Advanced Topics and Innovations in GWAS: From Multi-Omics Integration to Novel Breakthroughs

Genome-wide association study (GWAS), as the core tool to analyze the genetic basis of complex traits, has gradually developed from the simple association of single traits and single populations in the early stage to a multi-dimensional, cross-scale, and intelligent direction. In the field of life science research, the exploration of human genetic mysteries is endless, and the iterative upgrade of GWAS technology provides a new path for overcoming complex diseases and analyzing biodiversity. Early research on GWAS focused on a single disease or phenotype, looking for common genetic variation associated with traits in a small sample population. However, with the cost reduction and computing power improvement of sequencing technology, this technology has broken through the traditional research paradigm.

In the study of multi-traits and cross-populations, the common genetic mechanism of cross-species and cross-disease types can be mined by integrating multi-population and multi-phenotype data. Multiomics data integration breaks the limitation of single genomics, and comprehensively analyzes the regulation of genetic variation on biomolecular networks by collaborative analysis of multi-dimensional data such as transcription groups and protein groups.

The introduction of machine learning and artificial intelligence algorithm significantly improves the efficiency of complex data processing and helps to find weak genetic signals that are difficult to capture by traditional methods; The association analysis between GWAS and rare mutations makes up for the shortage of focusing only on common mutations in the early stage, and opens up a new direction for analyzing the genetic basis of rare diseases and complex diseases.

The article explores advanced topics and innovations in GWAS, including multi-trait and cross-population studies, multi-omics integration, machine learning and AI applications, and GWAS with rare variation correlation analysis, along with future trends.

Multi-Trait and Cross-Population GWAS

Traditional GWAS focuses on a single trait or a single population, and it is difficult to capture the genetic co-regulation mechanism between traits and the genetic heterogeneity between populations. Multi-trait and cross-population GWAS significantly improved the depth and breadth of genetic analysis by integrating multi-dimensional phenotype and population data.

Analysis Strategy of Multi-trait GWAS

By analyzing multiple related traits (such as height and weight, blood sugar and insulin levels) at the same time, multi-trait GWAS can mine the shared genetic loci that control "trait clusters" and reveal the synergistic regulatory network of complex traits. Its core method includes:

• Multivariate linear mixed model (MVLMM): This model takes multiple traits as response variables, and considers genetic correlation among individuals and environmental factors. By introducing a variance-covariance matrix, MVLMM can effectively capture the genetic covariance between traits, thus identifying pleiotropic loci affecting multiple traits.
• Bayesian method: A multi-trait analysis method based on the Bayesian framework, such as Bayesian Multi-trait Variable Selection (BMVS), models genetic effects by introducing a prior distribution. This method can effectively screen the real association sites in high-dimensional data, and describe the genetic structure of multiple traits in detail, especially suitable for the combination analysis of traits with complex genetic relationships.
• Principal component analysis (PCA) integration strategy: multiple traits are transformed into independent principal components through PCA, which reduces the data dimension while retaining the main variation information among traits. Subsequent GWAS analysis of principal components can not only reduce the computational complexity, but also find the genetic factors that affect the comprehensive variation of multiple traits, which is often used to deal with complex trait sets with high correlation.

Layout of principal component analysis (PCA)-based multiple-trait genome-wide association studies (GWAS) versus single-trait GWAS (Zhang et al., 2018)

Core Values and Challenges of Cross-group GWAS

GWAS shows its unique advantages in the field of genetic research by integrating population data with different genetic backgrounds (such as European, Asian, and African-American populations), which can be realized as follows:

• Improve the efficiency of genetic variation detection: The genetic background differences of different populations lead to significant differences in the distribution of genetic polymorphisms. Integrating multi-population data can break through the genetic bottleneck of a single population, find more low-frequency and rare mutation sites, and significantly improve the detection rate of genetic loci related to complex diseases.
• Enhance the universality of the results: The results of single-group research have limitations. Cross-group analysis can verify the stability of genetic association in different populations, avoid the research bias caused by population specificity, make the research results more universal, and provide a reliable basis for the practice of precision medicine in different populations around the world.
• Revealing the population-specific genetic mechanism: By comparing and analyzing different ancestral populations, we can identify the population-specific genetic loci affected by natural selection, environmental adaptation, and other factors, deeply understand the differences in disease genetic mechanisms among different populations, and provide new targets for personalized medical care, drug research, and development.
• Optimization of genetic risk prediction model: The risk prediction model based on multi-population genetic data can incorporate more genetic information and environmental factors, significantly improve the accuracy and reliability of disease risk prediction, and provide more effective tools for early warning and prevention of diseases.

Genetic signal similarity across 4 superpopulations (Zhang et al., 2018)

Integration with Omics Data

The significant loci of GWAS are mostly located in non-coding regions, and it is difficult to clarify their functional mechanism only by genotype-phenotype association. By integrating the data of transcriptome, phenotype, protein group, and other omics, we can build an association chain of "genotype-molecular phenotype-phenotype" and realize the leap from "location" to "mechanism".

Levels and Methods of Multi-group Integration

Transcriptome integration: Based on the analysis framework of expression quantitative trait loci (eQTL), the statistical correlation model between genome-wide association studies loci and gene expression level was constructed, and the potential candidate target genes were identified by colocalization analysis.

• Episomal integration: Combining high-throughput technologies such as chromatin accessibility sequencing (ATAC-seq) and DNA methylation immunoprecipitation sequencing (MeDIP-seq), the distribution characteristics of GWAS loci in genome regulatory elements (such as promoters and enhancers) were systematically analyzed. Through the prediction algorithm of cis-regulatory elements, the potential influence mechanism of genetic variation on gene expression regulation was evaluated, including the change of transcription factor binding site and chromatin conformation remodeling.
• Integration of protein Group and Metabonomic Group: With the help of protein Quantitative Trait Loci (pQTL) and Metabolite Quantitative Trait Loci (mQTL) analysis, a causal regulatory network of genetic variation, molecular phenotype, and clinical phenotype was constructed.

Genetic-Glycan joint principal components obtained with the OmicsPLS R-package. Loading values of each IgG1 glycan variable are depicted per component (Bouhaddani et al., 2018)

Integrate the Development of Platforms and Tools

In recent years, with the rapid development of quantitative biomedical technology in Qualcomm, the collection of various "omics" data has reached an unprecedented level of detail. In this context, the tools of multi-omics integration are constantly emerging.

• OmicsIntegrator: By constructing a joint analysis framework of multi-omics data, collaborative mining of genome, transcriptome, protein group, and metabolomics data can be realized, and multi-omics molecular markers related to complex diseases can be effectively identified.
• MixOmics: Based on a machine learning algorithm, it can find the potential association between variables in high-dimensional multi-disciplinary data and help researchers extract key biological pathways and regulatory networks from massive data.
• IGUIDE: Focus on integrating epigenome and transcriptome data, and use a deep learning model to analyze the regulation mechanism of epigenetic modification on gene expression, providing a new perspective for understanding the occurrence and development of diseases.
• Multi-Omics Factor Analysis (MOFA): Using a probability graph model, multiple omics data sets can be processed at the same time, the shared and specific biological signals among omics can be separated, and the multi-omics regulation mode behind complex phenotypes can be revealed.

Supervised Machine Learning Algorithm Training (Nicholls et al., 2020)

Machine Learning and AI in GWAS

Machine learning (ML) and artificial intelligence (AI) provide a new solution for GWAS to break through the limitations of traditional statistical models, especially in complex data analysis and predictive modeling.

• A. Data Preprocessing and Noise Filtering
  • a) GWAS data often contain noise such as technical errors and group mixing, and machine learning algorithms can efficiently optimize data quality
  • b) In the data preprocessing stage, based on a clustering algorithm (such as K-means), the abnormal samples can be accurately identified by quantitatively analyzing the multi-dimensional genetic characteristics of the samples (such as SNP locus genotype frequency and allele frequency distribution).
  • c) In the identification of related signals, the random forest algorithm, by virtue of its integrated learning advantage, effectively improves the ability to capture real related signals by constructing multiple decision trees and synthesizing voting results. Support Vector Machine (SVM) uses a nonlinear kernel function to find the optimal classification hyperplane in high-dimensional space, which can accurately distinguish the true correlation signal from the false correlation caused by group stratification, and at the same time, with Bonferroni correction and other strategies, it can significantly reduce the false negative problems caused by multiple tests.
  • d) Facing the millions of single-nucleotide polymorphism (SNP) sites in GWAS data, the self-encoder maps high-dimensional SNP data to a low-dimensional hidden space by constructing a neural network architecture including a coding layer and a decoding layer, which reduces the computational complexity by about 80% while retaining key genetic features. This dimension reduction not only accelerates the subsequent statistical analysis, but also effectively avoids the risk of over-fitting caused by the curse of dimensionality.

Hypothetical GWAS Locus with Two Signals that Affect Two Genes (Cannon et al., 2018)

• B. Mining Complex Association Patterns
  • a) Machine learning provides innovative methods for complex relationships that are difficult to capture in traditional models, such as gene-gene interaction (epistatic effect) and gene-environment interaction.
  • b) Deep learning models (such as a convolutional neural network, CNN) can automatically extract the nonlinear association features of SNP combinations and successfully identify five interaction sites missed by traditional methods in mental illness GWAS.
  • c) Gradient boosting decision tree (GBDT) integrates genotype and environmental factors (such as rainfall and temperature) in the study of crop yield traits by constructing a multivariate prediction model, and accurately predicts the genetic effects in different environments.
• C. Phenotypic Prediction and Functional Annotation
  • a) The machine learning prediction model based on GWAS markers (such as the deep learning optimized version of multi-gene risk score PRS) can significantly improve the accuracy of disease risk prediction (such as the AUC value of breast cancer risk prediction increased from 0.68 to 0.75).
  • b) Natural language processing (NLP) technology can automatically annotate the potential functions of GWAS loci by mining biomedical texts in the literature and databases, such as combining with the Gene Ontology database to predict the biological processes that the loci may participate in.

GWAS and Rare Variation Correlation Analysis

Traditional GWAS mainly focuses on common variation (allele frequency > 5%), but rare variation (frequency < 1%) plays an important role in complex diseases (such as rare diseases and some complex genetic diseases) and drug response differences. The correlation analysis between GWAS and rare mutations has become a research hotspot in the field.

• A. Technical Challenges of Rare Variation Association Analysis
  • a) The demand for sample size is huge: Due to the extremely low frequency of rare mutations in the population (usually allele frequency < 1%), the sample size of tens of thousands of people required by traditional GWAS is difficult to meet the statistical test requirements, and it is often necessary to include hundreds of thousands or even millions of samples to achieve sufficient test efficiency, resulting in a sharp increase in research costs and extremely difficult sample collection.
  • b) The burden of multiple tests is aggravated: When rare mutations are detected in the whole genome, the number of mutation sites that need to be tested at the same time increases sharply, which further aggravates the problem of a strict threshold caused by multiple tests and corrections, making the real correlation signal more easily buried in noise.
  • c) Complex genetic heterogeneity: Rare mutation usually has stronger population specificity and functional heterogeneity, and the pathogenic mechanism of the same rare mutation may be different in different individuals or populations, so it is difficult to accurately capture its association pattern with phenotype with a unified statistical model.

Depicted here are results from the multivariate analysis of pleiotropyFor each locus, the method returns the best fitting solution of which phenotypes were associated with that locus (Liu et al., 2019)

• B. Core Analysis Methods and Tools
  • a) Burden Tests: This method aggregates the rare mutations in the same gene in the case group and the control group, and judges whether the gene is related to the disease by comparing the number or frequency difference of the mutations between the two groups.
  • b) Association test based on set: This method not only pays attention to the quantity of variation, but also comprehensively considers the information, such as functional annotation and allele frequency of variation. By constructing a statistical model, the correlation between rare variation sets in genes and phenotypes was evaluated.
  • c) Stratification analysis strategy: According to the functional influence of variation (such as missense mutation, frame shift mutation, etc.), the position in the gene (such as coding region and non-coding region) or population subgroup, rare variation is stratified. Correlation analysis of different levels of variation sets is helpful to locate rare variations related to diseases more accurately.
  • d) Machine learning method: Using a machine learning algorithm to integrate multiple omics data, such as gene expression data, protein structure data, etc., combined with rare mutation information for disease association analysis.

Conclusion

Technological innovation of GWAS promotes the genetic research of complex traits to a new stage. Multi-trait cross-population analysis breaks the data barrier, multi-omics integrates the gene-phenotype pathway, machine learning enhances the efficiency of data analysis, rare mutation research improves the genetic map, and jointly constructs a multi-dimensional intelligent research system.

In the future, GWAS will present three development trends: first, cross-scale data fusion, combined with single-cell omics and other technologies to analyze genetic effects. Second, interdisciplinary collaborative innovation, integrated evolution and other theories reveal the significance of genetic variation. Third, the clinical transformation is accelerated, and accurate diagnosis and treatment of diseases is realized through correlation analysis.

Although challenges still exist, with the continuous breakthrough of technical methods, GWAS will continue to provide a powerful genetic analysis tool for analyzing the complexity of life and promoting the development of precision medicine and modern agriculture.

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