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

Stable pleotropic loci controlling the accumulation of multiple nutritional elements in wheat

Understanding the genetic basis of nutrient accumulation in wheat is crucial for improving its nutritional content and addressing global food security challenges. Here, we identified stable pleiotropic loci controlling the accumulation of 13 nutritional elements in wheat across diverse environments using a large wheat population of 1470 individuals. Our analysis revealed significant variability in SNP-based heritability values across 13 essential elements. Genetic correlations among elements uncovered complex relations, with positive correlations observed within two distinct groups, where calcium (Ca), cobalt (Co), potassium (K), and sodium (Na) formed one group, and copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), phosphorus (P), and zinc (Zn) formed the other. Negative correlations were observed among elements across both groups. Through MetaGWAS analysis, we identified stable QTL associated with individual elements and elements with high positive correlations. We identified 67 stable QTL across environments that are independent from grain yield, of which 56 were detected using the MetaGWAS analysis indicating their pleiotropic effect on multiple elements. A major QTL on chromosome 7D that can shift the phenotype up to one standard deviation compared to the mean phenotype in the population exhibited differential effects on multiple elements belonging to both groups. Our findings offer novel insights into the genetic architecture of nutrient accumulation in wheat and have practical implications for breeding programmes aimed at enhancing multiple nutrients simultaneously. By targeting stable QTL, breeders can develop wheat varieties with improved nutritional profiles, contributing to global food security and human health.

Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 49 are new. Variant-level fine mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants, and expression quantitative trait loci analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome and proteome-wide association studies, colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Gene prioritization analyses highlighted likely causal genes, including TMEM106B, CTNND1, AREL1 and so on. Pathway analysis indicated significant enrichment of depression risk genes in synapse-related pathways. Finally, knockdown of Tmem106b in mice resulted in depression-like behaviours, supporting the involvement of Tmem106b in depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.

Integrating large-scale meta-analysis of genome-wide association studies improve the genomic prediction accuracy for combined pig populations

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

Multi-ancestry GWAS reveals loci linked to human variation in LINE-1- and Alu-insertion numbers

LINE-1 (L1) and Alu are two families of transposable elements (TEs) occupying ~17% and ~11% of the human genome, respectively. Though only a small fraction of L1 copies is able to produce the machinery to mobilize autonomously, Alu and degenerate L1s can hijack their functional machinery and mobilize in trans. The expression and subsequent mobilization of L1 and Alu can exert pathological effects on their hosts. These features have made them promising focus subjects in studies of aging where they can become active. However, mechanisms regulating TE activity are incompletely characterized, especially in diverse human populations. To address these gaps, we leveraged genomic data from the 1000 Genomes Project to carry out a trans-ethnic GWAS of L1/Alu insertion singletons. These are rare, recently acquired insertions observed in only one person and which we used as proxies for variation in L1/Alu insertion numbers. Our approach identified SNVs in genomic regions containing genes with potential and known TE regulatory properties, and it enriched for SNVs in regions containing known regulators of L1 expression. Moreover, we identified reference TE copies and structural variants that associated with L1/Alu singletons, suggesting their potential contribution to TE insertion number variation. Finally, a transcriptional analysis of lymphoblastoid cells highlighted potential cell cycle alterations in a subset of samples harboring L1/Alu singletons. Collectively, our results suggest that known TE regulatory mechanisms may be active in diverse human populations, expand the list of loci implicated in TE insertion number variability, and reinforce links between TEs and disease.

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes

We assessed the reproducibility of differentially expressed genes (DEGs) in previously published Alzheimer's (AD), Parkinson's (PD), Schizophrenia (SCZ), and COVID-19 scRNA-seq studies. While transcriptional scores from DEGs of individual PD and COVID-19 datasets had moderate predictive power for case-control status of other datasets (AUC=0.77 and 0.75), genes from individual AD and SCZ datasets had poor predictive power (AUC=0.68 and 0.55). We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power (AUC=0.88, 0.91, 0.78, and 0.62). By multiple other metrics, specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. The DEGs revealed known and novel biological pathways, and we validate BCAT1 as down-regulated in AD mouse oligodendrocytes. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

Multi-ancestry GWAS reveals loci linked to human variation in LINE-1- and Alu-insertion numbers

LINE-1 (L1) and Alu are two families of transposable elements (TEs) occupying ~17% and ~11% of the human genome, respectively. Though only a small fraction of L1 copies is able to produce the machinery to mobilize autonomously, Alu and degenerate L1s can hijack their functional machinery and mobilize in trans. The expression and subsequent mobilization of L1 and Alu can exert pathological effects on their hosts. These features have made them promising focus subjects in studies of aging where they can become active. However, mechanisms regulating TE activity are incompletely characterized, especially in diverse human populations. To address these gaps, we leveraged genomic data from the 1000 Genomes Project to carry out a trans-ethnic GWAS of L1/Alu insertion singletons. These are rare, recently acquired insertions observed in only one person and which we used as proxies for variation in L1/Alu insertion numbers. Our approach identified SNVs in genomic regions containing genes with potential and known TE regulatory properties, and it enriched for SNVs in regions containing known regulators of L1 expression. Moreover, we identified reference TE copies and structural variants that associated with L1/Alu singletons, suggesting their potential contribution to TE insertion number variation. Finally, a transcriptional analysis of lymphoblastoid cells highlighted potential cell cycle alterations in a subset of samples harboring L1/Alu singletons. Collectively, our results suggest that known TE regulatory mechanisms may be active in diverse human populations, expand the list of loci implicated in TE insertion number variability, and reinforce links between TEs and disease.

Multi-ancestry genome-wide association analyses: a comparison of meta- and mega-analyses in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study

BACKGROUND

There is increasing need for effective incorporation of high-dimensional genetics data from individuals with varied ancestry in genome-wide association (GWAS) analyses. Classically, multi-ancestry GWAS analyses are performed using statistical meta-analysis to combine results conducted within homogeneous ancestry groups. The emergence of cosmopolitan reference panels makes collective preprocessing of GWAS data possible, but impact on downstream GWAS results in a mega-analysis framework merits investigation. We utilized GWAS data from the multi-national Hyperglycemia and Adverse Pregnancy Outcome Study to investigate differences in GWAS findings using a homogeneous ancestry meta-analysis versus a heterogeneous ancestry mega-analysis pipeline. Maternal fasting and 1-hr glucose and metabolomics measured during a 2-hr 75-gram oral glucose tolerance test during early third trimester pregnancy were evaluated as phenotypes.

RESULTS

For the homogeneous ancestry meta-analysis pipeline, variant data were prepared by identifying sets of individuals with similar ancestry and imputing to ancestry-specific reference panels. GWAS was conducted within each ancestry group and results were combined using random-effects meta-analysis. For the heterogeneous ancestry mega-analysis pipeline, data for all individuals were collectively imputed to the Trans-Omics for Precision Medicine (TOPMed) cosmopolitan reference panel, and GWAS was conducted using a unified mega-analysis. The meta-analysis pipeline identified genome-wide significant associations for 15 variants in a region close to GCK on chromosome 7 with maternal fasting glucose and no significant findings for 1-hr glucose. Associations in this same region were identified using the mega-analysis pipeline, along with a well-documented association at MTNR1B on chromosome 11 with both fasting and 1-hr maternal glucose. For metabolomics analyses, the number of significant findings in the heterogeneous ancestry mega-analysis far exceeded those from the homogeneous ancestry meta-analysis and confirmed many previously documented associations, but genomic inflation factors were much more variable.

CONCLUSIONS

For multi-ancestry GWAS, heterogeneous ancestry mega-analysis generates a rich set of variants for analysis using a cosmopolitan reference panel and results in vastly more significant, biologically credible and previously documented associations than a homogeneous ancestry meta-analysis approach. Genomic inflation factors do indicate that findings from the mega-analysis pipeline may merit cautious interpretation and further follow-up.

Identification of QTL-by-environment interaction by controlling polygenic background effect

The quantitative trait loci (QTL)-by-environment (Q × E) interaction effect is hard to detect because there are no effective ways to control the genomic background. In this study, we propose a linear mixed model that simultaneously analyzes data from multiple environments to detect Q × E interactions. This model incorporates two different kinship matrices derived from the genome-wide markers to control both main and interaction polygenic background effects. Simulation studies demonstrate that our approach is more powerful than the meta-analysis and inclusive composite interval mapping methods. We further analyze four agronomic traits of rice across four environments. A main effect QTL is identified for 1000-grain weight (KGW), while no QTL are found for tiller number. Additionally, a large QTL with a significant Q × E interaction is detected on chromosome 7 affecting grain number, yield, and KGW. This region harbors two important genes, PROG1 and Ghd7. Furthermore, we apply our mixed model to analyze lodging in barley across six environments. The six regions exhibiting Q × E interaction effects identified by our approach overlap with the SNPs previously identified using EM and MCMC-based Bayesian methods, further validating the robustness of our approach. Both simulation studies and empirical data analyses show that our method outperformed all other methods compared.

A meta-analysis of genome-wide association studies to identify candidate genes associated with feed efficiency traits in pigs

Pig production is an agricultural sector of great economic and social relevance to Brazil and global markets. Feed efficiency traits directly influence the sustainability of pig production due to the economic impact of feed costs on the production system and the environmental footprint of the industry. Therefore, breeding for improved feed efficiency has been a target of worldwide pig breeding programs. Genome-wide association studies (GWAS) enable the assessment of the genetic background of complex traits, which contributes to a better understanding of the biological mechanisms regulating their phenotypic expression. In this context, the primary objective of this study was to identify and validate genomic regions and candidate genes associated with feed conversion ratio (FCR) and residual feed intake (RFI) in pigs based on a comprehensive systematic review and meta-analysis of GWAS. The METAL software was used to implement the meta-analysis and the Bonferroni multiple testing correction considering a significance threshold 0.05. The significant single nucleotide polymorphisms (SNPs) in the meta-analysis were used to identify candidate genes, followed by a functional genomic enrichment analysis. The systematic review identified 13 studies, of which 7 evaluated FCR, 3 evaluated RFI, and 3 studies investigated both traits, with 160 and 96 SNPs identified for FCR and RFI, respectively. After the meta-analysis, 145 markers were significantly associated with FCR and 90 with RFI. The gene annotation process resulted in 105 and 114 genes for FCR and RFI, respectively. The enrichment analysis for FCR resulted in 16 significant gene ontology (GO) terms, while 6 terms were identified for RFI. The main GO terms were actin cytoskeleton (GO_BP:0030036), membrane (GO_CC:0016020), integral components of the peroxisomal membrane (GO_CC:0005779), and carbohydrate-binding (GO_MF:0030246). The main candidate genes identified were MED18, PHACTR4, ABCC2, TRHDE, FRS2, FAR2 and FIS1 for FCR, and ADGRL2, ASGR1, ASGR2, and MAN2B1 for RFI. These findings contribute to a better understanding of the genetic mechanisms associated with feed efficiency traits in pigs, providing a foundation for future improvements in pig breeding programs.

TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets

Available large-scale genome-wide association study (GWAS) summary datasets predominantly stem from European populations, while sample sizes for other ethnicities, notably Central/South Asian, East Asian, African, Hispanic, etc., remain comparatively limited, resulting in low precision of causal effect estimations within these ethnicities when using Mendelian randomization (MR). In this paper, we propose a trans-ethnic MR method, TEMR, to improve the statistical power and estimation precision of MR in a target population that is underrepresented, using trans-ethnic large-scale GWAS summary datasets. TEMR incorporates trans-ethnic genetic correlation coefficients through a conditional likelihood-based inference framework, producing calibrated p values with substantially improved MR power. In the simulation study, compared with other existing MR methods, TEMR exhibited superior precision and statistical power in causal effect estimation within the target populations. Finally, we applied TEMR to infer causal relationships between concentrations of 16 blood biomarkers and the risk of developing five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, and major depression disorder) in East Asian, African, and Hispanic/Latino populations, leveraging biobank-scale GWAS summary data obtained from individuals of European descent. We found that the causal biomarkers were mostly validated by previous MR methods, and we also discovered 17 causal relationships that were not identified using previously published MR methods.

metaGE: Investigating genotype x environment interactions through GWAS meta-analysis

Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments. In this context, we introduce metaGE, a flexible and computationally efficient meta-analysis approach for jointly analyzing single-environment GWAS of any MET experiment. The metaGE procedure accounts for the heterogeneity of quantitative trait loci (QTL) effects across the environmental conditions and allows the detection of QTL whose allelic effect variations are strongly correlated to environmental cofactors. We evaluated the performance of the proposed methodology and compared it to two competing procedures through simulations. We also applied metaGE to two emblematic examples: the detection of flowering QTLs whose effects are modulated by competition in Arabidopsis and the detection of yield QTLs impacted by drought stresses in maize. The procedure identified known and new QTLs, providing valuable insights into the genetic architecture of complex traits and QTL effects dependent on environmental stress conditions. The whole statistical approach is available as an R package.

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform

The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.

Capturing resilience from phenotypic deviations: a case study using feed consumption and whole genome data in pigs

BACKGROUND

In recent years, interest has grown in quantifying resilience in livestock by examining deviations in target phenotypes. This method is based on the idea that variability in these phenotypes reflects an animal's ability to adapt to external factors. By utilizing routinely collected time-series feed intake data in pigs, researchers can obtain a broad measure of resilience. This measure extends beyond specific conditions, capturing the impact of various unknown external factors that influence phenotype variations. Importantly, this method does not require additional phenotyping investments. Despite growing interest, the relationship between resilience indicators-calculated as deviations from longitudinally recorded target traits-and the mean of those traits remains largely unexplored. This gap raises the risk of inadvertently selecting for the mean rather than accurately capturing true resilience. Additionally, distinguishing between random phenotype fluctuations (white noise) and structural variations linked to resilience poses a challenge. With the aim of developing general resilience indicators applicable to commercial swine populations, we devised four resilience indicators utilizing daily feed consumption as the target trait. These include a canonical resilience indicator (BALnVar) and three novel ones (BAMaxArea, SPLnVar, and SPMaxArea), designed to minimize noise and ensure independence from daily feed consumption. We subsequently integrated these indicators with Whole Genome Sequencing using SLEMM algorithm, data from 1,250 animals to assess their efficacy in capturing resilience and their independence from the mean of daily feed consumption.

RESULTS

Our findings revealed that conventional resilience indicators failed to differentiate from the mean of daily feed consumption, underscoring potential limitations in accurately capturing true resilience. Notably, significant associations involving conventional resilience indicators were identified on chromosome 1, which is commonly linked to body weight.

CONCLUSION

We observed that deviations in feed consumption can effectively serve as indicators for selecting resilience in commercial pig farming, as confirmed by the identification of genes such as PKN1 and GYPC. However, the identification of other genes, such as RNF152, related to growth, suggests that common resilience quantification methods may be more closely related to the mean of daily feed consumption rather than capturing true resilience.

Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians

We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.

Development of DNA Markers for Acute Hepatopancreatic Necrosis Disease Tolerance in Litopenaeus vannamei through a Genome-Wide Association Study

Shrimp aquaculture is facing a serious disease, acute hepatopancreatic necrosis disease (AHPND), caused by Vibrio paraheamolyticus (VpAPHND). For sustainable shrimp aquaculture, massive losses of shrimp infected with VpAPHND must be prevented. Research and selection of shrimp tolerant to VpAPHND infection is a sustainable approach to reducing the risk of AHPND. This study focused on the identification and development of potential DNA markers associated with AHPND using DArT sequencing (DArTSeq) and a genome-wide association study. Three populations of post-larval Litopenaeus vannamei were immersed in VpAPHND to collect susceptible (D) and tolerant (S) samples. The 45 D and 48 S shrimp had their genotypes analyzed using DArTSeq. A total of 108,983 SNPs and 17,212 InDels were obtained from the DArTseq data, while the biallelic 516 SNPs and 2293 InDels were finally filtered with PIC < 0.1, MAF < 0.05, and a call rate ≥ 80%. The filtered variants were analyzed for their association with AHPND tolerance. Although there were no significantly associated SNPs and InDels above the Bonferroni correction threshold, candidate variants, four SNPs and 17 InDels corresponding to p < 0.01, were provided for further validation of the AHPND tolerance trait. The candidate SNPs are located on an exon of the zinc finger protein 239-like gene, an intron of an uncharacterized gene, and in intergenic regions. Most of the candidate InDels are in the intergenic regions, with fewer in the intronic and exonic regions. This study provides information on SNPs and InDels for white shrimp. These markers will support the variant database of shrimp and be useful in shrimp aquaculture for breeding selection.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Current approaches in CRISPR-Cas systems for diabetes

In the face of advancements in health care and a shift towards healthy lifestyle, diabetes mellitus (DM) still presents as a global health challenge. This chapter explores recent advancements in the areas of genetic and molecular underpinnings of DM, addressing the revolutionary potential of CRISPR-based genome editing technologies. We delve into the multifaceted relationship between genes and molecular pathways contributing to both type1 and type 2 diabetes. We highlight the importance of how improved genetic screening and the identification of susceptibility genes are aiding in early diagnosis and risk stratification. The spotlight then shifts to CRISPR-Cas9, a robust genome editing tool capable of various applications including correcting mutations in type 1 diabetes, enhancing insulin production in T2D, modulating genes associated with metabolism of glucose and insulin sensitivity. Delivery methods for CRISPR to targeted tissues and cells are explored, including viral and non-viral vectors, alongside the exciting possibilities offered by nanocarriers. We conclude by discussing the challenges and ethical considerations surrounding CRISPR-based therapies for DM. These include potential off-target effects, ensuring long-term efficacy and safety, and navigating the ethical implications of human genome modification. This chapter offers a comprehensive perspective on how genetic and molecular insights, coupled with the transformative power of CRISPR, are paving the way for potential cures and novel therapeutic approaches for DM.

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.

Brugada syndrome in Japan and Europe: a genome-wide association study reveals shared genetic architecture and new risk loci

BACKGROUND AND AIMS

Brugada syndrome (BrS) is an inherited arrhythmia with a higher disease prevalence and more lethal arrhythmic events in Asians than in Europeans. Genome-wide association studies (GWAS) have revealed its polygenic architecture mainly in European populations. The aim of this study was to identify novel BrS-associated loci and to compare allelic effects across ancestries.

METHODS

A GWAS was conducted in Japanese participants, involving 940 cases and 1634 controls, followed by a cross-ancestry meta-analysis of Japanese and European GWAS (total of 3760 cases and 11 635 controls). The novel loci were characterized by fine-mapping, gene expression, and splicing quantitative trait associations in the human heart.

RESULTS

The Japanese-specific GWAS identified one novel locus near ZSCAN20 (P = 1.0 × 10-8), and the cross-ancestry meta-analysis identified 17 association signals, including six novel loci. The effect directions of the 17 lead variants were consistent (94.1%; P for sign test = 2.7 × 10-4), and their allelic effects were highly correlated across ancestries (Pearson's R = .91; P = 2.9 × 10-7). The genetic risk score derived from the BrS GWAS of European ancestry was significantly associated with the risk of BrS in the Japanese population [odds ratio 2.12 (95% confidence interval 1.94-2.31); P = 1.2 × 10-61], suggesting a shared genetic architecture across ancestries. Functional characterization revealed that a lead variant in CAMK2D promotes alternative splicing, resulting in an isoform switch of calmodulin kinase II-δ, favouring a pro-inflammatory/pro-death pathway.

CONCLUSIONS

This study demonstrates novel susceptibility loci implicating potentially novel pathogenesis underlying BrS. Despite differences in clinical expressivity and epidemiology, the polygenic architecture of BrS was substantially shared across ancestries.

Altered salivary microbiota associated with high-sugar beverage consumption

The human oral microbiome may alter oral and systemic disease risk. Consuming high sugar content beverages (HSB) can lead to caries development by altering the microbial composition in dental plaque, but little is known regarding HSB-specific oral microbial alterations. Therefore, we conducted a large, population-based study to examine associations of HSB intake with oral microbiome diversity and composition. Using mouthwash samples of 989 individuals in two nationwide U.S. cohorts, bacterial 16S rRNA genes were amplified, sequenced, and assigned to bacterial taxa. HSB intake was quantified from food frequency questionnaires as low (< 1 serving/week), medium (1-3 servings/week), or high (> 3 servings/week). We assessed overall bacterial diversity and presence of specific taxa with respect to HSB intake in each cohort separately and combined in a meta-analysis. Consistently in the two cohorts, we found lower species richness in high HSB consumers (> 3 cans/week) (p = 0.027), and that overall bacterial community profiles differed from those of non-consumers (PERMANOVA p = 0.040). Specifically, presence of a network of commensal bacteria (Lachnospiraceae, Peptostreptococcaceae, and Alloprevotella rava) was less common in high compared to non-consumers, as were other species including Campylobacter showae, Prevotella oulorum, and Mycoplasma faucium. Presence of acidogenic bacteria Bifodobacteriaceae and Lactobacillus rhamnosus was more common in high consumers. Abundance of Fusobacteriales and its genus Leptotrichia, Lachnoanaerobaculum sp., and Campylobacter were lower with higher HSB consumption, and their abundances were correlated. No significant interaction was found for these associations with diabetic status or with microbial markers for caries (S. mutans) and periodontitis (P. gingivalis). Our results suggest that soft drink intake may alter the salivary microbiota, with consistent results across two independent cohorts. The observed perturbations of overrepresented acidogenic bacteria and underrepresented commensal bacteria in high HSB consumers may have implications for oral and systemic disease risk.

Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization

BACKGROUND

Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups.

METHODS

We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts.

RESULTS

Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]).

CONCLUSIONS

The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

Identification of novel genes associated with herbicide tolerance in Lentil (Lens culinaris ssp. culinaris Medik.)

Weeds pose a major constraint in lentil cultivation, leading to decrease farmers' revenues by reducing the yield and increasing the management costs. The development of herbicide tolerant cultivars is essential to increase lentil yield. Even though herbicide tolerant lines have been identified in lentils, breeding efforts are still limited and lack proper validation. Marker assisted selection (MAS) can increase selection accuracy at early generations. Total 292 lentil accessions were evaluated under different dosages of two herbicides, metribuzin and imazethapyr, during two seasons at Marchouch, Morocco and Terbol, Lebanon. Highly significant differences among accessions were observed for days to flowering (DF) and maturity (DM), plant height (PH), biological yield (BY), seed yield (SY), number of pods per plant (NP), as well as the reduction indices (RI) for PH, BY, SY and NP. A total of 10,271 SNPs markers uniformly distributed along the lentil genome were assayed using Multispecies Pulse SNP chip developed at Agriculture Victoria, Melbourne. Meta-GWAS analysis was used to detect marker-trait associations, which detected 125 SNPs markers associated with different traits and clustered in 85 unique quantitative trait loci. These findings provide valuable insights for initiating MAS programs aiming to enhance herbicide tolerance in lentil crop.

Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements

Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate these patterns to accurately detect markers and comprehensively understand the diseases. Integrative analysis has emerged as a promising approach to address this challenge. However, existing studies have been limited by ignoring the adjacency structure of genomic measurements, such as single nucleotide polymorphisms (SNPs) and DNA methylations. In this study, we propose a structured integrative analysis method that incorporates a spline type penalty to accommodate this adjacency structure. We utilize a fused lasso type penalty to identify both heterogeneity and commonality across the groups. Extensive simulations demonstrate its superiority compared to several direct competing methods. The analysis of The Cancer Genome Atlas melanoma data with DNA methylation measurements and GENEVA diabetes data with SNP measurements exhibit that the proposed analysis lead to meaningful findings with better prediction performance and higher selection stability.

Genetically Predicted Apolipoprotein E Levels with the Risk of Panvascular Diseases: A Mendelian Randomization Study

The aim of this study was to comprehensively assess the causal relationship between the overall genetic effect of circulating ApoE levels and panvascular lesions using newer genome-wide association data and two-sample bidirectional Mendelian randomization (MR) analysis. Two-way MR using single-nucleotide polymorphisms of circulating ApoE as instrumental variables was performed using the highest-priority Genome-wide association study (GWAS) data, with factor-adjusted and data-corrected statistics, to estimate causal associations between circulating ApoE levels and 10 pan-vascular diseases in > 500,000 UK Biobank participants, > 400,000 participants of Finnish ancestry, and numerous participants in a consortium of predominantly European ancestry. Meta-analysis was conducted to assess positive results. After correcting for statistical results, elevated circulating ApoE levels were shown to have a significant protective effect against Cerebral ischemia (CI) [IVW odds ratio (OR) 0.888, 95% Confidence Interval (CI): 0.823-0.958, p = 2.3 × 10-3], Coronary heart disease [IVW OR 0.950,95% CI: 0.924-0.976, p = 2.0 × 10-4] had a significant protective effect and potentially suggestive protective causality against Angina pectoris [IVW odds ratio (OR) 0.961, 95%CI: 0.931-0.991, p = 1.1 × 10-2]. There was a potential causal effect for increased risk of Heart failure (HF) [IVW ratio (OR) 1.040, 95%CI: 1.006-1.060, p = 1.8 × 10-2]. (Bonferroni threshold p < 0.0026, PFDR < 0.05) Reverse MR analysis did not reveal significant evidence of a causal effect of PVD on changes in circulating ApoE levels. Meta-analysis increases reliability of results. Elevated circulating ApoE levels were particularly associated with an increased risk of heart failure. Elevated ApoE levels reduce the risk of cerebral ischemia, coronary heart disease, and angina pectoris, reflecting a protective effect. The possible pathophysiological role of circulating ApoE levels in the development of panvascular disease is emphasized.

Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations

In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (pC ). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (pC  < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.

Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity

Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project

Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.

Drug target prediction through deep learning functional representation of gene signatures

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders

Anxiety disorders are the most prevalent mental disorders. However, the genetic etiology of anxiety disorders remains largely unknown. Here we conducted a genome-wide meta-analysis on anxiety disorders by including 74,973 (28,392 proxy) cases and 400,243 (146,771 proxy) controls. We identified 14 risk loci, including 10 new associations near CNTNAP5, MAP2, RAB9BP1, BTN1A1, PRR16, PCLO, PTPRD, FARP1, CDH2 and RAB27B. Functional genomics and fine-mapping pinpointed the potential causal variants, and expression quantitative trait loci analysis revealed the potential target genes regulated by the risk variants. Integrative analyses, including transcriptome-wide association study, proteome-wide association study and colocalization analyses, prioritized potential causal genes (including CTNND1 and RAB27B). Evidence from multiple analyses revealed possibly causal genes, including RAB27B, BTN3A2, PCLO and CTNND1. Finally, we showed that Ctnnd1 knockdown affected dendritic spine density and resulted in anxiety-like behaviours in mice, revealing the potential role of CTNND1 in anxiety disorders. Our study identified new risk loci, potential causal variants and genes for anxiety disorders, providing insights into the genetic architecture of anxiety disorders and potential therapeutic targets.

PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects

BACKGROUND

Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles.

RESULTS

We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry.

CONCLUSIONS

Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .

Multitrait meta-analyses identify potential candidate genes for growth-related traits in Holstein heifers

Understanding the underlying pleiotropic relationships among growth and body size traits is important for refining breeding strategies in dairy cattle for optimal body size and growth rate. Therefore, we performed single-trait GWAS for monthly-recorded body weight (BW), hip height, body length, and chest girth from birth to 12 mo of age in Holstein animals, followed by stepwise multiple regression of independent or lowly-linked markers from GWAS loci using conditional and joint association analyses (COJO). Subsequently, we conducted a multitrait meta-analysis to detect pleiotropic markers. Based on the single-trait GWAS, we identified 170 significant SNPs, in which 59 of them remained significant after the COJO analyses. The most significant SNP, located at BTA7:3,676,741, explained 2.93% of the total phenotypic variance for BW6 (BW at 6 mo of age). We identified 17 SNPs with potential pleiotropic effects based on the multitrait meta-analyses, which resulted in 3 additional SNPs in comparison to those detected based on the single-trait GWAS. The identified quantitative trait loci regions overlap with genes known to influence human growth-related traits. According to positional and functional analyses, we proposed HMGA2, HNF4G, MED13L, BHLHE40, FRZB, DMP1, TRIB3, and GATAD2A as important candidate genes influencing the studied traits. The combination of single-trait GWAS and meta-analyses of GWAS results improved the efficiency of detecting associated SNPs, and provided new insights into the genetic mechanisms of growth and development in Holstein cattle.

Genome-wide association study of placental weight identifies distinct and shared genetic influences between placental and fetal growth

A well-functioning placenta is essential for fetal and maternal health throughout pregnancy. Using placental weight as a proxy for placental growth, we report genome-wide association analyses in the fetal (n = 65,405), maternal (n = 61,228) and paternal (n = 52,392) genomes, yielding 40 independent association signals. Twenty-six signals are classified as fetal, four maternal and three fetal and maternal. A maternal parent-of-origin effect is seen near KCNQ1. Genetic correlation and colocalization analyses reveal overlap with birth weight genetics, but 12 loci are classified as predominantly or only affecting placental weight, with connections to placental development and morphology, and transport of antibodies and amino acids. Mendelian randomization analyses indicate that fetal genetically mediated higher placental weight is causally associated with preeclampsia risk and shorter gestational duration. Moreover, these analyses support the role of fetal insulin in regulating placental weight, providing a key link between fetal and placental growth.

Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection

The common bean (Phaseolus vulgaris L.) is a globally cultivated leguminous crop. Fusarium wilt (FW), caused by Fusarium oxysporum f. sp. phaseoli (Fop), is a significant disease leading to substantial yield loss in common beans. Disease-resistant cultivars are recommended to counteract this. The objective of this investigation was to identify single nucleotide polymorphism (SNP) markers associated with FW resistance and to pinpoint potential resistant common bean accessions within a core collection, utilizing a panel of 157 accessions through the Genome-wide association study (GWAS) approach with TASSEL 5 and GAPIT 3. Phenotypes for Fop race 1 and race 4 were matched with genotypic data from 4740 SNPs of BARCBean6K_3 Infinium Bea Chips. After ranking the 157-accession panel and revealing 21 Fusarium wilt-resistant accessions, the GWAS pinpointed 16 SNPs on chromosomes Pv04, Pv05, Pv07, Pv8, and Pv09 linked to Fop race 1 resistance, 23 SNPs on chromosomes Pv03, Pv04, Pv05, Pv07, Pv09, Pv10, and Pv11 associated with Fop race 4 resistance, and 7 SNPs on chromosomes Pv04 and Pv09 correlated with both Fop race 1 and race 4 resistances. Furthermore, within a 30 kb flanking region of these associated SNPs, a total of 17 candidate genes were identified. Some of these genes were annotated as classical disease resistance protein/enzymes, including NB-ARC domain proteins, Leucine-rich repeat protein kinase family proteins, zinc finger family proteins, P-loopcontaining nucleoside triphosphate hydrolase superfamily, etc. Genomic prediction (GP) accuracy for Fop race resistances ranged from 0.26 to 0.55. This study advanced common bean genetic enhancement through marker-assisted selection (MAS) and genomic selection (GS) strategies, paving the way for improved Fop resistance.

Sequence-based GWAS meta-analyses for beef production traits

BACKGROUND

Combining the results of within-population genome-wide association studies (GWAS) based on whole-genome sequences into a single meta-analysis (MA) is an accurate and powerful method for identifying variants associated with complex traits. As part of the H2020 BovReg project, we performed sequence-level MA for beef production traits. Five partners from France, Switzerland, Germany, and Canada contributed summary statistics from sequence-based GWAS conducted with 54,782 animals from 15 purebred or crossbred populations. We combined the summary statistics for four growth, nine morphology, and 15 carcass traits into 16 MA, using both fixed effects and z-score methods.

RESULTS

The fixed-effects method was generally more informative to provide indication on potentially causal variants, although we combined substantially different traits in each MA. In comparison with within-population GWAS, this approach highlighted (i) a larger number of quantitative trait loci (QTL), (ii) QTL more frequently located in genomic regions known for their effects on growth and meat/carcass traits, (iii) a smaller number of genomic variants within the QTL, and (iv) candidate variants that were more frequently located in genes. MA pinpointed variants in genes, including MSTN, LCORL, and PLAG1 that have been previously associated with morphology and carcass traits. We also identified dozens of other variants located in genes associated with growth and carcass traits, or with a function that may be related to meat production (e.g., HS6ST1, HERC2, WDR75, COL3A1, SLIT2, MED28, and ANKAR). Some of these variants overlapped with expression or splicing QTL reported in the cattle Genotype-Tissue Expression atlas (CattleGTEx) and could therefore regulate gene expression.

CONCLUSIONS

By identifying candidate genes and potential causal variants associated with beef production traits in cattle, MA demonstrates great potential for investigating the biological mechanisms underlying these traits. As a complement to within-population GWAS, this approach can provide deeper insights into the genetic architecture of complex traits in beef cattle.

Identifying QTLs involved in hybrid performance and heterotic group complementarity: new GWAS models applied to factorial and admixed diallel maize hybrid panels

KEY MESSAGE

An original GWAS model integrating the ancestry of alleles was proposed and allowed the detection of background specific additive and dominance QTLs involved in heterotic group complementarity and hybrid performance. Maize genetic diversity is structured into genetic groups selected and improved relative to each other. This process increases group complementarity and differentiation over time and ensures that the hybrids produced from inter-group crosses exhibit high performances and heterosis. To identify loci involved in hybrid performance and heterotic group complementarity, we introduced an original association study model that disentangles allelic effects from the heterotic group origin of the alleles and compared it with a conventional additive/dominance model. This new model was applied on a factorial between Dent and Flint lines and a diallel between Dent-Flint admixed lines with two different layers of analysis: within each environment and in a multiple-environment context. We identified several strong additive QTLs for all traits, including some well-known additive QTLs for flowering time (in the region of Vgt1/2 on chromosome 8). Yield trait displayed significant non-additive effects in the diallel panel. Most of the detected Yield QTLs exhibited overdominance or, more likely, pseudo-overdominance effects. Apparent overdominance at these QTLs contributed to a part of the genetic group complementarity. The comparison between environments revealed a higher stability of additive QTL effects than non-additive ones. Several QTLs showed variations of effects according to the local heterotic group origin. We also revealed large chromosomic regions that display genetic group origin effects. Altogether, our results illustrate how admixed panels combined with dedicated GWAS modeling allow the identification of new QTLs that could not be revealed by a classical hybrid panel analyzed with traditional modeling.

Association mapping in bambara groundnut [Vigna subterranea (L.) Verdc.] reveals loci associated with agro-morphological traits

BACKGROUND

Genome-wide association studies (GWAS) are important for the acceleration of crop improvement through knowledge of marker-trait association (MTA). This report used DArT SNP markers to successfully perform GWAS on agro-morphological traits using 270 bambara groundnut [Vigna subterranea (L.) Verdc.] landraces sourced from diverse origins. The study aimed to identify marker traits association for nine agronomic traits using GWAS and their candidate genes. The experiment was conducted at two different locations laid out in alpha lattice design. The cowpea [Vigna unguiculata (L.) Walp.] reference genome (i.e. legume genome most closely related to bambara groundnut) assisted in the identification of candidate genes.

RESULTS

The analyses showed that linkage disequilibrium was found to decay rapidly with an average genetic distance of 148 kb. The broadsense heritability was relatively high and ranged from 48.39% (terminal leaf length) to 79.39% (number of pods per plant). The GWAS identified a total of 27 significant marker-trait associations (MTAs) for the nine studied traits explaining 5.27% to 24.86% of phenotypic variations. Among studied traits, the highest number of MTAs was obtained from seed coat colour (6) followed by days to flowering (5), while the least is days to maturity (1), explaining 5.76% to 11.03%, 14.5% to 19.49%, and 11.66% phenotypic variations, respectively. Also, a total of 17 candidate genes were identified, varying in number for different traits; seed coat colour (6), days to flowering (3), terminal leaf length (2), terminal leaf width (2), number of seed per pod (2), pod width (1) and days to maturity (1).

CONCLUSION

These results revealed the prospect of GWAS in identification of SNP variations associated with agronomic traits in bambara groundnut. Also, its present new opportunity to explore GWAS and marker assisted strategies in breeding of bambara groundnut for acceleration of the crop improvement.

An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index

Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.

Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83-97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.

Molecular Mechanisms of Endometriosis Revealed Using Omics Data

Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.

Mediators of systemic sclerosis-associated interstitial lung disease (SSc-ILD): systematic review and meta-analyses

Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is rare, poorly understood, with heterogeneous characteristics resulting in difficult diagnosis. We aimed to systematically review evidence of soluble markers in peripheral blood or bronchoalveolar lavage fluid (BALF) as biomarkers in SSc-ILD.

METHOD

Five databases were screened for observational or interventional, peer-reviewed studies in adults published between January 2000 and September 2021 that assessed levels of biomarkers in peripheral blood or BALF of SSc-ILD patients compared with healthy controls. Qualitative assessment was performed using Critical Appraisal Skills Programme (CASP) checklists. Standardised mean difference (SMD) in biomarkers were combined in random-effects meta-analyses where multiple independent studies reported quantitative data.

RESULTS

768 published studies were identified; 38 articles were included in the qualitative synthesis. Thirteen studies were included in the meta-analyses representing three biomarkers: KL6, SP-D and IL-8. Greater IL-8 levels were associated with SSc-ILD in both peripheral blood and BALF, overall SMD 0.88 (95% CI 0.61 to 1.15; I2=1%). Greater levels of SP-D and KL-6 were both estimated in SSc-ILD peripheral blood compared with healthy controls, at an SMD of 1.78 (95% CI 1.50 to 2.17; I2=8%) and 1.66 (95% CI 1.17 to 2.14; I2=76%), respectively.

CONCLUSION

We provide robust evidence that KL-6, SP-D and IL-8 have the potential to serve as reliable biomarkers in blood/BALF for supporting the diagnosis of SSc-ILD. However, while several other biomarkers have been proposed, the evidence of their independent value in diagnosis and prognosis is currently lacking and needs further investigation.

PROSPERO REGISTRATION NUMBER

CRD42021282452.

Genetics of hair graying with age

Hair graying is an early and obvious phenotypic and physiological trait with age in humans. Several recent advances in molecular biology and genetics have increased our understanding of the mechanisms of hair graying, which elucidate genes related to the synthesis, transport, and distribution of melanin in hair follicles, as well as genes regulating these processes above. Therefore, we review these advances and examine the trends in the genetic aspects of hair graying from enrichment theory, Genome-Wide association studies, whole exome sequencing, gene expression studies, and animal models for hair graying with age, aiming to overview the changes in hair graying at the genetic level and establish the foundation for future research. Meanwhile, by summarizing the genetics, it's of great value to explore the possible mechanism, treatment, or even prevention of hair graying with age.

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite-Phenotype Associations

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn's disease, as well as between glycine and coronary heart disease.