Failure to replicate a genetic association may provide important clues about genetic architecture

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis

PURPOSE

Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.

METHODS

We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.

RESULTS

This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.

CONCLUSION

Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Mapping the genetic architecture of idiopathic pulmonary fibrosis: Meta-analysis and epidemiological evidence of case-control studies

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a rare and devastating fibrotic lung disorder with unknown etiology. Although it is believed that genetic component is an important risk factor for IPF, a comprehensive understanding of its genetic landscape is lacking. Hence, we aimed to highlight the susceptibility genes and pathways implicated in IPF pathogenesis through a two-staged systematic literature search of genetic association studies on IPF, followed by meta-analysis and pathway enrichment analysis.

METHODS

This study was performed based on PRISMA guidelines (PROSPERO, registration number: CRD42022297970). The first search was performed (using PubMed and Web of Science) retrieving a total of 5642 articles, of which 52 were eligible for inclusion in the first stage. The second search was performed (using PubMed, Web of Science and Scopus) for ten polymorphisms, identified from the first search, with 2 or more studies. Finally, seven polymorphisms, [rs35705950/MUC5B, rs2736100/TERT, rs2609255/FAM13A, rs2076295/DSP, rs12610495/DPP9, rs111521887/TOLLIP and rs1800470/TGF-β1] qualified for meta-analyses. The epidemiological credibility was evaluated using Venice criteria.

RESULTS

From the systematic review, 222 polymorphisms in 118 genes showed a significant association with IPF susceptibility. Meta-analyses findings revealed significant association of rs35705950/T [OR = 3.92(3.26-4.57)], rs2609255/G [OR = 1.50(1.18-1.82)], rs2076295/G [OR = 1.19(0.82-1.756)], rs12610495/G [OR = 1.28(1.12-1.44)], rs2736100/C [OR = 0.68(0.54-0.82), rs111521887/G [OR = 1.34(1.06-1.61)] and suggestive evidence for rs1800470/T [OR = 1.08(0.82-1.34)] with IPF susceptibility. Four polymorphisms- rs35705950/MUC5B, rs2736100/TERT, rs2076295/DSP and rs111521887/TOLLIP, exhibited substantial epidemiological evidence supporting their association with IPF risk. Gene ontology and pathway enrichment analysis performed on IPF risk-associated genes identified a critical role of genes in mucin production, immune response and inflammation, host defence, cell-cell adhesion and telomere maintenance.

CONCLUSIONS

Our findings present the most prominent IPF-associated genetic risk variants involved in alveolar epithelial injuries (MUC5B, TERT, FAM13A, DSP, DPP9) and epithelial-mesenchymal transition (TOLLIP, TGF-β1), providing genetic and biological insights into IPF pathogenesis. However, further experimental research and human studies with larger sample sizes, diverse ethnic representation, and rigorous design are warranted.

Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild-caught populations of Drosophila melanogaster

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding

Mendelian heredity is the cornerstone of plant breeding and has been used to develop new varieties of plants since the 19th century. However, there are several breeding cases, such as cytoplasmic inheritance, methylation, epigenetics, hybrid vigor, and loss of heterozygosity (LOH), where Mendelian heredity is not applicable, known as non-Mendelian heredity. This type of inheritance can be influenced by several factors besides the genetic architecture of the plant and its breeding potential. Therefore, exploring various non-Mendelian heredity mechanisms, their prevalence in plants, and the implications for plant breeding is of paramount importance to accelerate the pace of crop improvement. In this review, we examine the current understanding of non-Mendelian heredity in plants, including the mechanisms, inheritance patterns, and applications in plant breeding, provide an overview of the various forms of non-Mendelian inheritance (including epigenetic inheritance, cytoplasmic inheritance, hybrid vigor, and LOH), explore insight into the implications of non-Mendelian heredity in plant breeding, and the potential it holds for future research.

Inflammation and Oxidative Stress Gene Variability in Retinal Detachment Patients with and without Proliferative Vitreoretinopathy

This study investigated the association between certain genetic variations and the risk of developing proliferative vitreoretinopathy (PVR) after surgery. The study was conducted on 192 patients with primary rhegmatogenous retinal detachment (RRD) who underwent 3-port pars plana vitrectomy (PPV). The distribution of single nucleotide polymorphisms (SNPs) located in genes involved in inflammation and oxidative stress associated with PVR pathways were analyzed among patients with and without postoperative PVR grade C1 or higher. A total of 7 defined SNPs of 5 genes were selected for genotyping: rs4880 (SOD2); rs1001179 (CAT); rs1050450 (GPX1); rs1143623, rs16944, rs1071676 (IL1B); rs2910164 (MIR146A) using competitive allele-specific polymerase chain reaction. The association of SNPs with PVR risk was evaluated using logistic regression. Furthermore, the possible association of SNPs with postoperative clinical parameters was evaluated using non-parametric tests. The difference between two genotype frequencies between patients with or without PVR grade C1 or higher was found to be statistically significant: SOD2 rs4880 and IL1B rs1071676. Carriers of at least one polymorphic IL1B rs1071676 GG allele appeared to have better postoperative best-corrected visual acuity only in patients without PVR (p = 0.070). Our study suggests that certain genetic variations may play a role in the development of PVR after surgery. These findings may have important implications for identifying patients at higher risk for PVR and developing new treatments.

Sample Size Calculation in Genetic Association Studies: A Practical Approach

Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene-disease association.

Identification of 4 New Loci Associated With Primary Hyperparathyroidism (PHPT) and a Polygenic Risk Score for PHPT

CONTEXT

A hypothesis-free genetic association analysis has not been reported for patients with primary hyperparathyroidism (PHPT).

OBJECTIVE

We aimed to investigate genetic associations with PHPT using both genome-wide association study (GWAS) and candidate gene approaches.

METHODS

A cross-sectional study was conducted among patients of European White ethnicity recruited in Tayside (Scotland, UK). Electronic medical records were used to identify PHPT cases and controls, and linked to genetic biobank data. Genetic associations were performed by logistic regression models and odds ratios (ORs). The combined effect of the genotypes was researched by genetic risk score (GRS) analysis.

RESULTS

We identified 15 622 individuals for the GWAS that yielded 34 top single-nucleotide variations (formerly single-nucleotide polymorphisms), and LPAR3-rs147672681 reached genome-wide statistical significance (P = 1.2e-08). Using a more restricted PHPT definition, 8722 individuals with data on the GWAS-identified loci were found. Age- and sex-adjusted ORs for the effect alleles of SOX9-rs11656269, SLITRK5-rs185436526, and BCDIN3D-AS1-rs2045094 showed statistically significant increased risks (P < 1.5e-03). GRS analysis of 5482 individuals showed an OR of 2.51 (P = 1.6e-04), 3.78 (P = 4.0e-08), and 7.71 (P = 5.3e-17) for the second, third, and fourth quartiles, respectively, compared to the first, and there was a statistically significant linear trend across quartiles (P < 1.0e-04). Results were similar when stratifying by sex.

CONCLUSION

Using genetic loci discovered in a GWAS of PHPT carried out in a Scottish population, this study suggests new evidence for the involvement of genetic variants at SOX9, SLITRK5, LPAR3, and BCDIN3D-AS1. It also suggests that male and female carriers of greater numbers of PHPT-risk alleles both have a statistically significant increased risk of PHPT.

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in genome-wide association studies has been well-studied in the context of Winner's Curse, which is the inflation of effect size estimates for significant variants due to statistical chance. However, Winner's Curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes genome-wide association studies and replication studies to jointly model Winner's Curse and study-specific heterogeneity due to confounding factors. We apply this framework to 100 genome-wide association studies from the Human Genome-Wide Association Studies Catalog and observe that there is a large range in the level of estimated confounding. We demonstrate how this framework can be used to distinguish when studies fail to replicate due to statistical noise and when they fail due to confounding.

Alcohol use disorder is associated with DNA methylation-based shortening of telomere length and regulated by TESPA1: implications for aging

Chronic heavy alcohol consumption is associated with increased mortality and morbidity and often leads to premature aging; however, the mechanisms of alcohol-associated cellular aging are not well understood. In this study, we used DNA methylation derived telomere length (DNAmTL) as a novel approach to investigate the role of alcohol use on the aging process. DNAmTL was estimated by 140 cytosine phosphate guanines (CpG) sites in 372 individuals with alcohol use disorder (AUD) and 243 healthy controls (HC) and assessed using various endophenotypes and clinical biomarkers. Validation in an independent sample of DNAmTL on alcohol consumption was performed (N = 4219). Exploratory genome-wide association studies (GWAS) on DNAmTL were also performed to identify genetic variants contributing to DNAmTL shortening. Top GWAS findings were analyzed using in-silico expression quantitative trait loci analyses and related to structural MRI hippocampus volumes of individuals with AUD. DNAmTL was 0.11-kilobases shorter per year in AUD compared to HC after adjustment for age, sex, race, and blood cell composition (p = 4.0 × 10-12). This association was partially attenuated but remained significant after additionally adjusting for BMI, and smoking status (0.06 kilobases shorter per year, p = 0.002). DNAmTL shortening was strongly associated with chronic heavy alcohol use (ps < 0.001), elevated gamma-glutamyl transferase (GGT), and aspartate aminotransferase (AST) (ps < 0.004). Comparison of DNAmTL with PCR-based methods of assessing TL revealed positive correlations (R = 0.3, p = 2.2 × 10-5), highlighting the accuracy of DNAmTL as a biomarker. The GWAS meta-analysis identified a single nucleotide polymorphism (SNP), rs4374022 and 18 imputed ones in Thymocyte Expressed, Positive Selection Associated 1(TESPA1), at the genome-wide level (p = 3.75 × 10-8). The allele C of rs4374022 was associated with DNAmTL shortening, lower hippocampus volume (p < 0.01), and decreased mRNA expression in hippocampus tissue (p = 0.04). Our study demonstrates DNAmTL-related aging acceleration in AUD and suggests a functional role for TESPA1 in regulating DNAmTL length, possibly via the immune system with subsequent biological effects on brain regions negatively affected by alcohol and implicated in aging.

A multi-step genomic approach prioritized TBKBP1 gene as relevant for multiple sclerosis susceptibility

Background

Over 200 genetic loci have been associated with multiple sclerosis (MS) explaining ~ 50% of its heritability, suggesting that additional mechanisms may account for the “missing heritability” phenomenon.

Objective

To analyze a large cohort of Italian individuals to identify markers associated with MS with potential functional impact in the disease.

Methods

We studied 2571 MS and 3234 healthy controls (HC) of continental Italian origin. Discovery phase included a genome wide association study (1727 MS, 2258 HC), with SNPs selected according to their association in the Italian cohort only or in a meta-analysis of signals with a cohort of European ancestry (4088 MS, 7144 HC). Top associated loci were then tested in two Italian cohorts through array-based genotyping (903 MS, 884 HC) and pool-based target sequencing (588 MS, 408 HC). Finally, functional prioritization through conditional eQTL and mQTL has been performed.

Results

Top associated signals overlap with already known MS loci on chromosomes 3 and 17. Three SNPs (rs4267364, rs8070463, rs67919208), all involved in the regulation of TBKBP1, were prioritized to be functionally relevant.

Conclusions

No evidence of novel signal of association with MS specific for the Italian continental population has been found; nevertheless, two MS loci seems to play a relevant role, raising the interest to further investigations for TBKBP1 gene.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11109-8.

Genome-wide analysis of genetic predisposition to common polygenic cancers

Lung, breast, prostate, and colorectal cancers are among the most common and fatal malignancies worldwide. They are mainly caused by multifactorial mechanisms and are genetically heterogeneous. We investigated the genetic architecture of these cancers through genome-wide association, pathway-based, and summary-based transcriptome-/methylome-wide association analyses using three independent cohorts. Our genome-wide association analyses identified the associations of 33 single-nucleotide polymorphisms (SNPs) at P < 5E - 06, of which 32 SNPs were not previously reported and did not have proxy variants within their ± 1 Mb flanking regions. Moreover, other polymorphisms mapped to their closest genes were not previously associated with the same cancers at P < 5E - 06. Our pathway enrichment analyses revealed associations of 32 pathways; mainly related to the immune system, DNA replication/transcription, and chromosomal organization; with the studied cancers. Also, 60 probes were associated with these cancers in our transcriptome-wide and methylome-wide analyses. The ± 1 Mb flanking regions of most probes had not attained P < 5E - 06 in genome-wide association studies. The genes corresponding to the significant probes can be considered as potential targets for further functional studies. Two genes (i.e., CDC14A and PMEL) demonstrated stronger evidence of associations with lung cancer as they had significant probes in both transcriptome-wide and methylome-wide association analyses. The novel cancer-associated SNPs and genes identified here would advance our understanding of the genetic heterogeneity of the common cancers.

Estimating prevalence of human traits among populations from polygenic risk scores

The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population-level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population-specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRSs, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and was as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRSs performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants or spurious associations in the databases. We demonstrated that PRS-based analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.

Epistasis for head morphology in Drosophila melanogaster

Epistasis-gene-gene interaction-is common for mutations with large phenotypic effects in humans and model organisms. Epistasis impacts quantitative genetic models of speciation, response to natural and artificial selection, genetic mapping, and personalized medicine. However, the existence and magnitude of epistasis between alleles with small quantitative phenotypic effects are controversial and difficult to assess. Here, we use the Drosophila melanogaster Genetic Reference Panel of sequenced inbred lines to evaluate the magnitude of naturally occurring epistasis modifying the effects of mutations in jing and inv, two transcription factors that have subtle quantitative effects on head morphology as homozygotes. We find significant epistasis for both mutations and performed single marker genome-wide association analyses to map candidate modifier variants and loci affecting head morphology. A subset of these loci was significantly enriched for a known genetic interaction network, and mutations of the candidate epistatic modifier loci also affect head morphology.

Dissection of Allelic Variation Underlying Floral and Fruit Traits in Flare Tree Peony (Paeonia rockii) Using Association Mapping

Allelic variation in floral quantitative traits, including the elements of flowers and fruits, is caused by extremely complex regulatory processes. In the genetic improvement of flare tree peony (Paeonia rockii), a unique ornamental and edible oil woody species in the genus Paeonia, a better understanding of the genetic composition of these complex traits related to flowers and fruits is needed. Therefore, we investigated the genetic diversity and population structure of 160 P. rockii accessions and conducted single-marker association analysis for 19 quantitative flower and fruit traits using 81 EST-SSR markers. The results showed that the population had a high phenotypic diversity (coefficients of variation, 11.87-110.64%) and a high level of genetic diversity (mean number of alleles, N _A = 6.09). These accessions were divided into three subgroups by STRUCTURE analysis and a neighbor-joining tree. Furthermore, we also found a low level of linkage disequilibrium between these EST-SSRs and, by single-marker association analysis, identified 134 significant associations, including four flower traits with 11 EST-SSRs and 10 fruit traits with 32 EST-SSRs. Finally, based on the sequence alignment of the associated markers, P280, PS2, PS12, PS27, PS118, PS131, and PS145 may be considered potential loci to increase the yield of flare tree peony. These results laid the foundation for further analysis of the genetic structure of some key traits in P. rockii and had an obvious potential application value in marker-assisted selection breeding.

The Link between Type 2 Diabetes Mellitus and the Polymorphisms of Glutathione-Metabolizing Genes Suggests a New Hypothesis Explaining Disease Initiation and Progression

The present study investigated whether type 2 diabetes (T2D) is associated with polymorphisms of genes encoding glutathione-metabolizing enzymes such as glutathione synthetase (GSS) and gamma-glutamyl transferase 7 (GGT7). A total of 3198 unrelated Russian subjects including 1572 T2D patients and 1626 healthy subjects were enrolled. Single nucleotide polymorphisms (SNPs) of the GSS and GGT7 genes were genotyped using the MassArray-4 system. We found that the GSS and GGT7 gene polymorphisms alone and in combinations are associated with T2D risk regardless of sex, age, and body mass index, as well as correlated with plasma glutathione, hydrogen peroxide, and fasting blood glucose levels. Polymorphisms of GSS (rs13041792) and GGT7 (rs6119534 and rs11546155) genes were associated with the tissue-specific expression of genes involved in unfolded protein response and the regulation of proteostasis. Transcriptome-wide association analysis has shown that the pancreatic expression of some of these genes such as EDEM2, MYH7B, MAP1LC3A, and CPNE1 is linked to the genetic risk of T2D. A comprehensive analysis of the data allowed proposing a new hypothesis for the etiology of type 2 diabetes that endogenous glutathione deficiency might be a key condition responsible for the impaired folding of proinsulin which triggered an unfolded protein response, ultimately leading to beta-cell apoptosis and disease development.

Rare Variant in the SLC6A2 Encoding a Norepinephrine Transporter Is Associated with Elite Athletic Performance in the Polish Population

Numerous genetic factors have been shown to influence athletic performance, but the list is far from comprehensive. In this study, we analyzed genetic variants in two genes related to mental abilities, SLC6A2 (rs1805065) and SYNE1 (rs2635438) in a group of 890 athletes (320 endurance, 265 power, and 305 combat athletes) vs. 1009 sedentary controls. Genotyping of selected SNPs was performed using TaqMan SNP genotyping assays. SLC6A2 codes for norepinephrine transporter, a protein involved in modulating mood, arousal, memory, learning, and pain perception, while SYNE1 encodes protein important for the maintenance of the cerebellum—the part of the brain that coordinates complex body movements. Both SNPs (rs2635438 and rs1805065) showed no statistically significant differences between the frequencies of variants in the athletes and the sedentary controls (athletes vs. control group) or in the athlete subgroups (martial vs. control, endurance vs. control, and power vs. control). The rs1805065 T variant of SLC6A2 was found to be overrepresented in male high-elite martial sports athletes when compared to sedentary controls (OR = 6.56, 95%CI = 1.82–23.59, p = 0.010). This supports the hypothesis that genetic variants potentially affecting brain functioning can influence elite athletic performance and indicate the need for further genetic association studies, as well as functional analyses.

SINGLE NUCLEOTIDE POLYMORPHISMS IN RETINAL DETACHMENT PATIENTS WITH AND WITHOUT PROLIFERATIVE VITREORETINOPATHY

PURPOSE

To investigate differences in genotype distributions of single nucleotide polymorphisms within genes, encoding inflammatory mediators, among patients with rhegmatogenous retinal detachment (RRD) and patients with proliferative vitreoretinopathy (PVR).

METHODS

A genetic association study was performed on 191 Slovenian patients, divided into 2 groups: 113 RRD patients with PVR and 78 RRD patients without PVR. Genotype distributions were investigated within the following 13 single nucleotide polymorphisms: rs3760396 (CCL2), rs9990554 (FGF2), rs17561 (IL1A), rs2069763 (IL2), rs1800795 (IL6), rs1800871 (IL10), rs3008 (JAK3), rs2229094 (LTA), rs1042522 (TP53), rs7656613 (PDGFRA), rs7226855 (SMAD7), rs1800471 (TGFB1), and rs1800629 (TNF).

RESULTS

Differences in genotype distributions between patients with RRD with or without PVR were detected in rs1800795 (IL6) (P = 0.04), rs1800871 (in the vicinity of the IL10) (P = 0.034), and rs1800471 (TGFB1) (P = 0.032). After adjustment none of the 13 analyzed single nucleotide polymorphisms showed statistically significant associations in single nucleotide polymorphism genotype distributions between patients with RRD with and without PVR.

CONCLUSION

Further research is needed, particularly expanded multicentric population-based studies, to clarify the issue of genetic contribution to PVR from different genetic, clinical, and population-based aspects.

A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits

The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

Single-nucleotide polymorphism rs10036727 in the SLIT3 gene is associated with osteoporosis at the femoral neck in older Mexican postmenopausal women

AIMS

Osteoporosis (OP) remains a major public health problem worldwide. The most serious complications of this disease are fragility fractures, which increase morbidity and mortality. Management of OP represents an economic burden for health systems. Therefore, it is necessary to develop new screening strategies to identify the population at risk and implement preventive measures. We previously identified the SNPs rs3801387 in WNT16, rs7108738 in SOX6, rs10036727 in SLIT3 and rs7584262 in PKDCC as associated with bone mineral density in postmenopausal women through a genome-wide association study. The aim of this study was to validate those SNPs in two independent cohorts of non-related postmenopausal women.

MATERIALS AND METHODS

We included 1160 women classifying them as normal, osteopenic or osteoporotic and a group with hip fragility fracture. Genotyping was performed using predesigned TaqMan assays.

RESULTS

The variants rs10036727 and rs7108738 showed a significant association with BMD at the femoral neck. SLIT3 has been previously proposed as a potential biomarker and therapeutic resource.

CONCLUSIONS

Our results provide new evidence regarding a possible involvement of SLIT3 in bone metabolisms and encourage the development of more studies in different populations to support these observations.

The role of 5-HTTLPR in autism spectrum disorder: New evidence and a meta-analysis of this polymorphism in Latin American population with psychiatric disorders

The autism spectrum disorder (ASD) is a complex disorder encompassing a broad phenotypic and genotypic variability. The short (S)/long (L) 5-HTTLPR polymorphism has a functional role in the regulation of extracellular serotonin levels and both alleles have been associated to ASD. Most studies including European, American, and Asian populations have suggested an ethnical heterogeneity of this polymorphism; however, the short/long frequencies from Latin American population have been under-studied in recent meta-analysis. Here, we evaluated the 5-HTTLPR polymorphism in Colombian individuals with idiopathic ASD and reported a non-preferential S or L transmission and a non-association with ASD risk or symptom severity. Moreover, to recognize the allelic frequencies of an under-represented population we also recovered genetic studies from Latin American individuals and compared these frequencies with frequencies from other ethnicities. Results from meta-analysis suggest that short/long frequencies in Latin American are similar to those reported in Caucasian population but different to African and Asian regions.

Introducing Heuristic Information Into Ant Colony Optimization Algorithm for Identifying Epistasis

Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.

Association between ALDH2 Gene Polymorphism and Late-onset Alzheimer Disease: An Up-to-date Meta-analysis

OBJECTIVES

Previous case-control studies have focused on the relationship between ALDH2 gene polymorphism and late-onset Alzheimer's Disease (LOAD), but no definite unified conclusion has been reached. Therefore, the correlation between ALDH2 Glu504Lys polymorphism and LOAD remains controversial. To analyze the correlation between ALDH2 polymorphism and the risk of LOAD, we implemented this up-to-date meta-analysis to assess the probable association.

METHODS

Studies were searched through China National Knowledge Infrastructure (CNKI), VIP Database for Chinese Technical Periodicals, China Biology Medicine, PubMed, Cochrane Library, Clinical- Trials.gov, Embase, and MEDLINE from January 1, 1994 to December 31, 2018, without any restrictions on language and ethnicity.

RESULTS

Five studies of 1057 LOAD patients and 1136 healthy controls met our criteria for the analysis. Statistically, the ALDH2 GA/AA genotype was not linked with raising LOAD risk (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 0.96-2.28, p = 0.07). In subgroup analysis, the phenomenon that men with ALDH2*2 had higher risk for LOAD (OR = 1.72, 95%CI = 1.10-2.67, p = 0.02) was observed.

CONCLUSION

This study comprehends only five existing case-control studies and the result is negative. The positive trend might appear when the sample size is enlarged. In the future, more large-scale casecontrol or cohort studies should be done to enhance the association between ALDH2 polymorphism and AD or other neurodegenerative diseases.

Context-dependent genetic architecture of Drosophila life span

Understanding the genetic basis of variation in life span is a major challenge that is difficult to address in human populations. Evolutionary theory predicts that alleles affecting natural variation in life span will have properties that enable them to persist in populations at intermediate frequencies, such as late-life-specific deleterious effects, antagonistic pleiotropic effects on early and late-age fitness components, and/or sex- and environment-specific or antagonistic effects. Here, we quantified variation in life span in males and females reared in 3 thermal environments for the sequenced, inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and an advanced intercross outbred population derived from a subset of DGRP lines. Quantitative genetic analyses of life span and the micro-environmental variance of life span in the DGRP revealed significant genetic variance for both traits within each sex and environment, as well as significant genotype-by-sex interaction (GSI) and genotype-by-environment interaction (GEI). Genome-wide association (GWA) mapping in both populations implicates over 2,000 candidate genes with sex- and environment-specific or antagonistic pleiotropic allelic effects. Over 1,000 of these genes are associated with variation in life span in other D. melanogaster populations. We functionally assessed the effects of 15 candidate genes using RNA interference (RNAi): all affected life span and/or micro-environmental variance of life span in at least one sex and environment and exhibited sex-and environment-specific effects. Our results implicate novel candidate genes affecting life span and suggest that variation for life span may be maintained by variable allelic effects in heterogeneous environments.

A road map for understanding molecular and genetic determinants of osteoporosis

Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.

Embracing study heterogeneity for finding genetic interactions in large-scale research consortia

Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.

Discovering genetic interactions bridging pathways in genome-wide association studies

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

SNCA and mTOR Pathway Single Nucleotide Polymorphisms Interact to Modulate the Age at Onset of Parkinson's Disease

BACKGROUND

Single nucleotide polymorphisms (SNPs) in the α-synuclein (SNCA) gene are associated with differential risk and age at onset (AAO) of both idiopathic and Leucine-rich repeat kinase 2 (LRRK2)-associated Parkinson's disease (PD). Yet potential combinatory or synergistic effects among several modulatory SNPs for PD risk or AAO remain largely underexplored.

OBJECTIVES

The mechanistic target of rapamycin (mTOR) signaling pathway is functionally impaired in PD. Here we explored whether SNPs in the mTOR pathway, alone or by epistatic interaction with known susceptibility factors, can modulate PD risk and AAO.

METHODS

Based on functional relevance, we selected a total of 64 SNPs mapping to a total of 57 genes from the mTOR pathway and genotyped a discovery series cohort encompassing 898 PD patients and 921 controls. As a replication series, we screened 4170 PD and 3014 controls available from the International Parkinson's Disease Genomics Consortium.

RESULTS

In the discovery series cohort, we found a 4-loci interaction involving STK11 rs8111699, FCHSD1 rs456998, GSK3B rs1732170, and SNCA rs356219, which was associated with an increased risk of PD (odds ratio = 2.59, P < .001). In addition, we also found a 3-loci epistatic combination of RPTOR rs11868112 and RPS6KA2 rs6456121 with SNCA rs356219, which was associated (odds ratio = 2.89; P < .0001) with differential AAO. The latter was further validated (odds ratio = 1.56; P = 0.046-0.047) in the International Parkinson's Disease Genomics Consortium cohort.

CONCLUSIONS

These findings indicate that genetic variability in the mTOR pathway contributes to SNCA effects in a nonlinear epistatic manner to modulate differential AAO in PD, unraveling the contribution of this cascade in the pathogenesis of the disease. © 2019 International Parkinson and Movement Disorder Society.

The dopamine transporter VNTR polymorphism moderates the relationship between acute response to alcohol and future alcohol use disorder symptoms

Alcohol use disorder (AUD) is a genetically influenced disease with peak onset in young adulthood. Identification of factors that predict whether AUD symptoms will diminish or persist after young adulthood is a critical public health need. King and colleagues previously reported that acute response to alcohol predicted future AUD symptom trajectory. Genes associated with brain dopamine signaling, which underlies alcohol's rewarding effects, might influence this finding. This study analyzed whether variation at a variable number tandem repeat polymorphism in DAT1/SLC6A3, the gene encoding the dopamine transporter, moderated the predictive relationships between acute response to alcohol and future AUD symptoms among participants enrolled in the Chicago Social Drinking Project (first two cohorts). Heavy-drinking young adults (N = 197) completed an alcohol challenge, in which acute response (liking, wanting, stimulation, and sedation) was measured. Alcohol use disorder symptoms were assessed over the following 6 years. DAT1 genotype significantly moderated the interactions between follow-up time and alcohol liking (P = 0.006) and wanting (P = 0.006) in predicting future AUD symptoms. These predictive effects were strongest among participants who carried the DAT1 9-repeat allele, previously associated with enhanced striatal dopamine tone relative to the 10-repeat allele. Exploratory analyses indicated that DAT1 effects on the relationship between alcohol liking and AUD symptoms appeared stronger for females (n = 79) than males (n = 118) (P = 0.0496). These data suggest that heavy-drinking DAT1 9-repeat allele carriers who display high alcohol-induced reward in young adulthood may be predisposed to persistent AUD symptoms and support combining genotypic and phenotypic information to predict future AUD risk.

How to increase our belief in discovered statistical interactions via large-scale association studies?

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.

Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample sizes or confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using Bayesian random effect interaction models. The proposed methodology can be applied using shrinkage and variable selection methods, and produces useful information about effect heterogeneity in the form of whole-genome summaries (e.g., the proportions of variance of a complex trait explained by a set of SNPs and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations were well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by lifestyle, exhibited a greater extent of effect heterogeneity. For all the traits, we observed substantial variability in effect heterogeneity across SNPs, suggesting that effect heterogeneity varies between regions of the genome.

Gene-Gene and Gene-Environment Interactions

Identifying gene-gene and gene-environment interactions may help us to better describe the genetic architecture for complex traits. While advances have been made in identifying genetic variants associated with complex traits through more dense panels of genetic variants and larger sample sizes, genome-wide interaction analyses are still limited in power to detect interactions with small effect sizes, rare frequencies, and higher order interactions. This chapter outlines methods for detecting both gene-gene and gene-environment interactions both through explicit tests for interactions (i.e., ones in which the interaction is tested directly) and non-explicit tests (i.e., ones in which an interaction is allowed for in the test, but does not test for the interaction directly) as well as approaches for increasing power by reducing the search space. Issues relating to multiple test correction, replication, and the reporting of interaction results in publications.

Correlations Between Single Nucleotide Polymorphisms, Cognitive Dysfunction, and Postmortem Brain Pathology in Alzheimer's Disease Among Han Chinese

In this study, the distribution of five Alzheimer's disease (AD)-related single nucleotide polymorphisms (SNPs) in the Han population was examined in combination with the evaluation of clinical cognition and brain pathological analysis. The associations among SNPs, clinical daily cognitive states, and postmortem neuropathological changes were analyzed in 110 human brains from the Chinese Academy of Medical Sciences/Peking Union Medical College (CAMS/PUMC) Human Brain Bank. APOE ε4 (OR = 4.482, P = 0.004), the RS2305421 GG genotype (adjusted OR = 4.397, P = 0.015), and the RS10498633 GT genotype (adjusted OR = 2.375, P = 0.028) were associated with a higher score on the ABC (Aβ plaque score, Braak NFT stage, and CERAD neuritic plaque score) dementia scale. These results advance our understanding of the pathogenesis of AD, the relationship between pathological diagnosis and clinical diagnosis, and the SNPs in the Han population for future research.

Glutamate transporter gene polymorphisms and obsessive-compulsive disorder: A case-control association study

The etiology of obsessive-compulsive disorder (OCD) is largely unknown, but family, twin, neuroimaging, and pharmacological studies suggest that glutamatergic system plays a significant role on its underlying pathophysiology. We performed an association analysis of six Single Nucleotide Polymorphisms (SNPs) within SLC1A1 gene (rs12682807, rs2075627, rs3780412, rs301443, rs301430, rs301434) in a group of 199 patients and 200 healthy controls. Symptom profiles were evaluated using the Florida Obsessive-Compulsive Inventory (FOCI) and the Obsessive-Compulsive Inventory-Revised (OCI-R). SNPs were analyzed by Taqman® methodology (Thermo Fisher, Brazil). The genotype distributions were in Hardy-Weinberg equilibrium. The A-A-G (rs301434-rs3780412-rs301443) haplotype was twice as common in OCD as in controls (P = 0.02). We also found significant differences between male patients and controls for rs301443 in a dominant model (P = 0.04) and a protective effect of GG genotype of rs2072657 in women (P = 0.02). Regarding clinical characteristics, the G-A (rs301434-rs3780412) haplotype was almost twice more common in patients with vs. without hoarding (P = 0.04). Further analyses showed significant associations between hoarding and rs301434 (P = 0.04) and rs3780412 (P = 0.04) in women, both in a dominant model. A dominant effect was also observed on ordering dimension for rs301434 (P = 0.01, in women) and rs301443 (P = 0.04). Finally, the rs2072657 showed a recessive effect on neutralization (P = 0.04) and checking (P = 0.03, in men). These preliminary results demonstrated that the SLC1A1 may contribute to some extent the susceptibility to OCD and its symptoms. However, additional studies are still needed.

Genome-wide analysis of genetic predisposition to Alzheimer's disease and related sex disparities

BACKGROUND

Alzheimer's disease (AD) is the most common cause of dementia in the elderly and the sixth leading cause of death in the United States. AD is mainly considered a complex disorder with polygenic inheritance. Despite discovering many susceptibility loci, a major proportion of AD genetic variance remains to be explained.

METHODS

We investigated the genetic architecture of AD in four publicly available independent datasets through genome-wide association, transcriptome-wide association, and gene-based and pathway-based analyses. To explore differences in the genetic basis of AD between males and females, analyses were performed on three samples in each dataset: males and females combined, only males, or only females.

RESULTS

Our genome-wide association analyses corroborated the associations of several previously detected AD loci and revealed novel significant associations of 35 single-nucleotide polymorphisms (SNPs) outside the chromosome 19q13 region at the suggestive significance level of p < 5E-06. These SNPs were mapped to 21 genes in 19 chromosomal regions. Of these, 17 genes were not associated with AD at genome-wide or suggestive levels of associations by previous genome-wide association studies. Also, the chromosomal regions corresponding to 8 genes did not contain any previously detected AD-associated SNPs with p < 5E-06. Our transcriptome-wide association and gene-based analyses revealed that 26 genes located in 20 chromosomal regions outside chromosome 19q13 had evidence of potential associations with AD at a false discovery rate of 0.05. Of these, 13 genes/regions did not contain any previously AD-associated SNPs at genome-wide or suggestive levels of associations. Most of the newly detected AD-associated SNPs and genes were sex specific, indicating sex disparities in the genetic basis of AD. Also, 7 of 26 pathways that showed evidence of associations with AD in our pathway-bases analyses were significant only in females.

CONCLUSIONS

Our findings, particularly the newly discovered sex-specific genetic contributors, provide novel insight into the genetic architecture of AD and can advance our understanding of its pathogenesis.

A Solanum neorickii introgression population providing a powerful complement to the extensively characterized Solanum pennellii population

We present a complementary resource for trait fine-mapping in tomato to those based on the intra-specific cross between cultivated tomato and the wild tomato species Solanum pennellii, which have been extensively used for quantitative genetics in tomato over the last 20 years. The current population of backcross inbred lines (BILs) is composed of 107 lines derived after three backcrosses of progeny of the wild species Solanum neorickii (LA2133) and cultivated tomato (cultivar TA209) and is freely available to the scientific community. These S. neorickii BILs were genotyped using the 10K SolCAP single nucleotide polymorphism chip, and 3111 polymorphic markers were used to map recombination break points relative to the physical map of Solanum lycopersicum. The BILs harbor on average 4.3 introgressions per line, with a mean introgression length of 34.7 Mbp, allowing partitioning of the genome into 340 bins and thereby facilitating rapid trait mapping. We demonstrate the power of using this resource in comparison with archival data from the S. pennellii resources by carrying out metabolic quantitative trait locus analysis following gas chromatography-mass spectrometry on fruits harvested from the S. neorickii BILs. The metabolic candidate genes phenylalanine ammonia-lyase and cystathionine gamma-lyase were then tested and validated in F2 populations and via agroinfiltration-based overexpression in order to exemplify the fidelity of this method in identifying the genes that drive tomato metabolic phenotypes.

Replication of results from a cervical cancer genome-wide association study in Taiwanese women

Genetic epidemiological studies show that genetic factors contribute significantly to cervical cancer carcinogenesis. Several genome-wide association studies (GWAS) have revealed novel genetic variants associated with cervical cancer susceptibility. We aim to replicate 4 GWAS-identified single nucleotide polymorphisms (SNPs), which were associated with invasive cervical cancer in Chinese women, in a Taiwanese population. The rs13117307 C/T, rs8067378 A/G, rs4282438 G/T, and rs9277952 A/G SNPs were genotyped in 507 women with cervical squamous cell carcinoma (CSCC) and 432 age/sex matched healthy controls by using TaqMan PCR Assay. Human papillomavirus (HPV) DNA test and typing were performed in CSCC patients. Only the rs4282438 SNP was found to be significantly associated (G allele, odds ratio [OR] = 0.67, P = 1.5 × 10⁻⁵). This protective association remained in HPV-16 positive CSCC subgroup (G allele, OR = 0.60, P = 1.2 × 10⁻⁵). In conclusion, our study confirms the association of rs4282438 SNP with CSCC in a Taiwanese population. However, larger sample sets of other ethnic groups are required to confirm these findings.

Comparison of SNP Genotypes Related to Proliferative Vitreoretinopathy (PVR) across Slovenian and European Subpopulations

The present study investigated the distribution of genotypes within single nucleotide polymorphisms (SNPs) in genes, related to PVR pathogenesis across European subpopulations. Genotype distributions of 42 SNPs among 96 Slovenian healthy controls were investigated and compared to genotype frequencies in 503 European individuals (Ensembl database) and their subpopulations. Furthermore, a case-control status was simulated to evaluate effects of allele frequency changes on statistically significant results in gene-association studies investigating functional polymorphisms. In addition, 96 healthy controls were investigated within 4 SNPs: rs17561 (IL1A), rs2069763 (IL2), rs2229094 (LTA), and rs1800629 (TNF) in comparison to PVR patients. Significant differences (P < 0.05) in distribution of genotypes among 96 Slovenian participants and a European population were found in 10 SNPs: rs3024498 (IL10), rs315952 (IL1RN), rs2256965 (LST1), rs2256974 (LST1), rs909253 (LTA), rs2857602 (LTA), rs3138045 (NFKB1A), rs3138056 (NFKB1A), rs7656613 (PDGFRA), and rs1891467 (TGFB2), which additionally showed significant differences in genotype distribution among European subpopulations. This analysis also showed statistically significant differences in genotype distributions between healthy controls and PVR patients in rs17561 of the IL1A gene (OR, 3.00; 95% CI, 0.77–11.75; P = 0.036) and in rs1800629 of the TNF gene (OR, 0.48; 95% CI, 0.27–0.87; P = 0.014). Furthermore, we have shown that a small change (0.02) in minor allele frequency (MAF) significantly affects the statistical p value in case-control studies. In conclusion, the study showed differences in genotype distributions in healthy populations across different European countries. Differences in distribution of genotypes may have had influenced failed replication results in previous PVR-related SNP-association studies.

Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS

Although Genome Wide Association Studies (GWAS) have led to many valuable insights into the genetic bases of common diseases over the past decade, the issue of missing heritability has surfaced, as the discovered main effect genetic variants found to date do not account for much of a trait's predicted genetic component. We present a workflow, integrating epigenomics and topologically associating domain data, aimed at discovering trait-associated SNP pairs from GWAS where neither SNP achieved independent genome-wide significance. Each analyzed SNP pair consists of one SNP in a putative active enhancer and another SNP in a putative physically interacting gene promoter in a trait-relevant tissue. As a proof-of-principle case study, we used this approach to identify focused collections of SNP pairs that we analyzed in three independent Type 2 diabetes (T2D) GWAS. This approach led us to discover 35 significant SNP pairs, encompassing both novel signals and signals for which we have found orthogonal support from other sources. Nine of these pairs are consistent with eQTL results, two are consistent with our own capture C experiments, and seven involve signals supported by recent T2D literature.

Success and failure in replication of genotype-phenotype associations: How does replication help in understanding the genetic basis of phenotypic variation in outbred populations?

Recent developments in sequencing technologies have facilitated genomewide mapping of phenotypic variation in natural populations. Such mapping efforts face a number of challenges potentially leading to low reproducibility. However, reproducible research forms the basis of scientific progress. We here discuss the options for replication and the reasons for potential nonreproducibility. We then review the evidence for reproducible quantitative trait loci (QTL) with a focus on natural animal populations. Existing case studies of replication fall into three categories: (i) traits that have been mapped to major effect loci (including chromosomal inversion and supergenes) by independent research teams; (ii) QTL fine-mapped in discovery populations; and (iii) attempts to replicate QTL across multiple populations. Major effect loci, in particular those associated with inversions, have been successfully replicated in several cases within and across populations. Beyond such major effect variants, replication has been more successful within than across populations, suggesting that QTL discovered in natural populations may often be population-specific. This suggests that biological causes (differences in linkage patterns, allele frequencies or context-dependencies of QTL) contribute to nonreproducibility. Evidence from other fields, notably animal breeding and QTL mapping in humans, suggests that a significant fraction of QTL is indeed reproducible in direction and magnitude at least within populations. However, there is also a large number of QTL that cannot be easily reproduced. We put forward that more studies should explicitly address the causes and context-dependencies of QTL signals, in particular to disentangle linkage differences, allele frequency differences and gene-by-environment interactions as biological causes of nonreproducibility of QTL, especially between populations.

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)

Background

Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning analyses of GWAS can be hampered by biological and statistical factors, particularly so for the investigation of non-additive genetic interactions. Application of traditional cross validation to a GWAS data set may result in poor consistency between the training and testing data set splits due to an imbalance of the interaction genotypes relative to the data as a whole. We propose a new cross validation method, proportional instance cross validation (PICV), that preserves the original distribution of an independent variable when splitting the data set into training and testing partitions.

Results

We apply PICV to simulated GWAS data with epistatic interactions of varying minor allele frequencies and prevalences and compare performance to that of a traditional cross validation procedure in which individuals are randomly allocated to training and testing partitions. Sensitivity and positive predictive value are significantly improved across all tested scenarios for PICV compared to traditional cross validation. We also apply PICV to GWAS data from a study of primary open-angle glaucoma to investigate a previously-reported interaction, which fails to significantly replicate; PICV however improves the consistency of testing and training results.

Conclusions

Application of traditional machine learning procedures to biomedical data may require modifications to better suit intrinsic characteristics of the data, such as the potential for highly imbalanced genotype distributions in the case of epistasis detection. The reproducibility of genetic interaction findings can be improved by considering this variable imbalance in cross validation implementation, such as with PICV. This approach may be extended to problems in other domains in which imbalanced variable distributions are a concern.

Genetic background effects in quantitative genetics: gene-by-system interactions

Proper cell function depends on networks of proteins that interact physically and functionally to carry out physiological processes. Thus, it seems logical that the impact of sequence variation in one protein could be significantly influenced by genetic variants at other loci in a genome. Nonetheless, the importance of such genetic interactions, known as epistasis, in explaining phenotypic variation remains a matter of debate in genetics. Recent work from our lab revealed that genes implicated from an association study of toxin tolerance in Saccharomyces cerevisiae show extensive interactions with the genetic background: most implicated genes, regardless of allele, are important for toxin tolerance in only one of two tested strains. The prevalence of background effects in our study adds to other reports of widespread genetic-background interactions in model organisms. We suggest that these effects represent many-way interactions with myriad features of the cellular system that vary across classes of individuals. Such gene-by-system interactions may influence diverse traits and require new modeling approaches to accurately represent genotype-phenotype relationships across individuals.

Evaluation of the Role of -137G/C Single Nucleotide Polymorphism (rs187238) and Gene Expression Levels of the IL-18 in Patients with Coronary Artery Disease

Objectives

Interleukin-18 (IL-18) is a proinflammatory and proatherogenic cytokine, and its genetic variations may contribute to the development of coronary artery disease (CAD). We sought to investigate the role of -137G/C polymorphism and gene expression levels of IL-18 in patients with CAD.

Methods

The study population included 100 patients with angiographically proven CAD and 100 matched controls. Total RNA and DNA were extracted from leukocytes using appropriate kits. The genotype of -137G/C polymorphism and gene expression level of IL-18 was determined using allele-specific polymerase chain reaction (PCR) and real-time (RT)-PCR assay, respectively.

Results

The genotypic and allelic distribution of IL-18 -137G/C polymorphism was not significantly different between the two groups (p > 0.050). Moreover, the -137G/C polymorphism did not increase the risk of CAD in dominant and recessive genetic models (p > 0.050). However, subgroup analysis of CAD patients revealed that the IL-18 -137G/C polymorphism was significantly associated with increased risk of CAD in hypertensive patients (odds ratio (OR) = 7.51; 95% confidence interval (CI): 1.24-25.17; p = 0.019) and smokers (OR = 4.90; 95% CI: 1.21-19.70; p = 0.031) but not in the diabetic subpopulation (p = 0.261). The genotype distribution of IL-18 -137G/C genetic polymorphism was significantly different among patients with one, two, and three stenotic vessels (p < 0.050). The gene expression level of IL-18 was significantly higher in the CAD group than the control group (p < 0.001). Moreover, the carriers of CC genotype had significantly lower gene expression levels of IL-18 than carriers of GG genotype (p < 0.050).

Conclusions

The -137G/C polymorphism of IL-18 may be associated with the CAD risk in hypertensive and smoker subgroup of CAD patients. The -137G/C polymorphism seems to play an important role in determining the severity of CAD. Increased IL-18 gene expression level is a significant risk factor for the development of CAD. The CC genotype of -137G/C polymorphism is associated with lower IL-18 gene expression levels.

Low penetrance genetic polymorphisms as potential biomarkers for colorectal cancer predisposition

Colorectal cancer is a leading form of cancer in both males and females. Early detection of individuals at risk of colorectal cancer allows proper treatment and management of the disease to be implemented, which can potentially reduce the burden of colorectal cancer incidence, morbidity and mortality. In recent years, the role of genetic susceptibility factors in mediating predisposition to colorectal cancer has become more and more apparent. Identification of high-frequency, low-penetrance genetic polymorphisms associated with the cancer has therefore emerged as an important approach which can potentially aid prediction of colorectal cancer risk. However, the overwhelming amount of genetic epidemiology data generated over the past decades has made it difficult for one to assimilate the information and determine the exact genetic polymorphisms that can potentially be used as biomarkers for colorectal cancer. This review comprehensively consolidates, based primarily on results from meta-analyses, the recent progresses in the search of colorectal cancer-associated genetic polymorphisms, and discusses the possible mechanisms involved.

MEF2C rs190982 polymorphism with late-onset Alzheimer's disease in Han Chinese: A replication study and meta-analyses

The myocyte enhancer factor (MEF2) family of transcription factors plays a vital role in memory and learning due to its functions in regulating synapse number and reducing dendritic spines. Myocyte enhancer factor 2 C (MEF2C) is regarded as modulator of amyloid-protein precursor (APP) proteolytic processing, in which amyloid-β (Aβ) is produced. A common single nucleotide polymorphism (SNP, rs190982) in MEF2C gene was identified to be related to late-onset Alzheimer's disease (LOAD) in Caucasians in a large meta-analysis of genome-wide association studies (GWAS). Here, we recruited unrelated 984 LOAD patients and 1348 healthy controls matched for gender and age to ascertain whether the rs190982 polymorphism is related to LOAD in Han Chinese. No difference in the genotype and allele distributions of the MEF2C rs190982 polymorphism was found between LOAD cases and healthy controls (genotype: P = 0.861; allele: P = 0.862), even after stratification for APOE ε4 allele as well as statistical adjustment for age, gender and APOE ε4 status. Furthermore, the meta-analysis in 4089 Chinese individuals did not detect the association of rs190982 within MEF2C with the risk for LOAD (OR = 1.03, 95%CI = 0.90-1.18). Overall, the current evidence did not support the relation between rs190982 polymorphism within MEF2C and the LOAD risk in Northern Han Chinese.

Genome-Wide Association Studies and Meta-Analyses for Congenital Heart Defects

BACKGROUND

Maternal and inherited (ie, case) genetic factors likely contribute to the pathogenesis of congenital heart defects, but it is unclear whether individual common variants confer a large risk.

METHODS AND RESULTS

To evaluate the relationship between individual common maternal/inherited genotypes and risk for heart defects, we conducted genome-wide association studies in 5 cohorts. Three cohorts were recruited at the Children's Hospital of Philadelphia: 670 conotruncal heart defect (CTD) case-parent trios, 317 left ventricular obstructive tract defect (LVOTD) case-parent trios, and 406 CTD cases (n=406) and 2976 pediatric controls. Two cohorts were recruited through the Pediatric Cardiac Genomics Consortium: 355 CTD trios and 192 LVOTD trios. We also conducted meta-analyses using the genome-wide association study results from the CTD cohorts, the LVOTD cohorts, and from the combined CTD and LVOTD cohorts. In the individual genome-wide association studies, several genome-wide significant associations (P≤5×10^-8) were observed. In our meta-analyses, 1 genome-wide significant association was detected: the case genotype for rs72820264, an intragenetic single-nucleotide polymorphism associated with LVOTDs (P=2.1×10^-8).

CONCLUSIONS

We identified 1 novel candidate region associated with LVOTDs and report on several additional regions with suggestive evidence for association with CTD and LVOTD. These studies were constrained by the relatively small samples sizes and thus have limited power to detect small to moderate associations. Approaches that minimize the multiple testing burden (eg, gene or pathway based) may, therefore, be required to uncover common variants contributing to the risk of these relatively rare conditions.