If not accounted for, genotype x environment (G×E) interactions can decrease the accuracy of genetic evaluations and the efficiency of breeding schemes. These interactions are reflected by genetic correlations between countries lower than 1. In countries that are characterized by a heterogeneity of production systems, they are also likely to exist within country, especially when production systems are diverse, as is the case in South Africa. We illustrate several alternative approaches to assess the existence of G×E interactions for production traits and age at first calving in Holsteins in South Africa. Data from 257,836 first lactation cows were used. First, phenotypes that were collected in different regions were considered as separate traits and various multivariate animal models were fitted to calculate the estimates of heritability for each region and the genetic correlations between them. Second, a random regression approach using long-term averages of climatic variables at the herd level in a reaction norm model, was used as an alternative way to account for G×E interactions. Genetic parameter estimates and goodness-of-fit measures were compared.Genetic correlations between regions as low as 0.80 or even lower were found for production traits, which reflect strong G×E interactions within South Africa that can be linked to the production systems . A random regression model including average rainfall during several decades in the herd surroundings gave the best goodness-of-fit for production traits. This can be related to a preference for total mixed ration on farms with limited rainfall. For age at first calving, the best model was based on a random regression on maximum relative humidity and maximum temperature in summer.Our results indicate that G×E interactions can be accounted for when genetic evaluations of production traits are performed in South Africa, by either considering production records in different regions as different correlated traits or using a reaction norm model based on herd management characteristics. From a statistical point of view, climatic variables such as average rainfall over a long period can be included in a random regression model as proxies of herd production systems and climate.The online version contains supplementary material available at 10.1186/s12711-022-00735-5. With an average rainfall of only 464 mm, it is a relatively dry country. Most of the country has warm, sunny days and cool nights. Winters in South Africa occur between June and August )The purpose of this study was to illustrate the wide range of potential genetic evaluation models along this continuum in the particular context of G×E interactions in South African Holstein cows for production traits and age at first calving. The same phenotypes will be described as separate traits in different regions, or by including in the model a reaction norm involving climatic characteristics of the herds averaged over a long period. For simplicity, only first lactation records are considered in this study, to focus more easily on the detection of potential G×E interactions. This is in contrast with the current genetic evaluation model in South Africa, which is based on a 3-lactation fixed regression test-day model , 23.Data from 378,782 first lactation Holstein cows recorded over a period of 30 years 1982–2012) were used in the analysis. The traits analyzed were first lactation milk, fat and protein yields  and age at first calving (AFC) in months. Only herds in the seven major administrative regions (provinces) with dairy production were considered. Cows were required to have a known sire and an age at first calving between 20 and 40 months. Contemporary groups were defined within herd and included at least 20 cows that were the progeny of sires with at least 20 recorded daughters each in the complete data set. After editing, records from 257,836 first lactation cows from 702 herds were first grouped into four regions according to global climatic characteristics: Western Cape (1), Eastern Cape (2), Free State, Gauteng, North West and Limpopo (3), Kwazulu-Natal (4). Table 982–2012 In a first set of analyses, three linear models  were estimated as well as the specific genetic and residual variances from which a heritability could be derived for each region.The third model was also a multivariate model, but with a reduction of the rank of the correlation matrix that was imposed a priori. The Wombat software  reparameA second group of analyses was set up in order to assess whether the observed differences between regions in terms of production could be related to the climatic conditions that prevailed in each herd. Only milk yield was analyzed and a random regression analysis on climatic variables was used. First, as a base model, a unique (homogeneous) genetic variance and heterogeneous residual variances (one per region) were estimated. This is equivalent to the univariate analysis with a specific residual variance for each region. Then, the model was extended to a random regression model by including each climatic variable as a reaction norm variable: as a result, the genetic parameters of the random regression model included two genetic effects: a fixed term and a regression coefficient on the climatic variable (one at a time). The comparison of these models should reveal which climatic variables contribute to a G×E interaction. Then, two climatic variables were included simultaneously together with the fixed term and the most relevant combination of the two climatic variables was chosen. These random regression analyses were initially run assuming a full rank genetic variance–covariance matrix. This rank was then reduced to 1. In such a case, the first eigenvector of the resulting genetic covariance matrix can be interpreted as the optimal linear combination of the climatic variables describing the environmental component of the genetic variability.A summary of the statistics describing first lactation milk, fat and protein yield and age at first calving in the four regions is in Table Genetic parameter estimates for production traits and age at first calving measured in each of the four regions are in Table Table The most important climatic variable for all production traits is the average rainfall, contributing to about 10% of the total genetic variance (C1). In fact, the other climatic variables did not lead to any improvement of the BIC. In contrast, age at first calving was more influenced by the average maximum temperature in summer (C8) and by the average minimum relative humidity (C5), representing about a quarter of the total genetic variance, i.e., the two components related to THI.To highlight the impact of average rainfall, cows were partitioned into four groups depending on the average rainfall of their herd and a multivariate analysis was performed for milk production (RF4). The results are in Table All the models used were compared according to their goodness-of-fit [BIC, see Table After more than 50 years of uninterrupted development, genetic (and genomic) evaluations continue to play a fundamental role in the sustainable improvement of dairy cattle. To avoid computational complications, the first animal models were simple, based on strong assumptions, such as cow selection performed only through their sire-to-cow path or successive lactations considered as repetitions of a same trait. These unrealistic assumptions were progressively dropped in many countries with the development of, e.g., multiple-trait animal models and/or models based on test-day records (e.g. ). HoweveOur study is another illustration that strong G×E interactions do exist also within country—in our case, South-Africa—with genetic correlations between South-African environments as low as 0.8. In spite of the use of a quite simplistic lactation model, we could demonstrate that genetic correlations for lactation milk yield between regions with different climates could be of the same order of magnitude as the correlation considered in Interbull evaluations between South Africa as a whole and European or North American countries . A likelThe model that compared herds according to average rainfall over 50 years was statistically better than the model that opposed regions. Interestingly, in contrast with what is classically found in North America or Europe  where geThis study shows that important G×E interactions exist in Holstein cattle in South Africa that could result in large re-rankings of bulls. In absence of a generalized recording of the farms’s feeding systems, these interactions could be taken into consideration in national genetic evaluations by defining a heterogeneous residual variance by region and a genetic term involving a reaction norm on long-term averages of climatic variables for each herd. In local breeding programs, calculating a different breeding value depending on the environment (region or feeding system) should be considered.Additional file 1: Table S1. Estimates of heritability (in bold) and genetic correlations  for each region depending the model used (± SE) for Fat yield and Protein yield. Table S2. Contribution of the second eigenvalue of the genetic covariance matrix, correlation between the two genetic effects (± SE) and goodness-of-fit (BIC = 0 for the model without climatic variable) for reaction norm models including a constant and a regression on one climatic variable at a time, in the case of fat yield and protein yield; Lower values of BIC indicate a better fit.


In aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array.The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated , which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values.The low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population. Fillet yield is the ratio between the edible portion (meat) and the whole weight of the fish at harvest, and this trait is of primary economic importance in aquaculture. The price paid when fish are sold as fillet can be much higher than the price of whole fish; therefore, small changes in fillet yield can result in a significant economic impact on the production chain , 2. In fThe recent development of high-density single nucleotide polymorphism (SNP) panels has added an extra resource in the toolbox of breeding programs. It allows for the prediction of more accurate breeding values and ultimately leads to higher genetic gains . The useGonzalez-Pena et al.  performeData were collected by the experimental breeding program of the USDA National Center for Cool and Cold Water Aquaculture . Phenotypes were recorded for fillet yield  from 2010 to 2018 and for 13-month BW, (hereafter referred to as body weight)  from 2004 to 2016 , fourth- , and fifth-generation family  of the ‘Select’ line described in Leeds et al.  were pheThe breeding objective of the program changed from growth performance to fillet yield in 2014, thus the three generations of fillet yield data were used to estimate family-based breeding values for families hatched in 2014. Based on those family breeding values, a divergent selection was applied to develop contemporary high fillet yield (ARS-FY-H) and low fillet yield (ARS-FY-L) lines, starting from 2016. Breeding values were estimated for each generation using a three-trait animal model that included fillet yield, 10-month BW, and thermal growth coefficient (defined as function of growth in a growing period and the average water temperature in that period) , using Me is the vector of residuals. The The following two-trait animal model was used in the analyses:The traditional evaluation was performed using pedigree-based best linear unbiased prediction (PBLUP) and genomic evaluations were performed using single step genomic BLUP (ssGBLUP) , 24. In r et al. :\documenVanRaden :\documenr et al. . If basement}A22 . In the Variance components, heritability, and genetic correlations were estimated without genomic information using the model described above, under PBLUP, implemented in the AIREMLF90 software .We investigated the impact of including genomic information in FY and BW evaluations using two datasets to perform a mid-parent validation for FY and two forward validation methods based on young, genotyped animals for FY and BW. The datasets and validation strategies are described below.This practical validation strategy that directly estimates genetic improvement across generations is used in aquaculture when progeny phenotypes are available and can be used as a benchmark to compare with parent averages (PA), which are equal to the average genomic estimated breeding values (GEBV) of parents , 29. In For the 2018 hatch year, BW records were not available because the animals were only measured later at harvest. Since harvest weight is measured later than BW , we could not perform the mid-parent validation for BW.The correlation of PA and average phenotypes over all the 73 families is reported as a measure of prediction accuracy, and the coefficient of the regression of PA on (G)EBV is reported as a measure of dispersion bias of the breeding values.With the advent of genomic selection, and since the main goal of genetic evaluations is to predict future performance, using young, genotyped animals for validation became the standard method to validate predictions . In our For the validation based on adjusted phenotypes, the phenotypes were adjusted for the fixed effects calculated with PBLUP with the whole data, i.e. For the LR validation, traditional and genomic evaluations were run with whole (w) and partial (p) datasets. In the partial dataset, phenotypes for the validation animals are omitted. In this validation, we can evaluate the impact of including new data into subsequent evaluations and we can investigate the value of adding genomic data.(1) where (2) where (3) Once evaluations were run, the following four statistics described by Legarra and Reverter . were co(4) The slope of the regression of The higher this correlation is, the more consistent two subsequent evaluations are when new data are added.All four statistics were computed for the 2016 hatch year validation animals (N = 562), in both the traditional and genomic evaluations. The number of genotyped animals with phenotypes in the training population for the mid-parent validation was 1929 and for the validation on young animals it was 1366.From the ssGBLUP evaluation, GEBV can be backsolved into SNP effects which can help uncover the genetic architecture of a trait. In our study, SNP effects were calculated as in Wang et al. :\documenOnce SNP effects were calculated, the proportion of additive variance explained by windows of 20 adjacent SNP was calculated. The order of SNPs on the rainbow trout genome chromosomes was determined based on their position in the GenBank Assembly Accession GCA_002163495.1 . In addiWhen significantly associated important SNPs are identified, differential weights can be attributed to them in the genomic evaluation for potential increases in accuracy. Under the ssGBLUP framework, this can be done by using WssGBLUP over a few iterations, i.e., three to five, to optimize the weights applied in the construction of cument}G  by maximcument}G  and Legacument}G , as follThe two-trait model was used, and SNP effects and weights for programs .The estimates of variance components, heritabilities, and genetic correlations for FY and BW are in Table Estimating the genetic correlation between FY and BW is important to understand the relationship and potential impacts of selection for either of the traits. The correlations reported in the literature range from very low 0.04) to moderately positive (0.22)  to moder. In our Overall, the results of the three validation strategies used in our study agreed and the ssGBLUP evaluation always outperformed the traditional PBLUP. The results for the validation are in Tables Because BW phenotypes were not available for fish in hatch year 2018, the animals born in 2016 were used in the validation. For this trait, the gains in accuracy with ssGBLUP were higher with the LR validation (44%) than with adjusted phenotypes (11%). Although genomic evaluation outperformed traditional PBLUP for both traits in both validations, this discrepancy in the magnitude of the gains could be due to stronger selection on BW, which may not be as well accounted for by the LR validation. In LR, the denominator of the accuracy formula is used to consider selection; however, the formula we used is an approximation to the main formula . The denSimilar gains in prediction accuracy have been reported for a variety of traits in aquaculture species, for instance, Atlantic salmon , rainbowOur study provides the first estimates of validation accuracy from genomic evaluation models for fillet yield in aquaculture using mid-parent validation , 29. ThiBias and inflation results were not as consistent across validation methods. For the mid-parent validation (FY only), some bias 0.85 and 0.91) and over-dispersion 0.63 and 0.52) were present in both the traditional and genomic evaluations even with adjusted phenotypes  and for BW (0.68 to 0.52), as shown in Table Weighted ssGBLUP results for FY and BW are in Tables accuracy . Some staccuracy  or when Figures Other studies have investigated the genetic architecture of FY in rainbow trout. For example, Gonzalez-Pena et al. , using aThe low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No major SNPs were found to be significantly associated with the studied traits, which suggests that using all the SNPs available in the panel for genomic evaluations is a better strategy. Weighting SNPs differently provides only a marginal increase in prediction accuracy compared to the use of the unweighted single-step model. This indicates the existence of many loci with small effects on these traits in the genome. Past selection for growth rate, selective genotyping, and the relatively small number of genotyped animals in the current populations are possible sources of bias in the evaluation for fillet yield and body weight.


The SCZ PRS with the best predictive performance was ascertained in the Estonian Biobank  with LDpred. The infinitesimal SCZ PRS model showed the strongest capacity in discriminating SCZ cases from controls with one SD difference in SCZ PRS corresponding to an odds ratio (OR) of 1.73 . In 22q11.2 patients, random-effects ordinal regression modelling using longitudinal data showed SCZ PRS to have the strongest effect on social anhedonia , and occupational functioning  within the negative symptoms course, and dysphoric mood  and stress intolerance  within the general symptoms course. Genetic liability for SCZ was additionally associated with full scale cognitive decline  and with longitudinal volumetric reduction of the right and left hippocampi . Our results indicate that the polygenic contribution to SCZ acts upon the threshold-lowering first hit . It modifies the endophenotypes of 22q11.2DS and augments the derailment of developmental trajectories of negative and general symptoms, cognition, and hippocampal volume.While the recurrent 22q11.2 deletion is one of the strongest genetic risk factors for schizophrenia (SCZ), variability of its associated neuropsychiatric endophenotypes reflects its incomplete penetrance for psychosis development. To assess whether this phenotypic variability is linked to common variants associated with SCZ, we studied the association between SCZ polygenic risk score (PRS) and longitudinally acquired phenotypic information of the Swiss 22q11.2DS cohort ( Genomic studies have established the importance of copy number variants (CNVs) in rare disease aetiology, and particularly as causal factors for neurodevelopmental disorders . Despitede novo via non-allelic homologous recombination of low copy repeats [One of the most common genomic rearrangements is a recurrent hemizygous deletion on chromosome 22q11.2. In 90% of the cases, the deletion occurs  repeats . In addi repeats –9, it is repeats , 11. Suc repeats . Multipl repeats , 14. Whi repeats , hemizyg repeats , 17, and repeats , an incr repeats , 19, spe repeats , 21.Trait-associated SNV effects pooled into a single number, namely a polygenic risk score (PRS) could capture a meaningful proportion of phenotypic variance  and thuBuilding upon previous findings , 21, we Recruitment of participants within the Swiss 22q11.2DS longitudinal cohort  began inThe presence of attenuated psychotic symptoms was evaluated at each visit by an expert psychiatrist using the Structured Interview for Psychosis-risk Syndromes (SIPS) , which iAt each visit, all participants underwent the administration of the Wechsler Adult Intelligence Scale (WAIS-III and WAIS-IV)  or the W2, slice thickness = 1.5 mm, and 124 slices. The parameters for both 3 T scanners were TR = 2500 ms, TE = 3 ms, flip angle = 8°, acquisition matrix = 256 × 256, field of view = 23.5 cm, slice thickness = 3.2 mm, and 192 slices. To minimize any potential confounder effects, the scanner model was used as a covariate in statistical analyses using neuroimaging measurements.Due to the wide timespan of the study, MRI scans were acquired with three different scanners: 1.5 T Philips Intera scanner was used for the first 20 scans, 3 T Siemens Trio for the subsequent 94 scans, and 3 T Siemens Prisma for the remaining 93 scans. T1-weighted images were acquired at the Center for Biomedical Imaging in Geneva with a three-dimensional volumetric pulse. 1.5 T scanner parameters were TR = 35 ms, TE = 6 ms, flip angle = 45°, NEX = 1, matrix size = 256 × 192, field of view = 24 cmT1-weighted images underwent fully automated image processing with FreeSurfer v6.0, comprising skull stripping, intensity normalization, reconstruction of the internal and external cortical surface and parcellation of subcortical brain regions . Corticahttps://www.cog-genomics.org/plink/2.0/) using the following criteria: (i) exclusion of individuals with genotype call rate <95%; (ii) exclusion of single nucleotide variants (SNVs) with call rate <95%, Hardy-Weinberg equation (HWE) < 1e-4, minor allele frequency (MAF) < 0.01, and with A/T or G/C alleles to avoid strand issues; (iii) removal of outliers who deviated ± 3 standard deviations from the samples’ heterozygosity rate mean, and (iv) verification that the data did not contain closely related individuals (PI_HAT > 0.2) and that phenotype and genotype sex matched. Of first-degree relatives, one member of each related pair was excluded, preferentially retaining samples that had more complete phenotype data. Deletion carrier status was confirmed with bcftools cnv calling plugin  [https://qtltools.github.io/qtltools/). Haplotype Reference Consortium reference panel [http://www.haplotype-reference-consortium.org/) was used for array imputation with the following parameters: build hg19, reference panel apps@hrc-r1.1, population European, phasing eagle. After imputation, SNVs with low imputation quality score R2 < 0.3, HWE p < 1e-6 and MAF < 0.05 were filtered out. The final quality controlled SNV set contained 6,462,855 biallelic SNVs for 103 individuals. Six individuals were further excluded as no phenotype data was available either due to their young age for completing SIPS or due to sub-optimal MRI data, thus reducing the sample set to 97 patients.One hundred and twenty-two individuals whose DNA samples were available within the Swiss 22q11.2DS cohort, were subjected for whole-genome genotyping with the Illumina Global Diversity Array v1. Quality control was carried out with PLINK v2.0  (webpageng.html) . The 100ng.html)  was usedng.html)   wave3 by the Psychiatric Genomics Consortium conducted primarily on samples of European ancestry , phenotyp < 1e-4, MAF < 1%). Pre-phasing was carried out with Eagle v2.3 [EstBB is a population-based biobank in Northern Europe, comprising 201,146 individuals aged ≥18 years. All biobank participants have signed a broad informed consent form, which allows continuous updating of epidemiologic data through periodical linking to national electronic repositories , and recontacting of participants. Medical history and health status are recorded according to the International Classification of Diseases, Tenth Revision (ICD-10 codes) . EstBB pgle v2.3  and impugle v2.3  using thgle v2.3 .https://www.med.unc.edu/pgc/download-results). The EstBB SNV content was (i) filtered for the quality controlled SNV content captured in the Swiss 22q11.2DS genotype data to account for the uniform set of SNVs in both datasets , (ii) filtered for the quality controlled SNV content  in EstBB data , and (iii) clumped for maximum LD between SNV to reduce multicollinearity dimensions . Ten different SCZ PRSs were derived by varying the fraction of causal SNVs  and using the EstBB LD reference panel to account for LD between SNVs.Genome-wide SCZ PRSs were constructed with LDpred, a Bayesian approach that applies a continuous shrinkage model to modify effect sizes based on the strength of each variant’s association in the GWAS and the underlying linkage disequilibrium (LD) structure . We starn = 108,201). Individuals with mania (ICD-10 F30.* “Manic episodes” category) and bipolar disorder (F31.* “Bipolar disorder” category) were excluded from all sets given the considerable genetic overlap between these psychiatric disorders and SCZ [For testing and validating the SCZ PRSs in EstBB, we excluded EstBB participants whose data was included in the SCZ GWAS wave3, one member per related individual pairs (PI_HAT > 0.2) and individuals with non-European ancestry in reference to 1000 Genome Project samples . SCZ cas and SCZ  (furtherhttps://genomics.ut.ee/en/tools) by multiplying the genotype dosage of each risk allele for each SNV by its respective weight and then summing across all SNVs into a score. For determining the best predicting PRS, we considered ten standardized SCZ PRSs separately and used a logistic model with diagnosis status (SCZ case or control) as a dependent variable and sex, age, and five genotype PCs as covariates. The model with the highest odds ratio was selected for replication in the validation set. The score with the best discriminative capacity in the validation set was additionally assessed based on maximal area under the receiver-operator curve (AUC) for considered logistic regression models using R/pROC package [survival package [Next, two-thirds of the EstBB cohort were allocated into a testing set  and one-third into a validation set  for identifying and validating the best performing PRS, respectively  and standardized such that it followed a normal distribution with mean 0 and standard deviation 1  were used for calculating the SCZ PRS for 22q11.2DS patients with STEROID v0.1.1 (mass package (polr function) [ordinal package (clmm function) [2 was added in longitudinal analysis. SIPS data acquired at the timepoint in which the age was the closest to the median age of the 22q11.2DS cohort  were considered in cross-sectional analysis. Violation of proportional odds assumption was tested with Brant test that allows to assess whether the observed deviations from ordinal logistic regression model are larger than what could be attributed to chance alone using R/brant package [p > 0.05). To correct for multiple testing, false discovery rate (FDR) and Bonferroni correction were applied for cross-sectional and longitudinal analysis, respectively, accounting for 19 tests  was 14.51 with median 14.67 and SD 2.16; and the mean age of the older sub-group  was 21.72 with median 20.39 and SD 4.81. In longitudinal analysis, data of 76 individuals from 111 timepoints and 49 individuals from 102 timepoints were available for the younger and for the older sub-group, respectively.To test for an association between 19 SIPS variables (ordered factor dependent variables) and SCZ PRS, we used ordinal logistic regression implemented in R/unction)  for crosunction)  for long package . The pro package ). Given lme4 package (lmer function) [2, IQ test type/MRI scanner, and first three genotype PCs as covariates. For cognition, we first tested full scale IQ independently and then conducted a sub-analysis by considering verbal IQ and performance IQ measurements. For hippocampus, we carried out a secondary, region of interest analysis and considered fourteen volumetric hippocampal subfield variables. FDR correction [Linear regression was used to test for an association between SCZ PRS and IQ and MRI variables cross-sectionally using data from the timepoint in which the age was the closest to the median age of the cohort . SCZ PRS was regressed on age, IQ test type/MRI scanner, and first three genotype PCs. Next, we used longitudinal data for associating cognitive function and brain imaging variables captured at multiple timepoints with SCZ PRS. To this end, we used linear mixed modelling implemented in R/unction)  to accourrection  was applTo account for the five individuals with smaller 1.5 Mb deletion, we conducted a sensitivity analysis for all neuropsychiatric phenotypes considering 3 Mb deletion carriers only. While the test statistics show attenuation due to reduced power, these followed the same trend as in the main analyses  aged from 6 to 44 years  with a molecularly confirmed diagnosis of 22q11.2DS were included in the present study. Each participant was phenotypically assessed at an average of 2.2 timepoints (range = 1–5). Mean age at first visit was 15 years (SD = 6.66) and mean time interval between visits was 3.8 years . These results were in concordance with estimates when considering a lower number of SCZ cases determined with stricter SCZ diagnostic criteria  and “ideational richness”  within negative symptoms, and “dysphoric mood”  within general symptoms, but none of the items within the positive symptoms category, showed a significant association with SCZ PRS , than “avolition” of negative symptoms that only survived FDR 10% correction  within positive symptoms, “social anhedonia” , and “occupational functioning”  within negative symptoms, “impairment in personal hygiene”  within disorganized symptoms, and “dysphoric mood”  and “impaired intolerance to normal stress”  within general symptoms across time  0.11, P = 0.02, Table β = –0.25, SE 0.11, P = 0.02), rather than underperformance in visuospatial intellectual abilities  and longitudinal analysis , and right CA1 region  in modifying the endophenotypes of 22q11.2DS. It augments the derailment of the developmental trajectories for psychosis-risk symptoms, cognition, and hippocampal volume. While it remains to be investigated how the genetically predisposed molecular deviations captured in a PRS explicitly result in 22q11.2DS manifestation, our findings suggest the following implications.Firstly, we identified that the higher polygenic burden for SCZ was chiefly associated with the negative and the general rather than with the positive symptoms course, contradicting previous findings identified for 22q11.2 deletion carriers , 21, yetSecondly, as expected, the polygenic burden for SCZ amplified cognitive decline among 22q11.2DS patients. This result recapitulates the negative genetic correlation between cognition and SCZ –79 as weThirdly, the association between SCZ PRS and bilateral hippocampal volume reduction points out that the reduced hippocampal volume present in 22q11.2 patients , 84–86 iWe are fully aware of the limitations of the present study starting by its modest sample size. Our estimates display large confidence intervals that should be interpreted with caution. We could not compare the endophenotypic trajectories with those measured in healthy controls as the latter group consists of first-degree siblings in the Swiss 22q11.2 cohort. Nevertheless, the accrued comprehensive brain imaging and clinical measurements of deletion carriers over an extensive time period provides a unique dataset for association testing and for delineating longitudinal phenotype-specific trajectories rather than cross-sectional snapshot associations, while being at the same time sub-optimal for replication studies. To increase power in capturing the genetic liability for SCZ, we considered the latest wave of SCZ GWAS that utilized the largest case-control dataset to date , 21, 25,In conclusion, our findings support the notion that the phenotypic expression resulting from a large-effect genetic variant is modified by second lower-effect SNVs. We demonstrate here that the higher polygenic burden for SCZ is associated with a worsened symptoms course, cognitive decline, and hippocampal volume reduction in 22q11.2 deletion carriers. These results substantiate that a genome-wide integrative analysis of allelic variation across the entire frequency spectrum is required to fully comprehend the genetic architecture and phenotypic variability of developmental disorders caused by a high-effect genetic variant , 92–94. Supplementary Information


Genome-wide association studies (GWAS) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWAS in admixed populations, such as the need to correctly adjust for population stratification to balance type I error with statistical power. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes we find that modeling HetLanc in its absence reduces statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs HetLanc is not large enough for GWAS to benefit from modeling heterogeneity. Cohort and biobank studies are growing to reflect this diversity7, and a variety of techniques exist which incorporate populations of different continental ancestries into GWAS8. However, while admixture has been an important factor in other steps in the disease mapping process, such as fine-mapping9 and estimating heritability11, individuals of mixed ancestries  have largely been left out of traditional association studies. GWAS performed in admixed populations have greater power for discovery compared to similar sized GWAS in homogeneous populations13. Thus, excluding admixed individuals from association studies will not only increase health disparities, but will also disadvantage other populations. To prevent this exclusion, approaches to association studies have been developed specifically for admixed populations17. However, the impact of HetLanc  on GWAS methods remains underexplored. Of particular interest are recently admixed populations, defined as less than 20 generations of mixture between two ancestrally distinct populations. In such populations, the admixture process creates mosaic genomes comprised of chromosomal segments originating from each of the ancestral populations . Local ancestry segments are much larger than linkage disequilibrium (LD) blocks18; thus, LD patterns within each local ancestry block of an admixed genome reflect LD patterns of the ancestral population. Similarly, allele frequency estimates from segments of a particular local ancestry are expected to reflect allele frequencies of the ancestral population. Variation in local ancestry across the genome leads to variability in global ancestry . Such variability in local and global ancestries could pose a problem to GWAS in admixed populations as genetic ancestries are often correlated with socio-economic factors that also impact disease risk, thus yielding false positives in studies that do not properly correct for genetic ancestries. Because local and global ancestry are only weakly correlated19, complete control of confounding due to admixture requires conditioning on both local and global ancestry20. However, the success of admixture mapping indicates that the possibility of losing power due to over-correction for local ancestry stratification is serious21.The success of genomics in disease studies depends on our ability to incorporate diverse populations into large-scale genome-wide association studies (GWAS)3 in the absence of HetLanc while Tractor has higher power when causal effects are different by more than 60%15. However, the full impact of cross-ancestry genetic architecture on GWAS power in admixed populations remains underexplored.GWAS in admixed populations is typically performed either using a statistical test that ignores local ancestry altogether  or using a test that explicitly allows for HetLanc . The former provides superior power in the absence of HetLanc with the latter having great potential for discovery in its presence. However, these methods’ relative statistical power for discovery depends on the cross-ancestry genetic architecture of the trait: i.e., which variants are causal and what are those variants’ ancestry-specific frequencies, causal effects, and linkage disequilibrium patterns. For example, existing studies have found that ATT can yield a 25% increase in power over TractorIn this work, we use simulations to perform a comprehensive evaluation quantifying the impact of these factors on the power of GWAS approaches in admixed populations. We provide guidelines for when to use each test as a function of cross-ancestry genetic architecture. Elements of cross-ancestry genetic architecture such as allele frequencies, global ancestry ratios, and LD are known or can be calculated in advance of a GWAS to determine which of our simulation results apply in each case. Using extensive simulations, we find that ATT should be preferred when HetLanc is small or non-existent. We quantify the extent of HetLanc and the ancestry-specific allele frequency differences required for Tractor to overcome the extra degree of freedom penalty. We further validate our results using the African-European admixed population in the UK Biobank (UKBB). By examining the HetLanc of significant SNPs in the UKBB, we can understand how often it rises to a level that impacts the power of traditional GWAS.22. A more common form of HetLanc is observed at non-causal SNPs that tag the causal effect in a differential manner across ancestries. Differential linkage disequilibrium by local ancestry at these non-causal SNPs (tagged SNPs) can cause HetLanc even when allele frequencies and causal effect sizes are the same across ancestries. The extent to which HetLanc exists and the magnitude of these differences in effect sizes are yet uncertain38. However, the existence of HetLanc plays an important role in the power of GWAS methods to detect associations. Consider the example in i , β1 and β2 are ancestry-specific marginal effect sizes of the SNP, l is the vector of local ancestry counts at the locus, el is the effect size of l, α is a matrix of other covariates such as global ancestry, α, and ϵ is random environmental noise.We start with a formal definition for a full model relating genotype, phenotype, and ancestry for a single causal SNP:29. ADM tests for association between local ancestry and disease status in cases and controls or in a case-only fashion. This association is achieved by contrasting local ancestry deviation with expectations from per-individual global ancestry proportions. Therefore, ADM is often underpowered especially in situations in which allele frequency at the causal variant is similar across ancestral populations30. Genotype association testing is traditionally performed using an Armitage trend test (ATT). ATT tests for association between genotypes and disease status while correcting for global ancestry to account for stratification31. However, neither ADM nor ATT take advantage of the full disease association signal in admixed individuals. SNP1, SUM, and MIX are examples of association tests that combine local ancestry and genotype information. SNP1 regresses out local ancestry in addition to global ancestry to control for fine-scale population structure. This approach helps control for fine-scale population stratification but may remove the signal contained in local ancestry information32. SUM33 combines the SNP114 and ADM statistics into a 2 degree of freedom test. MIX14 is a case-control test that incorporates SNP and local ancestry information into a single degree of freedom test. Most recently Tractor15 conditions the effect size of each SNP on its local ancestry followed by a joint test allowing for different effects on different ancestral backgrounds. This step builds the possibility of HetLanc explicitly into the model, which may be particularly important when SNPs are negatively correlated across ancestries34. Other varieties of tests have also been developed using different types of frameworks, most notably BMIX34 which leverages a Bayesian approach to reduce multiple testing burden. These statistics have been compared at length35. However, existing comparisons do not consider HetLanc, nor do they thoroughly discuss allele frequency differences across ancestries.Variability across local and global ancestries has been leveraged in various statistical approaches for disease mapping in admixed populations. One of the first methods developed for association was admixture mapping (ADM)−2% for every association test. The maximum value was ≤ 0.6% for every association test. We next calculate power to detect SNPs with an odds ratio of OR1 = OR2 = 1.2 (see −16) than for SUM (power = 33.44%), ADM (power = 0.039%), or Tractor (power = 31.89%). Thus, we find that while these association statistics are all well controlled, power does substantially differ between them. In the absence of both HetLanc and allele frequency difference, 1 degree of freedom SNP association tests outperform 2 degree of freedom tests.First, we use simulations to compare type I error and power for each association statistic in  1.2 see . We find 1.2 see . The powMAF1 = 0.5 and MAF2 range from 0.0 to 1.0 with a 0.1 increment and plot power over MAF2  impact the power of ATT and Tractor in the case where true causal effect sizes are the same. We investigate the impact of varying MAF in each ancestry independently. Using our 10,000 simulated admixed individuals from the previous experiment, we simulate 1,000 quantitative phenotypes with a single causal SNP see . First, ver MAF2 . We findtability , S2, S3,ty  are included in the analysis. However, the overall power attenuation due to the inclusion of global ancestry is small compared to that due to local ancestry; thus, we shift our focus back to considering local ancestry-specific effects on power.While high levels of allele frequency differentiation drastically decrease the power of Tractor, ATT also has a smaller decrease in power at high levels of allele frequency differentiation, from 95% at equal allele frequencies to 93% when MAFF2 = 0.5 . This dehet), MAF difference, and percent difference in power is complex , BMI, cholesterol, erythrocyte count, HDL, height, LDL, leukocyte count, lymphocyte count, monocyte count, platelet count, and triglycerides. Then, we line up the histogram of these empirically derived values of Rhet with the heatmap. We find that for 69.3% of all significant SNPs, the empirical value for Rhet is within this  window. While this is an estimate, we predict the true difference between estimated marginal effect sizes might be smaller than indicated by these empirical values because Tractor is more powerful in identifying SNPs with heterogenous effect sizes. This result reflects previous findings that causal effects are similar across ancestries within admixed populations22. Due to this similarity in effect size, most of the significant SNPs sit in the center of the heatmap. This region of this heatmap predicts ATT will have more power than Tractor. We can compare the mean adjusted chi-square statistic of the SNPs found to be significant in this case. We find that this statistic is significantly larger for the ATT method than the Tractor method  or when effect sizes are in opposing directions (Rhet < −0.5), we find that Tractor out-performs ATT. For African-European admixed individuals in the UKBB, most significant loci have both small measured HetLanc and MAF differences. We find that across 12 quantitative traits, ATT finds more significant independent loci than Tractor. Furthermore, ATT has smaller p-values for the loci that it shares with Tractor. This suggests that on smaller datasets more of the shared loci would be found by ATT than by Tractor.In this work, we seek to understand the impact that estimated allelic effect-size heterogeneity by ancestry (HetLanc) has on the power of GWAS in admixed populations. Our main goal is to find whether conditioning disease mapping on local ancestry leads to an increase or decrease in power. We find that HetLanc and MAF differences are the two most important factors when considering various methods for disease mapping in admixed populations. We consider two association statistics - ATT, which ignores local ancestry, and Tractor, which conditions effect sizes on local ancestry. We find that in cases with small or absent levels of HetLanc, ATT is more powerful than Tractor in simulations of quantitative traits. This conclusion holds across a variety of global ancestry proportions and SNP heritabilities. We find that as MAF differentiation between ancestries increases, so does the improvement of power of ATT compared to Tractor. At high HetLanc  for 10,000 individuals where θ is the expected global ancestry proportion  of ancestry 2, and σ;2 is the variance of global ancestry in the population . We use σ;2 = 0.125 to reflect the variance of global ancestry found in the UK Biobank admixed population. α is coerced to 0 if it is negative and 1 if it’s larger than 1.Draw global ancestry proportions l ~ Binomial, where l represents the local ancestry count of ancestry 2.For each individual, draw a local ancestry count gi ~ Binomial, where fi represents the minor allele frequency at local ancestry i.For each local ancestry, draw a genotype We simulate phenotypes using the following procedure:Standardize genotypes so that they have a mean 0 and variance 1.β1, β2 calculate Varg = (β1g1 + β2g2)2, where Varg is the genetic variance component of the phenotypes.Given some effect sizes h2, calculate Vare is the environmental variance component of the phenotypes. This comes from the equation Given some heritability ϵ ~  where ϵ is the random noise to add to the phenotype to represent environmental variables.For each individual, draw Repeat for 1,000 replicates.We simulate genotypes using the following procedure:For our real data analysis, we used genotypes from the UK Biobank. We limited our study to participants with admixed African-European ancestry. Overall, we had 4,327 individuals with an average of 58.9% African and 41.1% European ancestry. We used the imputed genotypes for these individuals with a total of 16,584,433 SNPs. We calculated the top 10 PCs for these genotypes and added these PCs as covariates to all analyses as our global ancestry component. The phenotypes we used are also from the UK Biobank, and include aspartate transferase enzyme (AST), BMI, cholesterol, erythrocyte count, HDL, height, LDL, leukocyte count, lymphocyte count, monocyte count, platelet count, and triglycerides. We log transformed AST, BMI, HDL, leukocyte count, lymphocyte count, monocyte count, platelet count, and triglycerides to analyze all 12 traits as quantitative, continuous traits. We standardized all genotypes and phenotypes to be mean centered at 0.0 and have a standardized variance of 1.https://github.com/rachelmester/AdmixedAssociation. ATT is a 1 degree of freedom association test that uses the model β = 0 against a null hypothesis that includes global ancestry (α). Tractor is a two degree of freedom association test that uses the model β1 = 0 and β2 = 0 against a null hypothesis that includes local ancestry (l) and global ancestry (α). They can both be adapted to be used on case-control phenotypes or to adjust for additional covariates such as age and sex. For our simulations, we used global ancestry proportions as our measure of global ancestry (α) and did not need to adjust for any additional covariates such as age and sex as we did not model those factors in our simulations. For power calculations, we use a standard significance threshold of p-value < 5 × 10−8.We calculate the ATT and Tractor association tests on simulated data using scripts that can be found on https://kangchenghou.github.io/admix-kit/index.html) to perform the ATT and Tractor association tests on this data and extracted the p-values. In order to determine significant SNPs, we filtered for SNPs with a standard p-value of < 5 × 10−8. For the Manhattan plots, we plot all SNPs with a p-value < 1 × 10−5 for computational plotting purposes. For the Venn diagrams, in order to determine whether SNPs were part of the same loci, we grouped SNPs within a 500kB radius, and kept the most significant SNP from each test (ATT and Tractor) in that locus.We used admix-kit (In this work, we introduce several key measures that we use to compare our results. The formal definitions of these are the following:Percent difference in power:Adjusted chi square: We take the p-value from a χ2 statistic and convert it back to a Supplement 1


N = 329,671) and 13 neuropsychiatric traits . We found strong positive genetic correlations between DCC and lifetime cannabis use , alcohol use disorder identification test (AUDIT) total score , AUDIT_C score , AUDIT_P score , and strong negative genetic correlations between DCC and neuroticism , major depressed diseases , and insomnia . In the cross-trait meta-analysis, we identified 6, 5, 1, 1, 2, 31, and 27 shared loci between DCC and Insomnia, LCU, AUDIT_T, AUDIT_C, AUDIT_P, neuroticism, and MDD, respectively, which were mainly enriched in bone marrow, lymph node, cervix, uterine, lung, and thyroid gland tissues, T cell receptor signaling pathway, antigen receptor-mediated signaling pathway, and epigenetic pathways. A large of TWAS-significant associations were identified in tissues that are part of the nervous system, digestive system, and exo-/endocrine system. Our findings further indicated a causal influence of liability to DCC on LCU and low risk of MDD . We also observed that AUDIT_T and AUDIT_C were causally related to DCC . Meanwhile, insomnia and MDD had a causal negative influence on DCC . These findings provided evidence for the shared genetic basis and causality between DCC and neuropsychiatric diseases, and advance our understanding of the shared genetic mechanisms underlying their associations, as well as assisting with making recommendations for clinical works or health education.Coffee or caffeine consumption has been associated with neuropsychiatric disorders, implying a shared etiology. However, whether these associations reflect causality remains largely unknown. To understand the genetic structure of the association between decaffeinated coffee consumption (DCC) and neuropsychiatric traits, we examined the genetic correlation, causality, and shared genetic structure between DCC and neuropsychiatric traits using linkage disequilibrium score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis in large GWAS Consortia for coffee consumption ( Coffee, one of the most widely consumed beverages in the world, has received considerable attention on health consequences. Many different physiologically active compounds including caffeine, polyphenols, niacin, and others  have beeWith the increased availability of summary data from large-scale genome-wide association studies (GWAS) and methods estimating genome-wide genetic correlation by using only GWAS summary statistics –10, we cTherefore, we aimed to investigate the genetic correlation, causality, and shared genetic structure between decaffeinated coffee consumption and neuropsychiatric traits including insomnia, neuroticism, Alzheimer's disease (AD), BIP, anorexia nervosa (AN), MDD, SCZ, AUDIT score, ADHD, LCU, and amyotrophic lateral sclerosis (ALS) by using linkage disequilibrium (LD) score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis with GWAS summary statistics, to provide more knowledge about the shared molecular mechanism of them and detect the potential genetic liability or strong evidence for a causal relationship.http://www.nealelab.is/uk-biobank/. The GWAS summary statistics of insomnia and neuroticism items scores are available at https://ctg.cncr.nl/software/summary_statistics. We also could download GWAS summary statistics of AD , BIP , AN , MDD , SCZ , and AUDIT  from The Psychiatric Genomics Consortium (PGC) (Available online: http://www.med.unc.edu/pgc). Particularly, we selected three GWAS analysis results for AUDIT performed with AUDIT total score, AUDIT_C score, and AUDIT_P score. GWAS data on ADHD  was derived from the meta-analyses by PGC and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) released in June 2017. The GWAS summary statistics for LCU  and ALS  were downloaded from the International Cannabis Consortium  and Project MinE, an international collaboration of investigators aiming to unravel the genetic basis of ALS, respectively (Available online: http://databrowser.projectmine.com/). For all the meta-analyses of GWAS included in this study, we only focused on the data generated from the analysis of individuals from Europe. The original GWAS studies can be referred to as the publications presented in The workflow of our analysis was shown in To evaluate the genetic correlation between DCC and neuropsychiatric traits, we conducted a post-GWAS genome-wide genetic correlation analysis by LD score regression using LDSC software , 9. The Het and SHom. We used SHet as the main statistic in consideration of the heterogeneity of different traits in our study. SNPs with PSHet < 5 × 10−8 and trait-specific P < 0.01 were thought to have effects on both traits.To identify shared genetic loci between traits pair that showed significant genetic correlations in LD score regression analysis, we performed a genome-wide cross-trait meta-analysis using CPASSOC . This meTo make the identified regions more precise, we conducted the fine-mapping credible set analysis. We extracted variants within 500 kb of the index SNP and identified a 99% credible set of causal variants at each of the shared loci between DCC and neuropsychiatric traits using the Bayesian likelihood fine-mapping algorithm . This meWe used R “coloc” package to perform genetic colocalization analysis to detect whether the two traits we are interested in share common genetic variants in a given region. For each locus, we extracted variants within 500 kb of the index SNP. The probability H4 was calculated in this method and the co-localized loci were defined with a probability >0.5 –33.To calculate the tissue-specific gene enrichment and find out tissue of enriched expression of shared genes between DCC and neuropsychiatric traits, we performed a tissue enrichment analysis using the Tissue Enrich package , 35. TheWe used the WebGestalt tool to assess the over-represented enrichment of the identified shared gene from a cross-trait meta-analysis in Reactome pathways and GO biological processes , 37. BenP < 1 × 10−5 from the GWAS summary statistics for interested exposure as the genetic instruments, and selected independent genetic instruments . Considering the potential horizontal pleiotropy and heterogeneity, we used the intercept term in MR Egger regression to test a significant horizontal pleiotropy and used the IVW method and MR-Egger regression to detect heterogeneity. MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) method was also used as sensitivity analysis. We performed the MR analysis step by step as follows   as the p follows . First, P-value was corrected by the false discovery rate (FDR) and Benjamin–Hochberg procedure, and FDR <0.05 was considered significant.By using the FUSION software package based on 48 GTEx (version 7) tissue expression reference weights, we performed a TWAS to identify gene expression in specific tissues for each trait . The P-vP = 8.40 × 10−19), AUDIT_T , AUDIT_C , AUDIT_P , and weak positive genetic correlation between DCC and SCZ  (P = 7.27 × 10−05), MDD , and insomnia . No significant genetic correlations were observed of DCC with AD, ALS, ADHD, BIP, and AN  . In addi, and AN .P < 5 × 10−08 and single-trait P < 0.05. In this analysis, we identified 73 independent loci totally, 32 of which were not genome-wide significant in single-trait GWAS of both DCC and neuropsychiatric traits , neuroticism (Pmeta = 2.04 × 10−16), LCU (Pmeta = 1.05 × 10−19), AUDIT_T (Pmeta = 4.12 × 10−23), AUDIT_C (Pmeta = 6.87 × 10−22), AUDIT_P (Pmeta = 2.53 × 10−24), and MDD (Pmeta = 1.89 × 10−16). And rs13107325 was also the strongest signal observed for insomnia, LCU, AUDIT_T, AUDIT_C, and AUDIT_P. This locus was mapped to the protein-coding gene SLC39A8, which encodes for ZIP8, a divalent metal ion transporter. The encoded protein is glycosylated and found in the plasma membrane and mitochondria, and functions in the cellular import of zinc at the onset of inflammation (Pmeta = 1.20 × 10−08), neuroticism (Pmeta = 1.68 × 10−10), and MDD (Pmeta = 1.17 × 10−10) in the cross-trait meta-analysis. This locus was close to gene TCF4, which encodes a helix-loop-helix transcription factor widely expressed throughout the body and during neural development and plays an important role in nervous system development. Moreover, polymorphisms in TCF4 have been associated with many psychiatric and neurological conditions  was identified in the genome-wide cross-trait meta-analysis of DCC with insomnia . CELF4 was expressed primarily in excitatory neurons and encoded RNA-binding protein involved in pre- and postsynaptic neurotransmission, which played a vital role in human brain development and regulation of synaptic function . This gene encoded a cell adhesion protein which is a member of the immunoglobulin superfamily. The encoded protein is involved in cell-to-cell interactions as well as cell-matrix interactions during development and differentiation and played a role in the development of the nervous system by regulating neurogenesis, neurite outgrowth, and cell migration, as well as in immune surveillance by the expansion of immune cells . Most of ZNFs encoded the proteins that are transcription factors that function by binding to specific DNA sequences and mediating protein-protein interactions. Zinc finger genes have been reported to be associated with psychiatric disorders . Histones play a central role in transcription regulation, DNA repair, DNA replication, and chromosomal stability; these gene set also affects epigenetic pathways. The overlapped loci rs13107325 was the third strongest signal for DCC and MDD. The next strongest signal observed on Butyrophilin (BTN) family genes was another common finding in the genome-wide cross-trait meta-analysis of DCC and MDD . Among its related pathways are Butyrophilin (BTN) family interactions and Class I MHC-mediated antigen processing and presentation , isorders . The proelopment . OR2B6 eTo identify the tissue of enriched expression of shared genes between DCC and neuropsychiatric traits, we performed a tissue-specific enrichment analysis using RNA-Seq data from the Human Protein Atlas (HPA) and GTEx. A total of 13 tissues were found with an enriched expression of shared genes between DCC and neuroticism, in addition to six tissues for MDD. The mainly enriched tissues contained bone marrow, lymph node, cervix, uterine, cerebellar cortex, lung, tonsil, and thyroid gland, and main part of the nervous system, exo-/endocrine system, and hemic and immune system. We also found nine and eight enriched tissues identified for shared genes of DCC and insomnia, and LCU, respectively. Tissues like the cervix, uterine, tonsil, appendix, lung, lymph node, and thyroid gland were enriched. Meanwhile, shared genes of DCC and AUDIT were enriched in two tissues, uterine cervix, and lung .The GO analysis showed that shared genes between DCC and neuroticism or MDD were significantly enriched in several shared biological processes, such as nucleosome organization, protein-DNA complex assembly, T cell receptor signaling pathway, and antigen receptor-mediated signaling pathway . AdditioIn fine-mapping analysis, we got lists of credible set SNPs shown in P-value <1 in the MR-PRESSO outlier test (P = 9.06 × 10−5) and 1.27 , respectively. In the exploration of the causal effect of neuropsychiatric traits on DCC, we observed suggestive associations of higher genetically predicted AUDIT_T and AUDIT_C with DCC. For 1-SD increase in genetically predicted AUDIT_T and AUDIT_C, the OR was 1.83  and 1.80 , respectively. Meanwhile, our results suggested that genetically predicted insomnia and MDD were negatively associated with DCC . Besides, we found that there was no significant evidence for a causal relationship between DCC and other neuropsychiatric traits  and identified common variants underlying these associations. Functional enrichment analysis was performed to suggest functions of the mapped genes. In addition, our bidirectional MR analysis suggested that DCC had a significant causal effect on an increased risk of LCU and a protective effect on MDD risk. Higher AUDIT_T and AUDIT_C may increase habitual DCC, but insomnia and MDD may decrease habitual DCC. These results advance our understanding of the common genetic structure between DCC and neuropsychiatric traits and reveal the causality between these traits, which demonstrated the shared etiologies and mechanisms in co-occurring DCC and neuropsychiatric traits.Our cross-trait meta-analysis could capture the significant signals from both DCC and neuropsychiatric traits associated with GWAS summary statistics with great power. We identified 31 shared loci between DCC and neuroticism, 27 shared loci between DCC and MDD, six shared loci between DCC and insomnia, five shared loci between DCC and LCU, two shared loci between DCC and AUDIT_P, 1 shared locus between DCC and AUDIT_T, and 1 shared locus between DCC and AUDIT_C. Among them, 32 loci were not genome-wide significant in single-trait GWAS of both DCC and neuropsychiatric traits. Our results highlighted the important overlapped gene SLC39A8 (index SNP: rs13107325) identified by the cross-trait meta-analysis of seven genetically correlated trait pairs. Furthermore, our colocation analysis also showed that this locus had a great probability of containing shared causal variants of DCC and LCU (89%), as well as DCC and AUDIT (99%). SLC39A8 encodes a member of the SLC39 family of metal-ion transporters, which is glycosylated and found in the plasma membrane and mitochondria and involved in the cellular transport of zinc, the modulation of which could affect microglial inflammatory responses . Studiesvia their zinc finger domain, followed by transcriptional repression via the KRAB-box , regulates the expression of cell adhesion molecules to control neuronal positioning during brain development . PolymorKRAB-box . BTNs arnd INF-γ . BTN3A ind INF-γ , 58, as nd INF-γ . Taken tOur functional enrichment analysis provided several enriched tissues and biological pathways of the shared gene between DCC and neuropsychiatric traits involved. Tissue enrichment analysis showed that shared genes were enriched in nervous tissues (cerebellar cortex), exo-/endocrine tissues (thyroid gland), or hemic and immune tissues , and lung. These findings provide clues for further study to investigate the potential common genetics in these enriched tissues. Reactome enrichment of HATs acetylate histones, HDACs deacetylate histones, HDMs demethylate histones, DNA methylation, and developmental biology together suggested the effects of both genetic and epigenetics on DCC/MDD and DCC/neuroticism. A study has revealed the shared pathway including DNA methylation between MDD and Insomnia . InhibitIn single-trait TWAS, large significant associations were identified in the brain, nerve, esophagus, colon, thyroid, testis, and adipose tissue, which are part of the nervous system, digestive system, and exo-/endocrine System. Our results suggested that neuropsychiatric traits and DCC were regulated by a complex biological network. Among these associations, two gene-tissue pairs  were overlapped in TWAS of DCC and MDD. This suggested that studies need to pay more attention to the roles of the cerebellum region in DCC and MDD.From bidirectional MR analysis, our findings indicated a causal influence of liability to DCC on LCU and low risk of MDD. The effects of coffee are commonly attributed to coffee's caffeine content. However, a study with 2,232 middle-aged men in Finland found no association between intake of caffeine and depression . A recenHowever, some limitations existed. First, lack of data on the amounts of compounds like chlorogenic acids present in the decaffeinated coffee regularly consumed by study participants. But qualitative research still can provide strong evidence to some extent. Second, the GWAS data from meta-analysis studies have great power but reduces the homogeneity. Fortunately, the method CPASSOC can deal with population structure and cryptic relatedness.In conclusion, we found strong genetic correlations between DCC and neuropsychiatric disorders and identified shared common variants underlying these associations, suggesting a common genetic influence across these traits. Functional analysis showed epigenetic, developmental biology, or immune-related pathways in DCC and MDD or neuroticism, providing new directions in common biology process studies. MR analysis found strong evidence for a causal relationship between DCC and six neuropsychiatric traits . These findings advance our understanding of the shared genetic mechanisms and causality underlying their associations, assisting with making recommendations for clinical works or health education.The original contributions presented in the study are included in the article/BY performed the statistical analysis and wrote the manuscript. All authors designed the study, helped interpret the data, reviewed and edited the final paper, and approved the submission.The study was supported by grants from the Peking University Start-up Grant (BMU2018YJ002), the High-performance Computing Platform of Peking University. The funding organization had no role in the preparation of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N = 61294–91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed‐effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome‐covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed‐effects model  and a random effects model . Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual–covariate interactions, suggesting that covariate traits  typically interact with unmodelled variables, rather than a genome‐wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22%  of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic–environment interactions.Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene–environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank ( A significant G–C interaction means that the additive genetic component for symptoms of depression (G), which has been estimated internal to the data, varies with respect to a covariate trait (C)  between 2006 and 2010 .The SNP‐based heritability of depSympt is 8.5% , which is comparable to the SNP‐based heritability of depression, where estimates range from 5.5% to 11.2% depending on the definition of depression used  scores based on the International Physical Activity Questionnaire, which assesses the frequency, intensity, and duration of exercise in three categories: walking, moderate exercise, and vigorous exercise. Four variables of summed MET minutes per week were analysed: all activities , and the separate categories of walking , moderate exercise (MET mod), and vigorous exercise (MET vig). Other covariate traits from the baseline assessment were TDI, average sleep duration (sleep), neuroticism score, and pack years of smoking (smoking). Five biomarkers of low‐density lipoprotein (LDL) cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides, C‐reactive protein (CRP) and vitamin D were analysed. All biomarkers except for LDL were log‐transformed (see SM section 1.2). From the MHQ, a continuous variable summarising reported childhood trauma was analysed   is the simplest MRNM. In theory, the MRNM can incorporate multiple covariate traits, and so multiple G–C interactions, for the outcome trait via a multivariate mixed model. However, the computational burden increases with the number of covariates included  and the covariate trait (C), not explained by their respective fixed effects models, is partitioned into polygenic and residual random components. For the outcome trait, these polygenic and residual random effects can be a function of C, allowing heterogeneity of the polygenic and residual variance components for Y across C, thereby incorporating G–C and R–C interactions into the model. This random effects model can be written as follows:i: (1) α0i (β0i) is a random effect defining the random polygenic intercept for Yi (Ci), (2) τ0i (ε0i) is a random effect defining the random residual intercept for Yi (Ci), (3) α1i is a random effect capturing the interaction of the polygenic component for Yi with Ci (the G–C interaction/polygenic‐covariate interaction), and 4. τ1i is a random effect capturing the trend in the phenotypic variability of Yi across Ci that is not explained by the measured genetic variables . These random effects are random variables, characterised by a population multivariate normal distribution with mean zero, and a covariance matrix requiring estimation. The MRNM is therefore parameterised by estimating the covariance matrix between the random effects, which represent sources of (co)variation for Y and C within the population (see SM equation 5). Genetic  correlation between the two traits is included in the model by estimating the covariance parameters between the genetic  random effects for Y and for C. This is an advantage of the MRNM because not accounting for trait correlations can lead to inflation in the strength of the interactions  selected for inclusion . This is done for both the outcome trait  and the covariate traits (see SM section 1.2). Second, using a bivariate random effects model, the standardised residual variation in outcome  and population structure using the first 15 principal components. We also adjust for stressful and traumatic events in adulthood, due to their likely impact on depressive symptoms. SM section 1.2 details the complete list of fixed effects variables used for each depSympt‐covariate trait combination.Except for principal components, continuous fixed effects variables were allowed to have a nonlinear relationship with depSympt and the covariate traits by using fractional polynomials (FPs) .Covariate traits were analysed in five groups, each with a different sample size based on the missingness of covariate traits Figure . This ad2.8p value (p<0.05/17≈0.003) providing evidence that interactions explain a nonzero proportion of outcome variance.For each (fixed effects adjusted and standardised) covariate trait, we use MRNMs . A sensitivity analysis was performed by refitting the MRNMs using a rank‐based inverse normal transformation (RINT) of depSympt. Applying this transformation can control the type I error rate when the assumption of normality is violated , providing evidence that expected current symptoms of depression vary with these covariate traits. The effect sizes vary widely, ranging from 0.38% of the variability in depSympt explained by the HDL biomarker, to 21.55% of the variability in depSympt explained by neuroticism score. When FPs are considered, which allow covariate traits to have a nonlinear relationship with the expected value of depSympt, the effect of LDL on expected depSympt also becomes statistically significant, although the proportion of variability in depSympt explained is low (0.04%). The proportion of phenotypic variability in depSympt explained by each covariate trait does not greatly change between the main effects and the FP linear models, with average sleep duration having the largest absolute increase from 0.87% in the main effects model, to 2.24% in the FP model  can be found in Figures p values below the Bonferroni‐corrected significance level, which are presented in Table For each of the 17 covariate traits considered, MRNMs with and without interactions were run for depSympt, and evidence of G–C and/or R–C interactions was assessed using LRTs. A total of 11 of the 17 covariate traits had To assess if these covariate traits modulate the polygenic component and/or the residual component of depression symptoms , we use a measure of interaction strength: the proportion of variability in depSympt explained by an interaction effect  for waist to hip ratio, to 2.98%  for childhood trauma. These results show that a small, but significant, proportion of the residual variability in depSympt is modulated by the following covariate traits: neuroticism, childhood trauma, average sleep duration, BMI, waist circumference, smoking, waist‐to‐hip ratio, summed MET minutes per week walking and TDI.For the G–C interactions, only average sleep duration had a 95% CI that excluded zero Figure . This poFor the 11 covariates with significant interactions, R–C and G–C interactions account for only a small proportion of the variation in depSympt, and significant results are primarily driven by R–C interactions, not G–C interactions. These results imply that covariate traits exert more influence on depressive symptoms through the modulation of residual (unmodelled) effects compared to polygenic effects.We note the presence of negative variance component estimates, which are possible within random‐effects models including the MRNM , or with other omics‐type data (such as the transcriptome).Individually these R–C interactions are small, but cumulatively they could explain a large proportion of the variability in depressive symptoms. Random noise is not useful for prediction and further research to explain the residual heteroscedasticity is warranted. A potential route could be incorporating an environmental similarity matrix into the MRNM and looking for ‘exposome' effects by utilising shared environmental information , to estimates that were below zero (indicating no interaction).This is not the first study to identify a significant gene–sleep interaction for depression. A twin study by Watson et al.  found thAn initially surprising result is that this MRNM analysis does not support a nonzero G–C interaction effect for childhood trauma, despite G–C interactions of childhood trauma with depression previously having been identified  but we cannot exclude interactions with other biomarkers. Additionally, the modifying effect of covariate traits on depressive symptoms were considered in separate models. A joint model for interactions may provide a better fit, however, the computational burden and the required sample size for robust parameter estimation will increase. All statistical models require assumptions about variables to be analysed. We analysed a continuous measure of depression, as required by the MRNM, and chose to use a composite measure previously constructed and validated as highly correlated with MDD diagnosis. Other options would have been to take raw scores for numbers of depression symptoms reported, or the ordinal measure of presence/absence of the two core depression symptoms , as analysed in other genome‐wide association studies , suggesting that any modulation of the polygenic effects on depressive symptoms by the explored environmental variables is limited. Genome‐wide G–C interactions do not play a major role in the aetiology of depressive symptoms, and therefore, personalised lifestyle interventions using SNP profiles are not required.Cathryn Lewis sits on the Myriad Neuroscience Scientific Advisory Board. The other authors declare no conflicts of interest.https://data.broadinstitute.org/alkesgroup/LDSCORE/w_hm3.snplist.bz2LDSC HapMap 3 SNP‐list: https://sites.google.com/site/honglee0707/mtg2mtg2 software: Supporting information.Click here for additional data file.


The case-control study is a widely used method for investigating the genetic landscape of binary traits. However, the health-related outcome or disease status of participants in long-term, prospective cohort studies such as the UK Biobank are subject to change. Here, we develop an approach for the genetic association study leveraging disease liabilities computed from a deep patient phenotyping framework (AI-based liability). Analyzing 44 common traits in 261,807 participants from the UK Biobank, we identified novel loci compared to the conventional case-control (CC) association studies. Our results showed that combining liability scores with CC status was more powerful than the CC-GWAS in detecting independent genetic loci across different diseases. This boost in statistical power was further reflected in increased SNP-based heritability estimates. Moreover, polygenic risk scores calculated from AI-based liabilities better identified newly diagnosed cases in the 2022 release of the UK Biobank that served as controls in the 2019 version (6.2% percentile rank increase on average). These findings demonstrate the utility of deep neural networks that are able to model disease liabilities from high-dimensional phenotypic data in large-scale population cohorts. Our pipeline of genome-wide association studies with disease liabilities can be applied to other biobanks with rich phenotype and genotype data. The UK Biobank (UKBB) database has genotype data for about 500,000 participants and their socio-demographic, lifestyle and health-related phenotypes4. It provides a cohort of unparalleled size and scope, allowing the studies of human genetics research6. Over the past 15 years, genome-wide association studies (GWAS) of continuous and binary traits have provided new insights into the genetic architecture of a wide range of complex diseases5. However, while large-scale genotyping helps identify and localize common-to-rare variant signals, the proportion of phenotypic variance captured is limited7. In addition, recent studies have reported that the heritability estimates 9. This gap also labeled as “missing heritability” is substantial and can be due to multiple factors, such as sample size, omitting rare and structural variants, as well as gene-environment interactions10.The advent of biobanks has greatly reduced the cost and difficulty of collecting large genome-wide data13. Time-to-event analyses are computationally expensive and alternative approaches have focused on integrating family history to capture the underlying disease liability. Genome-wide association study by proxy (GWAX) where individuals who reported cases within family members are included as cases themselves can identify new risk loci14. GWAX has been particularly popular in the study of Alzheimer’s disease where 46,828 proxy-cases in addition to 2,447 diagnosed cases in the UKBB contributed to increased statistical power16. Going beyond a binary phenotype, more refined models have used family history to derive a continuous genetic liability phenotype where CC and family history configurations are differentiated more appropriately17.To unravel genetics’ role in disease studies, researchers typically compare patients with the disease (the cases) to individuals who are not (the controls). However, patient cohorts may progress beyond a dichotomous setting of “diseased”and “non-diseased”. Healthy patients can become diseased years down the line and GWAS studies explicitly modelling age-at-onset showed an increase in statistical power compared to case-control (CC) studies19. Our study seeks to improve genetic prediction with deep learning-based estimates of disease liability probabilities that leverage joint semantic and structure-based embeddings of phenotypes in the UKBB. To the best of our knowledge, this is the first demonstration of leveraging deep neural networks to present disease phenotypes quantitatively for genetic studies of complex traits.Although previous studies have identified thousands of disease-associated loci, researchers have yet to deeply explore the joint contribution of family history, clinical measures, and lifestyle factors to model disease liabilities. Machine learning techniques have advanced in recent years, allowing to reveal patterns in massive, high-dimensional datasets and capturing non-linear relationships20 to recognize a comprehensive set of phenotype labels in UK Biobank. Based on this framework, we present a methodology to improve the genetic prediction of complex traits using a continuous representation of the phenotypes based on individual-level data in the UKBB. Applied to 44 complex traits, our method improved statistical power in GWASs as demonstrated through higher heritability estimates, and higher polygenic risk score (PRS) for newly diagnosed cases. We also found many putative disease susceptibility variants for phenotypes such as essential hypertension, breast neoplasm, uterine leiomyoma, hypothyroidism, type 2 diabetes, alcoholism, myocardial infarction, and brain cancer compared to traditional CC studies.Recently, we developed a framework POPDx20 to extract multiple layers of information, including electronic medical records, laboratory tests, physical measurements, answered questionnaires and interviews about the socio-demographic status, lifestyle, and family history of 392,246 individuals in the UK Biobank. The overview of our artificial intelligence (AI)-based genetic analysis is illustrated in 20 for patient phenotyping in UK Biobank. The performance of the patient phenotyping is shown in We used a deep learning framework21. According to earlier research, CABLES2 has a crucial function in colorectal carcinogenesis22. 23. ZC3H4 (zinc finger CCCH domain-containing protein 4) is a candidate gene associated with susceptibility to type 2 diabetes (T2D) identified via the combined approach alone. zinc finger protein. Previous research indicates ZC3H4 having an essential role in mediating non-coding transcription in human cells24. It was also previously reported the association ZC3H4 and uncontrolled eating25. Additional results from the GWAS studies are provided in the We leveraged AI-based disease liabilities to conduct GWAS analyses and discovered independent risk loci for a wide range of complex and polygenic traits. Three methods of patient characterization were used to identify disease-associated risk loci: (1) cases and controls defined by the in-patient diagnosis record of the UK Biobank dataset (CC), 2) AI-based disease liabilities, (3) a combination of AI-based disease liabilities and case-control status (combined CC-liability).  AI-based28. For breast cancer , rs13281615 (PCAT1) is highly associated in both AI-based and combined approach. It was not identified in our CC analysis but was reported in other breast cancer studies30. Some SNPs such as rs10889338 (DOCK7), rs12916 (HMGCR), rs10455872 (LPA), rs15285 (LPL), rs507666 (ABO), and rs11057830 (SCARB1) were identified to be associated with hypercholesterolemia using the CC-liability method. These SNPs were found to be significant in the other GWAS studies of the UKBB31.We performed an in-depth investigation of our GWAS results (Supplementary Table GWAS Significant Loci). Most of the significant loci identified by the liability and CC-liability methods have been either reported in our CC analysis or other previous studies. Some SNPs are missed in the CC study but could be validated in the literature. For example, rs6544713 (ABCG8) and rs12190287 (TCF21) identified in our combined approach for coronary atherosclerosis were previously found to be associated with the risk for coronary artery disease32  to infer phecodes and calculate disease liability probabilities (section POPDx). Genetic data analyses were restricted to unrelated white British individuals that passed additional genetic quality control (QC) filters according to the UKBB . Furthermore, samples were excluded if the genetically inferred sex did not match the self-reported sex and if they had withdrawn their consent. Analyses were performed on autosomal genotype data provided by the UK Biobank with MAF > 0.01 . Further quality filters and overlapping with external datasets  could reduce this number to 577,395 markers. All reported genetic analyses are for 261,807 unrelated white British individuals on geq 577,395 genetic markers. Sex-specific traits were analysed on 142,844 females and 118,963 males, respectively.The UKBB is a cohort with rich phenotype data including International Classification of Diseases, Tenth Revision (ICD-10) codes from which we could derive phecodes38. In this study, we retrieved 44 specific phecodes that associate with more than 10,000 patients to represent 44 common diseases from the 2019 download of phenotype data. To obtain diagnostic labels for all the individuals in the UK Biobank, we mapped International Classification of Diseases Tenth Revision (ICD-10) codes to phecodes40. From the 2019 downloads, we received phenotype data from the UK Biobank. Again, we obtained an updated release of phenotype data in early 2022. On average, participants have more ICD-10 codes in their records. For each of the 44 complex traits, we have identified the individuals that are no longer part of the control group. For PRS validation, the new cases are compared to the controls that do not have the diagnostic code for both data lookups.ICD billing codes are commonly utilized to select individuals from large observational datasets such as the UK Biobank. Also, cases of multiple ICD codes are often put together as a group of patients to study a specific disease. Phecodes, combinations of relevant ICD codes used to describe phenotypes, have worked well in clinical research20. We trained the POPDx framework on the phenotypic and health-related patient data. The 1538 phecodes as gold standard labels correspond to 12,803 ICD-10 diagnosis codes from UKBB. We trained and tested POPDx on all the individuals based on the 2019 version of phenotype data.POPDx is an existing AI-based methodology that outputs the phenotype liability probabilities for each UK Biobank participantwww.ldak.org41). Prior regression, phenotypes were corrected for sex, age, age2, and PCs 1–40. The analysis was performed on 649,644 genetic markers with minor allele frequencies (MAF) ≥ 0.01.We performed GWAS analyses on all 44 complex traits for three phenotype definitions: i) original case-control status, ii) POPDx probability, iii) combined case-POPDx phenotype. GWAS were performed using linear regression as implemented in the LDAK software . Clumping was performed using LDAK and as reference panel we used the genotypes of 20,000 randomly selected individuals among the included 261,807 samples.We performed clumping on SNPs passing the genome-wide significance threshold of 5e-8 to obtain independent SNP associations  of all the protein-coding genes and non-coding RNA genes. The SNPs need to lie within the target genes to be annotated.We mapped the SNPs to genes under the guidance of a comprehensive reference annotation (GENCODE GRCh37 release 42)32. BLD-LDAK estimates We calculated SNP heritability ions see  using thFor each of the 261,807 individuals included in our analyses we calculated polygenic risk scores (PRS) for each trait. Per trait, two PRS were assigned to each person - the first based on original case-control status and the second based on POPDx probabilities. Since our aim was to compare which of the two phenotype definitions assigns a lower percentile rank  to emerging disease cases, estimation of per-predictor effect sizes and phenotype prediction were performed on the same individuals. Information as to whether an individual turns into a case is not available to either phenotype definition, and thus comparison at a later time point constitutes an independent validation analysis.33. First, we estimated per-predictor heritabilities which requires the summary statistic of the trait and phenotype definition of interest  following the LDAK-Bolt-Predict procedure as implemented in the LDAK software v5.2rest see , a SNP trest see  and the Per trait, we compared the two phenotype definitions by concentrating on the newly emerged cases (control in the earlier release and case in the new release). To this end, we first ranked the individuals relative to their PRS in the entire dataset. Each new case would thus have two ranks, once calculated from POPDx probabilities and once from the original case-control classification. For each trait, the difference in ranks of the new cases were then compared through a two-sided Wilcoxon signed-rank test.Supplement 1Supplement 2