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.

Parental Transmission Plays the Major Role in High Aggregation of Type 2 Diabetes in Iranian Families: Tehran Lipid and Glucose Study.

BACKGROUND: This study is the first to evaluate familial aggregation, heritability and inheritance mode of type 2 diabetes (T2D) in Tehran Lipid Glucose Study (TLGS) participants as a representative sample of the Iranian population. METHODS: From the ongoing family-based TLGS cohort, 13,741 individuals at least 20 years of age (mean ± standard deviation, 39.71±16.56) were assessed. After correcting family structures using genomic information from the Tehran Cardiometabolic Genetic Study, 2,594 constituent pedigrees were constructed. Familial aggregation was assessed based on genealogic index testing, familial intraclass correlation and positive family history. Family-based heritability was checked with 2 linear mixed models, including 2 different random components: the kinship matrix and the genomic relationship matrix. The mode of inheritance of T2D was investigated by complex segregation analysis (CSA). RESULTS: Familial aggregation of T2D was significant (p<0.05), and family-based heritability showed a high degree of genetic variation in T2D between individuals at 65% (standard error, 0.034). Within first-degree relatives (parent/offspring and siblings), the likelihood of a parental affect was higher than in siblings (odds ratio, 4.11 vs 1.65). Family history of T2D among first-degree relatives was more noteworthy than for second-degree relatives (odds ratio, 3.84 vs 0.59). CSA revealed that the polygenic model is best to illustrate the mode of inheritance of T2D for TLGS participants. CONCLUSIONS: Our findings demonstrate that the heritability of T2D with polygenic mode in the Iranian population is higher than the global average. We also found that T2D is transmitted equally into siblings, with parental affect the leading risk factor. These data suggest that policymakers should change individual-level to family-level prevention.

GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study.

In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs' subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.