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Dyslexia and developmental language disorders are important learning difficulties. However, their genetic basis remains poorly understood, and most genetic studies were performed on Europeans. There is a lack of genome-wide association studies (GWAS) on literacy phenotypes of Chinese as a native language and English as a second language (ESL) in a Chinese population. In this study, we conducted GWAS on 34 reading/language-related phenotypes in Hong Kong Chinese bilingual children (including both twins and singletons; total N = 1046). We performed association tests at the single-variant, gene, and pathway levels. In addition, we tested genetic overlap of these phenotypes with other neuropsychiatric disorders, as well as cognitive performance (CP) and educational attainment (EA) using polygenic risk score (PRS) analysis. Totally 5 independent loci (LD-clumped at r[superscript 2] = 0.01; MAF > 0.05) reached genome-wide significance (p < 5e-08; filtered by imputation quality metric Rsq>0.3 and having at least 2 correlated SNPs (r[superscript 2] > 0.5) with p < 1e-3). The loci were associated with a range of language/literacy traits such as Chinese vocabulary, character and word reading, and rapid digit naming, as well as English lexical decision. Several SNPs from these loci mapped to genes that were reported to be associated with EA and other neuropsychiatric phenotypes, such as "MANEA" and "PLXNC1." In PRS analysis, EA and CP showed the most consistent and significant polygenic overlap with a variety of language traits, especially English literacy skills. To summarize, this study revealed the genetic basis of Chinese and English abilities in a group of Chinese bilingual children. Further studies are warranted to replicate the findings.

A recent genome-wide association study on dyslexia in 51,800 affected European adults and 1,087,070 controls detected 42 genome-wide significant single nucleotide variants (SNPs). The association between rs2624839 in SEMA3F and reading fluency was replicated in a Chinese cohort. This study explores the genetic overlap between Chinese and English word reading, vocabulary knowledge and spelling, and aims at replicating the association in a unique cohort of bilingual (Chinese-English) Hong Kong Chinese twins. Our result showed an almost complete genetic overlap in vocabulary knowledge (r 2  = 0.995), and some genetic overlaps in word reading and spelling (r 2  = 0.846, 0.687) across the languages. To investigate the region near rs2624839, we tested proxy SNPs (rs1005678, rs12632110 and rs12494414) at the population level (n = 305-308) and the within-twin level (n = 342-344 [171-172 twin pairs]). All the three SNPs showed significant associations with quantitative Chinese and English vocabulary knowledge (p < 0.05). The strongest association after multiple testing correction was between rs12494414 and English vocabulary knowledge at the within-twin level (p = 0.004). There was a trend of associations with word reading and spelling in English but not in Chinese. Our result suggested that the region near rs2624839 is one of the common genetic factors across English and Chinese vocabulary knowledge and unique factors of English word reading and English spelling in bilingual Chinese twins. A larger sample size is required to validate our findings. Further studies on the relationship between variable expression of SEMA3F, which is important to neurodevelopment, and language and literacy are encouraged., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The interest in risk prediction using genomic profiles has surged recently. A proper interpretation of effect size measures in association studies is crucial to accurate risk prediction. In this study, we clarified the relationship between the odds ratio (OR), relative risk and incidence rate ratios in the context of genetic association studies. We demonstrated that under the common practice of sampling prevalent cases and controls, the resulting ORs approximate the incidence rate ratios. Based on this result, we presented a framework to compute the disease risk given the current age and follow-up period (including lifetime risk), with consideration of competing risks of mortality. We considered two extensions. One is correcting the incidence rate to reflect the person-years alive and disease-free, the other is converting prevalence to incidence estimates. The methodology was applied to an example of breast cancer prediction. We observed that simply multiplying the OR by the average lifetime risk estimates yielded a final estimate >100% (101%), while using our method that accounts for competing risks produces an estimate of 63% only. We also applied the method to risk prediction of Alzheimer's disease in Hong Kong. We recommend that companies offering direct-to-consumer genetic testing employ more rigorous prediction algorithms considering competing risks., (Copyright © 2010 S. Karger AG, Basel.)