Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression.

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

Evaluating the Joint Effects of Recurrent Copy Number Variants and Polygenic Scores on the Risk of Psychiatric Disorders in the iPSYCH2015 Case-Cohort Sample.

The impact of rare recurrent copy number variants (rCNVs) and polygenic background attributed to common variants, on the risk of psychiatric disorders is well-established in separate studies. However, it remains unclear how polygenic background modulates the effect of rCNVs. Using the population-representative iPSYCH2015 case-cohort sample (N=96,599), we investigated the association between absolute risk of psychiatric disorders and carriage of rCNVs and polygenic scores (PGS), as well as the interaction effect between the two on disease risk. Carriers of rCNVs with higher gene constraint scores had an increased absolute risk for autism, ADHD, and schizophrenia, but not depression, whereas an increase in PGS for each respective disorder was associated with higher absolute risk across all four disorders. Similarly, elevated absolute risks were observed with the increase of both PGS and gene constraints of rCNVs except in the case of depression. In contrast to some previous case-control studies, our joint analysis of rCNV groups and PGS revealed no indication of significant interactive effect between these two factors on disease risk. Also, we found no significant interactions of PGS with any of the most common individual rCNVs, except in the case of 16p13.11 duplication, which was found to attenuate the effect of ADHD-PGS on the absolute risk of ADHD. This study advances our understanding of the interplay between rare and common important genetic risk factors for major psychiatric disorders and sheds light on the importance of population-based samples in implementing precision medicine.

Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians.

Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Population-Based Risk of Psychiatric Disorders Associated With Recurrent Copy Number Variants.

Importance: Recurrent copy number variants (rCNVs) have been associated with increased risk of psychiatric disorders in case-control studies, but their population-level impact is unknown. Objective: To provide unbiased population-based estimates of prevalence and risk associated with psychiatric disorders for rCNVs and to compare risks across outcomes, rCNV dosage type (deletions or duplications), and locus features. Design, Setting, and Participants: This genetic association study is an analysis of data from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) case-cohort sample of individuals born in Denmark in 1981-2008 and followed up until 2015, including (1) all individuals (n = 92 531) with a hospital discharge diagnosis of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, major depressive disorder (MDD), or schizophrenia spectrum disorder (SSD) and (2) a subcohort (n = 50 625) randomly drawn from the source population. Data were analyzed from January 2021 to August 2023. Exposures: Carrier status of deletions and duplications at 27 autosomal rCNV loci was determined from neonatal blood samples genotyped on single-nucleotide variant microarrays. Main Outcomes and Measures: Population-based rCNV prevalence was estimated with a survey model using finite population correction to account for oversampling of cases. Hazard ratio (HR) estimates and 95% CIs for psychiatric disorders were derived using weighted Cox proportional hazard models. Risks were compared across outcomes, dosage type, and locus features using generalized estimating equation models. Results: A total of 3547 rCNVs were identified in 64 735 individuals assigned male at birth (53.8%) and 55 512 individuals assigned female at birth (46.2%) whose age at the end of follow-up ranged from 7.0 to 34.7 years (mean, 21.8 years). Most observed increases in rCNV-associated risk for ADHD, ASD, or SSD were moderate, and risk estimates were highly correlated across these disorders. Notable exceptions included high ASD-associated risk observed for Prader-Willi/Angelman syndrome duplications (HR, 20.8; 95% CI, 7.9-55). No rCNV was associated with increased MDD risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint but not with dosage type. Comparison with published case-control and community-based studies revealed a higher prevalence of deletions and lower associated increase in risk for several rCNVs in iPSYCH2015. Conclusions and Relevance: This study found that several rCNVs were more prevalent and conferred less risk of psychiatric disorders than estimated previously. Most case-control studies overestimate rCNV-associated risk of psychiatric disorders, likely because of selection bias. In an era where genetics is increasingly being clinically applied, these results highlight the importance of population-based risk estimates for genetics-based predictions.

Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians.

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Vittrup Man-The life-history of a genetic foreigner in Neolithic Denmark.

The lethally maltreated body of Vittrup Man was deposited in a Danish bog, probably as part of a ritualised sacrifice. It happened between c. 3300 and 3100 cal years BC, i.e., during the period of the local farming-based Funnel Beaker Culture. In terms of skull morphological features, he differs from the majority of the contemporaneous farmers found in Denmark, and associates with hunter-gatherers, who inhabited Scandinavia during the previous millennia. His skeletal remains were selected for transdisciplinary analysis to reveal his life-history in terms of a population historical perspective. We report the combined results of an integrated set of genetic, isotopic, physical anthropological and archaeological analytical approaches. Strontium signature suggests a foreign birthplace that could be in Norway or Sweden. In addition, enamel oxygen isotope values indicate that as a child he lived in a colder climate, i.e., to the north of the regions inhabited by farmers. Genomic data in fact demonstrates that he is closely related to Mesolithic humans known from Norway and Sweden. Moreover, dietary stable isotope analyses on enamel and bone collagen demonstrate a fisher-hunter way of life in his childhood and a diet typical of farmers later on. Such a variable life-history is also reflected by proteomic analysis of hardened organic deposits on his teeth, indicating the consumption of forager food (seal, whale and marine fish) as well as farmer food (sheep/goat). From a dietary isotopic transect of one of his teeth it is shown that his transfer between societies of foragers and farmers took place near to the end of his teenage years.

The selection landscape and genetic legacy of ancient Eurasians.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

100 ancient genomes show repeated population turnovers in Neolithic Denmark.

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1-4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5-7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Population genomics of post-glacial western Eurasia.

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes-mainly from the Mesolithic and Neolithic periods-from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a 'great divide' genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 BP, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a 'Neolithic steppe' cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder.

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.

Metabolic signature of the pathogenic 22q11.2 deletion identifies carriers and provides insight into systemic dysregulation.

Large deletions at chromosome 22q11.2 are known to cause severe clinical conditions collectively known as 22q11.2 deletion syndrome. Notwithstanding the pathogenicity of these deletions, affected individuals are typically diagnosed in late childhood or early adolescence, and little is known of the molecular signaling cascades and biological consequences immediately downstream of the deleted genes. Here, we used targeted metabolomics to compare neonatal dried blood spot samples from 203 individuals clinically identified as carriers of a deletion at chromosome 22q11.2 with 203 unaffected individuals. A total of 173 metabolites were successfully identified and used to inform on systemic dysregulation caused by the genomic lesion and to discriminate carriers from non-carriers. We found 84 metabolites to be differentially abundant between carriers and non-carriers of the 22q11.2 deletion. A predictive model based on all 173 metabolites achieved high Accuracy (89%), Area Under the Curve (93%), F1 (88%), Positive Predictive Value (94%), and Negative Predictive Value (84%) with tyrosine and proline having the highest individual contributions to the model as well as the highest interaction strength. Targeted metabolomics provides insight into the molecular consequences possibly contributing to the pathology underlying the clinical manifestations of the 22q11 deletion and is an easily applicable approach to first-pass screening for carrier status of the 22q11 to prompt subsequent verification of the genomic diagnosis.

Population-based Risk of Psychiatric Disorders Associated with Recurrent CNVs.

Recurrent copy number variants (rCNVs) are associated with increased risk of neuropsychiatric disorders but their pathogenic population-level impact is unknown. We provide population-based estimates of rCNV-associated risk of neuropsychiatric disorders for 34 rCNVs in the iPSYCH2015 case-cohort sample (n=120,247). Most observed significant increases in rCNV-associated risk for ADHD, autism or schizophrenia were moderate (HR:1.42-5.00), and risk estimates were highly correlated across these disorders, the most notable exception being high autism-associated risk with Prader-Willi/Angelman Syndrome duplications (HR=20.8). No rCNV was associated with significant increase in depression risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint. Comparison with published rCNV studies suggests that prevalence of some rCNVs is higher, and risk of psychiatric disorders lower, than previously estimated. In an era where genetics is increasingly being clinically applied, our results highlight the importance of population-based risk estimates for genetics-based predictions.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores.

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

DanMAC5: a browser of aggregated sequence variants from 8,671 whole genome sequenced Danish individuals.

OBJECTIVES: Allele counts of sequence variants obtained by whole genome sequencing (WGS) often play a central role in interpreting the results of genetic and genomic research. However, such variant counts are not readily available for individuals in the Danish population. Here, we present a dataset with allele counts for sequence variants (single nucleotide variants (SNVs) and indels) identified from WGS of 8,671 (5,418 females) individuals from the Danish population. The data resource is based on WGS data from three independent research projects aimed at assessing genetic risk factors for cardiovascular, psychiatric, and headache disorders. To enable the sharing of information on sequence variation in Danish individuals, we created summarized statistics on allele counts from anonymized data and made them available through the European Genome-phenome Archive (EGA, https://identifiers.org/ega. DATASET: EGAD00001009756 ) and in a dedicated browser, DanMAC5 (available at www.danmac5.dk ). The summary level data and the DanMAC5 browser provide insight into the allelic spectrum of sequence variants segregating in the Danish population, which is important in variant interpretation. DATA DESCRIPTION: Three WGS datasets with an average coverage of 30x were processed independently using the same quality control pipeline. Subsequently, we summarized, filtered, and merged allele counts to create a high-quality summary level dataset of sequence variants.

Associations of psychiatric disorders with sex chromosome aneuploidies in the Danish iPSYCH2015 dataset: a case-cohort study.

BACKGROUND: Increased prevalence of mental illness has been reported in clinical studies of sex chromosome aneuploidies, but accurate population-based estimates of the prevalence and clinical detection rate of sex chromosome aneuploidies and the associated risks of psychiatric disorders are needed. In this study, we provide such estimates, valid for children and young adults of the contemporary Danish population. METHODS: We used the iPSYCH2015 case-cohort dataset, which is based on a source population of single-born individuals born in Denmark between May 1, 1981, and Dec 31, 2008. The case sample comprises all individuals from the source population with a diagnosis of any index psychiatric disorder (schizophrenia spectrum disorder, bipolar disorder, major depressive disorder, autism spectrum disorder, or ADHD) by the end of follow-up (Dec 31, 2015), registered in the hospital-based Danish Psychiatric Central Research Register. The cohort consists of individuals randomly selected from the source population, and overlaps with the case sample. Biobanked blood samples for individuals in the case and cohort samples underwent genotyping and quality-control filtering, after which we analysed microarray data to detect sex chromosome aneuploidy karyotypes (45,X, 47,XXX, 47,XXY, and 47,XYY). We estimated the population-valid prevalence of these karyotypes from the cohort sample. Weighted Cox proportional hazards models were used to estimate the risks of each index psychiatric disorder associated with each sex chromosome aneuploidy karyotype, by use of date of first hospitalisation with the index disorder in the respective case group and the cohort as outcome. The clinical detection rate was determined by comparing records of clinical diagnoses of genetic conditions from the Danish National Patient Register with sex chromosome aneuploidy karyotype determined by our study. FINDINGS: The assessed sample comprised 119 481 individuals (78 726 in the case sample and 43 326 in the cohort) who had genotyped and quality-control-filtered blood samples, including 64 533 (54%) people of gonadal male sex and 54 948 (46%) of gonadal female sex. Age during follow-up ranged from 0 to 34·7 years (mean 10·9 years [SD 3·5 years]). Information on ethnicity was not available. We identified 387 (0·3%) individuals as carriers of sex chromosome aneuploidies. The overall prevalence of sex chromosome aneuploidies was 1·5 per 1000 individuals. Each sex chromosome aneuploidy karyotype was associated with an increased risk of at least one index psychiatric disorder, with hazard ratios (HRs) of 2·20 (95% CI 1·42-3·39) for 47,XXY; 2·73 (1·25-6·00) for 47,XXX; 3·56 (1·01-12·53) for 45,X; and 4·30 (2·48-7·55) for 47,XYY. All karyotypes were associated with an increased risk of ADHD (HRs ranging from 1·99 [1·24-3·19] to 6·15 [1·63-23·19]), autism spectrum disorder (2·72 [1·72-4·32] to 8·45 [2·49-28·61]), and schizophrenia spectrum disorder (1·80 [1·15-2·80] to 4·60 [1·57-13·51]). Increased risk of major depressive disorder was found for individuals with 47,XXY (1·88 [1·07-3·33]) and 47,XYY (2·65 [1·12-5·90]), and of bipolar disorder for those with 47,XXX (4·32 [1·12-16·62]). The proportion of sex chromosome aneuploidy carriers who had been clinically diagnosed was 93% for 45,X, but lower for 47,XXY (22%), 47,XXX (15%), and 47,XYY (15%). Among carriers, the risk of diagnosis of at least one index psychiatric disorder did not significantly differ between those who had and had not been clinically diagnosed with sex chromosome aneuploidies (p=0·65). INTERPRETATION: Increased risks of psychiatric disorders associated with sex chromosome aneuploidies, combined with low rates of clinical diagnosis of sex chromosome aneuploidies, compromise the adequate provision of necessary health care and counselling to affected individuals and their families, which might be helped by increased application of genetic testing in clinical settings. FUNDING: Lundbeck Foundation and National Institutes of Health.

Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks.

Sample recruitment for research consortia, biobanks, and personal genomics companies span years, necessitating genotyping in batches, using different technologies. As marker content on genotyping arrays varies, integrating such datasets is non-trivial and its impact on haplotype estimation (phasing) and whole genome imputation, necessary steps for complex trait analysis, remains under-evaluated. Using the iPSYCH dataset, comprising 130,438 individuals, genotyped in two stages, on different arrays, we evaluated phasing and imputation performance across multiple phasing methods and data integration protocols. While phasing accuracy varied by choice of method and data integration protocol, imputation accuracy varied mostly between data integration protocols. We demonstrate an attenuation in imputation accuracy within samples of non-European origin, highlighting challenges to studying complex traits in diverse populations. Finally, imputation errors can bias association tests, reduce predictive utility of polygenic scores. Carefully optimized data integration strategies enhance accuracy and replicability of complex trait analyses in complex biobanks.

Accurate and Effective Detection of Recurrent Copy Number Variants in Large SNP Genotype Datasets.

Structural variations, including recurrent Copy Number Variants (CNVs) at specific genomic loci, have been found to be associated with increased risk of several diseases and syndromes. CNV carrier status can be determined in large collections of samples using SNP arrays and, more recently, sequencing data. Although there is some consensus among researchers about the essential steps required in such analysis (i.e., CNV calling, filtering of putative carriers, and visual validation using intensity data plots of the genomic region), standard methodologies and processes to control the quality and consistency of the results are lacking. Here, we present a comprehensive and user-friendly protocol that we have refined from our extensive research experience in the field. We cover every aspect of the analysis, from input data curation to final results. For each step, we highlight which parameters affect the analysis the most and how different settings may lead to different results. We provide a pipeline to run the complete analysis with effective (but customizable) pre-sets. We present software that we developed to better handle and filter putative CNV carriers and perform visual inspection to validate selected candidates. Finally, we describe methods to evaluate the critical sections and actions to counterbalance potential problems. The current implementation is focused on Illumina SNP array data. All the presented software is freely available and provided in a ready-to-use docker container. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: From raw intensity data files to CNV calls Basic Protocol 2: From CNV calls to validated CNV carriers. Basic Protocol 3: Quality control and quality assessment Basic Protocol 4: Install the necessary software.

Infection Polygenic Factors Account for a Small Proportion of the Relationship Between Infections and Mental Disorders.

BACKGROUND: Several recent studies have suggested a role for infections in the development of mental disorders; however, the genetic contribution to this association is understudied. METHODS: We use the iPSYCH case-cohort genotyped sample (n = 65,534) and Danish health care registry data to study the genetic association between infections and mental disorders. To test the hypothesis that these associations are due to genetic pleiotropy, we estimated the genetic correlation between infection and mental disorders. Polygenic risk scores (PRSs) were used to assess whether genetic pleiotropy of infections and mental disorders was mediated by actual infection diagnoses. RESULTS: We observed that schizophrenia, attention-deficit/hyperactivity disorder, major depressive disorder, bipolar disorder, and posttraumatic stress disorder (rg ranging between 0.18 and 0.83), but not autism spectrum disorder and anorexia nervosa, were significantly genetically correlated with infection diagnoses. PRSs for infections were associated with modest increase in risk of attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia in the iPSYCH case-cohort (hazard ratios = 1.04 to 1.10) but was not associated with risk of anorexia, autism, or bipolar disorder. Using mediation analysis, we show that infection diagnoses account for only a small proportion (6%-14%) of the risk for mental disorders conferred by infection PRSs. CONCLUSIONS: Infections and mental disorders share a modest genetic architecture. Infection PRSs can predict risk of certain mental disorders; however, this effect is moderate. Finally, recorded infections partially explain the relationship between infection PRSs and mental disorders.

A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy.

For more than half a century, Denmark has maintained population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system. We leverage this resource to reconstruct the genealogy of the entire nation based on all individuals legally residing in Denmark since 1968. We cross-reference 6,691,426 individuals with nationwide health care registers to estimate heritability and genetic correlations of 10 broad diagnostic categories involving all major organs and systems. Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134]). The average genetic correlation of each of the 10 diagnostic categories with the other nine was highest for gastrointestinal conditions (average rg = 0.567, 95% CI = [0.566, 0.567]) and lowest for urogenital conditions (average rg = 0.386, 95% CI = [0.385, 0.388]). Mental, pulmonary, gastrointestinal, and neurological conditions had similar genetic correlation profiles.