Genetics in Ischemic Stroke: Current Perspectives and Future Directions.

Ischemic stroke is a heterogeneous condition influenced by a combination of genetic and environmental factors. Recent advancements have explored genetics in relation to various aspects of ischemic stroke, including the alteration of individual stroke occurrence risk, modulation of treatment response, and effectiveness of post-stroke functional recovery. This article aims to review the recent findings from genetic studies related to various clinical and molecular aspects of ischemic stroke. The potential clinical applications of these genetic insights in stratifying stroke risk, guiding personalized therapy, and identifying new therapeutic targets are discussed herein.

Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders.

Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.

Functional genomics elucidates regulatory mechanisms of Parkinson's disease-associated variants.

BACKGROUND: Genome-wide association studies (GWASs) have identified multiple risk loci for Parkinson's disease (PD). However, identifying the functional (or potential causal) variants in the reported risk loci and elucidating their roles in PD pathogenesis remain major challenges. To identify the potential causal (or functional) variants in the reported PD risk loci and to elucidate their regulatory mechanisms, we report a functional genomics study of PD. METHODS: We first integrated chromatin immunoprecipitation sequencing (ChIP-Seq) (from neuronal cells and human brain tissues) data and GWAS-identified single-nucleotide polymorphisms (SNPs) in PD risk loci. We then conducted a series of experiments and analyses to validate the regulatory effects of these (i.e., functional) SNPs, including reporter gene assays, allele-specific expression (ASE), transcription factor (TF) knockdown, CRISPR-Cas9-mediated genome editing, and expression quantitative trait loci (eQTL) analysis. RESULTS: We identified 44 SNPs (from 11 risk loci) affecting the binding of 12 TFs and we validated the regulatory effects of 15 TF binding-disrupting SNPs. In addition, we also identified the potential target genes regulated by these TF binding-disrupting SNPs through eQTL analysis. Finally, we showed that 4 eQTL genes of these TF binding-disrupting SNPs were dysregulated in PD cases compared with controls. CONCLUSION: Our study systematically reveals the gene regulatory mechanisms of PD risk variants (including widespread disruption of CTCF binding), generates the landscape of potential PD causal variants, and pinpoints promising candidate genes for further functional characterization and drug development.

SNP eQTL status and eQTL density in the adjacent region of the SNP are associated with its statistical significance in GWA studies.

BACKGROUND: Over the relatively short history of Genome Wide Association Studies (GWASs), hundreds of GWASs have been published and thousands of disease risk-associated SNPs have been identified. Summary statistics from the conducted GWASs are often available and can be used to identify SNP features associated with the level of GWAS statistical significance. Those features could be used to select SNPs from gray zones (SNPs that are nominally significant but do not reach the genome-wide level of significance) for targeted analyses. METHODS: We used summary statistics from recently published breast and lung cancer and scleroderma GWASs to explore the association between the level of the GWAS statistical significance and the expression quantitative trait loci (eQTL) status of the SNP. Data from the Genotype-Tissue Expression Project (GTEx) were used to identify eQTL SNPs. RESULTS: We found that SNPs reported as eQTLs were more significant in GWAS (higher -log10p) regardless of the tissue specificity of the eQTL. Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant in the GWAS compared to those reported as eQTL in only one tissue type. eQTL density in the ±5 kb adjacent region of a given SNP was also positively associated with the level of GWAS statistical significance regardless of the eQTL status of the SNP. We found that SNPs located in the regions of high eQTL density were more likely to be located in regulatory elements (transcription factor or miRNA binding sites). When SNPs were stratified by the level of statistical significance, the proportion of eQTLs was positively associated with the mean level of statistical significance in the group. The association curve reaches a plateau around -log10p ≈ 5. The observed associations suggest that quasi-significant SNPs (10- 5 < p < 5 × 10- 8) and SNPs at the genome wide level of statistical significance (p < 5 × 10- 8) may have a similar proportions of risk associated SNPs. CONCLUSIONS: The results of this study indicate that the SNP's eQTL status, as well as eQTL density in the adjacent region are positively associated with the level of statistical significance of the SNP in GWAS.

Deregulation of long noncoding RNA SNHG17 and TTC28-AS1 is associated with type 2 diabetes mellitus.

Long noncoding RNAs (lncRNAs) have emerged as key players in several biological processes and complex diseases including type 2 diabetes mellitus (T2DM). The purpose of this study was to investigate the expression levels of SNHG17 and TTC28-AS1 in T2DM patients. Quantitative real-time RT-PCR analysis was performed using peripheral blood mononuclear cells (PBMCs) samples from patients diagnosed with T2DM and healthy controls. Binary logistic regression analysis was carried out to determine the odds of development of T2DM based on expression levels of lncRNAs and clinical characteristic of the subjects. Spearman's correlation analysis was used to clarify the correlation between SNHG17 and TTC28-AS1 expressions to metabolic features. We found that SNHG17 and TTC28-AS1were down-regulated in the T2DM group compared to the healthy control group. The logistic regression revealed that body mass index (BMI), systolic blood pressure (SBP), fasting blood glucose (FBG) and TTC28-AS1 expression substantially affect T2DM susceptibility. Furthermore, expression of SNHG17 was negatively correlated with high-density lipoprotein cholesterol (HDL-C) and expression of TTC28-AS1 was positively correlated with low-density lipoprotein cholesterol (LDL-C). Decreased expressions of lncRNAs TTC28-AS1 and SNHG17 in T2DM are possibly associated with the development of T2DM.

Difficulty in determining the association of a single nucleotide polymorphism in the ZNF512B gene with the risk and prognosis of amyotrophic lateral sclerosis.

The advent of next-generation sequencing technology is expected to accelerate the identification of novel genes, and this technology will likely supersede Sanger sequencing. Thus, genome-wide association studies (GWASs) are performed more routinely in an effort to identify disease-susceptibility genes for sporadic amyotrophic lateral sclerosis (ALS). Previously, a Japanese team conducted a large-scale GWAS with 1,305 Japanese ALS patients and discovered a new single nucleotide polymorphism (SNP) rs2275294 associated with susceptibility to sporadic ALS (sALS) in the ZNF512B gene on chromosome 20q13.33. Ju et al. recently performed a case-control study to examine the possible association of rs2275294 with the risk of sALS. Their results, however, indicated that the SNP in ZNF512B is not associated with sALS susceptibility in the Chinese population. A precise diagnosis of neurodegenerative diseases, especially ALS, is highly challenging. For GWASs and other clinical research studies that require a large sample size, if true ALS patients are not selected initially, then all subsequent research is futile. Here, I evaluate the factors that are likely responsible for the inconsistent results obtained by GWASs and propose the development of a new classification system and diagnostic criteria for ALS as the first step towards conducting better clinical studies on ALS. I have attempted to explain the reasons for the inconsistent association between rs2275294 and ALS progression by listing the gene-gene/gene-environment interactions, age of onset, sample size, odds ratio, and inappropriate ALS diagnosis criteria for stratifying this heterogeneous disease in this review.

A review of molecular genetic studies of neurocognitive deficits in schizophrenia.

Schizophrenia is a complex and debilitating illness with strong genetic loading. In line with its heterogeneous symptomatology, evidence suggests genetic etiologies for the phenotypes in schizophrenia. A search across endophenotypes has pointed towards consistent findings in its neurocognitive deficits. Extensive literature has demonstrated impaired cognition including executive function, attention, and memory in schizophrenia patients when compared to healthy subjects. This review (1) provides an overview of recent studies and (2) develops an up-to-date conceptualization of genetic variations influencing neurocognitive functions in schizophrenia patients. Several neurotransmitter system genes have been examined given knowledge of their role in brain functions and their reported genetic associations with schizophrenia and cognition. Several genetic variations have emerged as having preliminary effects on neurocognitive deficits in schizophrenia. These include genes in the neurotrophic, serotonin, cell adhesion, and sodium channel systems. Limited evidence also suggests the dopaminergic system genes, with the most studied catechol-o-methytransferase (COMT) gene showing inconsistent findings. Further investigations with larger samples and replications are required to elucidate genetic risk for cognitive deficits in schizophrenia.

Analysis of Positive Selection at Single Nucleotide Polymorphisms Associated with Body Mass Index Does Not Support the "Thrifty Gene" Hypothesis.

The "thrifty gene hypothesis" suggests genetic susceptibility to obesity arises because of positive selection for alleles that favored fat deposition and survival during famines. We used public domain data to locate signatures of positive selection based on derived allele frequency, genetic diversity, long haplotypes, and differences between populations at SNPs identified in genome-wide association studies (GWASs) for BMI. We used SNPs near the lactase (LCT), SLC24A5, and SLC45A2 genes as positive controls and 120 randomly selected SNPs as negative controls. We found evidence for positive selection (p < 0.05) at nine out of 115 BMI SNPs. However, five of these involved positive selection for the protective allele (i.e., for leanness). The widespread absence of signatures of positive selection, combined with selection favoring leanness at some alleles, does not support the suggestion that obesity provided a selective advantage to survive famines, or any other selective advantage.

ABCA7 p.G215S as potential protective factor for Alzheimer's disease.

Genome-wide association studies (GWASs) have been effective approaches to dissect common genetic variability underlying complex diseases in a systematic and unbiased way. Recently, GWASs have led to the discovery of over 20 susceptibility loci for Alzheimer's disease (AD). Despite the evidence showing the contribution of these loci to AD pathogenesis, their genetic architecture has not been extensively investigated, leaving the possibility that low frequency and rare coding variants may also occur and contribute to the risk of disease. We have used exome and genome sequencing data to analyze the single independent and joint effect of rare and low-frequency protein coding variants in 9 AD GWAS loci with the strongest effect sizes after APOE (BIN1, CLU, CR1, PICALM, MS4A6A, ABCA7, EPHA1, CD33, and CD2AP) in a cohort of 332 sporadic AD cases and 676 elderly controls of British and North-American ancestry. We identified coding variability in ABCA7 as contributing to AD risk. This locus harbors a low-frequency coding variant (p.G215S, rs72973581, minor allele frequency = 4.3%) conferring a modest but statistically significant protection against AD (p-value = 0.024, odds ratio = 0.57, 95% confidence interval = 0.41-0.80). Notably, our results are not driven by an enrichment of loss of function variants in ABCA7, recently reported as main pathogenic factor underlying AD risk at this locus. In summary, our study confirms the role of ABCA7 in AD and provides new insights that should address functional studies.

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants.

In spite of the success of genome-wide association studies (GWASs), only a small proportion of heritability for each complex trait has been explained by identified genetic variants, mainly SNPs. Likely reasons include genetic heterogeneity (i.e., multiple causal genetic variants) and small effect sizes of causal variants, for which pathway analysis has been proposed as a promising alternative to the standard single-SNP-based analysis. A pathway contains a set of functionally related genes, each of which includes multiple SNPs. Here we propose a pathway-based test that is adaptive at both the gene and SNP levels, thus maintaining high power across a wide range of situations with varying numbers of the genes and SNPs associated with a trait. The proposed method is applicable to both common variants and rare variants and can incorporate biological knowledge on SNPs and genes to boost statistical power. We use extensively simulated data and a WTCCC GWAS dataset to compare our proposal with several existing pathway-based and SNP-set-based tests, demonstrating its promising performance and its potential use in practice.