Intellectual disability refers to significantly subaverage intellectual function (intelligence quotient < 70) with impairment of adaptive function. The IQSEC2 gene is one of the pathogenic genes located on chromosome Xp11.22. IQSEC2 is an X-linked gene correlated with intellectual disability and epilepsy. In this study, we reported a 2-year-old male patient presented with reacting sluggishly with people and surroungdings. Active electroencephalogram showed the background of epileptic activity. Brain MRI revealed patchy hyperintensity of bilateral parietal lobe white matter on fluid-attenuated inversion recovery image and widened ventricle, cistern and sulci on T2-weighted image. Delayed myelination was considered. The diagnosis of intellectual disability and epilepsy was made. Whole exome-sequencing was conducted and identified a novel frameshift mutation in exon 15 of IQSEC2 (NM_001111125.2: c.4164dupC: p.Ile1389 Hisfs*218). The variant resulted in the deletion of termination codon, and the protein was extended to termination after stretch of 218 amino acids.This study expands the mutation spectrum of IQSEC2. It supports the published data suggesting that IQSEC2 plays a significant part in patients with intellectual disability and epilepsy. IQSEC2 should be detected in patients with intellectual disability and epilepsy.

Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.

We aimed to test the association of polymorphisms in HTR2A, TPH1, and TPH2 genes with attempted suicide in rural China. On the basis of a case-control study, we recruited 1200 pairs of participants from Shandong Province, China. The blood samples of 712 suicide attempters and 739 nonsuicide attempters were collected finally. We tested seven single nucleotide polymorphisms: rs6313 and rs6311 in HTR2A, rs4537731, rs1800532, and rs1799913 in TPH1, and rs4448731 and rs4641527 in TPH2. In univariate analysis, allele C of rs4537731 was associated negatively with attempted suicide among total and male samples; however, the association was not statistically significant in multivariate analysis after adjusting for other potential confounding factors. No association between other six single nucleotide polymorphisms and attempted suicide was found in the total, male, or female samples. This study did not support the effect of these seven serotonergic gene polymorphisms on attempted suicide in rural China.

The clinical comorbidity of alcohol dependence (AD) and major depressive disorder (MDD) is well established, whereas genetic factors influencing co-occurrence remain unclear. A recent study using polygenic risk scores (PRS) calculated based on the first-wave Psychiatric Genomics Consortium MDD meta-analysis (PGC-MDD1) suggests a modest shared genetic contribution to MDD and AD. Using a (~10 fold) larger discovery sample, we calculated PRS based on the second wave (PGC-MDD2) of results, in a severe AD case–control target sample. We found significant associations between AD disease status and MDD-PRS derived from both PGC-MDD2 (most informative P-threshold=1.0, P=0.00063, R2=0.533%) and PGC-MDD1 (P-threshold=0.2, P=0.00014, R2=0.663%) meta-analyses; the larger discovery sample did not yield additional predictive power. In contrast, calculating PRS in a MDD target sample yielded increased power when using PGC-MDD2 (P-threshold=1.0, P=0.000038, R2=1.34%) versus PGC-MDD1 (P-threshold=1.0, P=0.0013, R2=0.81%). Furthermore, when calculating PGC-MDD2 PRS in a subsample of patients with AD recruited explicitly excluding comorbid MDD, significant associations were still found (n=331; P-threshold=1.0, P=0.042, R2=0.398%). Meanwhile, in the subset of patients in which MDD was not the explicit exclusion criteria, PRS predicted more variance (n=999; P-threshold=1.0, P=0.0003, R2=0.693%). Our findings replicate the reported genetic overlap between AD and MDD and also suggest the need for improved, rigorous phenotyping to identify true shared cross-disorder genetic factors. Larger target samples are needed to reduce noise and take advantage of increasing discovery sample size.

Animal models of mental disorders are important for the development of new treatment and biomarkers. Animal models should satisfy three validity criteria. The choice of species used for animal models depends on the purposes of the study. There are several established models of chromosomal abnormalities for autism and schizophrenia. Stress-induced animal models of depression are controversial. There have been no established models for bipolar disorder, but the authors recently proposed a genetic animal model showing recurrent spontaneous depression-like episodes. Optogenetic manipulation of neural circuit is also used for modeling of mental disorders. Progress in psychiatric genetics will lead to the generation of valid animal models of psychiatric disorders.

The field of psychiatric epidemiology has advanced both methodological and substantive knowledge in our understanding of mental disorders through the following contributions: (1) development of standardized tools that operationalize diagnostic criteria in order to obtain reliable estimates; (2) estimation of the magnitude, correlates and service patterns of mental disorders; (3) documentation of patterns of comorbidity; (4) quantification of disability attributable to mental disorders; and (5) identification of risk and protective factors for mental disorders and their core domains. Community surveys using standardized tools for ascertaining psychiatric disorders have shown that mental disorders are highly prevalent in the general population. With the growing success in identifying genetic risk factors for chronic human disorders, the field of epidemiology will play an important role in defining study designs, appropriate samples, population generalizability, and statistical tools that will facilitate our ability to identify the joint influence of genetic and environmental factors on the susceptibility to mental disorders.

The basic idea in linkage analysis is that a disease gene will segregate in a family with a close (linked) marker, and typing this marker will lead to its detection. The successes using this approach have been largely confined to Mendelian monogenic disorders or complex disorders with Mendelian subforms. During the last decade, psychiatric genetics abandoned linkage analysis and moved to case-control studies of association, with remarkable success in identifying susceptibility genes for mental disorders. In this chapter, we review the statistical underpinnings of linkage and association and discuss important issues such as population stratification, imputation, data cleaning, the genomic inflation factor, and QQ and Manhattan plots. The challenge for the next decade will be to understand the biology of these GWAS (genome-wide association study) hits.

Over more than the last decade, hypothesis-free genome-wide association studies (GWAS) have been widely used to detect genetic factors influencing phenotypes of interest. The basic principle of GWAS has been unchanged since the beginning: a series of univariate tests is conducted on all genetic variants available across the genome. We present study designs and commonly used methods for genome-wide studies, with a focus on the analysis of common variants. The basic concepts required for an application of GWAS in psychiatric genetics are introduced, from power calculation to meta-analysis. This chapter will help the reader in gaining the knowledge required for participation in and realization of GWAS of both qualitative and quantitative traits.

ZNF804A has been investigated widely as a candidate susceptibility gene for mental disorders in individuals of different ethnicities. However, in the Han Chinese population, most studies of this gene have focused on associations of the common single nucleotide polymorphism (SNP) rs1344706. To investigate additional common variants within ZNF804A, we carried out a case-control study of 13 SNPs distributed across the whole gene, in 1330 schizophrenic patients, 1045 major depressive disorder patients, and 1235 normal controls. We found that rs12476147 (P=0.0078) was associated significantly with schizophrenia, but no SNPs showed statistically significant associations with major depressive disorder after Bonferroni correction. Moreover, we also found that haplotype block 2, which included rs12476147 and rs1344706, was associated significantly with schizophrenia and major depressive disorder. Nevertheless, we could not replicate the association of rs1344706 with schizophrenia. In conclusion, the common variant rs12476147 and the related haplotype block in ZNF804A were associated significantly with schizophrenia in the Han Chinese population.