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.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments.

BACKGROUND: Symptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity. METHODS: Using data from the UK Biobank (n = 41,948-109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms. RESULTS: Corresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences. CONCLUSIONS: MDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder.

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Polygenic analyses show important differences between MDD symptoms collected using PHQ9 and CIDI-SF.

Symptoms of Major Depressive Disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire 9 (PHQ9, for current symptoms), and the Composite International Diagnostic Interview Short-Form (CIDI-SF, for lifetime worst-episode symptoms). Using data from the UKBiobank, we show that corresponding symptoms endorsed through PHQ9 and CIDI-SF have low to moderate genetic correlations (rG=0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip-structure in CIDI-SF. Through a combination of Mendelian Randomization (MR) and polygenic prediction analyses, we find that PHQ9 symptoms are more associated with traits which reflect general dysphoria, while the skip-structure in CIDI-SF allows for the identification of heterogeneity among likely MDD cases. This has important implications on factor analyses performed on their respective genetic covariance matrices for the purpose of identification of genetic factors behind MDD symptom dimensions and heterogeneity.

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.

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity.

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.

Genome-wide by Environment Interaction Study of Stressful Life Events and Hospital-Treated Depression in the iPSYCH2012 Sample.

Background: Researchers have long investigated a hypothesized interaction between genetic risk and stressful life events in the etiology of depression, but studies on the topic have yielded inconsistent results. Methods: We conducted a genome-wide by environment interaction study (GWEIS) in 18,532 patients with depression from hospital-based settings and 20,184 population controls. All individuals were drawn from the iPSYCH2012 case-cohort study, a nationally representative sample identified from Danish national registers. Information on stressful life events including family disruption, serious medical illness, death of a first-degree relative, parental disability, and child maltreatment was identified from the registers and operationalized as a time-varying count variable. Hazard ratios for main and interaction effects were estimated using Cox regressions weighted to accommodate the case-cohort design. Our replication sample included 22,880 depression cases and 50,378 controls from the UK Biobank. Results: The GWEIS in the iPSYCH2012 sample yielded three novel, genome-wide-significant (p < 5 × 10-8) loci located in the ABCC1 gene (rs56076205, p = 3.7 × 10-10), the AKAP6 gene (rs3784187, p = 1.2 × 10-8), and near the MFSD1 gene (rs340315, p = 4.5 × 10-8). No hits replicated in the UK Biobank (rs56076205: p = .87; rs3784187: p = .93; rs340315: p = .71). Conclusions: In this large, population-based GWEIS, we did not find any replicable hits for interaction. Future gene-by-stress research in depression should focus on establishing even larger collaborative GWEISs to attain sufficient power.

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Genome-wide association study of febrile seizures implicates fever response and neuronal excitability genes.

Febrile seizures represent the most common type of pathological brain activity in young children and are influenced by genetic, environmental and developmental factors. In a minority of cases, febrile seizures precede later development of epilepsy. We conducted a genome-wide association study of febrile seizures in 7635 cases and 83 966 controls identifying and replicating seven new loci, all with P < 5 × 10-10. Variants at two loci were functionally related to altered expression of the fever response genes PTGER3 and IL10, and four other loci harboured genes (BSN, ERC2, GABRG2, HERC1) influencing neuronal excitability by regulating neurotransmitter release and binding, vesicular transport or membrane trafficking at the synapse. Four previously reported loci (SCN1A, SCN2A, ANO3 and 12q21.33) were all confirmed. Collectively, the seven novel and four previously reported loci explained 2.8% of the variance in liability to febrile seizures, and the single nucleotide polymorphism heritability based on all common autosomal single nucleotide polymorphisms was 10.8%. GABRG2, SCN1A and SCN2A are well-established epilepsy genes and, overall, we found positive genetic correlations with epilepsies (rg = 0.39, P = 1.68 × 10-4). Further, we found that higher polygenic risk scores for febrile seizures were associated with epilepsy and with history of hospital admission for febrile seizures. Finally, we found that polygenic risk of febrile seizures was lower in febrile seizure patients with neuropsychiatric disease compared to febrile seizure patients in a general population sample. In conclusion, this largest genetic investigation of febrile seizures to date implicates central fever response genes as well as genes affecting neuronal excitability, including several known epilepsy genes. Further functional and genetic studies based on these findings will provide important insights into the complex pathophysiological processes of seizures with and without fever.

Pleiotropy between language impairment and broader behavioral disorders-an investigation of both common and rare genetic variants.

BACKGROUND: Language plays a major role in human behavior. For this reason, neurodevelopmental and psychiatric disorders in which linguistic ability is impaired could have a big impact on the individual's social interaction and general wellbeing. Such disorders tend to have a strong genetic component, but most past studies examined mostly the linguistic overlaps across these disorders; investigations into their genetic overlaps are limited. The aim of this study was to assess the potential genetic overlap between language impairment and broader behavioral disorders employing methods capturing both common and rare genetic variants. METHODS: We employ polygenic risk scores (PRS) trained on specific language impairment (SLI) to evaluate genetic overlap across several disorders in a large case-cohort sample comprising ~13,000 autism spectrum disorder (ASD) cases, including cases of childhood autism and Asperger's syndrome, ~15,000 attention deficit/hyperactivity disorder (ADHD) cases, ~3000 schizophrenia cases, and ~21,000 population controls. We also examine rare variants in SLI/language-related genes in a subset of the sample that was exome-sequenced using the SKAT-O method. RESULTS: We find that there is little evidence for genetic overlap between SLI and ADHD, schizophrenia, and ASD, the latter being in line with results of linguistic analyses in past studies. However, we observe a small, significant genetic overlap between SLI and childhood autism specifically, which we do not observe for SLI and Asperger's syndrome. Moreover, we observe that childhood autism cases have significantly higher SLI-trained PRS compared to Asperger's syndrome cases; these results correspond well to the linguistic profiles of both disorders. Our rare variant analyses provide suggestive evidence of association for specific genes with ASD, childhood autism, and schizophrenia. CONCLUSIONS: Our study provides, for the first time, to our knowledge, genetic evidence for ASD subtypes based on risk variants for language impairment.

Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly heritable neurodevelopmental disorders that frequently co-occur. Both rare and common genetic variants are important for ASD and ADHD risk but their combined contribution to clinical heterogeneity is unclear. In a sample of 39 ASD and/or ADHD families we estimated the overall variance explained by known rare copy number variants (CNVs) and polygenic risk score (PRS) from common variants to be 10% in comorbid ASD/ADHD, 4% in ASD and 2% in ADHD. We show that burden of large, rare CNVs and PRS is significantly higher in adult ASD and/or ADHD patients with sustained need for specialist care compared to their unaffected relatives, while affected relatives fall in-between the two.

Genetic factors underlying the bidirectional relationship between autoimmune and mental disorders - Findings from a Danish population-based study.

BACKGROUND: Previous studies have indicated the bidirectionality between autoimmune and mental disorders. However, genetic studies underpinning the co-occurrence of the two disorders have been lacking. In this study, we examined the potential genetic contribution to the association between autoimmune and mental disorders and investigated the genetic basis of overall autoimmune disease. METHODS: We used diagnostic information from patients with seven autoimmune diseases and six mental disorders from the Danish population-based case-cohort sample (iPSYCH2012). We explored the epidemiological association using survival analysis and modelled the effect of polygenic risk scores (PRSs) on autoimmune and mental diseases. Genetic factors were investigated using GWAS and imputed HLA alleles in the iPSYCH cohort. RESULTS: Of 64,039 individuals, a total of 43,902 (68.6%) were diagnosed with mental disorders and 1383 (2.2%) with autoimmune diseases. There was a significant comorbidity between the two disease classes (P = 2.67 × 10-7, OR = 1.38, 95%CI = 1.22-1.56), with an overall bidirectional association, wherein individuals with autoimmune diseases had an increased risk of subsequent mental disorders (HR = 1.13, 95%CI: 1.07-1.21, P = 7.95 × 10-5) and vice versa (HR = 1.27, 95%CI = 1.16-1.39, P = 8.77 × 10-15). Adding PRSs to these adjustment models did not have an impact on the associations. PRSs for autoimmune diseases were only slightly associated with increased risk of mental disorders (HR = 1.01, 95%CI: 1.00-1.02, p = 0.038), whereas PRSs for mental disorders were not associated with autoimmune diseases overall. Our GWAS highlighted 12 loci on chromosome 6 (minimum P = 2.74 × 10-36, OR = 1.80, 95% CI: 1.64-1.96), which were implicated in gene regulation through bioinformatic functional analyses, thereby identifying new candidate genes for overall autoimmune disease. Moreover, we observed 20 human leukocyte antigen (HLA) alleles strongly associated, either positively or negatively, with overall autoimmune disease, but we did not find significant evidence of their associations with overall mental disorders. A GWAS of a comorbid diagnosis of an autoimmune disease and a mental disorder identified a genome-wide significant locus on chromosome 7 as well (P = 1.43 × 10-8, OR = 10.65, 95%CI = 3.21-35.36). CONCLUSIONS: Our findings confirm the overall comorbidity and bidirectionality between autoimmune diseases and mental disorders and identify HLA genes which are significantly associated with overall autoimmune disease. Additionally, we identified several new candidate genes for overall autoimmune disease and ranked them based on their association with the investigated diseases.

Genetic predictors of educational attainment and intelligence test performance predict voter turnout.

Although the genetic influence on voter turnout is substantial (typically 40-50%), the underlying mechanisms remain unclear. Across the social sciences, research suggests that 'resources for politics' (as indexed notably by educational attainment and intelligence test performance) constitute a central cluster of factors that predict electoral participation. Educational attainment and intelligence test performance are heritable. This suggests that the genotypes that enhance these phenotypes could positively predict turnout. To test this, we conduct a genome-wide complex trait analysis of individual-level turnout. We use two samples from the Danish iPSYCH case-cohort study, including a nationally representative sample as well as a sample of individuals who are particularly vulnerable to political alienation due to psychiatric conditions (n = 13,884 and n = 33,062, respectively). Using validated individual-level turnout data from the administrative records at the polling station, genetic correlations and Mendelian randomization, we show that there is a substantial genetic overlap between voter turnout and both educational attainment and intelligence test performance.

A large population-based investigation into the genetics of susceptibility to gastrointestinal infections and the link between gastrointestinal infections and mental illness.

Gastrointestinal infections can be life threatening, but not much is known about the host's genetic contribution to susceptibility to gastrointestinal infections or the latter's association with psychiatric disorders. We utilized iPSYCH, a genotyped population-based sample of individuals born between 1981 and 2005 comprising 65,534 unrelated Danish individuals (45,889 diagnosed with mental disorders and 19,645 controls from a random population sample) in which all individuals were linked utilizing nationwide population-based registers to estimate the genetic contribution to susceptibility to gastrointestinal infections, identify genetic variants associated with gastrointestinal infections, and examine the link between gastrointestinal infections and psychiatric and neurodevelopmental disorders. The SNP heritability of susceptibility to gastrointestinal infections ranged from 3.7% to 6.4% on the liability scale. Significant correlations were found between gastrointestinal infections and the combined group of mental disorders (OR = 2.09; 95% CI: 1.82-2.4, P = 1.87 × 10-25). Correlations with autism spectrum disorder, attention deficit hyperactivity disorder, and depression were also significant. We identified a genome-wide significant locus associated with susceptibility to gastrointestinal infections (OR = 1.13; 95% CI: 1.08-1.18, P = 2.9 × 10-8), where the top SNP was an eQTL for the ABO gene. The risk allele was associated with reduced ABO expression, providing, for the first time, genetic evidence to support previous studies linking the O blood group to gastrointestinal infections. This study also highlights the importance of integrative work in genetics, psychiatry, infection, and epidemiology on the road to translational medicine.

Association between GLP-1 receptor gene polymorphisms with reward learning, anhedonia and depression diagnosis.

BACKGROUND: Glucagon-like peptide-1 receptors (GLP-1Rs) are widely expressed in the brain. Evidence suggests that they may play a role in reward responses and neuroprotection. However, the association of GLP-1R with anhedonia and depression diagnosis has not been studied. Here, we examined the association of GLP-1R polymorphisms with objective and subjective measures of anhedonia, as well as depression diagnosis. METHODS: Objective [response bias assessed by the probabilistic reward task (PRT)] and subjective [Snaith-Hamilton Pleasure Scale (SHAPS)] measures of anhedonia, clinical variables and DNA samples were collected from 100 controls and 164 patients at McLean Hospital. An independent sample genotyped as part of the Psychiatric Genomics Consortium (PGC) was used to study the effect of putative GLP-1R polymorphisms linked to response bias in PRT on depression diagnosis. RESULTS: The C allele in rs1042044 was significantly associated with increased PRT response bias, when controlling for age, sex, case-control status and PRT discriminability. AA genotype of rs1042044 showed higher anhedonia phenotype based on SHAPS scores. However, analysis of PGC major depressive disorder data showed no association between rs1042044 and depression diagnosis. CONCLUSION: Findings suggest a possible association of rs1042044 with anhedonia but no association with depression diagnosis.

Genetics of suicide attempts in individuals with and without mental disorders: a population-based genome-wide association study.

Family studies have shown an aggregation of suicidal behavior in families. Yet, molecular studies are needed to identify loci accounting for genetic heritability. We conducted a genome-wide association study and estimated single nucleotide polymorphisms (SNP) heritability for a suicide attempt. In a case-cohort study, national data on all individuals born in Denmark after 1981 and diagnosed with severe mental disorders prior to 2013 (n = 57,377) and individuals from the general population (n = 30,000) were obtained. After quality control, the sample consisted of 6024 cases with an incidence of suicide attempt and 44,240 controls with no record of a suicide attempt. Suggestive associations between SNPs, rs6880062 (p-value: 5.4 × 10-8) and rs6880461 (p-value: 9.5 × 10-8), and suicide attempt were identified when adjusting for socio-demographics. Adjusting for mental disorders, three significant associations, all on chromosome 20, were identified: rs4809706 (p-value: 2.8 × 10-8), rs4810824 (p-value: 3.5 × 10-8), and rs6019297 (p-value: 4.7 × 108). Sub-group analysis of cases with affective disorders revealed SNPs associated with suicide attempts when compared to the general population for gene PDE4B. All SNPs explained 4.6% [CI-95: 2.9-6.3%] of the variation in suicide attempt. Controlling for mental disorders reduced the heritability to 1.9% [CI-95: 0.3-3.5%]. Affective and autism spectrum disorders exhibited a SNP heritability of 5.6% [CI-95: 1.9-9.3%] and 9.6% [CI-95: 1.1-18.1%], respectively. Using the largest sample to date, we identified significant SNP associations with suicide attempts and support for a genetic transmission of suicide attempt, which might not solely be explained by mental disorders.

A large-scale genomic investigation of susceptibility to infection and its association with mental disorders in the Danish population.

Infections and mental disorders are two of the major global disease burdens. While correlations between mental disorders and infections have been reported, the possible genetic links between them have not been assessed in large-scale studies. Moreover, the genetic basis of susceptibility to infection is largely unknown, as large-scale genome-wide association studies of susceptibility to infection have been lacking. We utilized a large Danish population-based sample (N = 65,534) linked to nationwide population-based registers to investigate the genetic architecture of susceptibility to infection (heritability estimation, polygenic risk analysis, and a genome-wide association study (GWAS)) and examined its association with mental disorders (comorbidity analysis and genetic correlation). We found strong links between having at least one psychiatric diagnosis and the occurrence of infection (P = 2.16 × 10-208, OR = 1.72). The SNP heritability of susceptibility to infection ranged from ~2 to ~7% in samples of differing psychiatric diagnosis statuses (suggesting the environment as a major contributor to susceptibility), and polygenic risk scores moderately but significantly explained infection status in an independent sample. We observed a genetic correlation of 0.496 (P = 2.17 × 10-17) between a diagnosis of infection and a psychiatric diagnosis. While our GWAS did not identify genome-wide significant associations, we found 90 suggestive (P ≤ 10-5) associations for susceptibility to infection. Our findings suggest a genetic component in susceptibility to infection and indicate that the occurrence of infections in individuals with mental illness may be in part genetically driven.

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration.

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10-14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.