ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS

Gene-Based Association Mapping for Dental Caries in The GENEVA Consortium

OBJECTIVE

Dental caries is a multifactorial disease with high prevalence in both children and adults. Recent genome-wide association studies (GWASs) have revealed that genetic factors play an important role in caries incidence. However, existing methods are not sufficient to identify caries-associated genes, due to the complex correlation structure of caries GWAS data, and lack of appropriate summarization at the gene level. This paper attempts to address that by analyzing data from the Gene, Environment Association Studies (GENEVA) consortium.

METHODS

We investigated gene-based genetic associations for dental caries based on genome-wide data derived from the GENEVA database, with adjustment to covariates, linkage disequilibrium among single-nucleotide polymorphisms, and family relations, in sampled individuals.

RESULTS

Several suggestive genes were identified, in which some of them have been previously found to have potential biological functions on cariogenesis.

CONCLUSIONS

By comparing the gene sets identified from gene-based and SNP-based association testing methods, we found a non-negligible overlap, which indicates that our gene-based analysis can provide substantial supplement to the traditional GWAS analysis.

Gene-based association analysis for bivariate time-to-event data through functional regression with copula models

Several gene-based association tests for time-to-event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene-based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age-related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age-related Eye Disease Study, and to identify the gene regions that are associated with AMD progression.

Rare variants in SLC6A4 cause susceptibility to major depressive disorder with suicidal ideation in Han Chinese adolescents and young adults

BACKGROUND

Suicidal ideation (SI) is the most serious symptom of major depressive disorder (MDD) and considered an extreme state. The serotonin transporter gene (SLC6A4) plays a significant role in MDD and suicide pathophysiology. Previous studies have revealed an association between common variants of SLC6A4 with the risk of MDD and suicide. However, very few studies have so far focused on the degree to which rare variants of SLC6A4 are responsible for the depression observed in adolescent and young adult suicide patients. The aim of this study was to examine the impact of common and rare variants of SLC6A4 on the risk of Han Chinese adolescents and young adults suffering MDD with SI.

METHODS

Targeted sequencing of the SLC6A4 gene was conducted using FastTarget technology in Han Chinese adolescents and young adults, of which 74 were MDD patients with SI and 150 were healthy controls. Gene-based association analyses of rare variants were performed using enrichment analysis and a cumulative allele test. An allele association study was performed against common variants.

RESULTS

After sequencing and bioinformatics analysis, a total of 15 single nucleotide variants (SNVs) were detected in the targeted regions from all participants, including 9 common and 6 rare variants. Among these, 5 rare variants were identified within the study group. Enrichment analysis of rare variants demonstrated a statistical difference (p = 0.042) between the study and control groups. Using cumulative allele analysis, alternative alleles in the SLC6A4 gene exhibited an association with MDD patients with SI (cumulative allele: OR = 10.18, 95% CI = 1.18-87.32, p = 0.017). No significant association was found between the 9 common SLC6A4 variants and MDD patients with SI.

CONCLUSIONS

Our results suggest that rare variants of SLC6A4 may contribute to a genetic risk of adolescents and young adults suffering MDD with SI.