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The exome sequences of approximately 8,000 children with autism spectrum disorder (ASD) and/or attention deficit hyperactivity disorder (ADHD) and 5,000 controls were analyzed, finding that individuals with ASD and individuals with ADHD had a similar burden of rare protein-truncating variants in evolutionarily constrained genes, both significantly higher than controls. This motivated a combined analysis across ASD and ADHD, identifying microtubule-associated protein 1A (MAP1A) as a new exome-wide significant gene conferring risk for childhood psychiatric disorders.

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with onset in childhood (childhood ADHD); two-thirds of affected individuals continue to have ADHD in adulthood (persistent ADHD), and sometimes ADHD is diagnosed in adulthood (late-diagnosed ADHD). We evaluated genetic differences among childhood ( n  = 14,878), persistent ( n  = 1,473) and late-diagnosed ( n  = 6,961) ADHD cases alongside 38,303 controls, and rare variant differences in 7,650 ADHD cases and 8,649 controls. We identified four genome-wide significant loci for childhood ADHD and one for late-diagnosed ADHD. We found increased polygenic scores for ADHD in persistent ADHD compared with the other two groups. Childhood ADHD had higher genetic overlap with hyperactivity and autism compared with late-diagnosed ADHD and the highest burden of rare protein-truncating variants in evolutionarily constrained genes. Late-diagnosed ADHD had a larger genetic overlap with depression than childhood ADHD and no increased burden in rare protein-truncating variants. Overall, these results suggest a genetic influence on age at first ADHD diagnosis, persistence of ADHD and the different comorbidity patterns among the groups. Analyses of the polygenic architecture of childhood, persistent and late-diagnosed attention-deficit hyperactivity disorder (ADHD) in a Danish population-based case–cohort sample identify differences among ADHD subgroups with respect to common and rare variants.

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD. Cross-disorder genetic association analyses identify five loci differentiating attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Individuals diagnosed with both ADHD and ASD are double-loaded with genetic risk for both disorders.

There is great interest in the role epigenetic variation induced by non-genetic exposures may play in the context of health and disease. In particular, DNA methylation has previously been shown to be highly dynamic during the earliest stages of development and is influenced by in utero exposures such as maternal smoking and medication. In this study we sought to identify the specific DNA methylation differences in blood associated with prenatal and birth factors, including birth weight, gestational age and maternal smoking. We quantified neonatal methylomic variation in 1263 infants using DNA isolated from a unique collection of archived blood spots taken shortly after birth (mean = 6.08 days; s.d. = 3.24 days). An epigenome-wide association study (EWAS) of gestational age and birth weight identified 4299 and 18 differentially methylated positions (DMPs) respectively, at an experiment-wide significance threshold of p < 1 × 10 −7 . Our EWAS of maternal smoking during pregnancy identified 110 DMPs in neonatal blood, replicating previously reported genomic loci, including AHRR . Finally, we tested the hypothesis that DNA methylation mediates the relationship between maternal smoking and lower birth weight, finding evidence that methylomic variation at three DMPs may link exposure to outcome. These findings complement an expanding literature on the epigenomic consequences of prenatal exposures and obstetric factors, confirming a link between the maternal environment and gene regulation in neonates. This article is part of the theme issue ‘Developing differences: early-life effects and evolutionary medicine’.

In Denmark, a nationwide COVID-19 lockdown was implemented on March 12, 2020 and eased on April 14, 2020. The COVID-19 lockdown featured reduced prevalence of extremely preterm or extremely low birthweight births. This study aims to explore the impact of this COVID-19 lockdown on term birthweights in Denmark. We conducted a nationwide register-based cohort study on 27,870 live singleton infants, born at term (weeks 37–41), between March 12 and April 14, 2015–2020, using data from the Danish Neonatal Screening Biobank. Primary outcomes, corrected for confounders, were birthweight, small-for-gestational-age (SGA), and large-for-gestational-age (LGA), comparing the COVID-19 lockdown to the previous five years. Data were analysed using linear regression to assess associations with birthweight. Multinomial logistic regression was used to assess associations with relative-size-for-gestational-age (xGA) categories. Adjusted mean birthweight was significantly increased by 16.9 g (95% CI = 4.1–31.3) during the lockdown period. A dip in mean birthweight was found in gestational weeks 37 and 38 balanced by an increase in weeks 40 and 41. The 2020 lockdown period was associated with an increased LGA prevalence (aOR 1.13, 95% CI = 1.05–1.21). No significant changes in proportions of xGA groups were found between 2015 and 2019. The nationwide COVID-19 lockdown resulted in a small but significant increase in birthweight and proportion of LGA infants, driven by an increase in birthweight in gestational weeks 40 and 41.

Stored neonatal dried blood spot (DBS) samples from neonatal screening programmes are a valuable diagnostic and research resource. Combined with information from national health registries they can be used in population-based studies of genetic diseases. DNA extracted from neonatal DBSs can be amplified to obtain micrograms of an otherwise limited resource, referred to as whole-genome amplified DNA (wgaDNA). Here we investigate the robustness of exome sequencing of wgaDNA of neonatal DBS samples. We conducted three pilot studies of seven, eight and seven subjects, respectively. For each subject we analysed a neonatal DBS sample and corresponding adult whole-blood (WB) reference sample. Different DNA sample types were prepared for each of the subjects. Pilot 1: wgaDNA of 2x3.2mm neonatal DBSs (DBS_2x3.2) and raw DNA extract of the WB reference sample (WB_ref). Pilot 2: DBS_2x3.2, WB_ref and a WB_ref replica sharing DNA extract with the WB_ref sample. Pilot 3: DBS_2x3.2, WB_ref, wgaDNA of 2x1.6 mm neonatal DBSs and wgaDNA of the WB reference sample. Following sequencing and data analysis, we compared pairwise variant calls to obtain a measure of similarity--the concordance rate. Concordance rates were slightly lower when comparing DBS vs WB sample types than for any two WB sample types of the same subject before filtering of the variant calls. The overall concordance rates were dependent on the variant type, with SNPs performing best. Post-filtering, the comparisons of DBS vs WB and WB vs WB sample types yielded similar concordance rates, with values close to 100%. WgaDNA of neonatal DBS samples performs with great accuracy and efficiency in exome sequencing. The wgaDNA performed similarly to matched high-quality reference--whole-blood DNA--based on concordance rates calculated from variant calls. No differences were observed substituting 2x3.2 with 2x1.6 mm discs, allowing for additional reduction of sample material in future projects.

Background Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed. Methods We developed double batched sequencing where DNA samples are batch-sequenced twice — directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative ( n = 100) and a validation ( n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data. Results We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91–1.00 and 95% CI, 0.98–1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1 , TP53 , BRCA2 , APC , and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing. Conclusions Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

The objective of the present study was to investigate whether the polygenic liability for attention-deficit/hyperactivity disorder (ADHD) and the psychosocial environment impact the risk of ADHD in interaction or independently of each other. We conducted a register- and biobank-based cohort study of 13,725 individuals with ADHD and 20,147 randomly drawn population-based controls. These 33,872 cohort members were genotyped on the Infinium PsychChip v1.0 array (Illumina). Subsequently, we calculated the polygenic risk score (PRS) for ADHD and extracted register data regarding the following risk factors pertaining to the psychosocial environment for each cohort member at the time of birth: maternal/paternal history of mental disorders, maternal/paternal education, maternal/paternal work status, and maternal/paternal income. We used logistic regression analyses to assess the main effects of the PRS for ADHD and the psychosocial environment on the risk of ADHD. Subsequently, we evaluated whether the effect of the PRS and the psychosocial environment act independently or in interaction upon the risk of ADHD. We found that ADHD was strongly associated with the PRS (odds ratio: 6.03, 95%CI: 4.74–7.70 for highest vs. lowest 2% liability). All risk factors pertaining to the psychosocial environment were associated with an increased risk of ADHD. These associations were only slightly attenuated after mutual adjustments. We found no statistically significant interaction between the polygenic liability and the psychosocial environment upon the risk of ADHD. In conclusion, we found main effects of both polygenic liability and risk factors pertaining to the psychosocial environment on the risk of ADHD—in the expected direction.

Pharmacogenetics aims to improve clinical care by studying the relationship between genetic variation and variable drug response. Large population-based datasets could improve our current understanding of pharmacogenetics from selected study populations. We provide real-world pharmacogenetic frequencies of genotypes and (combined) phenotypes of a large Danish population-based case-cohort sample (iPSYCH2012; data of the Integrative Psychiatric Research consortium). The genotyped sample consists of 77,684 individuals, of which 51,464 individuals had diagnoses of severe mental disorders (SMD case-cohort) and 26,220 were individuals randomly selected from the Danish population (population cohort). Array-based genotype data imputed to 8.4 million genetic variants was searched for a selected pharmacogenetic panel of 42 clinically relevant variants and a CYP2D6 gene deletion and duplication. We identified 19 of 42 variants. Minor allele frequencies (MAFs) were consistent with previously reported MAFs, and did not differ between SMD cases and population cohorts. Almost all individuals carried at least one genetic variant (> 99.9%) and 87% carried three or more genetic variants. When genotypes were translated into phenotypes, also > 99.9% of individuals had at least one divergent phenotype (i.e. divergent from the common phenotypes considered normal, e.g. extensive metabolizer). The high number of identified individuals with at least one pharmacogenetic variant or divergent phenotype indicates the importance of pharmacogenetic panel-based genotyping. Combined CYP2C19-CYP2D6 phenotypes revealed that 72.7% of individuals had divergent phenotypes for one or both enzymes. As CYP2D6 and CYP2C19 have an important role in the metabolism of psychotropic drugs, this indicates the relevance of pharmacogenetic testing specifically in individuals using psychotropic drugs.

Mitochondria play a significant role in human diseases. However, disease associations with mitochondrial DNA (mtDNA) SNPs have proven difficult to replicate. An analysis of eight schizophrenia-associated mtDNA SNPs, in 23,743 Danes without a psychiatric diagnosis and 2,538 schizophrenia patients, revealed marked inter-allelic differences in mitochondrial haplogroup affiliation and nuclear ancestry. This bi-genomic dependence could entail population stratification. Only two mitochondrial SNPs, m.15043A and m.15218G, were significantly associated with schizophrenia. However, these associations disappeared when corrected for haplogroup affiliation and nuclear ancestry. The extensive bi-genomic dependence documented here is a major concern when interpreting historic, as well as designing future, mtDNA association studies.