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

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

A 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 (PRSCHD) for 5 genetic ancestry groups.

Methods

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

Results

Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]).

Conclusions

Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.

Genome-wide association study identifies a new susceptibility locus in PLA2G4C for Multiple System Atrophy

To elucidate the molecular basis of multiple system atrophy (MSA), a neurodegenerative disease, we conducted a genome-wide association study (GWAS) in a Japanese MSA case/control series followed by replication studies in Japanese, Korean, Chinese, European and North American samples. In the GWAS stage rs2303744 on chromosome 19 showed a suggestive association ( P = 6.5 × 10 ^-7 ) that was replicated in additional Japanese samples ( P = 2.9 × 10 ^-6 . OR = 1.58; 95% confidence interval, 1.30 to 1.91), and then confirmed as highly significant in a meta-analysis of East Asian population data ( P = 5.0 × 10 ^-15 . Odds ratio= 1.49; 95% CI 1.35 to 1.72). The association of rs2303744 with MSA remained significant in combined European/North American samples ( P =0.023. Odds ratio=1.14; 95% CI 1.02 to 1.28) despite allele frequencies being quite different between these populations. rs2303744 leads to an amino acid substitution in PLA2G4C that encodes the cPLA2γ lysophospholipase/transacylase. The cPLA2γ-Ile143 isoform encoded by the MSA risk allele has significantly decreased transacylase activity compared with the alternate cPLA2γ-Val143 isoform that may perturb membrane phospholipids and α-synuclein biology.

Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus

Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.

Causal effect of adiposity on the risk of 19 gastrointestinal diseases: a Mendelian randomization study

OBJECTIVE

Although the association between adiposity and gastrointestinal (GI) diseases has been explored, the causal effects of adiposity on GI diseases are largely unknown.

METHODS

Mendelian randomization was conducted using single-nucleotide polymorphisms associated with BMI and waist circumference (WC) as instrumental variables, and the causal associations of BMI or WC with GI conditions were estimated among >400,000 UK Biobank participants, >170,000 Finnish-descent participants, and numerous consortia participants of predominantly European ancestry.

RESULTS

Genetically predicted BMI was robustly associated with increased risk of nonalcoholic fatty liver disease (NAFLD), cholecystitis, cholelithiasis, and primary biliary cholangitis. For the diseases, the odds ratio per 1-SD increase in genetically predicted BMI (4.77 kg/m2 ) ranged from 1.22 (95% CI: 1.12-1.34; p < 0.0001) for NAFLD to 1.65 (95% CI: 1.31-2.06; p < 0.0001) for cholecystitis. Genetically predicted WC was robustly associated with increased risk of NAFLD, alcoholic liver disease, cholecystitis, cholelithiasis, colon cancer, and gastric cancer. Alcoholic liver disease was consistently associated with WC even after adjusting for alcohol consumption in a multivariable Mendelian randomization analysis. The odds ratio per 1-SD increase in genetically predicted WC (12.52 cm) for such associations ranged from 1.41 (95% CI: 1.17-1.70; p = 0.0015) for gastric cancer to 1.74 (95% CI: 1.21-1.78; p < 0.0001) for cholelithiasis.

CONCLUSIONS

High genetically predicted adiposity was causally associated with an increased risk of GI abnormalities, particularly of hepatobiliary organs (liver, biliary tract, and gallbladder) that are functionally related to fat metabolism.

Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta-analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL-YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL-YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL-YC identified were co-located with 210 SNP peak positions reported via genome-wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL-YC with MQTL-YC reported in soybean and pea allowed the identification of 14 orthologous MQTL-YC shared across species. The integration of MQTL-YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.

The genetic era of childhood cancer: Identification of high-risk patients and germline sequencing approaches

Childhood cancer is a leading cause of death by disease in children ages 5-14, for which there are no preventive strategies. Due to early-age of diagnosis and short period of exposure to environmental factors, increasing evidence suggests childhood cancer could have strong association with germline alterations in predisposition cancer genes but, their frequency and distribution are largely unknown. Several efforts have been made to develop tools to identify children with increased risk of cancer who may benefit from genetic testing but their validation and application on a large scale is necessary. Research on genetic bases of childhood cancer is ongoing, in which several approaches for the identification of genetic variants related to cancer predisposition have been used. In this paper, we discuss the updated efforts, strategies, molecular mechanisms and clinical implications for germline predisposition gene alterations and the characterization of risk variants in childhood cancer.

Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis

Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress.

Utilizing Variants Identified with Multiple Genome-Wide Association Study Methods Optimizes Genomic Selection for Growth Traits in Pigs

Improving the prediction accuracies of economically important traits in genomic selection (GS) is a main objective for researchers and breeders in the livestock industry. This study aims at utilizing potentially functional SNPs and QTLs identified with various genome-wide association study (GWAS) models in GS of pig growth traits. We used three well-established GWAS methods, including the mixed linear model, Bayesian model and meta-analysis, as well as 60K SNP-chip and whole genome sequence (WGS) data from 1734 Yorkshire and 1123 Landrace pigs to detect SNPs related to four growth traits: average daily gain, backfat thickness, body weight and birth weight. A total of 1485 significant loci and 24 candidate genes which are involved in skeletal muscle development, fatty deposition, lipid metabolism and insulin resistance were identified. Compared with using all SNP-chip data, GS with the pre-selected functional SNPs in the standard genomic best linear unbiased prediction (GBLUP), and a two-kernel based GBLUP model yielded average gains in accuracy by 4 to 46% (from 0.19 ± 0.07 to 0.56 ± 0.07) and 5 to 27% (from 0.16 ± 0.06 to 0.57 ± 0.05) for the four traits, respectively, suggesting that the prioritization of preselected functional markers in GS models had the potential to improve prediction accuracies for certain traits in livestock breeding.

A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test (metaUSAT) software for genetically correlated phenotypes (N = 397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and four loci were newly suggested. The LDL receptor related protein 1 (LRP1)-Signal Transducer and Activator of Transcription 6 (STAT6)-S hort chain D ehydrogenase/R eductase family 9C member 7 (SDR9C7) region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24 × 10-62 of rs11172113. The One Cut homeobox 2 (ONECUT2) gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29 × 10-9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches. This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00078-7.

Cortical morphometric vulnerability to generalised epilepsy reflects chromosome- and cell type-specific transcriptomic signatures

AIMS

Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level.

METHODS

We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas.

RESULTS

GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS.

CONCLUSIONS

By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.

An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices

The generation of unprecedented amounts of data brings new challenges in data management, but also an opportunity to accelerate the identification of processes of multiple science disciplines. One of these challenges is the harmonization of high-dimensional unbalanced and heterogeneous data. In this manuscript, we propose a statistical approach to combine incomplete and partially-overlapping pieces of covariance matrices that come from independent experiments. We assume that the data are a random sample of partial covariance matrices sampled from Wishart distributions and we derive an expectation-maximization algorithm for parameter estimation. We demonstrate the properties of our method by (i) using simulation studies and (ii) using empirical datasets. In general, being able to make inferences about the covariance of variables not observed in the same experiment is a valuable tool for data analysis since covariance estimation is an important step in many statistical applications, such as multivariate analysis, principal component analysis, factor analysis, and structural equation modeling.

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.

Association of several loci of SMAD7 with colorectal cancer: A meta-analysis based on case-control studies

BACKGROUND

Sma-and mad-related protein 7 (SMAD7) can affect tumor progression by closing transforming growth factor-beta intracellular signaling channels. Despite the extensive research on the correlation between SMAD7 polymorphisms and colorectal cancer (CRC), the conclusions of studies are still contradictory. We conducted a study focusing on the association of SMAD7 polymorphisms rs4939827, rs4464148, and rs12953717 with CRC.

METHODS

We searched through 5 databases for articles and used odd ratios (ORs) and 95% confidence intervals (CIs) to discuss the correlation of SMAD7 polymorphisms with CRC risk. The heterogeneity will be appraised by subgroup analysis and meta-regression. Contour-enhanced funnel plot, Begg test and Egger test were utilized to estimate publication bias, and the sensitivity analysis illustrates the reliability of the outcomes. We performed False-positive report probability and trial sequential analysis methods to verify results. We also used public databases for bioinformatics analysis.

RESULTS

We conclusively included 34 studies totaling 173251 subjects in this study. The minor allele (C) of rs4939827 is a protective factor of CRC (dominant, OR/[95% CI] = 0.89/[0.83-0.97]; recessive, OR/[95% CI] = 0.89/[0.83-0.96]; homozygous, OR/[95% CI] = 0.84/[0.76-0.93]; heterozygous, OR/[95% CI] = 0.91/[0.85-0.97]; additive, OR/[95% CI] = 0.91/[0.87-0.96]). the T allele of rs12953717 (recessive, OR/[95% CI] = 1.22/[1.15-1.28]; homozygous, OR/[95% CI] = 1.25/[1.13-1.38]; additive, OR/[95% CI] = 1.11/[1.05-1.17]) and the C allele of rs4464148 (heterozygous, OR/[95% CI] = 1.13/[1.04-1.24]) can enhance the risk of CRC.

CONCLUSION

Rs4939827 (T > C) can decrease the susceptibility to CRC. However, the rs4464148 (T > C) and rs12953717 (C > T) variants were connected with an enhanced risk of CRC.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

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.

Overall assessment for selected markers from high-throughput data

Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined P $$ P $$ -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses.

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10-4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.

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.

Genome-wide association studies uncover genes associated with litter traits in the pig

Litter traits are critical economic variables in the pig industry as they represent a production indicator that can serve to determine sow fertility. In this study, a genome-wide association study on litter traits, including total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), and piglet uniformity (PU), was carried out on two pig breeds (Yorkshire and Landrace). A total of 3 637 pigs of both breeds were genotyped using the GeneSeek GGP Porcine 50K SNP BeadChip. A mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) were employed in the genome-wide association studies for litter traits using combined data from the two pig breeds and data from each breed separately. Additionally, the heritability of traits was estimated using three methods-pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step best linear unbiased prediction (ssGBLUP)-and was found to lie between 0.065 and 0.1289, 0.0478 and 0.0938, 0.0793 and 0.0935, 0.1862 and 0.2163, and 0.0327 and 0.0419 for TNB, NBA, LBW, ABW, and PU, respectively. We also compared the genomic prediction accuracies and unbiasedness for litter traits of the three BLUP models. Our results indicated that the ssGBLUP method provided higher predictive accuracies and more rational unbiasedness compared with the PBLUP and GBLUP methodologies. Furthermore, based on their possible roles, eight candidate genes (INHBA, LEPR, HDHD2, CTNND2, RNF216, HMX1, PAPPA2, and NTN1) were identified as being linked with litter traits. In the middle of the test, these genes were found to be connected with pig metabolism and ovulation rate. Our results provide the insights into the genetic architecture of litter traits in pigs, and the potential single nucleotide polymorphisms (SNPs) and candidate genes identified may benefit economic profits in pig-breeding industry and contribute to improve litter traits.

Exploring the genetic overlap between twelve psychiatric disorders

The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies1-5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies6-10. To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability11-13, we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P-value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders.

A methodological review with meta-epidemiological analysis of preclinical systematic reviews with meta-analyses

Systematic reviews and meta-analyses have been proposed as an approach to synthesize the literature and counteract the lack of power of small preclinical studies. We aimed to evaluate (1) the methodology of these reviews, (2) the methodological quality of the studies they included and (3) whether study methodological characteristics affect effect size. We searched MEDLINE to retrieve 212 systematic reviews with meta-analyses of preclinical studies published from January, 2018 to March, 2020. Less than 15% explored the grey literature. Selection, data extraction and risk of bias assessment were performed in duplicate in less than two thirds of reviews. Most of them assessed the methodological quality of included studies and reported the meta-analysis model. The risk of bias of included studies was mostly rated unclear. In meta-epidemiological analysis, none of the study methodological characteristics was associated with effect size. The methodological characteristics of systematic reviews with meta-analyses of recently published preclinical studies seem to have improved as compared with previous assessments, but the methodological quality of included studies remains poor, thus limiting the validity of their results. Our meta-epidemiological analysis did not show any evidence of a potential association between methodological characteristics of included studies and effect size.

Genome-wide analysis-based single nucleotide polymorphism marker sets to identify diverse genotypes in cabbage cultivars (Brassica oleracea var. capitata)

Plant variety protection is essential for breeders' rights granted by the International Union for the Protection of New Varieties of Plants. Distinctness, uniformity, and stability (DUS) are necessary for new variety registration; to this end, currently, morphological traits are examined, which is time-consuming and laborious. Molecular markers are more effective, accurate, and stable descriptors of DUS. Advancements in next-generation sequencing technology have facilitated genome-wide identification of single nucleotide polymorphisms. Here, we developed a core set of single nucleotide polymorphism markers to identify cabbage varieties and traits of test guidance through clustering using the Fluidigm assay, a high-throughput genotyping system. Core sets of 87, 24, and 10 markers are selected based on a genome-wide association-based approach. All core markers could identify 94 cabbage varieties and determine 17 DUS traits. A genotypes database was validated using the Fluidigm platform for variety identification, population structure analysis, cabbage breeding, and DUS testing for plant cultivar protection.

Simultaneous detection of novel genes and SNPs by adaptive p-value combination

Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.

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.

Primary dysmenorrhoea in adolescents and young women: A twin family study of maternal transmission, genetic influence and associations

AIMS

The extent to which maternal transmission of primary dysmenorrhoea is genetically determined in adolescents and young women has yet to be determined. We aimed to assess heritability and associations relevant to primary pain syndromes using a twin family study.

METHODS

Participants were young menstruating female twins, and their oldest sisters and mothers, whose families were registered with Twins Research Australia and previously participated in a twin family study of primary paediatric pain disorders. Questionnaire packs were mailed, assessing current maximum and average menstrual pain intensity, current pain interference with activities and retrospective dysmenorrhea secondary symptoms.

RESULTS

The sample comprised 206 twin individuals (57 monozygous (MZ) and 46 dizygous (DZ) pairs) aged 10-22 years, eldest siblings (n = 38) aged 13-28 years and mothers (n = 101) aged 32-61 years. The estimated regression coefficient of the relationship between mother-daughter and twin-sibling dyads indicated significant associations for the measures of dysmenorrhea and supported heritability. Adjusted for age, the within twin-pair correlation for MZ twins was generally more than twice that of DZ twins. Heritability estimates were maximal pain intensity 0.67 (P = 3.8 × 10^-11 ), average pain intensity 0.63 (P = 3.7 × 10^-10 ), pain interference 0.57 (P = 1.8 × 10^-8 ) and retrospective symptoms 0.57 (P = 1.8 × 10^-8 ). Twin individuals with a lifetime (three-month) history of iron deficiency and those with painless restless legs syndrome (RLS) were significantly more likely to have more intense pain associated with menstruation.

CONCLUSION

Primary dysmenorrhea in adolescents and young women was shown to be relatively strongly genetically influenced and associated especially with a history of iron deficiency and painless RLS which have potential therapeutic implications.

Inframe insertion and splice site variants in MFGE8 associate with protection against coronary atherosclerosis

Cardiovascular diseases are the leading cause of premature death and disability worldwide, with both genetic and environmental determinants. While genome-wide association studies have identified multiple genetic loci associated with cardiovascular diseases, exact genes driving these associations remain mostly uncovered. Due to Finland's population history, many deleterious and high-impact variants are enriched in the Finnish population giving a possibility to find genetic associations for protein-truncating variants that likely tie the association to a gene and that would not be detected elsewhere. In a large Finnish biobank study FinnGen, we identified an association between an inframe insertion rs534125149 in MFGE8 (encoding lactadherin) and protection against coronary atherosclerosis. This variant is highly enriched in Finland, and the protective association was replicated in meta-analysis of BioBank Japan and Estonian biobank. Additionally, we identified a protective association between splice acceptor variant rs201988637 in MFGE8 and coronary atherosclerosis, independent of the rs534125149, with no significant risk-increasing associations. This variant was also associated with lower pulse pressure, pointing towards a function of MFGE8 in arterial aging also in humans in addition to previous evidence in mice. In conclusion, our results suggest that inhibiting the production of lactadherin could lower the risk for coronary heart disease substantially.

COVID-19 in pediatrics: Genetic susceptibility

The uptick in SARS-CoV-2 infection has resulted in a worldwide COVID-19 pandemic, which has created troublesome health and economic problems. We performed case-control meta-analyses in both African and European ethnicity COVID-19 disease cases based on laboratory test and phenotypic criteria. The cases had laboratory-confirmed SARS-CoV-2 infection. We uniquely investigated COVID infection genetics in a pediatric population. Our cohort has a large African ancestry component, also unique to our study. We tested for genetic variant association in 498 cases vs. 1,533 controls of African ancestry and 271 cases vs. 855 controls of European ancestry. We acknowledge that the sample size is relatively small, owing to the low prevalence of COVID infection among pediatric individuals. COVID-19 cases averaged 13 years of age. Pediatric genetic studies enhance the ability to detect genetic associations with a limited possible environment impact. Our findings support the notion that some genetic variants, most notably at the SEMA6D, FMN1, ACTN1, PDS5B, NFIA, ADGRL3, MMP27, TENM3, SPRY4, MNS1, and RSU1 loci, play a role in COVID-19 infection susceptibility. The pediatric cohort also shows nominal replication of previously reported adult study results: CCR9, CXCR6, FYCO1, LZTFL1, TDGF1, CCR1, CCR2, CCR3, CCR5, MAPT-AS1, and IFNAR2 gene variants. Reviewing the biological roles of genes implicated here, NFIA looks to be the most interesting as it binds to a palindromic sequence observed in both viral and cellular promoters and in the adenovirus type 2 origin of replication.

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

The tar spot complex (TSC) is a devastating disease of maize (Zea mays L.), occurring in 17 countries throughout Central, South, and North America and the Caribbean, and can cause grain yield losses of up to 80%. As yield losses from the disease continue to intensify in Central America, Phyllachora maydis, one of the causal pathogens of TSC, was first detected in the United States in 2015, and in 2020 in Ontario, Canada. Both the distribution and yield losses due to TSC are increasing, and there is a critical need to identify the genetic resources for TSC resistance. The Seeds of Discovery Initiative at CIMMYT has sought to combine next-generation sequencing technologies and phenotypic characterization to identify valuable alleles held in the CIMMYT Germplasm Bank for use in germplasm improvement programs. Individual landrace accessions of the “Breeders' Core Collection” were crossed to CIMMYT hybrids to form 918 unique accessions topcrosses (F1 families) which were evaluated during 2011 and 2012 for TSC disease reaction. A total of 16 associated SNP variants were identified for TSC foliar leaf damage resistance and increased grain yield. These variants were confirmed by evaluating the TSC reaction of previously untested selections of the larger F1 testcross population (4,471 accessions) based on the presence of identified favorable SNPs. We demonstrated the usefulness of mining for donor alleles in Germplasm Bank accessions for newly emerging diseases using genomic variation in landraces.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease

Background: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. Objective: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. Methods: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. Results: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. Conclusion: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer’s disease.

Leveraging the local genetic structure for trans-ancestry association mapping

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.

Evaluating statistical significance in a meta-analysis by using numerical integration

Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy of statistical significance estimation. In this study, we proposed a numerical integration method and examined its theoretical properties. Simulation studies were conducted to evaluate its Type I error, statistical power, computational efficiency, and estimation accuracy, and the results were compared with those of other methods. The results demonstrate that our proposed method performs well in terms of Type I error, statistical power, computing efficiency (regardless of sample size), and statistical significance estimation accuracy. P-value data from multiple large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs) were analyzed. The results demonstrate that our proposed method can be used to identify critical genomic regions associated with rheumatoid arthritis and asthma, increase statistical significance in individual GWASs and TWASs, and control for false-positives more effectively than can Fisher's method under an independence assumption. We created the software package Pbine, available at GitHub (https://github.com/Yinchun-Lin/Pbine).

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics

BACKGROUND

Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method.

RESULTS

We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy.

CONCLUSIONS

We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model.

Translation of Cellular Senescence to Novel Therapeutics: Insights From Alternative Tools and Models

Increasing chronological age is the greatest risk factor for human diseases. Cellular senescence (CS), which is characterized by permanent cell-cycle arrest, has recently emerged as a fundamental mechanism in developing aging-related pathologies. During the aging process, senescent cell accumulation results in senescence-associated secretory phenotype (SASP) which plays an essential role in tissue dysfunction. Although discovered very recently, senotherapeutic drugs have been already involved in clinical studies. This review gives a summary of the molecular mechanisms of CS and its role particularly in the development of cardiovascular diseases (CVD) as the leading cause of death. In addition, it addresses alternative research tools including the nonhuman and human models as well as computational techniques for the discovery of novel therapies. Finally, senotherapeutic approaches that are mainly classified as senolytics and senomorphics are discussed.

Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato

The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity.

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.

sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies

Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink .

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Meta-Analysis for Epigenome-Wide Association Studies

With the rapid development of methylation profiling technology, many datasets are generated to quantify genome-wide methylation patterns. Given the heavy burden of multiple testing of hundreds of thousands of DNA methylation markers, individual studies often have limited sample sizes and power. The EWAS meta-analysis is an approach that combines results from multiple studies on the same scientific question. It helps to improve statistical power by combining information from individual studies and reduce the chances of false positives. This chapter introduces commonly used meta-analysis methods and analytical tools with application to EWAS data.

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.

GWAS Case Studies in Wheat

With the advancements in next-generation sequencing technologies, leading to millions of single nucleotide polymorphisms in all crop species including wheat, genome-wide association study (GWAS) has become a leading approach for trait dissection. In wheat, GWAS has been conducted for a plethora of traits and more and more studies are being conducted and reported in journals. While application of GWAS has become a routine in wheat using the standardized approaches, there has been a great leap forward using newer models and combination of GWAS with other sets of data. This chapter has reviewed all these latest advancements in GWAS in wheat by citing the most important studies and their outputs. Specially, we have focused on studies that conducted meta-GWAS, multilocus GWAS, haplotype-based GWAS, Environmental- and Eigen-GWAS, and/or GWAS combined with gene regulatory network and pathway analyses or epistatic interactions analyses; all these have taken the association mapping approach to new heights in wheat.

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.

A Genome-Wide Association Study of a Korean Population Identifies Genetic Susceptibility to Hypertension Based on Sex-Specific Differences

Genome-wide association studies have expanded our understanding of the genetic variation of hypertension. Hypertension and blood pressure are influenced by sex-specific differences; therefore, genetic variants may have sex-specific effects on phenotype. To identify the genetic factors influencing the sex-specific differences concerning hypertension, we conducted a heterogeneity analysis of a genome-wide association study (GWAS) on 13,926 samples from a Korean population. Using the Illumina exome chip data of the population, we performed GWASs of the male and female population independently and applied a statistical test that identified heterogeneous effects of the variants between the two groups. To gain information about the biological implication of the genetic heterogeneity, we used gene set enrichment analysis with GWAS catalog and pathway gene sets. The heterogeneity analysis revealed that the rs11066015 of ACAD10 was a significant locus that had sex-specific genetic effects on the development of hypertension. The rs2074356 of HECTD4 also showed significant genetic heterogeneity in systolic blood pressure. The enrichment analysis showed significant results that are consistent with the pathophysiology of hypertension. These results indicate a sex-specific genetic susceptibility to hypertension that should be considered in future genetic studies of hypertension.

Reviewing and assessing existing meta-analysis models and tools

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.