Changes of neurofilament light chain in patients with alcohol dependence following withdrawal and the genetic effect from ALDH2 Polymorphism.

Neurofilament light chain (NFL), as a measure of neuroaxonal injury, has recently gained attention in alcohol dependence (AD). Aldehyde dehydrogenase 2 (ALDH2) is the major enzyme which metabolizes the alcohol breakdown product acetaldehyde. An ALDH2 single nucleotide polymorphism (rs671) is associated with less ALDH2 enzyme activity and increased neurotoxicity. We examined the blood NFL levels in 147 patients with AD and 114 healthy controls using enzyme-linked immunosorbent assay and genotyped rs671. We also followed NFL level, alcohol craving and psychological symptoms in patients with AD after 1 and 2 weeks of detoxification. We found the baseline NFL level was significantly higher in patients with AD than in controls (mean ± SD: 264.2 ± 261.8 vs. 72.1 ± 35.6 pg/mL, p < 0.001). The receiver operating characteristic curve revealed that NFL concentration could discriminate patients with AD from controls (area under the curve: 0.85; p < 0.001). The NFL levels were significantly reduced following 1 and 2 weeks of detoxification, with the extent of reduction correlated with the improvement of craving, depression, and anxiety (p < 0.001). Carriers with the rs671 GA genotype, which is associated with less ALDH2 activity, had higher NLF levels either at baseline or after detoxification compared with GG carriers. In conclusion, plasma NFL level was increased in patients with AD and reduced after early abstinence. Reduction in NFL level corroborated well with the improvement of clinical symptoms. The ALDH2 rs671 polymorphism may play a role in modulating the extent of neuroaxonal injury and its recovery.

Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

Exploring concepts and trends in informal caregiver burden: systematic review using citation network and content analysis.

BACKGROUND: With the increase in the aging population, informal caregivers have become an essential pillar for the long-term care of older individuals. However, providing care can have a negative impact and increase the burden on caregivers, which is a cause for concern. OBJECTIVE: This study aimed to comprehensively depict the concept of "informal caregiver burden" through bibliometric and content analyses. METHODS: We searched the Web of Science (WoS) database to obtain bibliometric data and included only papers published between 2013 and 2022. We used content analysis to extract and identify the core concepts within the text systematically. RESULTS: Altogether, 934 papers were included in the bibliometric analysis, from which we selected 19 highly impactful papers for content analysis. The results indicate that researchers have focused on exploring the factors that impact informal caregiver burden. Meanwhile, there has been a widespread discussion regarding the caregiver burden among those caring for recipients with specific illnesses, such as dementia, Alzheimer's disease, and cancer, as these illnesses can contribute to varying levels of burden on informal caregivers. In addition, questionnaires and interviews emerged as the predominant methods for data collection in the realm of informal caregiver research. Furthermore, we identified 26 distinct assessment tools specifically tailored for evaluating burden, such as caregiver strain index (CSI). CONCLUSION: For future studies, we suggest considering the intersectionality of factors contributing to the burden on informal caregivers. This approach could enhance the well-being of both caregivers and older care recipients.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. METHODS: A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics. RESULTS: The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel). CONCLUSIONS: The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Association of the D-amino acid oxidase gene with methadone dose in heroin dependent patients under methadone maintenance treatment.

Methadone is a synthetic opioid used for the maintenance treatment (MMT) of heroin dependence. It primarily binds to the µ-opioid receptor (MOR; with its gene, namely OPRM1). Methadone is also an N-methyl-D-aspartate (NMDA) receptor antagonist. The role of NMDA receptor in the regulatory mechanisms of methadone dosage in heroin dependent patients is so far not clear. D-amino acid oxidase (DAO) is an important enzyme that indirectly activates the NMDA receptor through its effect on the D-serine level. To test the hypothesis that genetic polymorphisms in the DAO gene are associated with methadone treatment dose and responses, we selected four single nucleotide polymorphisms (SNPs) in DAO from the literature reports of the Taiwanese population. SNPs were genotyped in 344 MMT patients. In this study, we identified a functional SNP rs55944529 in the DAO gene that reveals a modest but significant association with the methadone dosage in the recessive model of analysis (P = 0.003) and plasma concentrations (P = 0.003) in MMT patients. However, it did not show association with plasma methadone concentration in multiple linear regression analysis. It is also associated with the methadone adverse reactions of dry mouth (P = 0.002), difficulty with urination (P = 0.0003) in the dominant model, and the withdrawal symptoms of yawning (P = 0.005) and gooseflesh skin (P = 0.004) in the recessive model. Our results suggest a role of the indirect regulatory mechanisms of the NMDA reporter, possibly via the DAO genetic variants, in the methadone dose and some adverse reactions in MMT patients.

Increase in plasma CCL11 (Eotaxin-1) in patients with alcohol dependence and changes during detoxification.

BACKGROUND: Alcohol is known to modulate the immune system. Neuroinflammatory cytokine dysregulation plays an essential role in the pathophysiology of alcohol dependence (AD). Preclinical studies have indicated that alcohol consumption upregulates the pro-inflammatory cytokine CC motif ligand 11 (CCL11, also known as eotaxin-1). We examined CCL11 levels in patients with AD and in mice administered alcohol. METHODS: The plasma CCL11 levels of 151 patients with AD and 116 healthy controls were measured. In addition, we followed the CCL11 levels, alcohol cravings and psychological symptoms in patients with AD after 1 and 2 weeks of detoxification. Furthermore, we examined CCL11 changes in mice administered alcohol for 5 days. RESULTS: CCL11 levels were higher in patients with AD than in controls and declined during detoxification. CCL11 levels were positively correlated with AD severity (p < 0.001). Furthermore, mice exposed to alcohol exhibited a higher CCL11 level. The receiver operating characteristic curve revealed that a CCL11 level of 72.5 pg/mL could significantly differentiate patients with AD from controls (area under the curve: 0.77; p < 0.001). Reductions in CCL11 levels during detoxification were correlated with reductions in alcohol craving, depression, and anxiety. CONCLUSIONS: Our data from humans and mice suggest that chronic alcohol consumption is associated with an increase in CCL11 levels. CCL11 levels are correlated with AD severity and may be a potential indicator of AD. The CCL11 reduction after alcohol discontinuation is associated with alleviation of clinical symptoms. Collectively, our findings suggest that CCL11 is involved in the neurobiological mechanisms underlying AD.

Splice-Site Variants in the Gene Encoding GABA-A Receptor Delta Subunit Are Associated with Amphetamine Use in Patients under Methadone Maintenance Treatment.

Chronic opioid use disorder patients often also use other substances such as amphetamines. The gene-based analysis method was applied in the genomic database obtained from our previous study with 343 methadone maintenance treatment (MMT) patients. We found that the gene encoding gamma-aminobutyric acid type A receptors (GABA-A receptor) delta subunit isoforms (GABRD) was associated with amphetamine use in heroin dependent patients under MMT in Taiwan. A total of 15% of the 343 MMT patients tested positive for amphetamine in the urine toxicology test. Two genetic variants in the GABRD, rs2889475 and rs2376805, were found to be associated with the positive urine amphetamine test. They are located in the exon 1 of the splice variant and altered amino acid compositions (T126I, C/T, for rs2889475, and R252Q, G/A, for rs2376805). The CC genotype carriers of rs2889475 showed a four times higher risk of amphetamine use than those with TT genotype. The GG genotype carriers of rs2376805 showed a three times higher risk of amphetamine use than the AA genotype carriers. To our knowledge, this is the first report that demonstrated an association of the delta splice variant isoform in the GABA-A receptor with an increased risk of amphetamine use in MMT patients. Our results suggest that rs2889475 and rs2376805 may be indicators for the functional role and risk of amphetamine use in MMT patients.

Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose.

AIMS/HYPOTHESIS: An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses. METHODS: We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data. RESULTS: Our meta-analysis identified 18 significant (p < 5 × 10-8) and independent SNPs for fasting glucose. Interestingly, all 18 SNPs were meQTLs. The MR analysis identified seven CpGs near the G6PC2 gene that mediated the effects of a significant SNP (rs2232326) in the gene on fasting glucose. The MR effects for two CpGs were replicated using summary data based on the European population, using an exonic SNP rs2232328 in G6PC2 as the instrument. CONCLUSIONS/INTERPRETATION: Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.

pWGBSSimla: a profile-based whole-genome bisulfite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions.

MOTIVATION: DNA methylation plays an important role in regulating gene expression. DNA methylation is commonly analyzed using bisulfite sequencing (BS-seq)-based designs, such as whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and oxidative bisulfite sequencing (oxBS-seq). Furthermore, there has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e. methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e. allele-specific methylation or ASM) and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate different BS-seq data types (e.g. WGBS, RRBS and oxBS-seq) while modeling meQTLs, ASM and DMRs. RESULTS: We developed profile-based whole-genome bisulfite sequencing data simulator (pWGBSSimla), a profile-based bisulfite sequencing data simulator, which simulates WGBS, RRBS and oxBS-seq data for different cell types based on real data. meQTLs and ASM are modeled based on the block structures of the methylation status at CpGs, whereas the simulation of DMRs is based on observations of methylation rates in real data. We demonstrated that pWGBSSimla adequately simulates data and allows performance comparisons among different methylation analysis methods. AVAILABILITY AND IMPLEMENTATION: pWGBSSimla is available at https://omicssimla.sourceforge.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic polymorphisms in the opioid receptor delta 1 (OPRD1) gene are associated with methadone dose in methadone maintenance treatment for heroin dependence.

Delta opioid receptor (DOR) is well known to be involved in heroin dependence. This study tested the hypothesis that single nucleotide polymorphisms (SNPs) in the opioid receptor delta 1 (OPRD1) gene coding region are associated with treatment responses in a methadone maintenance therapy (MMT) cohort in Taiwan. Three hundred forty-four MMT patients were recruited. Diastolic/systolic blood pressure, heart rate, methadone dosage, and plasma concentrations of methadone were recorded. Twenty-five SNPs located within the OPRD1 genetic region were selected and genotyped from the genomic DNA of all 344 participants. After pairwise tagger analyses, tagger SNP rs204047 showed a significant association with methadone dosage (P = 0.0019), and tagger SNPs rs204047 and rs797397 were significantly associated with plasma R, S-methadone concentrations (P < 0.0006) in patients tested negative in the urine morphine test, which indicated patients with a better response to MMT. The major genotype carriers showed a higher methadone dosage and higher plasma concentrations of R, S-methadone than the minor genotype carriers. The results indicated that OPRD1 genetic variants were associated with methadone dosage and methadone plasma concentration in MMT patients with a negative morphine test result.

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci.

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.

GRK5 Is Associated with the Regulation of Methadone Dosage in Heroin Dependence.

Background: There is no countable biomarker for opioid dependence treatment responses thus far. In this study, we recruited Taiwanese methadone maintenance treatment patients to search for genes involving the regulatory mechanisms of methadone dose by genome-wide association analyses. Methods: A total of 344 Taiwanese methadone maintenance treatment patients were included in a genome-wide association study. The involvement of GRK5 in opioid dependence was then further confirmed by gene expression study on lymphoblastoid cell lines derived from 3 independent age- and gender-matched groups: methadone maintenance treatment patients, medication-free former heroin abusers, and normal controls. Results: The results indicated that GRK5, the gene encoding an enzyme related to µ-opioid receptor desensitization, is associated with methadone dose by additive model of gene-based association analysis (P=6.76×10-5). We found that 6 of the 55 single nucleotide polymorphisms from the genome-wide genotype platform and 2 single nucleotide polymorphisms from the 29 additionally selected single nucleotide polymorphisms were significantly associated with methadone maintenance dose in both genotype and allele type (P ≤ .006), especially in patients who tested negative in the urine morphine test. The levels of GRK5 gene expression were similar between methadone maintenance treatment patients and medication-free former heroin abusers. However, the normal controls showed a significantly lower level of GRK5 gene expression than the other groups (P=.019). Conclusions: The results suggested an important role for GRK5 in the regulatory mechanisms of methadone dose and course of heroin dependence.

IGF1 Gene Is Associated With Triglyceride Levels In Subjects With Family History Of Hypertension From The SAPPHIRe And TWB Projects.

Chromosome 12q23-q24 has been linked to triglyceride (TG) levels by previous linkage studies, and it contains the Insulin-like growth factor 1 (IGF1) gene. We investigated the association between IGF1 and TG levels using two independent samples collected in Taiwan. First, based on 954 siblings in 397 families from the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe), we found that rs978458 was associated with TG levels (ß = -0.049, p = 0.0043) under a recessive genetic model. Specifically, subjects carrying the homozygous genotype of the minor allele had lower TG levels, compared with other subjects. Then, a series of stratification analyses in a large sample of 13,193 unrelated subjects from the Taiwan biobank (TWB) project showed that this association appeared in subjects with a family history (FH) of hypertension (ß = -0.045, p = 0.0000034), but not in subjects without such an FH. A re-examination of the SAPPHIRe sample confirmed that this association appeared in subjects with an FH of hypertension (ß = -0.068, p = 0.0025), but not in subjects without an FH. The successful replication in two independent samples indicated that IGF1 is associated with TG levels in subjects with an FH of hypertension in Taiwan.

Genome-wide copy number variation analysis identified deletions in SFMBT1 associated with fasting plasma glucose in a Han Chinese population.

BACKGROUND: Fasting glucose and fasting insulin are glycemic traits closely related to diabetes, and understanding the role of genetic factors in these traits can help reveal the etiology of type 2 diabetes. Although single nucleotide polymorphisms (SNPs) in several candidate genes have been found to be associated with fasting glucose and fasting insulin, copy number variations (CNVs), which have been reported to be associated with several complex traits, have not been reported for association with these two traits. We aimed to identify CNVs associated with fasting glucose and fasting insulin. RESULTS: We conducted a genome-wide CNV association analysis for fasting plasma glucose (FPG) and fasting plasma insulin (FPI) using a family-based genome-wide association study sample from a Han Chinese population in Taiwan. A family-based CNV association test was developed in this study to identify common CNVs (i.e., CNVs with frequencies ≥ 5%), and a generalized estimating equation approach was used to test the associations between the traits and counts of global rare CNVs (i.e., CNVs with frequencies <5%). We found a significant genome-wide association for common deletions with a frequency of 5.2% in the Scm-like with four mbt domains 1 (SFMBT1) gene with FPG (association p-value = 2×10-4 and an adjusted p-value = 0.0478 for multiple testing). No significant association was observed between global rare CNVs and FPG or FPI. The deletions in 20 individuals with DNA samples available were successfully validated using PCR-based amplification. The association of the deletions in SFMBT1 with FPG was further evaluated using an independent population-based replication sample obtained from the Taiwan Biobank. An association p-value of 0.065, which was close to the significance level of 0.05, for FPG was obtained by testing 9 individuals with CNVs in the SFMBT1 gene region and 11,692 individuals with normal copies in the replication cohort. CONCLUSIONS: Previous studies have found that SNPs in SFMBT1 are associated with blood pressure and serum urate concentration, suggesting that SFMBT1 may have functional implications in some metabolic-related traits.

SeqSIMLA2_exact: simulate multiple disease sites in large pedigrees with given disease status for diseases with low prevalence.

UNLABELLED: It is difficult for current simulation tools to simulate sequence data in a pre-specified pedigree structure and pre-specified affection status. Previously, we developed a flexible tool, SeqSIMLA2, for simulating sequence data in either unrelated case-control or family samples with different disease and quantitative trait models. Here we extended the tool to efficiently simulate sequences with multiple disease sites in large pedigrees with a given disease status for each pedigree member, assuming that the disease prevalence is low. AVAILABILITY AND IMPLEMENTATION: SeqSIMLA2_exact is implemented with C++ and is available at http://seqsimla.sourceforge.net.

An efficient gene-gene interaction test for genome-wide association studies in trio families.

MOTIVATION: Several efficient gene-gene interaction tests have been developed for unrelated case-control samples in genome-wide association studies (GWAS), making it possible to test tens of billions of interaction pairs of single-nucleotide polymorphisms (SNPs) in a reasonable timeframe. However, current family-based gene-gene interaction tests are computationally expensive and are not applicable to genome-wide interaction analysis. RESULTS: We developed an efficient family-based gene-gene interaction test, GCORE, for trios (i.e. two parents and one affected sib). The GCORE compares interlocus correlations at two SNPs between the transmitted and non-transmitted alleles. We used simulation studies to compare the statistical properties such as type I error rates and power for the GCORE with several other family-based interaction tests under various scenarios. We applied the GCORE to a family-based GWAS for autism consisting of approximately 2000 trios. Testing a total of 22 471 383 013 interaction pairs in the GWAS can be finished in 36 h by the GCORE without large-scale computing resources, demonstrating that the test is practical for genome-wide gene-gene interaction analysis in trios. AVAILABILITY AND IMPLEMENTATION: GCORE is implemented with C ++ and is available at http://gscore.sourceforge.net CONTACT: rchung@nhri.org.tw SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease Studies.

In disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net.

GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs.

BACKGROUND: A computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS). Current tools for GWAS interaction analysis are mainly developed for unrelated case-control samples. Relatively fewer tools for interaction analysis are available for complex disease studies with family-based design, and these tools tend to be computationally expensive. RESULTS: We developed a fast gene-gene interaction test, GCORE-sib, for discordant sib pairs and implemented the test into an efficient tool. We used simulations to demonstrate that the GCORE-sib has correct type I error rates and has comparable power to that of the regression-based interaction test. We also showed that the GCORE-sib can run more than 10 times faster than the regression-based test. Finally, the GCORE-sib was applied to a GWAS dataset with approximately 2,000 discordant sib pairs, and the GCORE-sib finished testing 19,368,078,382 pairs of SNPs within 6 days. CONCLUSIONS: An efficient gene-gene interaction tool for discordant sib pairs was developed. It will be very useful for genome-wide gene-gene interaction analysis in GWAS using discordant sib pairs. The tool can be downloaded for free at http://gcore-sib.sourceforge.net .

SeqSIMLA2: simulating correlated quantitative traits accounting for shared environmental effects in user-specified pedigree structure.

Simulation tools that simulate sequence data in unrelated cases and controls or in families with quantitative traits or disease status are important for genetic studies. The simulation tools can be used to evaluate the statistical power for detecting the causal variants when planning a genetic epidemiology study, or to evaluate the statistical properties for new methods. We previously developed SeqSIMLA version 1 (SeqSIMLA1), which simulates family or case-control data with a disease or quantitative trait model. SeqSIMLA1, and several other tools that simulate quantitative traits, do not specifically model the shared environmental effects among relatives on a trait. However, shared environmental effects are commonly observed for some traits in families, such as body mass index. SeqSIMLA1 simulates a fixed three-generation family structure. However, it would be ideal to simulate prespecified pedigree structures for studies involving large pedigrees. Thus, we extended SeqSIMLA1 to create SeqSIMLA2, which can simulate correlated traits and considers the shared environmental effects. SeqSIMLA2 can also simulate prespecified large pedigree structures. There are no restrictions on the number of individuals that can be simulated in a pedigree. We used a blood pressure example to demonstrate that SeqSIMLA2 can simulate realistic correlation structures between the systolic and diastolic blood pressure among relatives. We also showed that SeqSIMLA2 can simulate large pedigrees with large chromosomal regions in a reasonable time frame.

A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families.

BACKGROUND: Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease. Therefore, a challenge for the multi-SNP analysis is how to effectively select a subset of SNPs with promising association signals from the SNP set. RESULTS: We developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT). The OPTPDT uses general nuclear families. A variable p-value threshold algorithm is used to determine an optimal p-value threshold for selecting a subset of SNPs. A permutation procedure is used to assess the significance of the test. We used simulations to verify that the OPTPDT has correct type I error rates. Our power studies showed that the OPTPDT can be more powerful than the set-based test in PLINK, the multi-SNP FBAT test, and the p-value based test GATES. We applied the OPTPDT to a family-based autism GWAS dataset for gene-based association analysis and identified MACROD2-AS1 with genome-wide significance (p-value=2.5×10(-6)). CONCLUSIONS: Our simulation results suggested that the OPTPDT is a valid and powerful test. The OPTPDT will be helpful for gene-based or pathway association analysis. The method is ideal for the secondary analysis of existing GWAS datasets, which may identify a set of SNPs with joint effects on the disease.