Northwestern University biophysicists have developed a new computational tool for identifying gene combinations underlying complex illnesses such as diabetes, cancer, and asthma. Unlike single-gene disorders, complex conditions are influenced by a network of multiple genes, which means there is a huge number of possible gene combinations, making it incredibly difficult for researchers to pinpoint those that cause disease.
Using a generative artificial intelligence (AI) model, the new method amplifies limited gene expression data, enabling researchers to resolve patterns of gene activity that cause complex traits. The team said this information could lead to new and more effective disease treatments involving molecular targets associated with multiple genes.
“Many diseases are determined by a combination of genes—not just one,” said Adilson Motter, PhD, the Charles E. and Emma H. Morrison Professor of Physics at Northwestern’s Weinberg College of Arts and Sciences and the director of the Center for Network Dynamics. “You can compare a disease like cancer to an airplane crash. In most cases, multiple failures need to occur for a plane to crash, and different combinations of failures can lead to similar outcomes. This complicates the task of pinpointing the causes. Our model helps simplify things by identifying the key players and their collective influence.”
An expert on complex systems, Motter is senior author of the team’s published paper in PNAS, titled “Generative prediction of causal gene sets responsible for complex traits.” The other authors of the study, who are all associated with Motter’s Lab, are postdoctoral researcher Benjamin Kuznets-Speck, PhD, graduate student Buduka Ogonor, and research associate Thomas Wytock, PhD.
Complex traits are polygenic, orchestrated by networks of interacting genes that work together to produce phenotypic variation, the authors explained. “An outstanding question in the study of such traits is the identification of the specific combinations of gene variants that give rise to the different phenotypic expressions.” For decades, scientists have struggled to unravel the genetic underpinnings of such complex human traits and diseases. “Researchers have long sought to bridge the gap between phenotypes and the genotypes that cause them.” However, the investigators further stated, “The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype–phenotype mappings.”
Even non-disease traits such as height, intelligence, and hair color depend on collections of genes. Existing methods, such as genome-wide association studies, try to find individual genes linked to a trait. But they lack the statistical power to detect the collective effects of groups of genes. “The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power,” the team stated.
“The Human Genome Project showed us that we only have six times as many genes as a single-cell bacterium,” Motter said. “But humans are much more sophisticated than bacteria, and the number of genes alone does not explain that. This highlights the prevalence of multigenic relationships, and that it must be the interactions among genes that give rise to complex life.”
Added Wytock, “Identifying single genes is still valuable. But there is only a very small fraction of observable traits, or phenotypes, that can be explained by changes in single genes. Instead, we know that phenotypes are the result of many genes working together. Thus, it makes sense that multiple genes typically contribute to the variation of a trait.”
To help bridge the long-standing knowledge gap between genotype (genetic makeup) and phenotype (observable traits), the research team developed a sophisticated approach that combines machine learning with optimization. Called the transcriptome-wide conditional variational auto-encoder (TWAVE), the model leverages generative AI to identify patterns from limited gene expression data in humans. Accordingly, it can emulate diseased and healthy states so that changes in gene expression can be matched with changes in phenotype. Instead of examining the effects of individual genes in isolation, the model identifies groups of genes that collectively cause a complex trait to emerge. The method then uses an optimization framework to pinpoint specific gene changes that are most likely to shift a cell’s state from healthy to diseased or vice versa.
“Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power,” they wrote. “A key aspect of our approach is the use of increasingly available trait-labeled transcriptomic data from bulk and single-cell RNA-Seq experiments, which contends with the biological networks that influence complex traits.”
Wytock noted, “We’re not looking at gene sequence but gene expression. We trained our model on data from clinical trials, so we know which expression profiles are healthy or diseased. For a smaller number of genes, we also have experimental data that tells how the network responds when the gene is turned on or off, which we can match with the expression data to find the genes implicated in the disease.”
Focusing on gene expression has multiple benefits. First, it bypasses patient privacy issues. Genetic data—a person’s actual DNA sequence—is inherently unique to an individual, providing a highly personal blueprint of health, genetic predispositions, and family relationships. Expression data, on the other hand, is more like a dynamic snapshot of cellular activity. Second, gene expression data implicitly accounts for environmental factors, which can turn genes “up” or “down” to perform various functions. “Environmental factors might not affect DNA, but they definitely affect gene expression,” Motter said. “So, our model has the benefit of indirectly accounting for environmental factors.”
Their resulting framework, the scientists stated, “… reveals groups of gene perturbations that most influence phenotypic variation, pinpointing the molecular underpinnings that determine complex traits … Our approach accounts for limited data, heterogeneity within phenotypes, confounding biological variation, and combinatorial explosion in gene sets in ways that traditional methods cannot.”
To demonstrate TWAVE’s effectiveness, the team tested it across several complex diseases. The method successfully identified the genes, some of which were missed by existing methods, that caused those diseases. TWAVE also revealed that different sets of genes can cause the same complex disease in different people. That finding suggests personalized treatments could be tailored to a patient’s specific genetic drivers of disease.
“A disease can manifest similarly in two different individuals,” Motter said. “But, in principle, there could be a different set of genes involved for each person owing to genetic, environmental, and lifestyle differences. This information could orient personalized treatment.”
In their paper, the team concluded, “Ultimately, our approach provides a tool to investigate genotype–phenotype relationships in complex traits, which is applicable across a range of organisms and traits. In humans, our approach also lays the groundwork for the design of next-generation multitarget strategies for the treatment of complex diseases.”
The post AI Tool Uncovers Gene Combinations Underlying Genotype–Phenotype Relationships in Complex Traits appeared first on GEN - Genetic Engineering and Biotechnology News.
Using a generative artificial intelligence (AI) model, the new method amplifies limited gene expression data, enabling researchers to resolve patterns of gene activity that cause complex traits. The team said this information could lead to new and more effective disease treatments involving molecular targets associated with multiple genes.
“Many diseases are determined by a combination of genes—not just one,” said Adilson Motter, PhD, the Charles E. and Emma H. Morrison Professor of Physics at Northwestern’s Weinberg College of Arts and Sciences and the director of the Center for Network Dynamics. “You can compare a disease like cancer to an airplane crash. In most cases, multiple failures need to occur for a plane to crash, and different combinations of failures can lead to similar outcomes. This complicates the task of pinpointing the causes. Our model helps simplify things by identifying the key players and their collective influence.”
An expert on complex systems, Motter is senior author of the team’s published paper in PNAS, titled “Generative prediction of causal gene sets responsible for complex traits.” The other authors of the study, who are all associated with Motter’s Lab, are postdoctoral researcher Benjamin Kuznets-Speck, PhD, graduate student Buduka Ogonor, and research associate Thomas Wytock, PhD.
Complex traits are polygenic, orchestrated by networks of interacting genes that work together to produce phenotypic variation, the authors explained. “An outstanding question in the study of such traits is the identification of the specific combinations of gene variants that give rise to the different phenotypic expressions.” For decades, scientists have struggled to unravel the genetic underpinnings of such complex human traits and diseases. “Researchers have long sought to bridge the gap between phenotypes and the genotypes that cause them.” However, the investigators further stated, “The challenge arises from complex traits being determined by a combination of multiple genes (or loci), which leads to an explosion of possible genotype–phenotype mappings.”
Even non-disease traits such as height, intelligence, and hair color depend on collections of genes. Existing methods, such as genome-wide association studies, try to find individual genes linked to a trait. But they lack the statistical power to detect the collective effects of groups of genes. “The primary techniques to resolve these mappings are genome/transcriptome-wide association studies, which are limited by their lack of causal inference and statistical power,” the team stated.
“The Human Genome Project showed us that we only have six times as many genes as a single-cell bacterium,” Motter said. “But humans are much more sophisticated than bacteria, and the number of genes alone does not explain that. This highlights the prevalence of multigenic relationships, and that it must be the interactions among genes that give rise to complex life.”
Added Wytock, “Identifying single genes is still valuable. But there is only a very small fraction of observable traits, or phenotypes, that can be explained by changes in single genes. Instead, we know that phenotypes are the result of many genes working together. Thus, it makes sense that multiple genes typically contribute to the variation of a trait.”
To help bridge the long-standing knowledge gap between genotype (genetic makeup) and phenotype (observable traits), the research team developed a sophisticated approach that combines machine learning with optimization. Called the transcriptome-wide conditional variational auto-encoder (TWAVE), the model leverages generative AI to identify patterns from limited gene expression data in humans. Accordingly, it can emulate diseased and healthy states so that changes in gene expression can be matched with changes in phenotype. Instead of examining the effects of individual genes in isolation, the model identifies groups of genes that collectively cause a complex trait to emerge. The method then uses an optimization framework to pinpoint specific gene changes that are most likely to shift a cell’s state from healthy to diseased or vice versa.
“Here, we develop an approach that combines transcriptional data endowed with causal information and a generative machine learning model designed to strengthen statistical power,” they wrote. “A key aspect of our approach is the use of increasingly available trait-labeled transcriptomic data from bulk and single-cell RNA-Seq experiments, which contends with the biological networks that influence complex traits.”
Wytock noted, “We’re not looking at gene sequence but gene expression. We trained our model on data from clinical trials, so we know which expression profiles are healthy or diseased. For a smaller number of genes, we also have experimental data that tells how the network responds when the gene is turned on or off, which we can match with the expression data to find the genes implicated in the disease.”
Focusing on gene expression has multiple benefits. First, it bypasses patient privacy issues. Genetic data—a person’s actual DNA sequence—is inherently unique to an individual, providing a highly personal blueprint of health, genetic predispositions, and family relationships. Expression data, on the other hand, is more like a dynamic snapshot of cellular activity. Second, gene expression data implicitly accounts for environmental factors, which can turn genes “up” or “down” to perform various functions. “Environmental factors might not affect DNA, but they definitely affect gene expression,” Motter said. “So, our model has the benefit of indirectly accounting for environmental factors.”
Their resulting framework, the scientists stated, “… reveals groups of gene perturbations that most influence phenotypic variation, pinpointing the molecular underpinnings that determine complex traits … Our approach accounts for limited data, heterogeneity within phenotypes, confounding biological variation, and combinatorial explosion in gene sets in ways that traditional methods cannot.”
To demonstrate TWAVE’s effectiveness, the team tested it across several complex diseases. The method successfully identified the genes, some of which were missed by existing methods, that caused those diseases. TWAVE also revealed that different sets of genes can cause the same complex disease in different people. That finding suggests personalized treatments could be tailored to a patient’s specific genetic drivers of disease.
“A disease can manifest similarly in two different individuals,” Motter said. “But, in principle, there could be a different set of genes involved for each person owing to genetic, environmental, and lifestyle differences. This information could orient personalized treatment.”
In their paper, the team concluded, “Ultimately, our approach provides a tool to investigate genotype–phenotype relationships in complex traits, which is applicable across a range of organisms and traits. In humans, our approach also lays the groundwork for the design of next-generation multitarget strategies for the treatment of complex diseases.”
The post AI Tool Uncovers Gene Combinations Underlying Genotype–Phenotype Relationships in Complex Traits appeared first on GEN - Genetic Engineering and Biotechnology News.