Meta-analysis methods for genome-wide association studies and beyond

CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

Association Analysis and Meta-Analysis of Multi-Allelic Variants for Large-Scale Sequence Data

There is great interest in understanding the impact of rare variants in human diseases using large sequence datasets. In deep sequence datasets of >10,000 samples, ~10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease-relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results. We discuss practical issues and methods to encode multi-allelic sites, conduct single-variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of ~18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single-variant association tests among methods that can properly estimate allele effects, and enhanced gene-level tests over existing approaches. Software packages implementing these methods are available online.

Meta-Analysis of Vaginal Microbiome Data Provides New Insights Into Preterm Birth

Preterm birth (PTB) is defined as the birth of an infant before 37 weeks of gestational age. It is the leading cause of perinatal morbidity and mortality worldwide. In this study, we present a comprehensive meta-analysis of vaginal microbiome in PTB. We integrated raw longitudinal 16S rRNA vaginal microbiome data from five independent studies across 3,201 samples and were able to gain new insights into the vaginal microbiome state in women who deliver preterm in comparison to those who deliver at term. We found that women who deliver prematurely show higher within-sample variance in vaginal microbiome abundance, with the most significant difference observed during the first trimester. Modeling the data longitudinally revealed a number of microbial genera as associated with PTB, including several that were previously known and two newly identified by this meta-analysis: Olsenella and Clostridium sensu stricto. New hypotheses emerging from this integrative analysis can lead to novel diagnostics to identify women who are at higher risk for PTB and potentially inform new therapeutic interventions.

Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering

When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred "Flint-Dent" panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.

Is useful research data usually shared? An investigation of genome-wide association study summary statistics

Primary data collected during a research study is often shared and may be reused for new studies. To assess the extent of data sharing in favourable circumstances and whether data sharing checks can be automated, this article investigates summary statistics from primary human genome-wide association studies (GWAS). This type of data is highly suitable for sharing because it is a standard research output, is straightforward to use in future studies (e.g., for secondary analysis), and may be already stored in a standard format for internal sharing within multi-site research projects. Manual checks of 1799 articles from 2010 and 2017 matching a simple PubMed query for molecular epidemiology GWAS were used to identify 314 primary human GWAS papers. Of these, only 13% reported the location of a complete set of GWAS summary data, increasing from 3% in 2010 to 23% in 2017. Whilst information about whether data was shared was typically located clearly within a data availability statement, the exact nature of the shared data was usually unspecified. Thus, data sharing is the exception even in suitable research fields with relatively strong data sharing norms. Moreover, the lack of clear data descriptions within data sharing statements greatly complicates the task of automatically characterising shared data sets.

A novel estimator of between-study variance in random-effects models

BACKGROUND

With the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance.

RESULTS

This paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the [Formula: see text] in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypothesis settings. The results show that DSLD2 is a suitable method for identifying differentially expressed genes under the first hypothesis. The DSLD2 method is also applied to Alzheimer's microarray datasets. The differentially expressed genes detected by the DSLD2 method are significantly enriched in neurological diseases.

CONCLUSIONS

The results from both simulationes and an application show that DSLD2 is a suitable method for detecting differentially expressed genes under the first hypothesis.

Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.

Comparison of rigid versus foldable iris-fixed phakic intraocular lens implantation for high myopia: A systematic review and meta-analysis

BACKGROUND

This study aimed to assess the efficacy of rigid versus foldable iris-fixed phakic intraocular lens (PIOL) implantation in the treatment of high myopia.

METHODS

A systematic search based on electronic databases such as Pubmed, Embase, and Cochrane Library was conducted to identify relevant studies published up to January 11, 2019. The pooled odds ratios and weighted mean differences (WMDs) with corresponding 95% confidence intervals were calculated.

RESULTS

Eight comparative studies with 835 participants were included in this meta-analysis. The overall WMD showed statistical significance in terms of postoperative uncorrected distance visual acuity (UDVA), mean postoperative spherical equivalence (SE), and mean postoperative intraocular higher-order aberrations (HOA) (μm) for a 6-mm pupil, suggesting that foldable PIOL group showed significant improvement of high myopia, compared to rigid PIOL group. Besides, compared with rigid PIOL group, foldable PIOL group had beneficial effect on the proportion of eyes with central endothelial cell density (ECD) loss in patients with high myopia.

CONCLUSION

This meta-analysis provided the up-to-date evidence and found that foldable PIOL group had significant beneficial effect on UDVA, SE, HOA, contrast sensitivity, and ECD, except best spectacle-corrected visual acuity, and safety in the treatment of high myopia over rigid PIOL group.

Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy

BACKGROUND

A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic studies.

METHODS

We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10-8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were two-sided.

RESULTS

Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10-10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10-10), and rs11122573 with hematuria (Pmeta = 1.8 × 10-8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10-6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese cohorts.

CONCLUSIONS

This study increases the understanding of the architecture of common genetic variants affecting radiotoxicity, points to novel radio-pathogenic mechanisms, and develops risk models for testing in clinical studies. Further multinational radiogenomics studies in larger cohorts are worthwhile.

Meta-Analysis of SNP-Environment Interaction With Overlapping Data

Meta-analysis, which combines the results of multiple studies, is an important analytical method in genome-wide association studies. In genome-wide association studies practice, studies employing meta-analysis may have overlapping data, which could yield false positive results. Recent studies have proposed models to handle the issue of overlapping data when testing the genetic main effect of single nucleotide polymorphism. However, there is still no meta-analysis method for testing gene-environment interaction when overlapping data exist. Inspired by the methods of testing the main effect of gene with overlapping data, we proposed an overlapping meta-regulation method to address the issue in testing the gene-environment interaction. We generalized the covariance matrices of the regular meta-regression model by employing Lin’s and Han’s correlation structures to incorporate the correlations introduced by the overlapping data. Based on our proposed models, we further provided statistical significance tests of the gene-environment interaction as well as joint effects of the gene main effect and the interaction. Through simulations, we examined type I errors and statistical powers of our proposed methods at different levels of data overlap among studies. We demonstrated that our method well controls the type I error and simultaneously achieves statistical power comparable with the method that removes overlapping samples a priori before the meta-analysis, i.e., the splitting method. On the other hand, ignoring overlapping data will inflate the type I error. Unlike the splitting method that requires individual-level genotype and phenotype data, our proposed method for testing gene-environment interaction handles the issue of overlapping data effectively and statistically efficiently at the meta-analysis level.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Perspectives on Low Temperature Tolerance and Vernalization Sensitivity in Barley: Prospects for Facultative Growth Habit

One option to achieving greater resiliency for barley production in the face of climate change is to explore the potential of winter and facultative growth habits: for both types, low temperature tolerance (LTT) and vernalization sensitivity are key traits. Sensitivity to short-day photoperiod is a desirable attribute for facultative types. In order to broaden our understanding of the genetics of these phenotypes, we mapped quantitative trait loci (QTLs) and identified candidate genes using a genome-wide association studies (GWAS) panel composed of 882 barley accessions that was genotyped with the Illumina 9K single-nucleotide polymorphism (SNP) chip. Fifteen loci including 5 known and 10 novel QTL/genes were identified for LTT-assessed as winter survival in 10 field tests and mapped using a GWAS meta-analysis. FR-H1, FR-H2, and FR-H3 were major drivers of LTT, and candidate genes were identified for FR-H3. The principal determinants of vernalization sensitivity were VRN-H1, VRN-H2, and PPD-H1. VRN-H2 deletions conferred insensitive or intermediate sensitivity to vernalization. A subset of accessions with maximum LTT were identified as a resource for allele mining and further characterization. Facultative types comprised a small portion of the GWAS panel but may be useful for developing germplasm with this growth habit.

Post-GWAS analysis of six substance use traits improves the identification and functional interpretation of genetic risk loci

BACKGROUND

Little is known about the functional mechanisms through which genetic loci associated with substance use traits ascertain their effect. This study aims to identify and functionally annotate loci associated with substance use traits based on their role in genetic regulation of gene expression.

METHODS

We evaluated expression Quantitative Trait Loci (eQTLs) from 13 brain regions and whole blood of the Genotype-Tissue Expression (GTEx) database, and from whole blood of the Depression Genes and Networks (DGN) database. The role of single eQTLs was examined for six substance use traits: alcohol consumption (N = 537,349), cigarettes per day (CPD; N = 263,954), former vs. current smoker (N = 312,821), age of smoking initiation (N = 262,990), ever smoker (N = 632,802), and cocaine dependence (N = 4,769). Subsequently, we conducted a gene level analysis of gene expression on these substance use traits using S-PrediXcan.

RESULTS

Using an FDR-adjusted p-value <0.05 we found 2,976 novel candidate genetic loci for substance use traits, and identified genes and tissues through which these loci potentially exert their effects. Using S-PrediXcan, we identified significantly associated genes for all substance traits.

DISCUSSION

Annotating genes based on transcriptomic regulation improves the identification and functional characterization of candidate loci and genes for substance use traits.

Common alleles of CMT2 and NRPE1 are major determinants of CHH methylation variation in Arabidopsis thaliana

DNA cytosine methylation is an epigenetic mark associated with silencing of transposable elements (TEs) and heterochromatin formation. In plants, it occurs in three sequence contexts: CG, CHG, and CHH (where H is A, T, or C). The latter does not allow direct inheritance of methylation during DNA replication due to lack of symmetry, and methylation must therefore be re-established every cell generation. Genome-wide association studies (GWAS) have previously shown that CMT2 and NRPE1 are major determinants of genome-wide patterns of TE CHH methylation. Here we instead focus on CHH methylation of individual TEs and TE-families, allowing us to identify the pathways involved in CHH methylation simply from natural variation and confirm the associations by comparing them with mutant phenotypes. Methylation at TEs targeted by the RNA-directed DNA methylation (RdDM) pathway is unaffected by CMT2 variation, but is strongly affected by variation at NRPE1, which is largely responsible for the longitudinal cline in this phenotype. In contrast, CMT2-targeted TEs are affected by both loci, which jointly explain 7.3% of the phenotypic variation (13.2% of total genetic effects). There is no longitudinal pattern for this phenotype, however, because the geographic patterns appear to compensate for each other in a pattern suggestive of stabilizing selection.

DNA methylation is a major component of transposon silencing, and essential for genomic integrity. Recent studies revealed large-scale geographic variation as well as the existence of major trans-acting polymorphisms that partly explained this variation. In this study, we re-analyze previously published data (The 1001 Epigenomes), focusing on CHH methylation patterns of individual TEs and TE families rather than on genome-wide averages (as was done in previous studies). GWAS of the patterns reveals the underlying regulatory networks, and allowed us to comprehensively characterize trans-regulation of CHH methylation and its role in the striking geographic pattern for this phenotype.

Meta-Analysis of SNP-Environment Interaction with Heterogeneity

Meta-analyses are widely used in genome-wide association studies to combine the results obtained from multiple studies. Classical random-effects methods treat genetic heterogeneity as a random effect and consider it as a portion of the variance associated with a fixed effect of the variant. Recent work suggests performing hypothesis testing with the null hypothesis under which neither fixed nor random effects exist for a variant. This method has been shown to perform better than classical random-effects methods. In this work, we propose a meta-analysis of testing single nucleotide polymorphism (SNP)-environment interaction in the presence of genetic heterogeneity. We introduced the random effects of the SNP and SNP-environment interaction under test into a meta-regression model to account for heterogeneity. A test for the SNP-environment interaction was formulated to test for fixed and random effects of the interaction simultaneously. Similarly, a test for total genetic effects was formulated to test for fixed effects of the SNP and the SNP-environment interaction together with their random effects. We performed simulations to study the null distribution and statistical power of the proposed tests. We show that the new methods have higher power than classical random-effects and fixed-effects meta-regression methods when heterogeneity effects are large.

Available Software for Meta-analyses of Genome-wide Expression Studies

Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

Combining multi-population datasets for joint genome-wide association and meta-analyses: The case of bovine milk fat composition traits

In genome-wide association studies (GWAS), sample size is the most important factor affecting statistical power that is under control of the investigator, posing a major challenge in understanding the genetics underlying difficult-to-measure traits. Combining data sets available from different populations for joint or meta-analysis is a promising alternative to increasing sample sizes available for GWAS. Simulation studies indicate statistical advantages from combining raw data or GWAS summaries in enhancing quantitative trait loci (QTL) detection power. However, the complexity of genetics underlying most quantitative traits, which itself is not fully understood, is difficult to fully capture in simulated data sets. In this study, population-specific and combined-population GWAS as well as a meta-analysis of the population-specific GWAS summaries were carried out with the objective of assessing the advantages and challenges of different data-combining strategies in enhancing detection power of GWAS using milk fatty acid (FA) traits as examples. Gas chromatography (GC) quantified milk FA samples and high-density (HD) genotypes were available from 1,566 Dutch, 614 Danish, and 700 Chinese Holstein Friesian cows. Using the joint GWAS, 28 additional genomic regions were detected, with significant associations to at least 1 FA, compared with the population-specific analyses. Some of these additional regions were also detected using the implemented meta-analysis. Furthermore, using the frequently reported variants of the diacylglycerol acyltransferase 1 (DGAT1) and stearoyl-CoA desaturase (SCD1) genes, we show that significant associations were established with more FA traits in the joint GWAS than the remaining scenarios. However, there were few regions detected in the population-specific analyses that were not detected using the joint GWAS or the meta-analyses. Our results show that combining multi-population data set can be a powerful tool to enhance detection power in GWAS for seldom-recorded traits. Detection of a higher number of regions using the meta-analysis, compared with any of the population-specific analyses also emphasizes the utility of these methods in the absence of raw multi-population data sets to undertake joint GWAS.

The Genetics of Neuropathic Pain from Model Organisms to Clinical Application

Neuropathic pain (NeuP) arises due to injury of the somatosensory nervous system and is both common and disabling, rendering an urgent need for non-addictive, effective new therapies. Given the high evolutionary conservation of pain, investigative approaches from Drosophila mutagenesis to human Mendelian genetics have aided our understanding of the maladaptive plasticity underlying NeuP. Successes include the identification of ion channel variants causing hyper-excitability and the importance of neuro-immune signaling. Recent developments encompass improved sensory phenotyping in animal models and patients, brain imaging, and electrophysiology-based pain biomarkers, the collection of large well-phenotyped population cohorts, neurons derived from patient stem cells, and high-precision CRISPR generated genetic editing. We will discuss how to harness these resources to understand the pathophysiological drivers of NeuP, define its relationship with comorbidities such as anxiety, depression, and sleep disorders, and explore how to apply these findings to the prediction, diagnosis, and treatment of NeuP in the clinic.

Replication and generalization in applied neuroimaging

There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

BACKGROUND

A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

RESULTS

We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

CONCLUSIONS

Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

Genome-Wide Association Analyses of Equine Metabolic Syndrome Phenotypes in Welsh Ponies and Morgan Horses

Equine metabolic syndrome (EMS) is a complex trait for which few genetic studies have been published. Our study objectives were to perform within breed genome-wide association analyses (GWA) to identify associated loci in two high-risk breeds, coupled with meta-analysis to identify shared and unique loci between breeds. GWA for 12 EMS traits identified 303 and 142 associated genomic regions in 264 Welsh ponies and 286 Morgan horses, respectively. Meta-analysis demonstrated that 65 GWA regions were shared across breeds. Region boundaries were defined based on a fixed-size or the breakdown of linkage disequilibrium, and prioritized if they were: shared between breeds or across traits (high priority), identified in a single GWA cohort (medium priority), or shared across traits with no SNPs reaching genome-wide significance (low priority), resulting in 56 high, 26 medium, and seven low priority regions including 1853 candidate genes in the Welsh ponies; and 39 high, eight medium, and nine low priority regions including 1167 candidate genes in the Morgans. The prioritized regions contained protein-coding genes which were functionally enriched for pathways associated with inflammation, glucose metabolism, or lipid metabolism. These data demonstrate that EMS is a polygenic trait with breed-specific risk alleles as well as those shared across breeds.

A genome-wide association and meta-analysis reveal regions associated with seed size in cowpea [Vigna unguiculata (L.) Walp]

This paper combined GWAS, meta-analysis and sequence homology comparison with common bean to identify regions associated with seed size variation in domesticated cowpea. Seed size is an important trait for yield and commercial value in dry-grain cowpea. Seed size varies widely among different cowpea accessions, and the genetic basis of such variation is not yet well understood. To better decipher the genetic basis of seed size, a genome-wide association study (GWAS) and meta-analysis were conducted on a panel of 368 cowpea diverse accessions from 51 countries. Four traits, including seed weight, length, width and density were evaluated across three locations. Using 51,128 single nucleotide polymorphisms covering the cowpea genome, 17 loci were identified for these traits. One locus was common to weight, width and length, suggesting pleiotropy. By integrating synteny-based analysis with common bean, six candidate genes (Vigun05g036000, Vigun05g039600, Vigun05g204200, Vigun08g217000, Vigun11g187000, and Vigun11g191300) which are implicated in multiple functional categories related to seed size such as endosperm development, embryo development, and cell elongation were identified. These results suggest that a combination of GWAS meta-analysis with synteny comparison in a related plant is an efficient approach to identify candidate gene (s) for complex traits in cowpea. The identified loci and candidate genes provide useful information for improving cowpea varieties and for molecular investigation of seed size.

A genome-wide association study of reproduction traits in four pig populations with different genetic backgrounds

Objective

Genome-wide association study and two meta-analysis based on GWAS performed to explore the genetic mechanism underlying variation in pig number born alive (NBA) and total number born (TNB).

Methods

Single trait GWAS and two meta-analysis (single-trait meta analysis and multi-trait meta analysis) were used in our study for NBA and TNB on 3,121 Yorkshires from 4 populations, including three different American Yorkshire populations (n = 2,247) and one British Yorkshire populations (n = 874).

Results

The result of single trait GWAS showed that no significant associated single nucleotide polymorphisms (SNPs) were identified. Using single-trait meta analysis and multi-trait meta analysis within populations, 11 significant loci were identified associated with target traits. Spindlin 1, vascular endothelial growth factor A, forkhead box Q1, msh homeobox 1, and LHFPL tetraspan submily member 3 are five functionally plausible candidate genes for NBA and TNB. Compared to the single population GWAS, single-trait Meta analysis can improve the detection power to identify SNPs by integrating information of multiple populations. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits.

Conclusion

In total, our findings identified novel genes to be validated as candidates for NBA and TNB in pigs. Also, it enabled us to enlarge population size by including multiple populations with different genetic backgrounds and increase the power of GWAS by using meta analysis.

The prognostic value of hypoxia-inducible factor-1α in advanced cancer survivors: a meta-analysis with trial sequential analysis

Background:

Expression of hypoxia-inducible factors (HIFs) has been observed, but their prognostic role in advanced cancers remains uncertain. We conducted a meta-analysis to establish the prognostic effect of HIFs and to better guide treatment planning for advanced cancers.

Methods:

Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Trial sequential analysis (TSA) was also performed. The clinical outcomes included overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), relapse/recurrence-free survival (RFS), and metastasis-free survival (MFS) in patients with advanced tumors according to multivariate analysis.

Results:

A total of 31 studies including 3453 cases who received chemotherapy, radiotherapy, or chemoradiotherapy were identified. Pooled analyses revealed that HIF-1α expression was correlated with worse OS (HR = 1.61, p < 0.001), DFS (HR = 1.61, p < 0.001), PFS (HR = 1.49, p = 0.01), CSS (HR = 1.65, p = 0.056), RFS (HR = 2.10, p = 0.015), or MFS (HR = 2.36, p = 0.002) in advanced cancers. HIF-1α expression was linked to shorter OS in the digestive tract, epithelial ovarian, breast, non-small cell lung, and clear cell renal cell carcinomas. Subgroup analysis by study region showed that HIF-1α expression was correlated with poor OS in Europeans and Asians, while an analysis by histologic subtypes found that HIF-1α expression was not associated with OS in squamous cell carcinoma. No relationship was found between HIF-2α expression and OS, DFS, PFS, or CSS.

Conclusions:

Targeting HIF-1α may be a useful therapeutic approach to improve survival for advanced cancer patients. Based on TSA, more randomized controlled trials are strongly suggested.

Association of dietary fibre intake and gut microbiota in adults

Increasing evidence indicates that gut microbiota may influence colorectal cancer risk. Diet, particularly fibre intake, may modify gut microbiota composition, which may affect cancer risk. We investigated the relationship between dietary fibre intake and gut microbiota in adults. Using 16S rRNA gene sequencing, we assessed gut microbiota in faecal samples from 151 adults in two independent study populations: National Cancer Institute (NCI), n 75, and New York University (NYU), n 76. We calculated energy-adjusted fibre intake based on FFQ. For each study population with adjustment for age, sex, race, BMI and smoking, we evaluated the relationship between fibre intake and gut microbiota community composition and taxon abundance. Total fibre intake was significantly associated with overall microbial community composition in NYU (P=0·008) but not in NCI (P=0·81). In a meta-analysis of both study populations, higher fibre intake tended to be associated with genera of class Clostridia, including higher abundance of SMB53 (fold change (FC)=1·04, P=0·04), Lachnospira (FC=1·03, P=0·05) and Faecalibacterium (FC=1·03, P=0·06), and lower abundance of Actinomyces (FC=0·95, P=0·002), Odoribacter (FC=0·95, P=0·03) and Oscillospira (FC=0·96, P=0·06). A species-level meta-analysis showed that higher fibre intake was marginally associated with greater abundance of Faecalibacterium prausnitzii (FC=1·03, P=0·07) and lower abundance of Eubacterium dolichum (FC=0·96, P=0·04) and Bacteroides uniformis (FC=0·97, P=0·05). Thus, dietary fibre intake may impact gut microbiota composition, particularly class Clostridia, and may favour putatively beneficial bacteria such as F. prausnitzii. These findings warrant further understanding of diet-microbiota relationships for future development of colorectal cancer prevention strategies.

Environmental factors and risk of multiple sclerosis: Findings from meta-analyses and Mendelian randomization studies

Multiple sclerosis (MS) is a chronic demyelinating disease that is associated with permanent disability and low quality of life. Development of MS is attributed to a combination of genetic and environmental factors. Genome-wide association studies revealed more than 200 variants that are associated with risk of MS. An umbrella review showed that smoking, history of infectious mononucleosis, and anti-Epstein–Barr virus nuclear antigen (anti-EBNA) immunoglobulin G (IgG) seropositivity are credible risk factors of MS. In the present narrative review, we updated our published umbrella review, showing that body mass index in childhood and adolescence and anti-viral capsid antigen (anti-VCA) IgG seropositivity are additional credible risk factors of MS. In addition, we discuss the findings from Mendelian randomization studies, which present evidence for a potential causal role of serum vitamin D and adulthood body mass index on risk of MS. Finally, we discuss the potential limitations of meta-analyses, umbrella reviews, and Mendelian randomization studies in the search for risk factors of MS.

New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. Here we conducted a meta-analysis of genome-wide association studies of alcohol consumption (g d-1) from the UK Biobank, the Alcohol Genome-Wide Consortium and the Cohorts for Heart and Aging Research in Genomic Epidemiology Plus consortia, collecting data from 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 new common loci and investigated their potential functional importance using magnetic resonance imaging data and gene expression studies. We identify genetic pathways associated with alcohol consumption and suggest genetic mechanisms that are shared with neuropsychiatric disorders such as schizophrenia.

Investigation of MiR-92a as a Prognostic Indicator in Cancer Patients: a Meta-Analysis

Background: MiR-92a has been discovered to be involved in the malignant behavior of various types of cancers. However, the particular clinical and prognostic roles of miR-92a in tumors still need to be identified more precisely. The current meta-analysis assessed the prognostic value of miR-92a in various carcinomas.

Methods: Systematic literature searches of PubMed, PMC, Web of Science (WOS), Embase in English and Wanfang, SinoMed and the China National Knowledge Infrastructure (CNKI) in Chinese up to Jan 15^th 2019 were conducted for eligible studies. Twenty studies involving a total of 2573 patients were included in the analysis. Pooled hazard ratios (HR) for overall survival (OS) and disease-free survival (DFS), progression-free survival (PFS) and recurrence-free survival (RFS) were assessed using fixed-effects and random-effects models. Meta-regression and subgroup analyses were carried out to explore the source of heterogeneity. Odds ratio (OR) and 95%CIs were applied to evaluate the relationship between miR-92a expression levels and clinicopathological characteristics.

Results: A significant association between miR-92a levels and OS (HR=2.18) was identified. The random pooling model also revealed significance of consistency (HR=2.14), indicating that the stability of the results. Subgroup analyses were performed and the corresponding significance was recognized in Chinese cancer patients (HR=2.35), studies of specimen derived from tissues (HR=2.43), non-hematological cancer (HR=2.35), osteosarcoma (HR=2.54), non-small cell lung cancer (HR=2.33), hepatocellular carcinoma (HR=2.40) and so on. There were significant relations observed of the expression level of miR-92a to tumor size (≥5 vs <5 cm) (OR=2.13), lymph node metastasis (present vs. absent) (OR=1.87), distant metastasis (present vs. absent) (OR=2.99) and so on.

Conclusions: the over expression of miR-92a is associated with unfavorable prognosis of Chinese cancer patients. In addition, patients of elevated miR-92a expression level are likely to develop the cancers of more malignant behaviors.

Meta-Qtest: meta-analysis of quadratic test for rare variants

Background

In genome-wide association studies (GWASs), meta-analysis has been widely used to improve statistical power by combining the results of different studies. Meta-analysis can detect phenotype associated variants that are failed to be detected in single studies. Especially, in biomedical sciences, meta-analysis is often necessary not only for improving statistical power, but also for reducing unavoidable limitation in data collection. As next-generation sequencing (NGS) technology has been developed, meta-analysis of rare variants is proceeding briskly along with meta-analysis of common variants in GWASs. However, meta-analysis on a single variant that is commonly used in common variant association test is improper for rare variants. A sparse signal of rare variant undermines the association signal and its large number causes multiple testing problem. To over-come these problems, we propose a meta-analysis method at the gene-level rather than variant level.

Results

Among many methods that have been developed, we used the unified quadratic tests (Q-tests); Q-test is more powerful than or as powerful as other tests such as Sequence Kernel Association Tests (SKAT). Since there are three different versions of Q-test (QTest1, QTest2, QTest3), each assumes different relationships among multiple rare variants, we extended them into meta-study accordingly. For meta-analysis, we consider two types of approaches, the one is to combine regression coefficients and the other is to combine test statistics from each single study. We extend the Q-test for meta-analysis, proposing Meta Quadratic Test (Meta-Qtest). Meta Q-test avoids the limitations of MetaSKAT. It does not only consider genetic heterogeneity among studies as MetaSKAT but also reflects diverse real situations; since we extend three different Q-tests into meta-analysis respectively, flexible Meta Q-test suggests way to deal with gene-level rare variant meta-analysis efficiently From the results of real data analysis of blood pressure trait, our meta-analysis could successfully discovered genes, KCNA5 and CABIN1 that are already well known for relevance with hypertension disease and they are not detected in MetaSKAT.

Conclusion

As exemplified by an application to T2D Genes projects data set, Meta-Qtest more effectively identified genes associated with hypertension disease than MetaSKAT did.

Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle

We performed genome-wide association analyses for milk, fat, and protein yields and somatic cell score based on lactation stages in the first 3 parities of Canadian Ayrshire, Holstein, and Jersey cattle. The genome-wide association analyses were performed considering 3 different lactation stages for each trait and parity: from 5 to 95, from 96 to 215, and from 216 to 305 d in milk. Effects of single nucleotide polymorphisms (SNP) for each lactation stage, trait, parity, and breed were estimated by back-solving the direct breeding values estimated using the genomic best linear unbiased predictor and single-trait random regression test-day models containing only the fixed population average curve and the random genomic curves. To identify important genomic regions related to the analyzed lactation stages, traits, parities and breeds, moving windows (SNP-by-SNP) of 20 adjacent SNP explaining more than 0.30% of total genetic variance were selected for further analyses of candidate genes. A lower number of genomic windows with a relatively higher proportion of the explained genetic variance was found in the Holstein breed compared with the Ayrshire and Jersey breeds. Genomic regions associated with the analyzed traits were located on 12, 8, and 15 chromosomes for the Ayrshire, Holstein, and Jersey breeds, respectively. Especially for the Holstein breed, many of the identified candidate genes supported previous reports in the literature. However, well-known genes with major effects on milk production traits (e.g., diacylglycerol O-acyltransferase 1) showed contrasting results among lactation stages, traits, and parities of different breeds. Therefore, our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the analyzed traits across breeds, parities, and lactation stages. Further functional studies are needed to validate our findings in independent populations.

Combining CDKN1A gene expression and genome-wide SNPs in a twin cohort to gain insight into the heritability of individual radiosensitivity

Individual variability in response to radiation exposure is recognised and has often been reported as important in treatment planning. Despite many efforts to identify biomarkers allowing the identification of radiation sensitive patients, it is not yet possible to distinguish them with certainty before the beginning of the radiotherapy treatment. A comprehensive analysis of genome-wide single-nucleotide polymorphisms (SNPs) and a transcriptional response to ionising radiation exposure in twins have the potential to identify such an individual. In the present work, we investigated SNP profile and CDKN1A gene expression in blood T lymphocytes from 130 healthy Caucasians with a complex level of individual kinship (unrelated, mono- or dizygotic twins). It was found that genetic variation accounts for 66% (95% CI 37-82%) of CDKN1A transcriptional response to radiation exposure. We developed a novel integrative multi-kinship strategy allowing investigating the role of genome-wide polymorphisms in transcriptomic radiation response, and it revealed that rs205543 (ETV6 gene), rs2287505 and rs1263612 (KLF7 gene) are significantly associated with CDKN1A expression level. The functional analysis revealed that rs6974232 (RPA3 gene), involved in mismatch repair (p value = 9.68e-04) as well as in RNA repair (p value = 1.4e-03) might have an important role in that process. Two missense polymorphisms with possible deleterious effect in humans were identified: rs1133833 (AKIP1 gene) and rs17362588 (CCDC141 gene). In summary, the data presented here support the validity of this novel integrative data analysis strategy to provide insights into the identification of SNPs potentially influencing radiation sensitivity. Further investigations in radiation response research at the genomic level should be therefore continued to confirm these findings.

Genomics of disease risk in globally diverse populations

Risk of disease is multifactorial and can be shaped by socio-economic, demographic, cultural, environmental and genetic factors. Our understanding of the genetic determinants of disease risk has greatly advanced with the advent of genome-wide association studies (GWAS), which detect associations between genetic variants and complex traits or diseases by comparing populations of cases and controls. However, much of this discovery has occurred through GWAS of individuals of European ancestry, with limited representation of other populations, including from Africa, The Americas, Asia and Oceania. Population demography, genetic drift and adaptation to environments over thousands of years have led globally to the diversification of populations. This global genomic diversity can provide new opportunities for discovery and translation into therapies, as well as a better understanding of population disease risk. Large-scale multi-ethnic and representative biobanks and population health resources provide unprecedented opportunities to understand the genetic determinants of disease on a global scale.

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

The genomics era has brought useful tools to dissect the genetic architecture of complex traits. Here we propose a multivariate reaction norm model (MRNM) to tackle genotype–covariate (G–C) correlation and interaction problems. We apply MRNM to the UK Biobank data in analysis of body mass index using smoking quantity as a covariate, finding a highly significant G–C correlation, but only weak evidence for G–C interaction. In contrast, G–C interaction estimates are inflated in existing methods. It is also notable that there is significant heterogeneity in the estimated residual variances (i.e., variances not attributable to factors in the model) across different covariate levels, i.e., residual–covariate (R–C) interaction. We also show that the residual variances estimated by standard additive models can be inflated in the presence of G–C and/or R–C interactions. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses.

Complex traits are often influenced by genetic and non-genetic factors (such as environmental exposures), which are themselves interconnected. Here, the authors develop a method for disentangling genotype–covariate correlation and interaction, and investigate their effects on estimating statistical genetic parameters.

GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data

Summary

Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGI's input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses.

Availability and and implementation

GIGI-Quick is open source and publicly available via: https://cse-git.qcri.org/Imputation/GIGI-Quick.

Contact

msaad@hbku.edu.qa.

Supplementary information

Supplementary data are available at Bioinformatics online.

Association between PTPN22-1123G/C and susceptibility to rheumatoid arthritis: A systematic review and meta-analysis

BACKGROUND

The incidence of rheumatoid arthritis (RA) varies greatly among different ethnic groups, suggesting genetic susceptibility. The several genetic variants of protein tyrosine phosphatase, non-receptor type 22 (PTPN22-1123G/C, rs2488457) have been widely examined. We systematically evaluated the association of PTPN22-1123 and RA risk by pooling the related studies conducted in different races.

METHODS

Literature was searched using PubMed, EMBASE, Cochrane Library, Korean scientific database, Chinese medical databases, and the Indian medical database to identify eligible studies for determining the association of PTPN22-1123 and RA risk. The association was assessed in five genetic random effects models including the allelic (AG), recessive (RG), dominant (DG), homozygous (HMG), and heterozygous (HTG) genetic models. Subgroup analyses stratified by ethnicity (Asians and non-Asians) were assessed.

RESULTS

A total of 10 articles were selected that met the criteria including Hardy-Weinberg equilibrium. Subjects included 14 186 healthy controls and 5735 with RA. The AG, RG, DG, and HMG genetic models showed no heterogeneity, but the HTG model showed heterogeneity. AG and RG did not exhibit publication bias in any of the studies including Asian and non-Asian subgroups. The overall effect of PTPN22-1123 on RA risk in all genetic random models showed significant positive associations (AG: odds ratio [OR]: 1.24; CI: 1.08-1.42; P = 0.002; RG: OR: 1.35; CI: 1.15-1.59; P = 0.0003; DG: OR: 1.42; CI: 1.09-1.85; P = 0.009; HMG: OR: 1.69; CI: 1.22-2.34; P = 0.002). A significant association when pooling the studies was only revealed in non-Asians (P < 0.05), but no significant relationship was shown in Asians.

CONCLUSIONS

People with C allele in PTPN22-1123 increased the risk of RA only in non-Asians.

Meta-analysis of genome-wide association studies provides insights into genetic control of tomato flavor

Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor, we report the meta-analysis of genome-wide association studies (GWAS) using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discover 305 significant associations for the contents of sugars, acids, amino acids, and flavor-related volatiles. We demonstrate that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. We suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis.

Flavor is one of the most important traits for improving tomato sensory quality and consumer acceptability. Here, the authors report meta-analysis of genome-wide association studies of flavor related traits and show genetic insights into the influence of human selection during domestication and improvement.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

With the increasing obtainability of multi-OMICs data comes the need for easy to use data analysis tools. Here, the authors introduce Metascape, a biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets.

Statistical Methods and Software for Substance Use and Dependence Genetic Research

BACKGROUND

Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

METHODS

Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

CONCLUSION

In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Impact of polymorphisms within genes involved in regulating DNA methylation in patients with metastatic colorectal cancer enrolled in three independent, randomised, open-label clinical trials: a meta-analysis from TRIBE, MAVERICC and FIRE-3

BACKGROUND

CpG island DNA hypermethylation and global DNA hypomethylation are hallmark characteristics of colorectal cancer (CRC). Therefore, we aim to explore the effect of genetic variations within the genes that regulate the DNA methylation and demethylation pathways on outcomes in patients with metastatic CRC (mCRC) treated with first-line therapy and enrolled in three independent, randomised, open-label clinical trials.

METHODS

A total of 884 patients with mCRC enrolled in TRIBE, MAVERICC and FIRE-3 trials were included. Single-nucleotide polymorphisms (SNPs) within genes involved in DNA methylation and demethylation pathways were analysed. The prognostic value of each SNP across all treatment arms was quantified using the inverse-variance-weighted effect size, a meta-analysis approach implemented in the METASOFT software.

RESULTS

In the meta-analysis, DNMT3A rs11681717 was significantly associated with overall survival (hazard ratio = 1.26; 95% confidence interval [CI] 1.08-1.46; P = 0.002; false discovery rate [FDR] = 0.016), accounting for seven tests in the DNA methylation pathway. In addition, there was suggestive evidence of association for ten-eleven translocation (TET) genes variance with tumour response (TET1 rs3814177, odds ratio [OR] = 0.76, 95% CI 0.59-0.97, P = 0.025, FDR = 0.087; TET3 rs7560668, OR = 1.44; 95% CI 1.10-1.89; P = 0.009; FDR = 0.062).

CONCLUSIONS

We showed that polymorphisms within the genes responsible for the DNA methylation and demethylation machineries are correlated with outcomes in patients with mCRC who were enrolled in three independent, randomised, open-label, phase II/III clinical trials. In addition, we demonstrated the feasibility of a meta-analysis approach to identify stronger and more convincing association between gene polymorphisms and outcome, potentially leading the way to a new method of analysis for similar data set.

Prognostic value of ALDH1 and Nestin in advanced cancer: a systematic meta-analysis with trial sequential analysis

Background

Novel prognostic markers and therapeutic targets for advanced cancer are urgently needed. This report with trial sequential analysis (TSA) was first conducted to provide robust estimates of the correlation between aldehyde dehydrogenase 1 (ALDH1) and Nestin and clinical outcomes of advanced cancer patients.

Methods

Hazard ratios (HRs) with 95% confidence intervals (CIs) were summarized for overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), relapse/recurrence-free survival (RFS), and metastasis-free survival (MFS) from multivariable analysis. TSA was performed to control for random errors.

Results

A total of 20 studies with 2050 patients (ALDH1: 15 studies with 1557 patients and Nestin: 5 studies with 493 patients) were identified. ALDH1 (HR = 2.28, p < 0.001) and Nestin (HR = 2.39, p < 0.001) were associated with a worse OS, as confirmed by TSA. Nestin positivity was linked to a poor PFS (HR = 2.08, p < 0.001), but ALDH1 was not linked to DFS, RFS, MFS, or PFS, and TSA showed that more studies were needed. Subgroup analysis by tumor type indicated that ALDH1 positivity may be associated with shorter OS in breast, head and neck cancers, but there was no association with colorectal cancer. Subgroup analysis by study source showed that ALDH1 positivity was correlated with a worse OS for Japanese (HR = 1.94, p = 0.002) and European patients (HR = 4.15, p < 0.001), but there was no association for Chinese patients. Subgroup analysis by survival rate showed that ALDH1 positivity correlated with poor OS at ⩾ 5 years (HR = 2.33, p < 0.001) or 10 years (HR = 1.76, p = 0.038).

Conclusions

ALDH1 may be more valuable as an effective therapeutic target than Nestin for improving the long-term survival rate of advanced cancer. Additional prospective clinical trials are needed across different cancer types.

Robust meta-analysis of biobank-based genome-wide association studies with unbalanced binary phenotypes

With the availability of large-scale biobanks, genome-wide scale phenome-wide association studies are being instrumental in discovering novel genetic variants associated with clinical phenotypes. As increasing number of such association results from different biobanks become available, methods to meta-analyse those association results is of great interest. Because the binary phenotypes in biobank-based studies are mostly unbalanced in their case-control ratios, very few methods can provide well-calibrated tests for associations. For example, traditional Z-score-based meta-analysis often results in conservative or anticonservative Type I error rates in such unbalanced scenarios. We propose two meta-analysis strategies that can efficiently combine association results from biobank-based studies with such unbalanced phenotypes, using the saddlepoint approximation-based score test method. Our first method involves sharing the overall genotype counts from each study, and the second method involves sharing an approximation of the distribution of the score test statistic from each study using cubic Hermite splines. We compare our proposed methods with a traditional Z-score-based meta-analysis strategy using numerical simulations and real data applications, and demonstrate the superior performance of our proposed methods in terms of Type I error control.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Prognostic Value of CD44 and Its Isoforms in Advanced Cancer: A Systematic Meta-Analysis With Trial Sequential Analysis

Objective: Cancer stem cell marker CD44 and its variant isoforms (CD44v) may be correlated with tumor growth, metastasis, and chemo-radiotherapy resistance. However, the prognostic power of CD44 and CD44v in advanced cancer remains controversial. Therefore, the purpose of our study was to generalize the prognostic significance of these cancer stem cell markers in advanced cancer patients.

Methods: Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated from multivariable analysis to assess the associations among CD44, CD44v6, and CD44v9 positivity and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), and recurrence-free survival (RFS). Trial sequential analysis (TSA) was also conducted.

Results: We included 15 articles that reported on 1,201 patients with advanced cancer (CD44: nine studies with 796 cases, CD44v6: three studies with 143 cases, and CD44v9: three studies with 262 cases). CD44 expression was slightly linked to worse OS (HR = 2.03, P = 0.027), but there was no correlation between CD44 expression and DFS, RFS, or PFS. Stratified analysis showed that CD44 expression was not correlated with OS at ≥5 years or OS in patients receiving adjuvant therapy. CD44v6 expression was not associated with OS. CD44v9 expression was closely associated with poor 5-years CSS in patients treated with chemo/radiotherapy (HR = 3.62, P < 0.001). However, TSA suggested that additional trials were needed to confirm these conclusions.

Conclusions: CD44 or CD44v9 might be novel therapeutic targets for improving the treatment of advanced cancer patients. Additional prospective clinical trials are strongly needed across different cancer types.

Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control

To identify genetic variants associated with diabetic retinopathy (DR), we performed a large multiethnic genome-wide association study. Discovery included eight European cohorts (n = 3,246) and seven African American cohorts (n = 2,611). We meta-analyzed across cohorts using inverse-variance weighting, with and without liability threshold modeling of glycemic control and duration of diabetes. Variants with a P value <1 × 10-5 were investigated in replication cohorts that included 18,545 European, 16,453 Asian, and 2,710 Hispanic subjects. After correction for multiple testing, the C allele of rs142293996 in an intron of nuclear VCP-like (NVL) was associated with DR in European discovery cohorts (P = 2.1 × 10-9), but did not reach genome-wide significance after meta-analysis with replication cohorts. We applied the Disease Association Protein-Protein Link Evaluator (DAPPLE) to our discovery results to test for evidence of risk being spread across underlying molecular pathways. One protein-protein interaction network built from genes in regions associated with proliferative DR was found to have significant connectivity (P = 0.0009) and corroborated with gene set enrichment analyses. These findings suggest that genetic variation in NVL, as well as variation within a protein-protein interaction network that includes genes implicated in inflammation, may influence risk for DR.

Variants in ABCG8 and TRAF3 genes confer risk for gallstone disease in admixed Latinos with Mapuche Native American ancestry

Latin Americans and Chilean Amerindians have the highest prevalence of gallstone disease (GSD) and gallbladder cancer (GBC) in the world. A handful of loci have been associated with GSD in populations of predominantly European ancestry, however, they only explain a small portion of the genetic component of the disease. Here, we performed a genome-wide association study (GWAS) for GSD in 1,095 admixed Chilean Latinos with Mapuche Native American ancestry. Disease status was assessed by cholecystectomy or abdominal ultrasonography. Top-10 candidate variants surpassing the suggestive cutoff of P < 1 × 10⁻⁵ in the discovery cohort were genotyped in an independent replication sample composed of 1,643 individuals. Variants with positive replication were further examined in two European GSD populations and a Chilean GBC cohort. We consistently replicated the association of ABCG8 gene with GSD (rs11887534, P = 3.24 × 10⁻⁸, OR = 1.74) and identified TRAF3 (rs12882491, P = 1.11 × 10⁻⁷, OR = 1.40) as a novel candidate gene for the disease in admixed Chilean Latinos. ABCG8 and TRAF3 variants also conferred risk to GBC. Gene expression analyses indicated that TRAF3 was significantly decreased in gallbladder (P = 0.015) and duodenal mucosa (P = 0.001) of GSD individuals compared to healthy controls, where according to GTEx data in the small intestine, the presence of the risk allele contributes to the observed effect. We conclude that ABCG8 and TRAF3 genes are associated with GSD and GBC in admixed Latinos and that decreased TRAF3 levels could enhance gallbladder inflammation as is observed in GSD and GSD-associated GBC.