africa, Asia, and South America (15.99%) . After duplicate removal and screening, 139 publications were included,ICF-CY,11 there were enough data from independent samples for meta-analysis (≥5 samples). Results are reported in k = 22) than observer-rated data (cohort k = 12) , followed by attention functions , complex interpersonal interactions , family relationships , and emotional functions . Remaining categories had lower estimates with 95% CIs above 0 , apart from mental functions of language and sleep functions, which had CIs overlapping 0  (The highest heritability estimate was for psychomotor functions (pooled ctively) .2c, 0.59; 95% CI, 0.24-0.86; P = .001), growth maintenance , basic cognitive functions , and sleep functions  had high shared environment estimates. Complex interpersonal interactions had a lower estimate , and estimates for psychomotor, attention, and emotional functions; family relationships; and basic interpersonal interactions had CIs overlapping with 0   .2e, 0.42; 95% CI, 0.33-0.50; P < .001), family relationships , basic interpersonal interactions , and attention functions . Remaining categories had lower estimates with CIs above 0   .2I range, 0.19%-12.44%) , and between-cohort heterogeneity contributed a lower amount  to these outcomes. The remaining 7 categories each had substantial between-cohort heterogeneity  and lower within-cohort heterogeneity   . Within--42.04%) .ICF-CY (with data from ≥5 samples) and 3 phenotypic categories (with separate parent and observer data from ≥5 samples) by rater (for 6 subgroups: 3 with parent report and 3 with observer report). Full findings are reported in eResults and eTables 8 and 9 in the To reduce heterogeneity, we analyzed 10 subcategories of the Possible publication bias was detected in the unexpected direction across all categories. Findings are in eResults, eTables 10 to 11, and eFigures 14 to 18 in the 156 included in the meta-analysis was 75.58% (13.83%).Quality assessment results are displayed in eFigure 19 in the 7 shared environment estimates were high across several important domains of infant development.Drawing on a systematically-retrieved pooled sample of 79 044 twins, this systematic review and meta-analysis found evidence that most domains of functioning, disability, and health in psychological and developmental milestones were heritable in infancy and had moderate to high nonshared estimates. Contrary to evidence in older ages,7 all meta-analyzed categories had heritability estimates with 95% CIs above 0 in infancy, apart from sleep and language functions. Estimates were high (≥40%) for important areas of development , suggesting that these categories may be particularly suitable candidates for gene mapping.Consistent with evidence in older samples,157 In accordance with the very high heritability of autism,158 a neurodevelopmental condition involving differences in social interaction, social cues in relationships, and regulating behaviors within interactions were among the most heritable subcategories. Absence of evidence that infant language was heritable was consistent with evidence that the heritability of cognition, including language, is low in early development, increasing with age.160High heritability in infancy of attention functions was consistent with the high heritability of attention-deficit/hyperactivity disorder (ADHD) and ADHD traits in older samples.161 Correlated rater bias that inflated MZ and DZ twin similarity equally would lead to inflated shared environment estimates. Without raw data from individual studies, it was not possible to test this by examining variance-covariance structures, which can uncover evidence of contrast and assimilation bias. Overall, our results suggest that individual differences in growth, motor, cognitive, and emotional development may be associated with genetic factors as early as the first 2 years of life.The higher heritability of parent-rated than observer-rated phenotypes may be driven by contrast bias in parental reports of phenotypes among children who were DZ twins, exaggerating DZ differences, or by assimilation bias in parental reports of MZ twins, exaggerating MZ similarity.7 shared environment estimates had CIs above 0 in several domains and were high for language, sleep, growth maintenance, and basic cognitive functions, reflecting a broader trend noted in the literature that shared environment estimates for language and cognition are higher in early development.160 This may have important implications for obesity prevention and efforts to promote intellectual outcomes, which are among the most robust estimators of health and longevity.162 Shared environment estimates had CIs overlapping with 0 for psychomotor, attention, and emotional functions; basic interpersonal interactions; and family relationships. This was consistent with pooled findings in older age groups7 and evidence that shared environments do not contribute as much to similarity between siblings as genetics and do not contribute as much to differences between siblings as nonshared environments.164Contrary to evidence in older age groups,165 and may reflect the importance of each twin’s unique experience in the expression of phenotypes specifically when rated by observers. Alternatively, given that nonshared estimates also include measurement error, higher observer-rated estimates may reflect increased error in observational measurement.Nonshared environment estimates had 95% CIs above 0 for all phenotypic categories and were high for emotional and attention functions, family relationships, and basic interpersonal interactions. Higher nonshared estimates for observer ratings than parent ratings were consistent with wider research167 However, given that our aim was to examine individual differences rather than investigate how and why groups differed, mean differences between twins and singletons may not indicate issues with generalizability.This study has several limitations. Given that research designs all have limitations and biases, establishing robust evidence ideally involves triangulation of methods. However, the classical twin design is currently the only quantitative genetic method that has produced data from enough independent samples to conduct adequately powered meta-analyses across a comprehensive range of infant traits. The generalizability of twin findings may be limited because some infant phenotypes  develop differently in twins compared with individuals from singleton births.168Although the twin method can be used to examine genotype-environment correlation or interaction, we did not synthesize findings on these outcomes. In twin modeling, ignored interaction between genotype and shared environment is estimated as heritability and ignored interaction between genotype and nonshared environment will be estimated as nonshared environment, potentially contributing to biased estimates.169 Furthermore, for any given sample size, there is more power to estimate 2e than 2h and 2c, which may explain the narrower intervals around 2e. CIs are also associated with the number of samples included in a meta-analysis; while adding studies may improve precision, it may also increase heterogeneity, decreasing precision. Crucially, in multilevel modeling, CIs are also dependent on the degree of between-study heterogeneity. High variability in estimates across studies is associated with wider CIs, and ignoring such heterogeneity tends to overestimate precision. The heterogeneity we observed was generally high, and so CIs were comparatively wide.CIs for some estimates were wide. In meta-analysis, CIs depend on the precision of included studies, which are influenced by sample size and, in the case of twin modeling, the ratio of MZ to DZ pairs and relative contribution of each parameter.A downside of the comprehensive approach we took is that it may have increased between-study heterogeneity. We attempted to reduce this in the narrower subcategory and rater analyses. However, between-study heterogeneity was substantial in all categories and subcategories, suggesting that between-study differences likely created considerable noise in our estimates. Understanding and reducing heterogeneity will be important for future research to provide more precise twin estimates in infancy. Possible publication bias was also detected across multiple outcome domains. The impact of this on the estimates is difficult to rigorously assess.Although individuals from Western, educated, industrialized, rich, and democratic societies represent 12% of the world’s population, twins from these areas of the world constituted more than 80% of our sample. Infants in Africa, Asia, and South America combined represented approximately 16% of our sample, highlighting a need for twin research on infants in these continents.There was an imbalance in the amount of research conducted on the synthesized categories; for example, far more was conducted on anthropometric phenotypes included in growth maintenance functions (which included data from 24 of 52 included samples) than other domains. Important areas in which research was lacking included nonsocial autistic traits and dysregulation, eating behavior, memory, higher-level cognitive functions, and brain structure.To our knowledge, this systematic review and meta-analysis is the first study to synthesize the large and comprehensive infant twin literature on psychological traits and developmental milestones, offering insight into the possible earliest manifestations of phenotypic variance associated with genetic and environmental factors. This has the potential to improve public perceptions on nature and nurture by, for example, dispelling widely held beliefs that infants are shaped entirely by their environments or that family history entirely predetermines child health, beliefs that may place undue pressure on parents. For researchers, these results may offer a guide for future gene discovery research and efforts to uncover the causes of variation in infant traits. For clinicians, these findings may provide an indication of how family history and environmental conditions may estimate infant outcomes, including outcomes that may be early markers associated with subsequent healthy or pathological development.