Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility.

Transcriptome-wide association studies and colocalization analysis are popular computational approaches for integrating genetic-association data from molecular and complex traits. They show the unique ability to go beyond variant-level genetic-association evidence and implicate critical functional units, e.g., genes, in disease etiology. However, in practice, when the two approaches are applied to the same molecular and complex-trait data, the inference results can be markedly different. This paper systematically investigates the inferential reproducibility between the two approaches through theoretical derivation, numerical experiments, and analyses of four complex trait GWAS and GTEx eQTL data. We identify two classes of inconsistent inference results. We find that the first class of inconsistent results (i.e., genes with strong colocalization but weak transcriptome-wide association study [TWAS] signals) might suggest an interesting biological phenomenon, i.e., horizontal pleiotropy; thus, the two approaches are truly complementary. The inconsistency in the second class (i.e., genes with weak colocalization but strong TWAS signals) can be understood and effectively reconciled. To this end, we propose a computational approach for locus-level colocalization analysis. We demonstrate that the joint TWAS and locus-level colocalization analysis improves specificity and sensitivity for implicating biologically relevant genes.

A current guide to candidate gene association studies.

Important factors contribute to a gained momentum in candidate gene association studies (CGASs), including the generalized use of next-generation sequencing (NGS), growing opportunities for hospital-based research, and the availability of open-source databases and bioinformatics tools. This article summarizes the general principles and analytical methods as a guide to CGASs in today's favorable context.

MicroRNA-Mediated Suppression of Glial Cell Line-Derived Neurotrophic Factor Expression Is Modulated by a Schizophrenia-Associated Non-Coding Polymorphism.

Plasma levels of glial cell line-derived neurotrophic factor (GDNF), a pivotal regulator of differentiation and survival of dopaminergic neurons, are reportedly decreased in schizophrenia. To explore the involvement of GDNF in the pathogenesis of the disease, a case-control association analysis was performed between five non-coding single nucleotide polymorphisms (SNP) across the GDNF gene and schizophrenia. Of them, the 'G' allele of the rs11111 SNP located in the 3' untranslated region (3'-UTR) of the gene was found to associate with schizophrenia. In silico analysis revealed that the rs11111 'G' allele might create binding sites for three microRNA (miRNA) species. To explore the significance of this polymorphism, transient co-transfection assays were performed in human embryonic kidney 293T (HEK293T) cells with a luciferase reporter construct harboring either the 'A' or 'G' allele of the 3'-UTR of GDNF in combination with the hsa-miR-1185-1-3p pre-miRNA. It was demonstrated that in the presence of the rs11111 'G' (but not the 'A') allele, hsa-miR-1185-2-3p repressed luciferase activity in a dose-dependent manner. Deletion of the miRNA binding site or its substitution with the complementary sequence abrogated the modulatory effect. Our results imply that the rs11111 'G' allele occurring more frequently in patients with schizophrenia might downregulate GDNF expression in a miRNA-dependent fashion.

Leveraging a surrogate outcome to improve inference on a partially missing target outcome.

Sample sizes vary substantially across tissues in the Genotype-Tissue Expression (GTEx) project, where considerably fewer samples are available from certain inaccessible tissues, such as the substantia nigra (SSN), than from accessible tissues, such as blood. This severely limits power for identifying tissue-specific expression quantitative trait loci (eQTL) in undersampled tissues. Here we propose Surrogate Phenotype Regression Analysis (Spray) for leveraging information from a correlated surrogate outcome (eg, expression in blood) to improve inference on a partially missing target outcome (eg, expression in SSN). Rather than regarding the surrogate outcome as a proxy for the target outcome, Spray jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. We describe and implement an expectation conditional maximization algorithm for performing estimation in the presence of bilateral outcome missingness. Spray estimates the same association parameter estimated by standard eQTL mapping and controls the type I error even when the target and surrogate outcomes are truly uncorrelated. We demonstrate analytically and empirically, using simulations and GTEx data, that in comparison with marginally modeling the target outcome, jointly modeling the target and surrogate outcomes increases estimation precision and improves power.

Genetic variability of oxidative stress and DNA repair genes associated with pre-treatment cancer-related fatigue in women with breast cancer.

PURPOSE: Investigate potential relationships between pre-treatment cancer-related fatigue (CRF) in women with early-stage breast cancer and variation in genes involved with oxidative stress and DNA repair. METHODS: Investigated 39 functional and tagging single nucleotide polymorphisms (SNPs) in genes involved in oxidative stress (CAT, GPX1, SEPP1, SOD1, and SOD2) and DNA repair (ERCC2, ERCC3, ERCC5, and PARP1) in a sample (N = 219) that included n = 138 postmenopausal women diagnosed with early-stage breast cancer before initiation of therapy and n = 81 age- and education-matched healthy controls. Using the Profile of Mood States Fatigue/Inertia Subscale, fatigue occurrence and severity were evaluated in both groups. Regression analysis was used to independently identify significant SNPs for three outcomes: 1) any fatigue versus no fatigue, 2) clinically meaningful versus non-clinically meaningful fatigue, and 3) fatigue severity. Using a weighted multi-SNP method, genetic risk scores (GRS) were calculated for each participant, and GRS models were constructed for each outcome. Models were adjusted for age, pain, and symptoms of depression and anxiety. RESULTS: SEPP1rs3877899, ERCC2rs238406, ERCC2rs238416, ERCC2rs3916874, and ERCC3rs2134794 were associated with fatigue occurrence and had a significant GRS model (OR = 1.317, 95%CI [1.067, 1.675], P ≤ 0.05). One SNP, SOD2rs5746136, was significant for clinically meaningful fatigue; therefore, a GRS model could not be constructed. ERCC3rs4150407, ERCC3rs4150477, and ERCC3rs2134794 were associated with fatigue severity with a significant GRS model (b = 1.010, 95%CI [1.647, 4.577], R2 = 6.9%, P ≤ 0.01). CONCLUSIONS: These results may contribute to identifying patients who are at risk of developing CRF. Oxidative stress and DNA repair biological pathways may be involved with CRF.

Targeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea.

Rationale: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epidemiologic evidence supporting the importance of genetic factors influencing OSA but limited data implicating specific genes. Objectives: To search for rare variants contributing to OSA severity. Methods: Leveraging high-depth genomic sequencing data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the CFS (Cleveland Family Study), followed by multistage gene-based association analyses in independent cohorts for apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry. Measurements and Main Results: Linkage analysis in the CFS identified a suggestive linkage peak on chromosome 7q31 (LOD = 2.31). Gene-based analysis identified 21 noncoding rare variants in CAV1 (Caveolin-1) associated with lower AHI after accounting for multiple comparisons (P = 7.4 × 10-8). These noncoding variants together significantly contributed to the linkage evidence (P < 10-3). Follow-up analysis revealed significant associations between these variants and increased CAV1 expression, and increased CAV1 expression in peripheral monocytes was associated with lower AHI (P = 0.024) and higher minimum overnight oxygen saturation (P = 0.007). Conclusions: Rare variants in CAV1, a membrane-scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher CAV1 gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.

Shared genomic segment analysis with equivalence testing.

An important aspect of disease gene mapping is replication, that is, a putative finding in one group of individuals is confirmed in another set of individuals. As it can happen by chance that individuals share an estimated disease position, we developed a statistical approach to determine the p-value for multiple individuals or families to share a possibly small number of candidate susceptibility variants. Here, we focus on candidate variants for dominant traits that have been obtained by our previously developed heterozygosity analysis, and we are testing the sharing of candidate variants obtained for different individuals. Our approach allows for multiple pathogenic variants in a gene to contribute to disease, and for estimated disease variant positions to be imprecise. Statistically, the method developed here falls into the category of equivalence testing, where the classical null and alternative hypotheses of homogeneity and heterogeneity are reversed. The null hypothesis situation is created by permuting genomic locations of variants for one individual after another. We applied our methodology to the ALSPAC data set of 1,927 whole-genome sequenced individuals, where some individuals carry a pathogenic variant for the BRCA1 gene, but no two individuals carry the same variant. Our shared genomic segment analysis found significant evidence for BRCA1 pathogenic variants within ±5 kb of a given DNA variant.

A review of kernel methods for genetic association studies.

Evaluating the association of multiple genetic variants with a trait of interest by use of kernel-based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.

Heterozygosity mapping for human dominant trait variants.

Homozygosity mapping is a well-known technique to identify runs of homozygous variants that are likely to harbor genes responsible for autosomal recessive disease, but a comparable method for autosomal dominant traits has been lacking. We developed an approach to map dominant disease genes based on heterozygosity frequencies of sequence variants in the immediate vicinity of a dominant trait. We demonstrate through theoretical analysis that DNA variants surrounding an inherited dominant disease variant tend to have increased heterozygosity compared with variants elsewhere in the genome. We confirm existence of this phenomenon in sequence data with known dominant pathogenic variants obtained on family members and in unrelated population controls. A computer-based approach to estimating empirical significance levels associated with our test statistics shows genome-wide p-values smaller than 0.05 for many but not all of the individuals carrying a pathogenic variant.

On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows.

For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

KLK4 Gene and Dental Decay: Replication in a South Brazilian Population.

OBJECTIVE: The objective of this research was to identify and replicate the participation of KLK4 gene polymorphisms in the susceptibility to dental decay. METHODS: A total of 200 patients were recruited using ICDAS criteria - 100 of them with dental caries and 100 with no history of the disease. Buccal cells were collected and the DNA was extracted and amplified using PCR. RESULTS: During the descriptive analysis, the variables ethnicity, biofilm, and gingivitis and the markers rs2242670 and rs2978642 were statistically significant. In the multivariate analysis, the marker rs2242670 and the variable biofilm maintained statistical significance. CONCLUSION: Genetic variations in the KLK4 gene may contribute to dental decay.

Genome-wide analysis of spina bifida risk variants in a case-control study from Bangladesh.

BACKGROUND: Human studies of genetic risk factors for neural tube defects, severe birth defects associated with long-term health consequences in surviving children, have predominantly been restricted to a subset of candidate genes in specific biological pathways including folate metabolism. METHODS: In this study, we investigated the association of genetic variants spanning the genome with risk of spina bifida (i.e., myelomeningocele and meningocele) in a subset of families enrolled from December 2016 through December 2022 in a case-control study in Bangladesh, a population often underrepresented in genetic studies. Saliva DNA samples were analyzed using the Illumina Global Screening Array. We performed genetic association analyses to compare allele frequencies between 112 case and 121 control children, 272 mothers, and 128 trios. RESULTS: In the transmission disequilibrium test analyses with trios only, we identified three novel exonic spina bifida risk loci, including rs140199800 (SULT1C2, p = 1.9 × 10-7), rs45580033 (ASB2, p = 4.2 × 10-10), and rs75426652 (LHPP, p = 7.2 × 10-14), after adjusting for multiple hypothesis testing. Association analyses comparing cases and controls, as well as models that included their mothers, did not identify genome-wide significant variants. CONCLUSIONS: This study identified three novel single nucleotide polymorphisms involved in biological pathways not previously associated with neural tube defects. The study warrants replication in larger groups to validate findings and to inform targeted prevention strategies.

Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.

BACKGROUND: Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD. METHODS: Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses. RESULTS: Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets. CONCLUSIONS: Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.

Genetic variants in HHIP are associated with FEV1 in subjects with chronic obstructive pulmonary disease.

BACKGROUND AND OBJECTIVE: Chronic obstructive pulmonary disease (COPD) is a complex disease in which multiple genes and their interaction with environmental factors contribute to disease development. Recent genome-wide association studies in COPD revealed the chromosome 4 region near hedgehog interacting protein (HHIP). However, these studies were mostly performed in Caucasians, and additional studies in multiple ethnic groups are needed. We investigated genetic associations of HHIP in Korean COPD and control subjects. METHODS: Two separate case-control studies were performed. Firstly, 139 subjects with COPD and 199 control subjects were selected from the Soonchunhyang University Bucheon Hospital Biobank. Secondly, 219 individuals with COPD were recruited from the Korean Obstructive Lung Disease (KOLD) cohort. The control subjects consisted of 305 smokers or ex-smokers with normal lung function who were registered in the Korean Genome Epidemiology Study. Associations between COPD susceptibility and single-nucleotide polymorphism (SNP) genotypes were tested by logistic regression. Associations between lung function and SNP genotypes were tested by linear regression. RESULTS: In the first study, the mean forced expiratory volume in 1 s (FEV1 ) of these COPD subjects was 1.32 L. None of 15 SNP was significantly associated with COPD susceptibility. However, four SNP associated significantly with FEV1 in subjects with COPD. In the KOLD cohort study, two SNPs (rs11938704 and rs10013495) near HHIP were significantly associated with FEV1 (P = 0.0001 and 0.001, respectively) in subjects with COPD. CONCLUSIONS: Genetic variants in HHIP are associated with lung function in subjects with COPD.

SMAD3 rs36221701 T>C polymorphism impacts COPD susceptibility in the Kashi population.

Small mother against decapentaplegic (SMAD) family member 3 (SMAD3) is well correlated with the inflammatory response of chronic obstructive pulmonary disease (COPD). A previous study indicated that the single nucleotide polymorphism (SNP) rs36221701 of SMAD3 was related to the risk of inflammatory disease. Hence, given the pathogenesis of COPD is intently associated with smoking and gene polymorphism, this study aims to analyze the relationship between SMAD3 rs36221701 and COPD susceptibility, and to explore whether the interaction is related to smoking status. We studied the association between the rs36221701 and rs34307601 of SMAD3 and COPD susceptibility, a total of 541 COPD patients and 534 controls of the Uyghur population were recruited at the First People's Hospital and the village of Kashi. The interrelation of the two SNPs with the risk of COPD was determined by calculating odds ratio (OR) and 95% confidence interval (95% CI). We found a significant association between the rs36221701 and COPD risk in the non-smoking population. TC genotype showed a significant decreased association with COPD risk (OR = 0.59, 95% CI = 0.41-0.83, P < 0.05), but CC genotype can increased the COPD risk (OR > 1, P < 0.05). In addition, COPD susceptibility was not related to the genetic variations in the rs34307601 (P > 0.05). In conclusion, we confirmed that the SMAD3 rs36221701 may be associated with COPD susceptibility in the Chinese Uyghur population, especially among non-smokers. Our data provide new light for the relationship between SMAD3 gene polymorphisms and COPD susceptibility in the Chinese Uyghur population.

FOXN3 and GDNF Polymorphisms as Common Genetic Factors of Substance Use and Addictive Behaviors.

Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the "lifetime other drugs" variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.

DHCR7 rs12785878 T>C Polymorphism Is Associated With an Increased Risk of Early Onset of Alzheimer's Disease in Chinese Population.

Background: Vitamin D insufficiency has been considered a risk factor for Alzheimer's disease (AD) in several studies. Recently, four single-nucleotide polymorphisms (SNPs) to be genome-wide significant for 25-hydroxyvitamin D [25(OH)D] were identified to have an association with the risk of AD. These include GC rs2282679 A>C, CYP2R1 rs10741657 T>C, DHCR7 rs12785878 T>C, and CYP24A1 rs6013897 T>A. However, the association between these polymorphisms and AD susceptibility in the Chinese population remains unclear. Methods: A case-control cohort study was conducted in 676 AD patients (mean age at onset was 69.52 ± 10.90 years, male: 39.2%) and 551 healthy controls (mean age was 67.73 ± 6.02 years, male: 44.8%). Genotyping was determined by PCR and SNaPshot sequencing. To determine whether the four SNPs account for risks in AD in Chinese population, multivariate logistic regression models were performed. Stratified analysis was performed based on gender and age of onset of AD, separately. Statistical significance was set at 0.0125 (0.05/4) based on Bonferroni correction. Findings: DHCR7 rs12785878 T>C was found to be significantly associated with an increased risk of early-onset Alzheimer's disease (EOAD) (n = 300, risk allele C, adjusted OR = 1.542, adjusted 95% CI = 1.176-2.023, p = 0.002). There was no statistical significance of the other three SNPs between the two groups. Interpretation: Our results suggested that DHCR7 rs12785878 T>C might be associated with an increased risk of EOAD in the Chinese population, while other polymorphisms related to vitamin D insufficiency might not be. However, due to the limited data in this study, replication studies in a larger sample are required.

Evaluation of Male Fertility-Associated Loci in a European Population of Patients with Severe Spermatogenic Impairment.

Infertility is a growing concern in developed societies. Two extreme phenotypes of male infertility are non-obstructive azoospermia (NOA) and severe oligospermia (SO), which are characterized by severe spermatogenic failure (SpF). We designed a genetic association study comprising 725 Iberian infertile men as a consequence of SpF and 1058 unaffected controls to evaluate whether five single-nucleotide polymorphisms (SNPs), previously associated with reduced fertility in Hutterites, are also involved in the genetic susceptibility to idiopathic SpF and specific clinical entities. A significant difference in the allele frequencies of USP8-rs7174015 was observed under the recessive model between the NOA group and both the control group (p = 0.0226, OR = 1.33) and the SO group (p = 0.0048, OR = 1.78). Other genetic associations for EPSTI1-rs12870438 and PSAT1-rs7867029 with SO and between TUSC1-rs10966811 and testicular sperm extraction (TESE) success in the context of NOA were observed. In silico analysis of functional annotations demonstrated cis-eQTL effects of such SNPs likely due to the modification of binding motif sites for relevant transcription factors of the spermatogenic process. The findings reported here shed light on the molecular mechanisms leading to severe phenotypes of idiopathic male infertility, and may help to better understand the contribution of the common genetic variation to the development of these conditions.

Increased risk of hypercholesterolemia in a French and Lebanese population due to an interaction between rs2569190 in CD14 and gender.

RATIONALE: Since identifying gender-specific genetic associations may have a significant impact on public health, we studied the interaction between rs2569190 in CD14 (cluster of differentiation 14) and gender in relation to the lipid traits in two independent populations. METHODS: We first tested the interaction in a discovery population (SFS, n = 956), then replicated it in an independent population (LGP, n = 460), followed by a meta-analysis (n = 1,416). Finally, stratification according to gender was conducted to test the association between rs2569190 and lipid traits. Binary multiple logistic regression models were used while correcting for many confounders. Power calculations were also performed. RESULTS: An interaction between rs2569190 and gender, which increased the risk of total cholesterol levels in SFS, was found (OR = 2.151 and P = 0.05). This interaction was further replicated in the LGP (OR = 1.353 and P < 0.001), and the meta-analysis showed an overall significant interaction (OR = 1.436 and Pmeta = 0.02). Similarly, the meta-analysis showed an overall significant positive effect (OR = 1.204 and Pmeta = 0.004) for low-density lipoprotein cholesterol levels. Overall, 1,416 patients were evaluated, and the statistical heterogeneity was low, with I2 estimates ranging between 0% and 22.2%. In contrast, rs2569190 in CD14 did not show any significant interaction with gender influencing high-density lipoprotein levels and triglycerides levels in both populations. CONCLUSION: An interaction between rs2569190 in CD14 and gender increased the risk of hypercholesterolemia in two independent populations with a gender-specific effect in males.