Meta-analysis in genome-wide association studies

GWAS for identification of genomic regions and candidate genes in vegetable crops

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BACKGROUND

Chronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.

METHODS

GWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.

RESULTS

A total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.

CONCLUSIONS

Our integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies

SUMMARY

Harmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets.

AVAILABILITY AND IMPLEMENTATION

GRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.

Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

BACKGROUND

Mastitis is a disease that incurs significant costs in the dairy industry. A promising approach to mitigate its negative effects is to genetically improve the resistance of dairy cattle to mastitis. A meta-analysis of genome-wide association studies (GWAS) across multiple breeds for clinical mastitis (CM) and its indicator trait, somatic cell score (SCS), is a powerful method to identify functional genetic variants that impact mastitis resistance.

RESULTS

We conducted meta-analyses of eight and fourteen GWAS on CM and SCS, respectively, using 30,689 and 119,438 animals from six dairy cattle breeds. Methods for the meta-analyses were selected to properly account for the multi-breed structure of the GWAS data. Our study revealed 58 lead markers that were associated with mastitis incidence, including 16 loci that did not overlap with previously identified quantitative trait loci (QTL), as curated at the Animal QTLdb. Post-GWAS analysis techniques such as gene-based analysis and genomic feature enrichment analysis enabled prioritization of 31 candidate genes and 14 credible candidate causal variants that affect mastitis.

CONCLUSIONS

Our list of candidate genes can help to elucidate the genetic architecture underlying mastitis resistance and provide better tools for the prevention or treatment of mastitis, ultimately contributing to more sustainable animal production.

A mediation analysis framework based on variance component to remove genetic confounding effect

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Time-dependent gene-environment interactions are essential drivers of asthma initiation and persistence

Asthma is a clinical syndrome caused by heterogeneous underlying mechanisms with some of them having a strong genetic component. It is known that up to 82% of atopic asthma has a genetic background with the rest being influenced by environmental factors that cause epigenetic modification(s) of gene expression. The interaction between the gene(s) and the environment has long been regarded as the most likely explanation of asthma initiation and persistence. Lately, much attention has been given to the time frame the interaction occurs since the host response (immune or biological) to environmental triggers, differs at different developmental ages. The integration of the time variant into asthma pathogenesis is appearing to be equally important as the gene(s)-environment interaction. It seems that, all three factors should be present to trigger the asthma initiation and persistence cascade. Herein, we introduce the importance of the time variant in asthma pathogenesis and emphasize the long-term clinical significance of the time-dependent gene-environment interactions in childhood.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones

Background

Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify.

Methods

An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography - mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation.

Results

We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites.

Conclusions

HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

Introduction

The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).

Methods

In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.

Results

We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.

Discussion

Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.

Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections

With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.

Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks

Sample recruitment for research consortia, biobanks, and personal genomics companies span years, necessitating genotyping in batches, using different technologies. As marker content on genotyping arrays varies, integrating such datasets is non-trivial and its impact on haplotype estimation (phasing) and whole genome imputation, necessary steps for complex trait analysis, remains under-evaluated. Using the iPSYCH dataset, comprising 130,438 individuals, genotyped in two stages, on different arrays, we evaluated phasing and imputation performance across multiple phasing methods and data integration protocols. While phasing accuracy varied by choice of method and data integration protocol, imputation accuracy varied mostly between data integration protocols. We demonstrate an attenuation in imputation accuracy within samples of non-European origin, highlighting challenges to studying complex traits in diverse populations. Finally, imputation errors can bias association tests, reduce predictive utility of polygenic scores. Carefully optimized data integration strategies enhance accuracy and replicability of complex trait analyses in complex biobanks.

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

Beta-Meta: a meta-analysis application considering heterogeneity among genome-wide association studies

Many packages for a meta-analysis of genome-wide association studies (GWAS) have beendeveloped to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easilyprocess a meta-analysis by automatically selecting between a fixed effects and a randomeffects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in asingle process. It provides a step-by-step meta-analysis of GWAS for each association inthe following order: heterogeneity test, two different calculations of an effect size and ap-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. Thesemethods enable users to validate the results of individual studies with greater statisticalpower and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

Two decades of association mapping: Insights on disease resistance in major crops

Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.

Discerning asthma endotypes through comorbidity mapping

Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.

Harmonized Phenotypes for Anxiety, Depression, and Attention-Deficit Hyperactivity Disorder (ADHD)

In multi-cohort consortia, the problem often arises that a phenotype is measured using different questionnaires. This study aimed to harmonize scores based on the Child Behaviour Check List (CBCL) and the Strength and Difficulties Questionnaire (SDQ) for anxiety/depression and ADHD. To link the scales, we used parent reports on 1330 children aged 10–11.5 years from the Raine study on both SDQ and CBCL. Harmonization was done based on Item Response Theory. We started from existing CBCL and SDQ scales related to anxiety/depression and ADHD (theoretical approach). Next, we conducted a data-driven approach using factor analysis to validate the theoretical approach. Both approaches yielded similar scales, validating the combination of existing scales. In addition, we studied the impact of harmonized (IRT-based) scores on the statistical power of the results in meta-analytic gene-finding studies. The results showed that the IRT-based harmonized scores increased the statistical power of the results compared to sum scores, even with an equal sample size. These findings can help future researchers to harmonize data from different samples and/or different questionnaires that measure anxiety, depression, and ADHD, in order to obtain the larger sample sizes, to compare research results across subpopulations or to increase generalizability, the validity or statistical power of research results. We recommend using our item parameters to estimate harmonized scores that represent commensurate phenotypes across cohorts, and we explained in detail how other researchers can use our results to harmonize data in their studies.

The "Golden Age" of Behavior Genetics?

The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.

Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies

Growing genomic and phenotypic datasets require different groups around the world to collaborate and integrate these valuable resources to maximize their benefit and increase reference population sizes for genomic prediction and genome-wide association studies (GWAS). However, different studies use different genotyping techniques which requires a synchronizing step for the genotyped variants called "imputation" before combining them. Optimally, different GWAS datasets can be analysed within a meta-analysis, which recruits summary statistics instead of actual data. This chapter describes the general principles for genotypic imputation and meta-GWAS analysis with a description of study designs and command lines required for such analyses.

Identification and Validation of Candidate Genes from Genome-Wide Association Studies

Exploiting the statistical associations coming out from a GWAS experiment to identify and validate candidate genes may be potentially difficult and time consuming. To fill the gap between the identification of candidate genes toward their functional validation onto the trait performance, the prioritization of variants underlying the GWAS-associated regions is necessary. In parallel, recent developments in genomics and statistical methods have been achieved notably in human genetic and they are accordingly being adopted in plant breeding toward the study of the genetic architecture of traits to sustain genetic gains. In this chapter, we aim at providing both theoretical and practical aspects underlying three main options including (1) the MetaGWAS analysis, (2) the statistical fine mapping and (3) the integration of functional data toward the identification and validation of candidate genes from a GWAS experiment.

Flimma: a federated and privacy-aware tool for differential gene expression analysis

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Performing post-genome-wide association study analysis: overview, challenges and recommendations

Genome-wide association studies (GWAS) provide  huge information on statistically significant single-nucleotide polymorphisms (SNPs) associated with various human complex traits and diseases. By performing GWAS studies, scientists have successfully identified the association of hundreds of thousands to  millions of SNPs to a single phenotype. Moreover, the association of some SNPs with rare diseases has been intensively tested. However, classic GWAS studies have not yet provided solid, knowledgeable insight into functional and biological mechanisms underlying phenotypes or mechanisms of diseases. Therefore, several post-GWAS (pGWAS) methods have been recommended. Currently, there is no simple scientific document to provide a quick guide for performing pGWAS analysis. pGWAS is a crucial step for a better understanding of the biological machinery beyond the SNPs. Here, we provide an overview to performing pGWAS analysis and demonstrate the challenges behind each method. Furthermore, we direct readers to key articles for each pGWAS method and present the overall issues in pGWAS analysis.  Finally, we include a custom pGWAS pipeline to guide new users when performing their research.

Genomewide Association Studies in Pharmacogenomics

The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.

Meta-GWAS for quantitative trait loci identification in soybean

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Translating genetic association of lipid levels for biological and clinical application

PURPOSE OF REVIEW

This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction.

RECENT FINDINGS

Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes-PCSK9, LDLR, and APOB-with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction.

CONCLUSIONS

Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.

Genome wide association study of response to interval and continuous exercise training: the Predict-HIIT study

BACKGROUND

Low cardiorespiratory fitness (V̇O_2peak) is highly associated with chronic disease and mortality from all causes. Whilst exercise training is recommended in health guidelines to improve V̇O_2peak, there is considerable inter-individual variability in the V̇O_2peak response to the same dose of exercise. Understanding how genetic factors contribute to V̇O_2peak training response may improve personalisation of exercise programs. The aim of this study was to identify genetic variants that are associated with the magnitude of V̇O₂peak response following exercise training.

METHODS

Participant change in objectively measured V̇O₂peak from 18 different interventions was obtained from a multi-centre study (Predict-HIIT). A genome-wide association study was completed (n = 507), and a polygenic predictor score (PPS) was developed using alleles from single nucleotide polymorphisms (SNPs) significantly associated (P < 1 × 10^-5) with the magnitude of V̇O₂peak response. Findings were tested in an independent validation study (n = 39) and compared to previous research.

RESULTS

No variants at the genome-wide significance level were found after adjusting for key covariates (baseline V̇O₂peak_, individual study, principal components which were significantly associated with the trait). A Quantile-Quantile plot indicates there was minor inflation in the study. Twelve novel loci showed a trend of association with V̇O₂peak response that reached suggestive significance (P < 1 × 10^-5). The strongest association was found near the membrane associated guanylate kinase, WW and PDZ domain containing 2 (MAGI2) gene (rs6959961, P = 2.61 × 10^-7). A PPS created from the 12 lead SNPs was unable to predict V̇O₂peak response in a tenfold cross validation, or in an independent (n = 39) validation study (P > 0.1). Significant correlations were found for beta coefficients of variants in the Predict-HIIT (P < 1 × 10^-4) and the validation study (P <  × 10^-6), indicating that general effects of the loci exist, and that with a higher statistical power, more significant genetic associations may become apparent.

CONCLUSIONS

Ongoing research and validation of current and previous findings is needed to determine if genetics does play a large role in V̇O₂peak response variance, and whether genomic predictors for V̇O₂peak response trainability can inform evidence-based clinical practice. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR), Trial Id: ACTRN12618000501246, Date Registered: 06/04/2018, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374601&isReview=true .

Introduction of a Variant Classification System for Analysis of Genotype-Phenotype Relationships in Heritable Retinoblastoma

Simple Summary

Heritable retinoblastoma is a genetic disease that predisposes to develop multiple retinoblastomas in childhood and other extraocular tumors later in life. It is caused by genetic variants in the RB1 gene. Here we present a new classification for genetic variants in the RB1 gene (REC) that focuses on the variant’s effect. The different classes, REC-I to -V, correlate with different risks of tumor predisposition. REC correlated with different clinical courses when applied in our study cohort. REC aims to facilitate risk estimation for physicians, patients and their families, and researchers and to improve the definition of the necessity of screening examination.

Abstract

Constitutional haploinsufficiency of the RB1 gene causes heritable retinoblastoma, a tumor predisposition syndrome. Patients with heritable retinoblastoma develop multiple retinoblastomas early in childhood and other extraocular tumors later in life. Constitutional pathogenic variants in RB1 are heterogeneous, and a few genotype-phenotype correlations have been described. To identify further genotype-phenotype relationships, we developed the retinoblastoma variant effect classification (REC), which considers each variant’s predicted effects on the common causal mediator, RB1 protein pRB. For validation, the RB1 variants of 287 patients were grouped according to REC. Multiple aspects of phenotypic expression were analyzed, known genotype-phenotype associations were revised, and new relationships were explored. Phenotypic expression of patients with REC-I, -II, and -III was distinct. Remarkably, the phenotype of patients with variants causing residual amounts of truncated pRB (REC-I) was more severe than patients with complete loss of RB1 (REC-II). The age of diagnosis of REC-I variants appeared to be distinct depending on truncation’s localization relative to pRB structure domains. REC classes identify genotype-phenotype relationships and, therefore, this classification framework may serve as a tool to develop tailored tumor screening programs depending on the type of RB1 variant.

Powerful p-value combination methods to detect incomplete association

Meta-analyses increase statistical power by combining statistics from multiple studies. Meta-analysis methods have mostly been evaluated under the condition that all the data in each study have an association with the given phenotype. However, specific experimental conditions in each study or genetic heterogeneity can result in "unassociated statistics" that are derived from the null distribution. Here, we show that power of conventional meta-analysis methods rapidly decreases as an increasing number of unassociated statistics are included, whereas the classical Fisher's method and its weighted variant (wFisher) exhibit relatively high power that is robust to addition of unassociated statistics. We also propose another robust method based on joint distribution of ordered p-values (ordmeta). Simulation analyses for t-test, RNA-seq, and microarray data demonstrated that wFisher and ordmeta, when only a small number of studies have an association, outperformed existing meta-analysis methods. We performed meta-analyses of nine microarray datasets (prostate cancer) and four association summary datasets (body mass index), where our methods exhibited high biological relevance and were able to detect genes that the-state-of-the-art methods missed. The metapro R package that implements the proposed methods is available from both CRAN and GitHub ( http://github.com/unistbig/metapro ).

Dose HLA-B5, 7, 8, 27, and 51 Antigens Associated to Behcet's disease? A Study in Southwestern Iran

BACKGROUND

Behcet's disease is a potentially life threatening autoimmune disease with recurrent ulcers and unknown pathogenesis. Gender and human leukocyte antigen-B51 seem to have an effective role in the clinical features of the disease.

OBJECTIVE

The aim of this study is to evaluate the frequency of HLA-B5, 7, 8, 27 and 51 in behçet's disease in southwestern Iranian patients who visited the rheumatology clinic and to find the association between these HLA types and the disease.

METHODS

63 patients with behcet's disease participated in this study and peripheral blood samples were collected from them. The expression of each HLA antigen was evaluated by standard lymphocytotoxicity technique.

RESULTS

Compared to other studied antigens, the expression of HLA-B5 and HLA-B51 was more prevalent among our patients. According to the results, 25% and 21% of patients were positive for HLA-B5 and HLA-B51, respectively.

CONCLUSIONS

HLA-B5 and HLA-B51 are dominant positive HLA antigens among behcet's disease patients in the southwest of Iran; however, we cannot conclude that these antigens are valuable diagnostic or prognostic biomarkers due to our study limitations. We suggest studying the association between HLA-B antigens and inflammation severity in patients to determine the possible prognostic value of HLA-B antigens in Iranian population in the southwest and this region needs more studies in HLA subject among BD patients because of the frequency of BD to evaluate the value of HLA typing in BD prognosis.

Response and Toxicity to Cytarabine Therapy in Leukemia and Lymphoma: From Dose Puzzle to Pharmacogenomic Biomarkers

Simple Summary

In this review, the authors propose a crosswise examination of cytarabine-related issues ranging from the spectrum of clinical activity and severe toxicities, through updated cellular pharmacology and drug formulations, to the genetic variants associated with drug-induced phenotypes. Cytarabine (cytosine arabinoside; Ara-C) in multiagent chemotherapy regimens is often used for leukemia or lymphoma treatments, as well as neoplastic meningitis. Chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. The individual variability in clinical response to Leukemia & Lymphoma treatments among patients appears to be associated with intracellular accumulation of Ara-CTP due to genetic variants related to metabolic enzymes. The review provides exhaustive information on the effects of Ara-C-based therapies, the adverse drug reaction will also be provided including bone pain, ocular toxicity (corneal pain, keratoconjunctivitis, and blurred vision), maculopapular rash, and occasional chest pain. Evidence for predicting the response to cytarabine-based treatments will be highlighted, pointing at their significant impact on the routine management of blood cancers.

Abstract

Cytarabine is a pyrimidine nucleoside analog, commonly used in multiagent chemotherapy regimens for the treatment of leukemia and lymphoma, as well as for neoplastic meningitis. Ara-C-based chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. Several studies suggest that the individual variability in clinical response to Leukemia & Lymphoma treatments among patients, underlying either Ara-C mechanism resistance or toxicity, appears to be associated with the intracellular accumulation and retention of Ara-CTP due to genetic variants related to metabolic enzymes. Herein, we reported (a) the latest Pharmacogenomics biomarkers associated with the response to cytarabine and (b) the new drug formulations with optimized pharmacokinetics. The purpose of this review is to provide readers with detailed and comprehensive information on the effects of Ara-C-based therapies, from biological to clinical practice, maintaining high the interest of both researcher and clinical hematologist. This review could help clinicians in predicting the response to cytarabine-based treatments.

Meta-analysis of SNP-environment interaction with heterogeneity for overlapping data

Meta-analysis is a popular method used in genome-wide association studies, by which the results of multiple studies are combined to identify associations. This process generates heterogeneity. Recently, we proposed a random effect model meta-regression method (MR) to study the effect of single nucleotide polymorphism (SNP)-environment interactions. This method takes heterogeneity into account and produces high power. We also proposed a fixed effect model overlapping MR in which the overlapping data is taken into account. In the present study, a random effect model overlapping MR that simultaneously considers heterogeneity and overlapping data is proposed. This method is based on the random effect model MR and the fixed effect model overlapping MR. A new way of solving the logarithm of the determinant of covariance matrices in likelihood functions is also provided. Tests for the likelihood ratio statistic of the SNP-environment interaction effect and the SNP and SNP-environment joint effects are given. In our simulations, null distributions and type I error rates were proposed to verify the suitability of our method, and powers were applied to evaluate the superiority of our method. Our findings indicate that this method is effective in cases of overlapping data with a high heterogeneity.

Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

Genotype imputation using the Positional Burrows Wheeler Transform

Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model. Using the HRC reference panel, which has ∼65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.

Genome-wide association studies (GWAS) typically use microarray technology to measure genotypes at several hundred thousand positions in the genome. However reference panels of genetic variation consist of haplotype data at >100 fold more positions in the genome. Genotype imputation makes genotype predictions at all the reference panel sites using the GWAS data. Reference panels are continuing to grow in size and this improves accuracy of the predictions, however methods need to be able to scale this increased size. We have developed a new version of the popular IMPUTE software than can handle reference panels with millions of haplotypes, and has better performance than other published approaches. A notable property of the new method is that it scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.

The role of host genetics in susceptibility to severe viral infections in humans and insights into host genetics of severe COVID-19: A systematic review

BACKGROUND

Susceptibility to severe viral infections was reported to be associated with genetic variants in immune response genes using case reports and GWAS studies. SARS-CoV-2 is an emergent viral disease that caused millions of COVID-19 cases all over the world. Around 15 % of cases are severe and some of them are accompanied by dysregulated immune system and cytokine storm. There is increasing evidence that severe manifestations of COVID-19 might be attributed to human genetic variants in genes related to immune deficiency and or inflammasome activation (cytokine storm).

OBJECTIVE

Identify the candidate genes that are likely to aid in explaining severe COVID-19 and provide insights to understand the pathogenesis of severe COVID-19.

METHODS

In this article, we systematically reviewed genes related to viral susceptibility that were reported in human genetic studies (Case-reports and GWAS) to understand the role of host viral interactions and to provide insights into the pathogenesis of severe COVID-19.

RESULTS

We found 40 genes associated with viral susceptibility and 21 of them were associated with severe SARS-CoV disease and severe COVID-19. Some of those genes were implicated in TLR pathways, others in C-lectin pathways, and others were related to inflammasome activation (cytokine storm).

CONCLUSION

This compilation represents a list of candidate genes that are likely to aid in explaining severe COVID-19 which are worthy of inclusion in gene panels and during meta-analysis of different variants in host genetics studies of COVID-19. In addition, we provide several hypotheses for severe COVID-19 and possible therapeutic targets.

Effect of plasma polyunsaturated fatty acid levels on leukocyte telomere lengths in the Singaporean Chinese population

BACKGROUND

Shorter telomere length (TL) has been associated with poor health behaviors, increased risks of chronic diseases and early mortality. Excessive shortening of telomere is a marker of accelerated aging and can be influenced by oxidative stress and nutritional deficiency. Plasma n6:n3 polyunsaturated fatty acid (PUFA) ratio may impact cell aging. Increased dietary intake of marine n-3 PUFA is associated with reduced telomere attrition. However, the effect of plasma PUFA on leukocyte telomere length (LTL) and its interaction with genetic variants are not well established.

METHODS

A nested coronary artery disease (CAD) case-control study comprising 711 cases and 638 controls was conducted within the Singapore Chinese Health Study (SCHS). Samples genotyped with the Illumina ZhongHua-8 array. Plasma n-3 and n-6 PUFA were quantified using mass spectrometry (MS). LTL was measured with quantitative PCR method. Linear regression was used to test the association between PUFA and LTL. The interaction between plasma PUFAs and genetic variants was assessed by introducing an additional term (PUFA×genetic variant) in the regression model. Analysis was carried out in cases and controls separately and subsequently meta-analyzed using the inverse-variance weighted method. We further assessed the association of PUFA and LTL with CAD risk by Cox Proportional-Hazards model and whether the effect of PUFA on CAD was mediated through LTL by using structural equation modeling.

RESULTS

Higher n6:n3 ratio was significantly associated with shorter LTL (p = 0.018) and increased CAD risk (p = 0.005). These associations were mainly driven by elevated plasma total n-3 PUFAs, especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (p < 0.05). There was a statistically significant interaction for an intergenic single nucleotide polymorphism (SNP) rs529143 with plasma total n-3 PUFA and DHA on LTL beyond the genome-wide threshold (p < 5 ×  10- 8). Mediation analysis showed that PUFA and LTL affected CAD risk independently.

CONCLUSIONS

Higher plasma n6:n3 PUFA ratio, and lower EPA and DHA n-3 PUFAs were associated with shorter LTL and increased CAD risk in this Chinese population. Furthermore, genetic variants may modify the effect of PUFAs on LTL. PUFA and LTL had independent effect on CAD risk in our study population.

Interaction between a haptoglobin genetic variant and coronary artery disease (CAD) risk factors on CAD severity in Singaporean Chinese population

BACKGROUND

Haptoglobin (Hp) is a plasma protein with strong anti-inflammation and antioxidant activities. Its plasma level is known to be inversely associated with many inflammatory diseases, including cardiovascular diseases. However, the association of HP genetic variants with coronary artery disease (CAD) severity/mortality, and how they interact with common CAD risk factors are largely unknown.

METHODS

We conducted the analysis in a Singaporean Chinese CAD population with Gensini severity scores (N = 582) and subsequently evaluated the significant findings in an independent cohort with cardiovascular mortality (excluding stroke) as outcome (917 cases and 19,093 controls). CAD risk factors were ascertained from questionnaires, and stenosis information from medical records. Mortality was identified through linkage with the nationwide registry of births and deaths in Singapore. Linear regression analysis between HP genetic variant (rs217181) and disease outcome were performed. Interaction analyses were performed by introducing an interaction term in the same regression models.

RESULTS

Although rs217181 was not significantly associated with CAD severity and cardiovascular mortality (excluding stroke) in all subjects, when stratified by hypertension status, hypertensive individuals with the minor T allele have more severe CAD (β = 0.073, SE = 0.030, p = 0.015) and non-hypertensive individuals with the T allele have lower risk for mortality (odds ratio = 0.771 (0.607-0.980), p = 0.033).

CONCLUSION

HP genetic variant is not associated with CAD severity and mortality in the general population. However, hypertensive individuals with the rs217181 T allele associated with higher Hp levels had more severe CAD while non-hypertensive individuals with the same allele had lower risk for mortality in the Chinese population.

Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population

While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.

Critical Analysis of Genome-Wide Association Studies: Triple Negative Breast Cancer Quae Exempli Causa

Genome-wide association studies (GWAS) are useful in assessing and analyzing either differences or variations in DNA sequences across the human genome to detect genetic risk factors of diseases prevalent within a target population under study. The ultimate goal of GWAS is to predict either disease risk or disease progression by identifying genetic risk factors. These risk factors will define the biological basis of disease susceptibility for the purposes of developing innovative, preventative, and therapeutic strategies. As single nucleotide polymorphisms (SNPs) are often used in GWAS, their relevance for triple negative breast cancer (TNBC) will be assessed in this review. Furthermore, as there are different levels and patterns of linkage disequilibrium (LD) present within different human subpopulations, a plausible strategy to evaluate known SNPs associated with incidence of breast cancer in ethnically different patient cohorts will be presented and discussed. Additionally, a description of GWAS for TNBC will be presented, involving various identified SNPs correlated with miRNA sites to determine their efficacies on either prognosis or progression of TNBC in patients. Although GWAS have identified multiple common breast cancer susceptibility variants that individually would result in minor risks, it is their combined effects that would likely result in major risks. Thus, one approach to quantify synergistic effects of such common variants is to utilize polygenic risk scores. Therefore, studies utilizing predictive risk scores (PRSs) based on known breast cancer susceptibility SNPs will be evaluated. Such PRSs are potentially useful in improving stratification for screening, particularly when combining family history, other risk factors, and risk prediction models. In conclusion, although interpretation of the results from GWAS remains a challenge, the use of SNPs associated with TNBC may elucidate and better contextualize these studies.

Non-significant association between - 330 T/G polymorphism in interleukin-2 gene and chronic periodontitis: findings from a meta-analysis

BACKGROUND

Chronic periodontitis (CP) is an immune-inflammatory disease that promotes tissue damage around the teeth. Among the several inflammatory mediators that orchestrate the periodontitis, there is the interleukin (IL)-2. Genetic variations in IL2 gene may be associated with the risk and severity of the disease. Contrary results are available in the literature with inconclusive findings and none meta-analysis to gather these data.

METHODS

A literature search was performed for studies published before June 11, 2019 in diverse scientific and educational databases. The data was extracted by two investigators and the statistical evaluation was performed by Review Manager statistical program with heterogeneity (I²) and Odds Ratio (OR) with 95% of Confidence Intervals (CI) calculations and a sensitive analysis to assess the accuracy of the obtained results. The publication bias was evaluated by Begg' and Egger's test with Comprehensive meta-analysis software. The value of P < 0.05 was considered as significant.

RESULTS

Five studies were identified in diverse ethnical groups with 1425 participants. The - 330 T/G polymorphism in IL2 gene was not significantly associated with CP in allelic evaluation (P > 0.05) as well as in the genotypic comparisons (P = 0.15). The Begg's test and the linear regression Egger's test did not show any evidence of publication bias risk (P > 0.05) which was corroborated by the absence of obvious asymmetry in Funnel plot graphic.

CONCLUSIONS

This meta-analysis showed a non-significant association between - 330 T/G polymorphism in IL2 gene and CP in any allelic evaluation.

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.

An adaptive test for meta-analysis of rare variant association studies

Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data. One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github.com/ytzhong/metaRV and will be on CRAN soon.

Mitochondrial DNA Sequence Variants Associated With Blood Pressure Among 2 Cohorts of Older Adults

Background

Age‐related changes in blood pressure are associated with a variety of poor health outcomes. Genetic factors are proposed contributors to age‐related increases in blood pressure, but few genetic loci have been identified. We examined the role of mitochondrial genomic variation in blood pressure by sequencing the mitochondrial genome.

Methods and Results

Mitochondrial DNA (mtDNA) data from 1755 participants from the LIFE (Lifestyle Interventions and Independence for Elders) studies and 788 participants from the Health ABC (Health, Aging, and Body Composition) study were evaluated using replication analysis followed by meta‐analysis. Participants were aged ≥69 years, of diverse racial backgrounds, and assessed for systolic blood pressure (SBP), diastolic blood pressure, and mean arterial pressure. After meta‐analysis across the LIFE and Health ABC studies, statistically significant associations of mtDNA variants with higher SBP (m.3197T>C, 16S rRNA; P=0.0005) and mean arterial pressure (m.15924A>G, t‐RNA‐thr; P=0.004) were identified in white participants. Among black participants, statistically significant associations with higher SBP (m.93A>G, HVII; m.16183A>C, HVI; both P=0.0001) and mean arterial pressure (m.16172T>C, HVI; m.16183A>C, HVI; m.16189T>C, HVI; m.12705C>T; all P's<0.0004) were observed. Significant pooled effects on SBP were observed across all transfer RNA regions (P=0.0056) in white participants. The individual and aggregate variant results are statistically significant after multiple comparisons adjustment for the number of mtDNA variants and mitochondrial regions examined.

Conclusions

These results suggest that mtDNA‐encoded variants are associated with variation in SBP and mean arterial pressure among older adults. These results may help identify mitochondrial activities to explain differences in blood pressure in older adults and generate new hypotheses surrounding mtDNA variation and the regulation of blood pressure.

Clinical Trial Registration

URL: http://www.ClinicalTrials.gov. Unique identifiers: NCT01072500 and NCT00116194.

A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies

Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome-wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two-phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome-wide application involving colorectal cancer.

Finding associated variants in genome-wide association studies on multiple traits

Motivation

Many variants identified by genome-wide association studies (GWAS) have been found to affect multiple traits, either directly or through shared pathways. There is currently a wealth of GWAS data collected in numerous phenotypes, and analyzing multiple traits at once can increase power to detect shared variant effects. However, traditional meta-analysis methods are not suitable for combining studies on different traits. When applied to dissimilar studies, these meta-analysis methods can be underpowered compared to univariate analysis. The degree to which traits share variant effects is often not known, and the vast majority of GWAS meta-analysis only consider one trait at a time.

Results

Here, we present a flexible method for finding associated variants from GWAS summary statistics for multiple traits. Our method estimates the degree of shared effects between traits from the data. Using simulations, we show that our method properly controls the false positive rate and increases power when an effect is present in a subset of traits. We then apply our method to the North Finland Birth Cohort and UK Biobank datasets using a variety of metabolic traits and discover novel loci.

Availability and implementation

Our source code is available at https://github.com/lgai/CONFIT.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide association studies of severe P. falciparum malaria susceptibility: progress, pitfalls and prospects

BACKGROUND

P. falciparum malaria has been recognized as one of the prominent evolutionary selective forces of human genome that led to the emergence of multiple host protective alleles. A comprehensive understanding of the genetic bases of severe malaria susceptibility and resistance can potentially pave ways to the development of new therapeutics and vaccines. Genome-wide association studies (GWASs) have recently been implemented in malaria endemic areas and identified a number of novel association genetic variants. However, there are several open questions around heritability, epistatic interactions, genetic correlations and associated molecular pathways among others. Here, we assess the progress and pitfalls of severe malaria susceptibility GWASs and discuss the biology of the novel variants.

RESULTS

We obtained all severe malaria susceptibility GWASs published thus far and accessed GWAS dataset of Gambian populations from European Phenome Genome Archive (EGA) through the MalariaGen consortium standard data access protocols. We noticed that, while some of the well-known variants including HbS and ABO blood group were replicated across endemic populations, only few novel variants were convincingly identified and their biological functions remain to be understood. We estimated SNP-heritability of severe malaria at 20.1% in Gambian populations and showed how advanced statistical genetic analytic methods can potentially be implemented in malaria susceptibility studies to provide useful functional insights.

CONCLUSIONS

The ultimate goal of malaria susceptibility study is to discover a novel causal biological pathway that provide protections against severe malaria; a fundamental step towards translational medicine such as development of vaccine and new therapeutics. Beyond singe locus analysis, the future direction of malaria susceptibility requires a paradigm shift from single -omics to multi-stage and multi-dimensional integrative functional studies that combines multiple data types from the human host, the parasite, the mosquitoes and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research.

Associations between autistic-like traits and polymorphisms in NFKBIL1

OBJECTIVE

The immune system has been suggested to be associated with neuropsychiatric disorders; for example, elevated levels of cytokines and the inflammation-related transcription factor nuclear factor kappa-B (NF-κB) have been reported in individuals with autism spectrum disorder (ASD). The aim of this study was to investigate possible associations between autistic-like traits (ALTs) and single nucleotide polymorphisms (SNPs) in NFKB1 (encoding a subunit of the NF-κB protein complex) and NF-κB inhibitor-like protein 1 (NFKBIL1).

METHODS

The study was conducted in a cohort from the general population: The Child and Adolescent Twin Study in Sweden (CATSS, n = 12 319, 9-12 years old). The subjects were assessed by the Autism-Tics, ADHD, and Other Comorbidities Inventory. Five SNPs within the two genes were genotyped (NFKBIL1: rs2857605, rs2239707, rs2230365 and rs2071592; NFKB1: rs4648022).

RESULTS

We found significant associations for two SNPs in NFKBIL1: rs2239707 showed a significant distribution of genotype frequencies in the case-control analysis both for all individuals combined and in boys only, and rs2230365 was significantly associated with the ALTs-module language impairment in boys only. Furthermore, we found nominal association in the case-control study for rs2230365, replicating earlier association between this SNP and ASD in an independent genome-wide association study.

CONCLUSION

The shown associations between polymorphisms in NFKBIL1 and ALTs are supporting an influence of the immune system on neuropsychiatric symptoms.