A Comprehensive Family-Based Replication Study of Schizophrenia Genes

Schizophrenia (SCZ) is a devastating psychiatric condition. Identifying the specific genetic variants and pathways that increase susceptibility to SCZ is critical to improve disease understanding and address the urgent need for new drug targets

A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder

Background Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. Methods We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish populationregister data (N=77,905 cases, N=1,874,637 population controls). Results Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that females with ADHD may be at especially high risk of certain comorbid developmental conditions (i.e. autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score (PRS) analysis did not support a higher burden of ADHD common risk variants in female cases (OR=1.02 [0.98-1.06], p=0.28). In contrast, epidemiological sibling analyses revealed that the siblings of females with ADHD are at higher familial risk of ADHD than siblings of affected males (OR=1.14, [95% CI: 1.11-1.18], p=1.5E-15). Conclusions Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence

GWAS of Behavioral Traits

Over the past decade, genome-wide association studies (GWAS) have evolved into a powerful tool to investigate genetic risk factors for human diseases via a hypothesis-free scan of the genome. The success of GWAS for psychiatric disorders and behavioral traits have been somewhat mixed, partly owing to the complexity and heterogeneity of these traits. Significant progress has been made in the last few years in the development and implementation of complex statistical methods and algorithms incorporating GWAS. Such advanced statistical methods applied to GWAS hits in combination with incorporation of different layers of genomics data have catapulted the search for novel genes for behavioral traits and improved our understanding of the complex polygenic architecture of these traits. This chapter will give a brief overview on GWAS and statistical methods currently used in GWAS. The chapter will focus on reviewing the current literature and highlight some of the most important GWAS on psychiatric and other behavioral traits and will conclude with a discussion on future directions.</p