Some of the compounds involved in sexual steroids’ metabolic pathways  might accumulate in the adipose tissue of intact male pigs after sexual maturity, resulting in boar taint (BT). With a perspective future ban on surgical castration, in pig population where early slaughtering is not a viable option, the exploitation of genomic selection procedures might prevent this off-odor and off-flavor. The accuracy provided by the use of genomic information was equal or higher than the one obtained using pedigree information. This indicates that genomic selection could be beneficial for the traits investigated as it minimizes the need to collect individual measures of BT compounds. Several genomic regions, each with a small effect on BT compound concentrations, were identified. Genes previously associated with BT, reproduction traits, and fat metabolism are located in those genomic regions. Detection of candidate genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be ascribed to the pig line investigated, selected for ham quality and not for lean growth.With a perspective future ban on surgical castration in Europe, selecting pigs with reduced ability to accumulate boar taint (BT) compounds  in their tissues seems a promising strategy. BT compound concentrations were quantified in the adipose tissue of 1075 boars genotyped at 29,844 SNPs. Traditional and SNP-based breeding values were estimated using pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP), respectively. Heritabilities for BT compounds were moderate (0.30–0.52). The accuracies of GBLUP and PBLUP were significantly different for androstenone , but comparable for indole and skatole . Several SNP windows, each explaining a small percentage of the variance of BT compound concentrations, were identified in a genome-wide association study (GWAS). A total of 18 candidate genes previously associated with BT (MX1), reproduction traits , and fat metabolism  were identified in the post-GWAS analysis. The large number of genes related to fat metabolism might be explained by the relationship between sexual steroid levels and fat deposition and be partially ascribed to the pig line investigated, which is selected for ham quality and not for lean growth. Boar taint (BT) is a “urine/faecal-like”, unpleasant flavor and odor released during cooking or heating of pork meat, and it occurs in male pigs after puberty due to the accumulation in the adipose tissue of lipophilic components such as androstenone, skatole, and indole . The accSurgical castration of male piglets is normally performed within the first seven days of life of the animal and is largely used to prevent BT . Also, cSlaughtering intact male pigs before sexual maturity can prevent BT, but it is not a viable option for pigs intended for protected designation of origin (PDO) dry-cured ham production, since the minimum age at slaughter required by product specifications to meet quality standards is after sexual maturity ,14.Selective breeding might provide an effective and long-term approach to control BT, supporting the transition towards the gradual elimination of surgical castration in the pig production industry. According to Ducro-Steverink , selectiAccording to Lervik et al. , estimatRoutine BT compound phenotyping, required by traditional pedigree-based selection, is challenging and expensive, particularly when the number of selection candidates is large. In this context, the implementation of genomic selection procedures is greatly beneficial . In someSus scrofa chromosomes  revealed different quantitative trait loci (QTL) and candidate genes related to androstenone, skatole, and indole. QTL affecting BT compounds have been found on most e et al. ); this he et al. . Detectie et al. , wherease et al. ,32.Several candidate genes, resulting from differential gene expression studies, were identified as encoding receptors and enzymes involved in the metabolism of androstenone, skatole, and indole in the liver and testis tissues . MoreoveSo far, studies have focused on pig lines selected for lean growth, but since there is a well-known relationship between steroid hormone levels and adipogenesis , it is oHence, the aims of the study were: (1) to evaluate the accuracy of traditional pedigree-based and genomic-enabled methods in the prediction of BT compound concentrations in a purebred sire line selected for carcass and ham quality traits; (2) to identify QTL regions and candidate genes associated with BT compound concentrations, through a single-trait GWAS approach.The study was conducted on 1075 intact young boars of the C21 Goland sire line , selected to enhance carcass and ham quality traits and widely used in Italy to generate commercial heavy pigs intended for PDO dry-cured ham production. For BT compounds quantification, a subcutaneous adipose tissue sample of approximately 0.5 g was collected from the neck area of each young boar  using a biopsy device ; sampleReversed-phase HPLC with fluorescence detection was used to quantify BT compound concentrations. Sample preparation was performed following the method by Rostellato et al. . Two difThe frequency distribution of BT compound concentrations, measured as ng/g for indolic compounds and as µg/g for androstenone, were skewed and then normalized through log-transformation of the original data.A saliva sample was collected from each of the 1075 pigs and sent to GeneSeek Inc. laboratory  for DNA extraction and genotyping. Animals were genotyped using a high-density SNP chip (GGP Porcine 50K). All the animals were successfully genotyped. SNP markers with a minor allele frequency (MAF) lower than 1% and call rate lower than 95% were discarded. Missing genotypes were then imputed using the FImpute software  exploitiy is the vector of phenotypes ; µ is the average; 1 is an all-ones vector; b is the random effect of day of BT compound analysis (91 levels); X is an incidence matrix relating y to b; a is a vector of random animal additive genetic effects assumed to be N for BT compound concentrations were predicted with pedigree-based BLUP (PBLUP) and genomic BLUP (GBLUP) methods. Variance components, heritability and pedigree-based EBVs were estimated using the Average Information Restricted Maximum Likelihood method implemented in the BLUPf90+ suite of programs  with the matrix) . The dayadjy) using PREDICTF90 from the BLUPf90+ suite of programs [adjy) and Accuracy of PBLUP and GBLUP models was assessed in a 5-fold cross-validation. Firstly, phenotypes  were adjusted for the random effect of day of BT compound analysis (programs  using moams test  was usedA genome-wide association study (GWAS) was performed to test the association between the phenotype of each BT compound and SNP genotypes. The analyses were performed following a GBLUP GWAS procedure ,49 usingG was obtained as in VanRaden [Z is a matrix of gene content  adjusted for allele frequencies; m is the number of SNPs, and ip is the minor allele frequency of i-th SNP.VanRaden :(3)G=ZZ′p-values were obtained as described in Aguilar et al. [p-value = 0.05/m, thus applying a stringent Bonferroni correction.The allele substitution effect of each SNP was then derived from GEBV using postGSf90 :(4)g^=12r et al. :(5)p−vale et al. . In ordeExplained percentage of genetic variance was calculated using subsequent windows of adjacent SNPs within a distance of 0.4 Mb, which is the average haplotype block size in commercial pig lines . ManhattFor each BT compound, the 0.4 Mb windows that explained 0.5% or more of the total genetic variance were selected were considered as putative QTL and selected for candidate genes search. The threshold of 0.5% was chosen based on the literature ,55 and ohttps://www.ensembl.org/info/docs/tools/vep/index.html; accessed on 16 September 2022) was used to determine the effect of SNPs on genes, transcripts, protein sequence and regulatory regions. Genes located within 200 kb upstream and downstream of the lead SNP were considered putative candidate genes associated with the trait. The candidate genes nearby the genome-wide significant SNPs were identified by Ensembl database  using the gene annotation information of Sus scrofa reference genome (Sscrofa11.1), which is publicly available at NCBI. The genomic locations were downloaded from PigQTL database  and genes within the candidate region were considered as candidate genes.The Ensembl VEP (variant effect predictor) tool (2 ± SE): 0.30 ± 0.08 for AND, 0.43 ± 0.09 for IND, 0.51 ± 0.09 for SKA. The estimates indicates that moderate to high genetic gain could be obtained through selection against boar taint compounds in the evaluated population. These estimates are comparable to those reported in the literature: in Landrace, Large White, Duroc, and Pietrain breeds, estimates of the genetic parameters ranged between 0.39 and 0.73 for AND and between 0.32 and 0.69 for SKA [For each BT compound, the number of animals with phenotypic data, and the sample mean and standard deviation before and after log-transformation are reported in  for SKA ; in the  for SKA . Even th for SKA ; 0.39 [1 for SKA ) or sligadj on EBV (β), ranged between 0.89 (AND) and 1.00 (SKA), whereas GEBV tended to be inflated (β < 0.89). The accuracies  and 0.58% and 0.50% (SSC4) of the genetic variance, for IND and SKA, respectively.Myxovirus resistance 1, involved in the resistance to influenza virus in pigs [Considering the genomic regions associated with either AND, IND, or SKA , we iden in pigs ) has bee in pigs . In the The large majority of the GWAS performed on BT ,65,72,73Transcription Factor 21) is involved in testis growth and development in chicken [sex determining factor (SRY) and both have similar effects on Sertoli cell differentiation and embryonic testis development in rats [Nucleoside diphosphate kinase homolog 5, also known as NME/NM23 family member 5 gene) is considered the best candidate for sperm concentration in Landrace [Prostaglandin F Receptor) is known to affect sexual behavior and to stimulate testicular hormone secretion in boars [Potassium voltage-gated channel subfamily Q member 1) gene have been reported in association with poor semen traits or male infertility [In our study, several genes associated with sexual development  were identified. TCF21  has been reported to be associated with female fertility, and, in particular, with oocyte fecundity. It is a gonadotropin-responsive gene expressed in both oocytes and the thymic medulla in mice. Mice carrying an extra allele of UMODL1 have drastically reduced litter sizes and increased premature infertility [One of the identified genes  encodes for an aspartic lysosomal proteinase. Transcription of this gene is initiated from several sites, including one which is a start site for an estrogen-regulated transcript. The gene has been associated with growth, fat deposition, and production traits in the Italian Large White pig population [Synaptotagmin 8 (SYT8) encodes a member of the synaptotagmin protein family, which includes membrane proteins involved in neurotransmission and hormone secretion. The Ca2+ non-binding isoform syt 8 has been detected in insulin-secreting cells. Glucose stimulated expression of SYT8 in human islets and SYT8 knock-down have shown to impair insulin release [Several candidate genes  related to adipocyte biogenesis, lipid metabolic processes and deposition, and to variations in fat thickness and intramuscular fat were also identified. CTSD  gene has been associated with fat percentage, lean meat percentage, loin eye area, thorax–waist backfat thickness, and average backfat thickness in pigs [Cluster of differentiation 81) is a beige adipocyte progenitor cell marker and is required for de novo beige fat biogenesis; “beiging” of white adipose tissue is an adaptive process initiated as a response to cold temperatures in which numerous mitochondria-enriched thermogenic adipocytes with multi-locular lipid droplets  emerge within white adipose tissue [Early growth response 1) influences adipocyte differentiation and was found to have an anti-adipogenic action [Neuronal growth regulator 1 (NEGR1), Mitoguardin 1 , and GIPC PDZ domain containing family member 2 (GIPC2), are responsible for genetic predisposition to common forms of obesity in human, especially those resulting from deposition of subcutaneous fat, and they have been associated to subcutaneous fat thickness in a combined human and pig study [Coiled-Coil Serine Rich Protein 1 (CCSER1) was identified as a candidate gene for backfat thickness in Chuying-black pigs [Myotubularin related protein 2) was proposed as a functional candidate gene for intramuscular fat content in GWAS and signatures of selection studies in a Duroc pig population selected for intramuscular fat content [Lipoprotein lipase (LPL) can hydrolyze the lipoprotein triacylglycerols from cycling and release free fatty acid for skeletal muscle uptake. The increased expression of the gene indicates that the ability to uptake exogenous fatty acid and synthesize triacylglycerols could be increased, which could intensify fat deposition [Myonectin, also known as Erythroferrone) is associated with iron metabolism, but is also a key player in regulating local and systemic lipid metabolism [TNNI2 , reproduction traits , and fat metabolism . The large number of candidate genes (12 out of 18) related to fat metabolism might be explained by the well-known relationship between sexual steroid levels and fat deposition and be, in part, ascribed to the pig line investigated, which is selected for ham quality and not for lean growth.


Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For a plurality of individuals with ASD, it is likely that the primary causes involve multiple common inherited variants that individually account for only small levels of variation in phenotypic outcomes. This genetic landscape creates a major challenge for detecting small but important pathogenic effects associated with ASD. To address similar challenges, separate fields of medicine have identified endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition and leveraged the study of these traits to map polygenic mechanisms and advance more personalized therapeutic strategies for complex diseases. Endophenotypes represent a distinct class of biomarkers useful for understanding genetic contributions to psychiatric and developmental disorders because they are embedded within the causal chain between genotype and clinical phenotype, and they are more proximal to the action of the gene(s) than behavioral traits. Despite their demonstrated power for guiding new understanding of complex genetic structures of clinical conditions, few endophenotypes associated with ASD have been identified and integrated into family genetic studies. In this review, we argue that advancing knowledge of the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs with measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. We also emphasize that clarification of endophenotypic traits necessarily will involve integration of transdiagnostic approaches as candidate traits likely reflect liability for multiple clinical conditions and often are agnostic to diagnostic boundaries. Multiple candidate endophenotypes associated with ASD likelihood are described, and we propose a new focus on the analysis of “endophenotype trait domains” (ETDs), or traits measured across multiple levels  along the causal pathway from genes to behavior. To inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We also present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical autistic traits, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes associated with ASD as well as overlapping neurodevelopmental disorders. Autism spectrum disorder (ASD) is both clinically and etiologically diverse. Rare inherited and de novo pathogenic variants each have been repeatedly implicated and account for up to 20% of cases , 2. For One approach to gene discovery that has proven valuable in separate fields of medicine is the identification of condition-related biological traits that can be used to decompose complex clinical phenotypes into less genetically complex trait structures. Endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition, have been used to identify polygenic mechanisms of complex conditions and advance more personalized therapeutic strategies for heart disease , 13, obeIn this paper, we argue that understanding the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs integrating measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. Given that most, if not all behavioral traits associated with ASD also are implicated in separate behaviorally defined disorders  and show wide variation in neurotypical development, it is likely that endophenotypic traits associated with ASD will cut across diagnostic boundaries, and transdiagnostic designs will be critical.Multiple candidate endophenotypes associated with ASD likelihood are described herein, though the intent is not to provide a comprehensive review , buThe concept of ETDs is drawn from the NIMH Research Domain Criteria (RDoC) that focuses on the analysis of domains of behavior from molecular to self-report levels , but extTo inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD and neurodevelopmental disorders more broadly. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We then present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical traits associated with ASD, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes contributing to ASD or associated neurodevelopmental traits.+) and without ASD (FH−), strongly predicted ASD trait severity of their autistic sibling and categorical recurrence of ASD within participants’ families . These results suggest that genetic variation underlying non-specific neurodevelopmental traits may represent background ASD susceptibilities that are inherited and non-specific and may confer additive genetic risk alongside variants conferring specific ASD liability . These non-specific traits may be detectable earlier than other ASD-related behaviors and serve as critical early targets for intervention. Together, these results suggest that expanding our endophenotype definition to account for a broader range of traits and their developmental variance is crucial.The endophenotype concept was first introduced in insect biology to describe “microscopic and internal traits” that contrast “exophenotypes”, or behaviors that are directly observable . GottesmSeveral factors have impeded progress in identifying endophenotypic traits useful for gene discovery in ASD. First, studies aimed at understanding the genetics of ASD are inherently constrained by their reliance on categorical definitions that do not have a strong grounding in biology. The diagnostic standards developed by DSM task forces and the gold-standard assessment tools used to inform ASD classifications each primarily were intended to guide reliable, differential diagnoses based on behavioral observation. Historical beliefs that behaviorally defined categories would structure research that could eventually illuminate distinct pathogenic processes for select disorders have not been borne out, likely reflecting the high level of clinical overlap across diagnostic categories and the profound heterogeneity within categories. This realization was explicitly acknowledged by the American Psychological Association prior to publication of DSM-5 when they indicated “historical aspiration of achieving diagnostic homogeneity by progressive subtyping within disorder categories no longer is sensible” .quantitatively as a function of the age or developmental level at which individuals are studied. In support of this hypothesis, multiple studies have shown that some behavioral differences seen in young siblings of autistic individuals relative to non-autistic peers are not predictive of eventual affectation status ) elopment , 76.Additional examples of the power of family study designs for advancing gene discovery related to neurodevelopmental disorders can be found in adult psychiatry. For example, as described above, BSNIP takes a unique approach to family genetic research by developing hypothesis-driven and data-driven endophenotypes associated with psychosis that cut across diagnostic boundaries, deriving multi-level endophenotype factors that separate biologically separable clusters of patients, or “biotypes” , 78. TheWhile family studies are central in the search for endophenotypic traits useful for defining etiological mechanisms associated with familial ASD , their limitations also should be considered in the context of research aimed at understanding heritable influences. Family study methods provide only necessary, but not sufficient evidence that select traits are inherited. More specifically, family study designs are not capable of quantifying the impacts of shared environments and are thus not able to directly inform estimates of heritability. Family designs that focus on traits already shown to be highly heritable or less likely to be strongly influenced by social modeling or other environmental influences  may provide greater leverage in advancing endophenotype discovery.Trait heritability is best demonstrated through twin studies examining concordance in monozygotic (MZ) and dizygotic (DZ) twins raised in the same family environments. Based on the assumption that MZ twins share approximately double the proportion of genetic material as DZ twins, greater similarity  between MZ relative to DZ pairs on select traits serves as an index of variation attributable to genetic inheritance rather than shared environment. Large-scale twin studies offer rich information on the heritability of individual traits that may directly guide analyses of candidate endophenotypes for clinical conditions or dimensions and guide interpretation of studies examining autistic individuals and their unaffected family members. For example, basic attentional and sensorimotor traits implicated in ASD each show high levels of heritability based on population twin studies and thus offer strong candidates for endophenotype discovery e.g., , 80). Im. Im80]).R2 < 0.1). Trait concordance was high in non-autistic population control MZ twins (R2 = 0.6) indicating that trait heritability estimates from non-autistic twins may not be directly translatable to ASD populations, and that environmental influences on trait outcomes and development may be profound.Twin studies have proven invaluable for developing new knowledge on the heritability of ASD and autistic traits. For example, twin studies consistently have indicated that ASD represents perhaps the most heritable behaviorally defined condition identified in the DSM –85. TheyIn summary, family and twin studies each provide critical, complementary information for establishing endophenotypic traits associated with ASD. While family designs, including trio, quad, and infant sibling methods, each can yield important new insights into traits that “run in families”, they are not able to directly index heritability of candidate traits. Instead, heritability estimates from twin studies are important for understanding inheritance patterns and parsing environmental and genetic contributions to trait outcomes. Twin studies also are limited by difficulties ascertaining sufficient samples of autistic twins and evidence that heritability estimates from non-autistic twins may not be directly applicable to autistic twins. Large-scale twin studies that characterize carefully selected continuously distributed traits in the general population represent a promising future direction. More specifically, focus on analyses of early emerging heritable traits less influenced by stochastic or environmental processes will be important for identifying endophenotypic traits sensitive to inherited genetic processes.The majority of traits examined in family studies of ASD have been measured at the behavioral level. It is important to note that endophenotypes originally were differentiated from behavioral traits based on their “internal” and “unobservable” qualities allowing them to be objectively measured and suggesting they lie more proximal to gene action , 91, 92.Despite these caveats, we propose that familial behavioral traits are important to integrate in family genetic studies because they can be leveraged to focus the search for associated “internal” traits closer to genetic substructures. Studies of core autistic traits represent important examples of how understanding familial behavioral phenotypes may guide the search for endophenotypic traits. For example, considerable evidence shows that first-degree relatives of autistic individuals present mild traits qualitatively similar to those found in their autistic family members, justifying the definition of a “broader autism phenotype” (BAP) characterized by social aloofness, pragmatic communication difficulties, and a rigid personality style , 95). NoIn principle, identifying endophenotypic traits associated with BAP behaviors and those features closer to molecular and neurodevelopmental mechanisms contributing to ASD will increase resolution for detecting etiological pathways. This process of mapping ETDs by linking behavioral traits back to their genetic origins aims to define the full chains of causality contributing to traits that may collectively constitute ASD. To illustrate how this approach may enhance resolution for detecting genetic substructures, we integrate findings from case-control and family designs assessing the neurodevelopmental substrates of multiple behavioral traits associated with ASD. Our list of candidate ETDs is not intended to be exhaustive. Instead, we focus on discrete behaviors that are highly quantitative, have been studied across multiple levels, and also have shown promise for clarifying etiological processes based on studies of their heritability, familiality in ASD, underlying biology, and potential for back-translation to model systems  studies. Despite these challenges, studies of non-human primates have provided some insight into cellular and molecular processes associated with social gaze behavior. Chen and colleagues found that non-human primates with TALEN-edited ressions . Based oressions . Studiesressions  and incrressions .B enantiomer R-Baclofen reverses deficits in social approach in BTBR mice ).2a receptor agonist and a 5HT2a receptor antagonist in dorsomedial striatum implicating selective receptor targets for drug development as well as gene expression studies focused on cognitive/behavioral rigidity in ASD  , 147. Thy in ASD –149.Studies of inhibitory control show similar potential for translation across behavioral and imaging environments in ASD studies and for back-translation in lower-order species. Using tests of antisaccades, or the ability to consistently suppress prepotent oculomotor responses, multiple fMRI studies have detailed neural networks supporting top-down control of reactive behaviors, including frontal and parietal eye fields, anterior cingulate cortex, middle frontal gyrus, and dorsal striatum . IncreasSensorimotor functions frequently are impaired in ASD affecting a range of behaviors and effector systems including oculomotor, vestibulo-motor, and skeletomotor processes , 199. SeMultiple sensorimotor behaviors, including sensory feedback guided precision manual motor behaviors and sensorimotor sequence learning, each show high levels of heritability in twin studies . Analyse+ siblings relative to FH− siblings who show differences compared to controls )The ETDs described above each were developed based on descriptions of behavioral traits associated with ASD. While we propose that “top-down” mapping of ETDs starting with behavioral traits will accelerate progress in understanding inherited pathways, it also is likely that significant advances will be generated using a “bottom-up” approach in which traits with simpler genetic structures serve as the starting point for connecting genes to behavior. This approach involves developing ETDs that leverage known genetic/molecular targets, blood-based, neurophysiological, or cellular traits to understand neurobiological processes contributing to select behavioral outcomes , 241. Mu2a receptor function in platelet aggregation. The vast majority (99%) of blood 5HT is stored in platelets ).Historically, endophenotype criteria have stipulated that ETDs must be disorder-specific, but research in psychiatry has yielded strong evidence of phenotypic overlap at multiple levels of analysis. Population studies of non-clinical samples have indicated that, in many instances, self-endorsed clinical symptoms show similar levels of heterogeneity within diagnostic categories as between diagnostic categories . ConsistIt is our position that understanding the complex pathogenic processes associated with ASD can be accelerated by shifting away from traditional case–control designs for gene discovery and instead prioritizing transdiagnostic studies that integrate dimensional endophenotypes for participant stratification. One method for leveraging candidate endophenotypes to understand mechanisms of ASD inheritance is developing ETDs, or associated endophenotypes that cut across functional units of analysis. Similar approaches  have proven powerful for identifying more biologically homogeneous subgroups of individuals, but have not yet been leveraged to understand traits associated with neurodevelopmental disorders including ASD. The quest for ETDs involved in ASD should focus on traits that are discrete, quantitative, familial, closer to gene action than behavioral traits, and translational. The dimensional and condition-agnostic nature of ETDs may accelerate gene discovery and advance understanding of heritable, polygenic processes contributing to neurodevelopmental impairments.


Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes. Robust genome-wide association study (GWAS) methods that can utilise time-to-event information such as age-of-onset will help increase power in analyses for common health outcomes. Here, the authors propose a computationally efficient time-to-event model for GWAS. Most of these GWASs have modelled the outcome as a binary case-control variable in a logistic (or linear) regression while accounting for covariates such as age, sex, and genetic principal components. However, these models are generally not suited for modelling time-to-event data, as they do not account for certain types of missing or censored data. Time-to-event models are commonly used in epidemiology and many other fields, and have proven useful for both accounting for censoring, changes in disease incidence over time (cohort effects), and age-of-onset3. Time-to-event models can also be used to estimate absolute time-dependent risk (i.e. the probability of developing the disease as a function of time) conditional on individual features, and are therefore widely used to estimate disease risk in clinical settings4.Over the last decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants associated with human diseases8, their adoption has been limited in practice. One reason is that age-of-onset (AOO) information is often not made available. However, time-to-event data is becoming more readily available as more genotyped data are being linked to health records. Another reason is that fitting these models on large data is computationally intensive. However, several computationally efficient survival analysis GWAS methods have been proposed recently for large population-scale data. These include efficient Cox regression implementations10, and an efficient frailty (random effects) model11. The frailty model inherits some of its advantages from the mixed model15, and can both account for population structure and relatedness, as well as improve statistical power when sample sizes are large. A third reason is that time-to-event models are generally not expected to provide significant gains in power for rare health outcomes16. Indeed, the performance of Cox-based regressions in a GWAS setting is poorly understood, and they have only been viewed in comparison to other Cox-based regressions or logistic regression8. Importantly, these benchmarks have focused on the proportional hazards generative model and without significant case ascertainment, which is common in GWAS. In practice, when collecting data for GWAS it is common to oversample cases to increase the effective sample size and statistical power in the genetic analyses, leading to a case-control or case-cohort study design.Although time-to-event models have been proposed for GWAS18 (LTM) have proved to be for ascertained data19, we propose ADuLT (age-dependent liability threshold) as a computationally efficient time-to-event model for GWAS, and examine how it performs in the presence of case ascertainment. ADuLT is based on the liability threshold model and is the underlying model for the recently proposed LT-FH++ method20. ADuLT accounts for age-of-onset information, as well as sex and cohort effects by personalising the thresholds used to infer the case-control status for each individual. These thresholds are personalised by using population-based cumulative incidence proportions (CIPs) for the phenotype of interest as a function of age and additional information, such as sex and birth year (to model sex and cohort effects). We examine how ADuLT compares to SPACox and standard linear regression GWAS in terms of both statistical power and computational efficiency, using both simulations and real iPSYCH data, which is a psychiatric disorder case-cohort data with a strong case ascertainment bias where cases are about 20 times more likely to be sampled22.Here we examine to what extent case ascertainment in GWAS data affects Cox regression GWAS and standard case-control GWAS. Inspired by how robust liability threshold modelsgithub.com/EmilMiP/LTFHPlus), and is made highly scalable by relying on parallelization and the R package Rcpp, which offers a seamless integration of R and C++23.With an increasing integration between biobanks and electronic health records, it is important to utilise additional information in the best way possible, and we believe that knowledge about age-of-onset will be a common and powerful piece of information to include. Finally, ADuLT is implemented in an efficient R package called LTFHPlus . The performance of a simple case-control GWAS, SPACox, and the ADuLT phenotype used as the outcome in a linear regression-based GWAS was assessed under both generative models. Sex and age or age-of-onset were simulated for 1 million individuals, each with 20,000 independent SNPs. To examine the effect of ascertainment of cases, which is common in GWAS data, similar analyses were performed where the total number of individuals was randomly downsampling from 1 million to 20,000 individuals, leaving 10,000 controls and 10,000 cases in each case ascertained dataset. The following simulation results are based on 10 replications of each parameter setup.Figure As several phenotypes are assigned to the same set of genotypes, we will consider a paired power for comparison in the following sections. This means all calculations are done within each iteration when possible, as the naїve mean value would not be able to account for one method consistently identifying more SNPs. Without case ascertainment, for both 250 and 1000 causal SNPs, and all considered population prevalences, the power of all methods are within ~3% of one another, except for one parameter setup. The parameter setup with the largest difference without case ascertainment was under the proportional hazards model and with 1000 causal SNPs, where the ADuLT phenotype had ~9% lower power and case-control status was 19% lower. Under the proportional hazards model and without case ascertainment, SPACox had the highest power for all considered prevalences, while case-control status had the lowest. Under the LTM and without case ascertainment, ADuLT obtained the highest power for all prevalences, while SPACox had the lowest power.Under the LTM, with case ascertainment, and 1000 causal SNPs, the average increase in power was 117% with ADuLT over SPACox across all prevalences considered, and it was 97% for case-control status over SPACox. With case ascertainment and 250 causal SNPs, we observed an average increase of 34% in power over SPACox with ADuLT, and a 29% increase in power with case-control status, showing that SPACox has a comparatively low power for low effect sizes.Under the proportional hazards model and with case ascertainment, we were not able to use a paired power for comparisons, as SPACox was unable to identify any genome-wide significant SNPs in 36 out of 80 simulations . The ADuLT phenotype and case-control status were unable to identify genome-wide significant SNPs in only 6 out of 80 simulations, all of which are for 1000 causal SNPs. The following comparisons are based on the average un-paired power. Under the proportional hazards model simulations with case ascertainment and 1000 causal SNPs, ADuLT had a 317% higher power than SPACox, whereas case-control status had 256% higher power than SPACox. When simulating 250 causal SNPs, ADuLT resulted in a 234% higher power and case-control status had a 193% higher power compared to SPACox. Plots of the power as a function of MAF and the true effect size can be found in Supplementary Figs. χ2-statistics for the null SNPs is reported. The null SNPs are the SNPs with an effect size of 0(i.e. no effect). The expected average of these SNPs’ χ2-statistics is 1. Plots were achieved for 250 and 1000 causal SNPs, respectively, and each plot contains results for four different lifetime prevalences, with and without case ascertainment, and for both generative models. All models were well calibrated, since no inflation of the null statistics is observed. All methods controlled for type-1 errors  at varying significance levels. No false positives were observed for any method with a significance level of 5 × 10−8. All false positive results can be found in Supplementary Data In Supplementary Figs. 25 are reported. We used 4 CPU cores for both steps, which is a conservative number of cores. The SPACox implementation does not support parallelization, which is why SPACox was run sequentially. We find that ADuLT together with a linear regression is faster than SPACox, even with only modest parallelization. Logistic regression of a binary phenotype is slower than linear regression of the same phenotype25, which means ADuLT together with a linear regression may be faster and have higher power to detect causal SNPs.The computational time for estimating the ADuLT phenotype depends solely on the number of individuals. The running time for the GWAS step depends heavily on the implementation of the GWAS method used. In Fig. 22. This supplementary information was used to analyse four psychiatric traits, namely ADHD, autism, depression, and schizophrenia. For each of these phenotypes, population-based CIPs were obtained by birth year and sex . Figure Across the four psychiatric disorders, ADuLT found 20 independent genome-wide significant associations, while case-control status found 17 and SPACox found 8. The Manhattan plots for each of the methods, each of the remaining disorders , and with and without age as a covariate can be found in Supplementary Figs. p-values for validation since they are the only way to directly compare between GWAS models. We used the largest available summary statistics from PGC34 for the four considered phenotypes, used the SNPs that were present in the iPSYCH data, and performed LD clumping (using the same settings as before) and prioritising the SNPs with the lowest p-values from the meta-analysed PGC summary statistics to keep only independent hits. Then, we took the SNPs that were genome-wide significant in the LD-clumped PGC summary statistics and plotted the p-values of the three considered methods against each other. The plot can be found in Supplementary Figure p-values for ADuLT against case-control status and SPACox p-values on the SNPs that are independent genome-wide significant from the largest available PGC summary statistics, and they are therefore not necessarily genome-wide significant for either of the methods considered. The plot shows ADuLT has a slightly lower p-value than case-control and SPACox on the external genome-wide significant SNPs.Finally, as SPACox does not provide effect sizes, we opted to plot 35, iPSYCH22, FinnGen36, or Biobank Japan37 linking electronic health records to genetic data, there is an increased incentive to develop methods that can fully utilise this supplementary information. This includes details about age-of-onset, which can be used in time-to-event analyses to improve power. In epidemiology, time-to-event analyses are usually performed with a Cox-based regression, whereas time-to-event GWAS are still relatively uncommon. This has in part been due to computational challenges of applying Cox regression to GWAS, but recent developments of efficient Cox-based regression methods such as SPACox or GATE have largely resolved this limitation11. However, the performance of Cox-based regressions for GWAS has only been viewed in comparison to other Cox-based or logistic regression8, and not when the case-control cohort is sampled with ascertainment (e.g. where cases are oversampled). Evaluating their performance in ascertained case-control cohorts is important as such datasets are very common in genetics, e.g. the iPSYCH and FinnGen data.With biobanks such as the UK biobank20, as presented here it does not incorporate information on family members. However, the main focus of this paper was to compare the ADuLT model to a computationally efficient time-to-event GWAS method, SPACox, without accounting for information such as family history. ADuLT incorporates time-to-event information into the LTM by using liability thresholds that vary with age and sex. These personalised thresholds are derived from population-based estimates of the cumulative incidence proportions. Using this information, ADuLT first estimates individual posterior genetic liabilities, which are then used as a quantitative phenotype in GWAS. This final step can be performed with any continuous outcome GWAS software, which allows for ADuLT to benefit from using advanced GWAS methods, such as linear mixed models15. The computational cost of estimating the individual posterior liabilities is negligible when compared to the computational cost of performing even a simple GWAS with linear regression.In this paper, we have examined the proportional hazards model implemented in SPACox and found that in situations where cases are ascertained , the proportional hazards based model was less powerful than a simple linear regression. We proposed the age-dependent liability threshold (ADuLT) model as an efficient and robust alternative to Cox-based time-to-event GWAS. The ADuLT model is the model underlying the recently published LT-FH++ method8. When we applied all three methods to the iPSYCH data, which has a high degree of case ascertainment, the results were in agreement with the simulation results in that SPACox identified fewer genome-wide significant variants than the case-control or the ADuLT GWASs. Therefore, for identifying significant genome-wide associated variants, a Cox-regression GWAS can have less statistical power than linear regression with case-control status or the ADuLT phenotype. As a result, we recommend using more robust GWAS methods, such as on case-control status or the ADuLT phenotype when performing GWAS in ascertained samples, which includes most case-cohort and case-control datasets.Using simulations, we compared different GWAS methods, Cox regression as implemented in SPACox and a linear regression with the ADuLT phenotype and the case-control status. As expected we found a Cox-based time-to-event GWAS to provide most power under the proportional hazards generative model, however it was closely followed by the ADuLT GWAS and case-control GWAS, especially when disease prevalence is low. Conversely, when simulating under the LTM, the ADuLT GWAS had the greatest power, followed by Cox regression and case-control GWAS. However, when considering ascertainment of cases, we found SPACox to have the lowest power of all considered methods under both generative models and for all prevalences except one . Interestingly, the simulations also show that Cox regression and ADuLT provide little or no benefit over standard case-control GWAS (linear or logistic regression) when the prevalence is small (e.g. <2%). We note that these results are in line with previously reported comparison between Cox regression and linear regression in case-cohort studies38 (IPW). IPW results in unbiased estimates, but estimating their variance  can be difficult39. Furthermore, to the best of our knowledge, IPW is currently not implemented in computationally efficient Cox regression GWAS methods (e.g. SPACox). Instead, we considered the proportional hazards implementation available in the survival package for R40. We used the proportional hazards model as generative model and ascertained the cases, but found no difference between the results from the survival package and SPACox, even with IPW. The results of the IPW can be seen in Supplementary Figs. 42. The appropriate model for either iPSYCH or UKBB is not a straightforward application of the proportional hazards model without properly considering and accounting for case ascertainment, collider bias, or immortal time bias. Instead a method tailored to each biobank and their particular sampling strategy as they each have different challenges. However, these biobank-specific challenges are often ignored in favour of a one-size-fits-all approach. With the sex and birth year stratified population-representative CIPs, ADuLT is able to account for these biases.Although Cox regression GWAS may not be robust to ascertained samples, we note that it can still improve power in population cohorts (i.e. population representative samples) for relatively prevalent outcomes. In addition, the ability to estimate unbiased individual absolute or relative risk over a time-period is also an important benefit of time-to-event models. Furthermore, several adjustments have been proposed to Cox regression when applied to ascertained data, such as inverse probability weighting24, and it can be used in connection to risk prediction45. GWAS individual-level data can also be used to build polygenic scores based on efficient penalized regression models46; a future direction of research for us is to investigate whether a penalized linear regression using the ADuLT-inferred outcome would be preferable to using a Cox-based penalized regression as implemented in e.g. snpnet-Cox47. As other possible future directions, the ADuLT model may also provide an alternative framework for examining interactions between age and genetic variants48, and provide insight into the genetics underlying disease trajectory. Like LT-FH24 and LT-FH++20, ADuLT also has the advantage that it produces quantitative posterior liabilities which can be treated as quantitative phenotypes and analysed with advanced GWAS method, such as BOLT-LMM14, fastGWA13, or REGENIE15. However, ADuLT does have some limitations. First, ADuLT requires population-representative CIPs to be available for the disorder of interest, and preferably stratified by sex and birth year. Recent efforts to make such data publicly available for all diseases is therefore of great interest49. If population-representative CIPs are not available, it is possible to use CIPs from a similar population. An example of this could be using population-representative CIPs from the danish registers in UKBB or FinnGen. However, in such a case, we would caution against fixing the full liability of a case, but rather only setting the lower limit to be the individualised threshold and letting the upper limit be infinite. Second, the assumption that early onset cases have higher disease liability may not always be true. Although age-of-onset tends to be negatively genetically correlated with case-control status, the correlation is not always very strong50. Third, the model does not account for possible interactions between genotype (or environment) with age, but exploring methods that model this relationship is a future direction. Fourth, similar to LT-FH24 and LT-FH++20, ADuLT assumes the narrow sense (additive) heritability is known a priori for the outcome of interest. These can either be obtained from literature or estimated in the data, e.g. using family-based heritability estimates51. However, we have also previously shown that the model we use is robust to misspecification of prevalence information and heritability20. Finally, in this study we did not consider downsampling of cases or ascertainment of healthy controls, which might be relevant for many genetic datasets such as the UK biobank35 or the Danish blood donor study data52.In contrast, we find ADuLT to be a computationally efficient and robust time-to-event GWAS method that in terms of statistical power performs on par with or better than Cox-regression GWAS in simulations. Moreover, using the LTM, it is possible to account for family history informationIt is not necessary to include age as a covariate in a GWAS with ADuLT as the outcome, since the effect of age is already accounted for in the phenotype itself. Traditionally, age or some related variable is included in the analysis to account for a person’s lifespan and period of being at-risk. A common way to deal with such covariates in a regression is to project them out and consider only the univariate regression with the regressed outcome and predictor. Therefore, projecting out the covariates boils down to subtracting a value from the observations. This subtraction is not necessary with the ADuLT phenotype, as the effect of age and sex can be accounted for through the sex and birth year stratified CIPs. In fact, accounting for sex and age through the CIPs provide a more nuanced way of accounting for this information, as interactions are also considered. We have also performed the GWAS analyses without sex and age as covariates for the ADuLT phenotype, but it did not differ substantially from the other analysis.53. The ADuLT phenotype is quantitative, and is therefore not suitable for logistic regression (such as SAIGE or REGENIE), although it is often bimodal and may result in a clear separation between cases and controls. A potential solution is to employ rank-based inverse normal transformation to the ADuLT phenotype54, but it may lead to a loss in power. Therefore, we recommend not using the ADuLT phenotype for GWAS when the in-sample prevalence is lower than roughly 1/80, which is in line with the advice provided by Mbatchou et al.15 for the application of mixed linear models to binary outcomes.Across the GWAS sample, the ADuLT phenotype is often bimodal in practice. The bimodality is due to the underlying truncated normal distributions leading to a gap between the resulting mean genetic estimates of the cases and controls. It has been shown that binary phenotypes when analysed with linear mixed models can suffer from inflation when the in-sample prevalence is lowAs age information becomes more readily available, we expect time-to-event methods for GWAS that make use of such information to become more common. However, the benefit of these methods may depend on how the data was collected, as well as their ability to account for other confounders. We believe ADuLT provides both a robust, computationally efficient, and a flexible approach for time-to-event analyses of common diseases and outcomes in population-scale datasets.18, and is the model underlying our previously proposed LT-FH++ method20. To estimate an individual’s genetic liability, ADuLT utilises birth year, sex, phenotype-specific age-of-onset for cases and current age for controls, as well as population-based cumulative incidences (i.e. the probability of having developed the disease at a given age). In contrast to LT-FH++, the ADuLT model does not incorporate family history as presented here. Instead, we focus on comparing ADuLT to standard time-to-event GWAS methods. ADuLT can account for cohort effects (changes in disease incidence by birth year), as well as differences by sex. This however requires population-based estimates to be available by age, sex, and birth year for each phenotype of interest.The ADuLT model is an extension of the classical LTMℓ that follows a standard normal distribution in the population. When this liability is larger than a given threshold, ℓ ≥ T, where K is the trait’s lifetime prevalence, then the individual is a case (z = 1), otherwise it is a control (z = 0). Under the ADuLT model, the trait prevalence K is substituted by the population-representative CIP stratified by sex and birth year, if this information is available. If we let si denote the sex of the ith individual and bi the birth year, then we can denote the sex and birth year stratified CIP as K. It has the interpretation of being the proportion of individuals born in year bi and of sex si that have been experienced the phenotype at time t. In Supplementary Fig. Ti is then the ith individual’s threshold. It is a function of sex, birth year, and age or age-of-onset through the CIP, however this notation is suppressed for ease of notation. Additionally, we assume that the liability can be decomposed into two independent components, a genetic component, ℓg, and an environmental component, ℓe, such that ℓ = ℓg + ℓe. The genetic liability ℓg is normally distributed with mean 0 and variance h2, where h2 denotes the trait heritability on the liability scale. The environmental component is normally distributed with mean 0 and variance 1 − h2 and independent of ℓg.The ADuLT model extends the classical LTM by allowing the threshold used to determine case-control status to depend on sex, birth year, and (if available) age-of-onset for an individual. The LTM assumes that each individual has a liability Ti and excludes a range of possible liabilities, essentially resulting in a truncated multivariate normal distribution. The exclusion of liabilities can be thought of as “lived through risk". The case status determines if the integration is above or below the threshold. For a control, the interval . The genetic liability estimated under the ADuLT model estimates the aggregated latent genetic liability and will be referred to as the ADuLT phenotype. Estimating the ADuLT phenotype does not require any genotype information, as it relies solely on phenotypic information. In Supplementary Fig. ADuLT aims to estimate an expected genetic liability. We do this by expressing the liability as a 2-dimensional normal distribution given by:t. The model commonly used for GWAS is given byλ0(t) is the baseline hazard, X denotes the covariates, γ is the covariate effects, Gj is the genotype, and β is the SNP effect. We note that a baseline hazard affects everyone, and the model can then examine the influence of covariates and the SNP in comparison to the baseline. The association test of interest is H0: β = 0 vs HA: β ≠ 0. The baseline hazard is rarely known, but a common way to perform an association test in a proportional hazards model is with a likelihood ratio test, where the unknown baseline cancel out. A partial likelihood function is commonly used, which only maximises with respect to the variable of interest, here β.A proportional hazards model is commonly used to model the time to an event for various outcomes. It models the changes in the hazard function, which can be thought of as the instantaneous chance of experiencing the event at some point in time, 57. Recently, an efficient implementation of the proportional hazards model have been presented by Bi et al.9, which allows for GWAS of large biobanks . This is achieved by use of the saddlepoint approximation. The null model (with β = 0) is determined only once, and the p-values are then efficiently calculated across the entire genome using the saddlepoint approximation on the null model’s score test statistic. A trade-off of this approach is no effect sizes are available.However, maximum likelihood estimating can be very computationally expensive, which has been a limiting factor for most previous implementations of proportional hazards and mixed effects proportional hazards GWASIn our simulations we use two generative time-to-event models, namely the Cox proportional hazards model and the age-dependent liability threshold (ADuLT) model. In the simulation results, we refer to the Cox proportional hazards as “Hazard", and the ADuLT as “Liability". We consider both of these generative models in order not to favour one time-to-event method over the other (i.e. SPACox and ADuLT GWAS). When referencing simulation results under both of these models, we will simply call them generative models, unless otherwise specified.N = 1,000,000 individuals and M = 20,000 independent SNPs. The genotypes are sampled from a binomial distribution Binom with the probability parameter set to the allele frequency (AF) of a given SNP. The AFs are sampled from a uniform distribution on the interval . We chose not to include any low-frequency variants because the power to detect them in a GWAS setting is usually very small58. SNPs are standardised using the true AF, and for the scaled SNPs, the effect sizes of causal SNPs were drawn from the normal distribution N, where C denotes the number of causal SNPs and h2 denotes the liability-scale heritability. Each SNP is simulated independently, which means we do not consider linkage disequilibrium in these simulations, in order to have a clear separation of causal and null SNPs. We used h2 = 0.5 and either C = 250 or C = 1000 causal SNPs. For each choice of generative model and number of causal SNPs, we simulate 10 genotype data sets, which results in a total of 40 genotype data sets. With the simulated genotypes and causal effect sizes, we then generated synthetic phenotypes using the two generative models (details below). For each simulated genotype data set, phenotypes are derived under both generative models and for each disease prevalence considered. We consider four prevalences, three methods, and with and without case ascertainment.Initially, genotypes are simulated for zi equal to 1 if the liability exceeds the threshold, i.e. if ℓi > T, and 0 otherwise, where T is determined by the lifetime prevalence K. For instance, a lifetime prevalence of 5% and 10% results in thresholds T = 1.64 and T = 1.28, respectively. The relationship between the age-of-onset and the liabilities above the threshold T, is given by the logistic functionK denotes the maximal attainable value, k denotes the growth rate, and x0 denotes the median age-of-onset. Using the age of controls, we know how long they have lived without being diagnosed. This information allows us to exclude liabilities, i.e. the period of risk lived through so far. For both cases and controls, the personalised thresholds are calculated as Ti = Φ(1 − CIPi), where Ti is the personalised threshold and CIPi is the CIP for individual i. The liabilities below the personalised threshold are considered for controls and the liabilities above the threshold are considered for cases. If the population-representative CIPs are stratified by birth year and sex, the full liability for cases can be fixed at Ti. Ages for controls are sampled from a uniform distribution between 10 and 90. This resulted in 90% of individuals having an age between 14 and 86.Under the LTM, we set the trait status 9. First, we simulated the censoring times, ci, for each individual i from an exponential distribution with a scale parameter of 0.15. Next, we simulated onset times59, 60 as followsλ is the event rate, Ui ~ Unif, ℓg in the LTM. The case-control status zi is then 1 if λ was chosen such that the lifetime prevalence is fixed at e.g. 1% or 5%. The simulation of onset times depends on all causal SNPs, which deviates from the simulations of onset times in the SPACox paper, where the onset times depended on a single causal SNP only. This change was made in order for the full genetic load of an individual to influence the onset times, instead of just a single SNP. Next, we calculated the CIP of the simulated event times, i.e. the fraction of cases observed before a given point in time, then the proportions were converted to the ages-of-onset (in years) using the logistic function given by Eq. . We then apply case-ascertainment by drawing a sample of 20,000 individuals with a fixed case-control ratio of 50%, with 10,000 cases and 10,000 controls, regardless of what the population prevalence is in the full population.20, the age-dependent liability threshold model can be considered a survival model. More specifically, consider the survival function ti represents age-of-onset for cases or censoring time for the ith individual, whichever happens first. Therefore, t represents time on the scale of years. The probability that an individual has not become a case for a given t is equal to the probability that the individual’s liability is below the  liability threshold Ti, which is a shorthand notation for the age-dependent threshold given by Eq.  approaches positive infinity, but may instead simply become fixed for all values Ti above some threshold, e.g. if every individual in a cohort has died and no new event are possible. This is not necessarily a problem for the interpretation as Ti may still be piece-wise differentiable, and the hazard rate for all values t above this threshold then becomes 0.If we assume that the individual liability consists of a genetic and an environmental component, mentclass2pt{minim22. Both waves have been imputed with the RICOPILI imputation pipeline61, and were then combined into a single dataset. We restricted the analysis to SNPs that passed RICOPILI quality controls for both waves, resulting in a total of 8,785,478 SNPs for the GWAS. The analysis was restricted to a group of individuals with European ancestry, which were identified by calculating a robust Mahalanobis distance based on the first 20 PCs and restricting to a log-distance below 4.562. We filtered for relatedness by removing individuals (the second one in each pair) with a KING-relatedness above 0.088. Since the iPSYCH case-cohort has a population representative subcohort and oversampled cases for six major psychiatric disorders , we restricted each analysis to the individuals in the subcohort (which is a random sample of the entire population) and the cases for the phenotype being analysed, i.e. oversampled cases from the other psychiatric disorders were not used. The final number of individuals used for the GWAS of each phenotype is presented in Supplementary Table 25 for R and SPACox GWAS was performed using the original implementation in the SPACox package for R. We used 20 PCs, sex, and imputation wave as covariates for all analyses. We included age as a covariate when analysing case-control status. Age was not included as a covariate when using the ADuLT phenotype or SPACox. For SPACox, we did not include age as a covariate, as age was the timeline used. The ADuLT phenotype inherently accounts for age, which means it is not necessary to include as a covariate. We chose not to use a mixed model approach for GWAS with case-control status or ADuLT phenotypes, as SPACox did not have a similar option for random effects.With the second wave of genotyped individuals, the iPSYCH case-cohort reached ~143,000 individuals, up from ~80,00063 was used to identify individuals and contains all 9,251,071 individuals that lived in Denmark at some point between April 2, 1968 and December 31, 2016. The Danish Civil Registration System has continually recorded information since its launch in 1968, and includes information about sex, date of birth, date of death, and date of emigration, or immigration. Each individual has a unique identifier that can be used to link information of several registers. Information on psychiatric disorders was obtained from the Danish Psychiatric Central Research Register64. It contains all admissions to psychiatric inpatient facilities since 1969 and visits to outpatient psychiatric departments and emergency departments since 1995. From 1969 to 1993, the International Classification of Diseases, eighth revision (ICD-8) was used as the diagnostic system. From 1994 onwards, the tenth revision (ICD-10) was used. The four disorders of interest were identified by the following ICD-8 and ICD-10 codes: ADHD (308.01 and F90.0), autism , depression , and schizophrenia (295.x9 excluding 295.79 and F20). The age-of-onset was defined as the age of an individual at first contact with the psychiatric care system, either inpatient, outpatient, or emergency visits. In the analyses, each individual was followed from birth, immigration, or January 1, 1969 (whichever happened last) until death, emigration, or December 31, 2016 (whichever happened first). The cumulative incidence function was estimated separately for each sex and birth year, and the Aalen-Johansen approach was used with death and emigration as competing events65.The CIPs can be interpreted as the proportion of individuals diagnosed with a certain disorder before a given age. As a result, the CIPs are population and disorder specific and can be stratified by sex and birth year. The CIPs used here were stratified by sex and birth year to account for differences in incidences between sexes and for different birth years (cohorts). The CIPs were estimated from Danish population-based registers. The Danish Civil Registration SystemFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Ndiscovery = 24,336; Nreplication = 3412) and annotation on RSN-SC and RSN-FC. We identify genes for visual network-SC that are involved in axon guidance and synaptic functioning. Genetic variation in RSN-FC impacts biological processes relevant to brain disorders that previously were only phenotypically associated with RSN-FC alterations. Correlations of the genetic components of RSNs are mostly observed within the functional domain, whereas less overlap is observed within the structural domain and between the functional and structural domains. This study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint.Functional connectivity within resting-state networks (RSN-FC) is vital for cognitive functioning. RSN-FC is heritable and partially translates to the anatomic architecture of white matter, but the genetic component of structural connections of RSNs (RSN-SC) and their potential genetic overlap with RSN-FC remain unknown. Here, we perform genome-wide association studies ( Brain regions with synchronized activity can be clustered into distinct networks. We investigate which genetic effects contribute to structural (SC) and functional (FC) connectivity within seven networks and assess their degree of shared genetic signal. Multiple genetic effects are identified and highlight relevant biological processes for brain connectivity and brain disorders related to the networks. Overlap between the genetics of network connectivity is mostly observed within the functional domain. These results advance our biological understanding of the complex functional organization of the brain and its structural underpinnings, and their relevance for the genetics of neuropsychiatry.Structural (SC) and functional connectivity (FC) are vital for healthy cognitive behavior . Brain r2H = 20–40%; Studies have shown that genetic factors significantly contribute to RSN functional connectivity  GWAS are performed on the functional connectivity strength within seven well-known RSNs , we randomly assigned 5000 subjects to a holdout set for validation. Subsetting the total sample to subjects with all neuroimaging data necessary to construct our phenotypes as described below, resulted in Nfunctional = 37,017 and Nstructural = 36,645. We only included subjects for which the projected ancestry principal component score was closest to and <6 SD from the average principal component score of the European 1000 Genomes sample based on Mahalanobis distance. This procedure has been described in previous publications by our group (The UK Biobank (UKB) is a resource with genomic and imaging data of volunteer participants . The Natur group  and the ur group . FurtherThe genotype data used in this study for discovery and validation were obtained from the UK Biobank Axiom and the UK BiLEVE Axiom arrays. These Affymetrix arrays cover 812,428 unique genetic markers and overlap 95% in SNP content. This number of SNPs was increased to 92,693,895 by imputation conducted by UKB. Variants were imputed using the Haplotype Reference Consortium and the UK10K haplotype panel as reference. We applied our in-house quality control pipeline in addition to quality control performed by UKB. This procedure excluded SNPs with low imputation scores (INFO < 0.9), low minor allele frequency (MAF < 0.005) or high missingness (>0.05), multiallelic SNPs, indels, and SNPs without unique rs-identifiers. A total of 9,380,668 SNPs passed quality control and were converted to hard call SNPs using a certainty threshold of 0.9 for further analyses.The UKB scanning protocol and processing pipeline is described in the UKB Brain Imaging Documentation . For thiIn CATO’s structural pipeline, additional preprocessing of DWI files was performed in FSL  by compuThe functional pipeline in CATO consisted of similar steps. First, we computed an rsfMRI reference image by averaging all rsfMRI frames in FSL and subsequently registered this reference image and the T1 image in FreeSurfer. Second, we parcellated the surface based on the same atlas as in the structural pipeline  and we registered the T1 parcellation to the rsfMRI image. Third, motion metrics were estimated, and time-series were corrected for covariates  by regression. Fourth, time-series were passed through bandpass filtering (frequencies 0.01–0.1) and scrubbing . Fifth, the functional connectivity matrix was computed by the Pearson’s correlation coefficient of the average signal intensity of every pair of brain regions across the frames that survived filtering.The UKB scanning and preprocessing protocol includes filters for outliers based on manual QC and an advanced classifier described elsewhere . We exclIn this study, the phenotypes of interest were functional and structural connectivity properties  within seven resting-state networks (RSNs) that previously have been identified  and are r2 < 0.1), common (MAF > 0.01), and genotyped SNPs or SNPs with very high imputation quality (INFO = 1). The first 30 principal components were used as covariates in all GWASs, together with sex, age, genotype array, Townsend deprivation index (a proxy of socio-economic status), and general neuroimaging confounders as well as FC/SC specific covariates  and retained those principal components that explained > 99% of variance. Rare variants (MAF < 0.005) and SNPs with high missingness (>5%) were excluded from GWAS and male X variants were counted as 0/1. The genome-wide significance threshold was α =  3.13 × 10−9 according to the Bonferroni correction for multiple testing.To identify common genetic variants involved in FC within each of the seven RSN, we performed seven single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) in PLINK2 . Also, fSNP-based , we investigated whether certain functional categories in the human genome were enriched for r2 > 0.6 with an independent genome-wide significant SNPs (r2 < 0.6). Annotation was subsequently performed using ANNOVAR  mapping or chromatin interaction mapping. SNPs were positionally mapped to genes if their physical distance was <10 kb. Mapping based on eQTLs relied on known associations between SNPs and the gene expression of genes within a 1-Mb window, from BRAINEAC frontal, occipital, temporal and cerebral cortex  is a web-based platform that can be used to functionally map and annotate SNPs that appear significant during GWAS. We uploaded summary statistics to FUMA if GWAS identified at least one genome-wide significant SNP. Candidate SNPs were defined as all SNPs in linkage disequilibrium (LD)  ANNOVAR , Regulom ANNOVAR  and Chro ANNOVAR . Lead SNl cortex , GTEx v8l cortex  and xQTLl cortex . Chromatl cortex ]. To resterature .k = 10 and report on the 95% credible set of the model with k causal SNPs reaching the highest probability. LDstore  molecular functions, cellular components and biological processes, and curated gene-sets from MsigDB v7.0  analysis pipeline. This pipeline consisted of three steps. (1) In the first step, genome-wide rg between 42 combinations of RSNs were estimated using LDSC [α = (0.05/42=) 1.19 × 10−3]. The summary statistics of SNP-based GWAS were used as input for LDSC. We excluded FC within the visual network, because both the λ (<1.02) and ratio (>0.20) values were out of bound for LDSC. (2) For all RSNs included in a significant bivariate rg, additional rg with global FC/SC were calculated in LDSC. (3) If one or both RSNs from the significant bivariate rg showed additional significant rg with global FC/SC, we recalculated of the genome-wide rg between the two RSNs with global FC/SC taken into account. Since such residual genome-wide rg analyses are not implemented in LDSC, we applied genomic structural equation modelling  and female percentage in discovery and holdout samples for GWAS on RSN-FC/RSN-SC. Download 10.1523/ENEURO.0242-22.2023.f1-2Extended Data Figure 1-2Figure 1-2, XLS file.Sample size and exclusion criteria for discovery and holdout sample in GWAS for RSN-FC/RSN-SC. From all subjects in the latest neuroimaging release, we randomly assigned 5000 subjects to a holdout set for validation. Genotype quality control exclusion criteria included UKB-provided relatedness, discordant sex or sex aneuploidy. Outliers include “ununsable” subjects as well as outliers based on methods advised by UKB Neuroimaging Documentation. Download 10.1523/ENEURO.0242-22.2023.f1-3Extended Data Figure 1-3Figure 1-3, TIF file.Flowchart of methods involved in the current study. Functional and structural connectivity (FC/SC) within resting-state networks (RSN) as defined by 10.1523/ENEURO.0242-22.2023.tab1-1Extended Data Table 1-1rg. phen2 = one of the two phenotypes involved in the bivariate rg. z = the conditional phenotype in partial rg, rho = local genetic correlation. rho.lower and rho.upper = 95% confidence interval of rho. r2.phen1 z and r2.phen2 z = the proportion of genetic variance explained for phen1/phen2 by z. pcor = partial correlation coefficient. ci.lower and ci.lower = 95% confidence interval for partial correlation coefficient; P = p-value for local rg. Download Table 1-1, XLS file.Bivariate and partial local genetic correlations from LAVA between summary statistics of RSN-FC/RSN-SC. Locus = locus number. chr = chromosome of locus. start = basepair start position of the locus. stop = basepair end position of the locus. n.snps = number of snps within the locus. n.pcs = number of PCs within the locus. phen1 = one of the two phenotypes involved in the bivariate rg's described above are an average correlation of genetic effects across the genome, implicating that contrasting local rg’s are possibly cancelling each other out. Running rg analysis on a locus level has the potential to uncover loci that show genetic similarity between traits. For this purpose, we adopted a three-step local rg analysis pipeline similar to the genome-wide rg analysis approach described above. All three steps were performed in LAVA  to enable LAVA to model shared variance because of sample overlap as residual covariance and consequently remove upward bias in local rg estimates  in the locus were excluded from local bivariate rg analysis to ensure interpretability and reliability. A total of 774 bivariate tests were performed across 337 loci, leading to an adjusted significance threshold of α  = (0.05/774=) 6.46 × 10−5. In the second step, RSNs that showed significant local rg were additionally tested for rg in that locus with global FC/SC. Note that if this was not possible, because global FC/SC showed no significant heritability in that locus, the local bivariate rg between RSNs could not be biased by global FC/SC. If one or both RSNs did show additional significant rg with global FC/SC, we ran a partial local rg between the RSNs conditioned on the SC-global and/or FC-global phenotype in step three. If the partial local rg between the RSNs no longer remained significant, we concluded that the initial rg was driven by global FC/SC and did not reflect genetic overlap specific for these RSNs.The genome-wide  in LAVA , a localstimates . Since op-values unaffected by overfitting and therefore split our holdout sample in a target (N = 1818) and validation set (N = 1824). In phase 1, SNP-based summary statistics  from the discovery sample together with genotype data of the target set were used to find the optimal p-value threshold. PRSice-2 uses high-resolution thresholding and clumping of genotype data, and we included the same covariates as during discovery GWAS. In phase 2, the model with the best fit from phase 1 was applied on the validation set to obtain the variance explained (R2).The variance explained in RSN-FC/RSN-SC by our GWAS findings was investigated to test the robustness of our findings using polygenic score (PGS) estimation in PRSice-2 . We applNFC = 3408 and NSC = 3412 was reserved for PGS analysis. We applied an earlier described method . Within-RSN functional connectivity strength (from now on referred to as RSN-FC) was measured as the average correlation between the activation signals of brain regions within each RSN over time. A property of within-RSN structural connections (from now on RSN-SC) was measured as the average fractional anisotropy (FA) of white matter connections between brain regions within each RSN . FA values are believed to reflect a metric of efficiency of or capacity for information transfer across white matter pathways and are sensitive to myelin content (p < (5 × 10−8/16 =) 3.13 × 10−9 located in six genomic loci  or (right) significance after correcting for the number of genes tested per trait (p < 2.65 × 10−6). The red dashed horizontal line indicates significance after an additional correction for the number of traits tested  or . Download Figure 2-1, EPS file.Manhattan plots of SNP-based (left) and gene-based (right) GWAS for RSN-FC. The light grey dashed horizontal line indicates (left) traditional GWS  or (right) significance after correcting for the number of genes tested per trait (p < 2.65 × 10−6). The red dashed horizontal line indicates significance after an additional correction for the number of traits tested  or . Download Figure 2-2, EPS file.Manhattan plots of (left) SNP-based and (right) gene-based GWAS for RSN-SC. The light grey dashed horizontal line indicates (left) traditional GWS  for FC/SC within RSNs. Phase 1 = GWAS of FC/SC within RSNs in discovery sample. Phase 2 = best p-value threshold determination in half replication sample. Phase 3 = validation of best p-value threshold PRS in other half replication sample. Threshold = best p-value threshold. PRS R2 = variance explained by the PRS. Full R2 = variance explained by the full model (including the covariates). Null R2 = variance explained by the covariates. Coefficient = regression coefficient of the model. SE = SE of regression coefficient of the model. P = p-value of the model fit. N SNPs = number of SNPs included in the model. Download Figure 2-3, XLS file.Best 10.1523/ENEURO.0242-22.2023.f2-4Extended Data Figure 2-4t statistic. P = p-value. ALT_FREQS = frequency of alternative allele. RSID_UKB = rs identifier of the SNP. MAF_UKB = MAF as provided by UKB (full file). INFO_UKB = INFO as provided by UKB (full file). Download Figure 2-4, XLS file.Association statistics for discovery lead SNPs in the holdout set for replication. Phenotype = phenotype of the discovery lead SNP. lead SNP = original UKB variant name. CHR = chromosome. POS = position. REF = reference allele. ALT = alternative allele. A1 = effect allele. OBS_CT = per SNP sample size. BETA = effect size. SE = SE of the effect size. L95 = the lower endpoint of the confidence interval. U95 = the upper endpoint of the confidence interval. TSTAT = 10.1523/ENEURO.0242-22.2023.f2-5Extended Data Figure 2-5Figure 2-5, EPS file.Probability curves of discovery lead SNPs being significant in a replication sample of increasing size [log10(N) scale]. The current replication sample size of this study is represented by the vertical grey line. FC, functional connectivity; SC, structural connectivity; SMN, somatomotor network; LN, limbic network; FPN, frontoparietal network; VN, visual network. Download 10.1523/ENEURO.0242-22.2023.f2-6Extended Data Figure 2-6p-value. Start = locus BP start position of the locus. End = stop position of the locus. nSNPs = number of unique candidate SNPs in the genomic locus, including non-GWAS-tagged SNPs (which are available in the use selected reference panel). nGWASSNPs = number of the GWAS-tagged candidate SNPs within the genomic locus. This is a subset of “nSNPs.” nIndSigSNPs = number of the independent significant SNPs in the genomic locus. IndSigSNPs = rsID of independent significant SNPs in the genomic locus. nLeadSNPs = the number of lead SNPs in the genomic locus. LeadSNPs = rsID of lead SNPs in the genomic locus. Download Figure 2-6, XLS file.Genomic loci and lead variants in the GWAS of RSN-FC/RSN-SC as identified by FUMA. uniqID = ID of the top lead variant within the locus formatted as 'chromosome:base pair position:alleles in alphabetic order'. Chr = chromosome. Pos = base pair position lead SNP. P = 10.1523/ENEURO.0242-22.2023.f2-7Extended Data Figure 2-7p-value from cerebellar volume GWAS [non-GWAS tagged SNPs (extracted from the reference panel) are “NA”]. Beta = beta from cerebellar volume GWAS [non-GWAS tagged SNPs (extracted from the reference panel) are “NA”]. SE = SE of beta from cerebellar volume GWAS . Therefore, it cannot be concluded that these biological processes are involved in the genetics of FC and SC within RSNs.We looked for convergence of the genetic signal for RSN-FC/RSN-SC onto 7252 MSigDB  pathwaysrg) were estimated using SNP-based summary statistics  in the dorsal attention network and 0.23  in the frontoparietal network.As SC strength has been noted to correlate with FC strength on the phenotypic level , we sougatistics . We obse10.1523/ENEURO.0242-22.2023.f5-1Extended Data Figure 5-1rg = global genetic correlation. SE = SE of rg; Z = z-score of rg. P = p-value for rg. h2 obs = observed scale h2 for trait 2. h2 obs SE = SE of observed scale h2 for trait 2. h2 int = single-trait LD Score regression intercept for trait 2. h2 int SE = SE of single-trait LD score regression intercept for trait 2. gcov int = cross-trait LD score regression intercept. gcov int SE = SE of cross-trait LD score regression intercept. Download Figure 5-1, XLS file.Global genetic correlations between summary statistics of RSN-FC/RSN-SC in LDSC and genomic SEM. rg’s possibly may go undetected, since genome-wide rg’s are an average of the shared genetic association signal across the genome. We examined whether such relationships between SC and FC within any given RSN exist by performing local rg analysis (rg on a locus level either (Extended Data Table 1-1).Strongly localized or opposing local analysis , althougrg analyses . This association was not driven by global FC as neither default mode network-FC nor ventral attention network-FC were genetically correlated with global FC . Note that this positive rg does not imply simultaneous functional activation of these two RSNs or their involvement in similar cognitive tasks  across RSNs, although many of these were also correlated with global SC . As none of the residual rg estimates remained significant, we conclude that global SC likely accounts for the observed genetic overlap across RSN-SC.For the structural connectivity property, we observed multiple significant genome-wide rgs when comparing RSNs within the functional domain . From these six lead SNPs, we estimated to replicate three (exact 3.99) at α = (0.05/n lead SNPs per trait) in our holdout sample given their winner’s curse corrected effect size and the sample sizes of the discovery and replication samples. Observations showed three discovery lead SNPs to be significant  to 6.89% (FC) of the Additionally, the variance that could be explained in RSN-FC/RSN-SC by polygenic scores based our GWAS associations was considered. In PRSice-2, the summary statistics of the discovery GWAS were used to find the best le sizes .Mapping the genetic components of resting state networks (RSNs) may provide insight into the etiology of brain function and brain disorders. RSNs are typically defined using functional connectivity (FC) patterns across brain regions, and structural connectivity properties (SC) between these regions correlate with FC across RSNs . The aimAPOE as a genome-wide significant gene. The default mode network is hypothesized to relate to Alzheimer’s disease through the role of default mode network-FC in memory consolidation , we detect biologically interpretable results for the default mode and limbic network-FC. For default mode network-FC, we observe lidation  and throlidation . Here, wlidation  by now alidation  and has lidation . The gendisorder . A previ pathway . NOC3L eresponse , and oneresponse . These rPAX8. PAX8 regulates multiple genes involved in the production of thyroid hormone  and identify nine genes that suggest a role for synaptic transmission in the genetics of visual network-SC. For example, ignaling , the traasticity , and SEMelopment . The SEMelopment . Visual elopment  and relaelopment , but futWhen investigating the genetic relationship between SC and FC within each RSN, we find no significant genome-wide or local genetic correlations. Since the estimation of genetic correlations is dependent on sample size and the heritability estimates of both traits , studiesSeveral limitations must be considered while interpreting our results. First, the definition of RSN used in this univariate GWAS study reduces voxel-level diffusion and functional information to one phenotype by averaging potentially variable connectivity patterns. This could unequally affect more variable higher-order RSNs compared with less variable unimodal RSNs, which would lead to differential statistical power across the RSNs studied here . This coThis study examines the genetic architecture of RSNs, structurally and functionally. We observe several genetic effects for RSNs that highlight relevant biological processes for brain connectivity and related brain disorders. The complexity of structure-function coupling within RSNs is illustrated by the observation that, despite genetic overlap of RSNs within the functional domain, genetic overlap is less apparent within the structural domain and between the functional and structural domains. Altogether, this study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint.