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A new computational method improves the detection of genes linked to complex diseases and biological traits, according to researchers.
The method, PrediXcan, estimates gene expression levels across the whole genome and integrates this data with genome-wide association study (GWAS) data.
Researchers say PrediXcan has the potential to identify gene targets for therapeutic applications faster and with greater accuracy than traditional methods.
“PrediXcan tells us which genes are more likely to affect a disease or trait by learning the relationship between genotype, gene expression levels from large-scale transcriptome studies, and disease associations from GWAS studies,” said Hae Kyung Im, PhD, of the University of Chicago in Illinois.
“This is the first method that accounts for the mechanisms of gene regulation and can be applied to any heritable disease or phenotype.”
Dr Im and her colleagues described the method in Nature Genetics.
They said PrediXcan uses computational algorithms to learn how genome sequence influences gene expression, based on large-scale transcriptome datasets. This can then be used to create computational estimates of gene expression levels from any whole-genome sequence or chip dataset.
Genomes that have been sequenced as part of a GWAS can be run through PrediXcan to generate a gene expression level profile, which is then analyzed to determine the association between gene expression levels and the disease states or the trait of interest being studied.
The researchers said this method can reveal potentially causal genes and determine directionality—whether high or low levels of expression might cause the disease or trait.
As calculations are based on DNA sequence data and not physical measurements, PrediXcan can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environment.
The researchers said that, with PrediXcan, validation studies need to test a few thousand genes, instead of millions of potential single mutations. In addition, the method can be used to re-analyze existing genomic datasets, with a focus on mechanism, in a high-throughput manner.
“This integrates what we know about consequences of genetic variation in the transcriptome in order to discover genes, instead of just looking at mutations,” Dr Im said. “In a way, we’re modeling one mechanism through which genes affect disease or traits, which is the regulation of gene expression level.”
Dr Im noted that, because PrediXcan creates estimates based on genome sequence data, it is most accurate for strongly heritable traits. However, almost every complex trait or disease has a genetic component. The method can be used to predict the influence of that component, reducing the complexity of follow-up studies.
Dr Im is now working to improve the prediction of PrediXcan and applying it to mental health disorders. In addition, she is working to expand it beyond gene expression levels, to predict the links between diseases or traits and protein levels, epigenetics, and other measurements that can be estimated based on genomic data.
“GWAS studies have been incredibly successful at finding genetic links to disease, but they have been unable to account for mechanism,” Dr Im said. “We now have a computational method that allows us to understand the consequences of GWAS studies.” 
Photo by Darren Baker
A new computational method improves the detection of genes linked to complex diseases and biological traits, according to researchers.
The method, PrediXcan, estimates gene expression levels across the whole genome and integrates this data with genome-wide association study (GWAS) data.
Researchers say PrediXcan has the potential to identify gene targets for therapeutic applications faster and with greater accuracy than traditional methods.
“PrediXcan tells us which genes are more likely to affect a disease or trait by learning the relationship between genotype, gene expression levels from large-scale transcriptome studies, and disease associations from GWAS studies,” said Hae Kyung Im, PhD, of the University of Chicago in Illinois.
“This is the first method that accounts for the mechanisms of gene regulation and can be applied to any heritable disease or phenotype.”
Dr Im and her colleagues described the method in Nature Genetics.
They said PrediXcan uses computational algorithms to learn how genome sequence influences gene expression, based on large-scale transcriptome datasets. This can then be used to create computational estimates of gene expression levels from any whole-genome sequence or chip dataset.
Genomes that have been sequenced as part of a GWAS can be run through PrediXcan to generate a gene expression level profile, which is then analyzed to determine the association between gene expression levels and the disease states or the trait of interest being studied.
The researchers said this method can reveal potentially causal genes and determine directionality—whether high or low levels of expression might cause the disease or trait.
As calculations are based on DNA sequence data and not physical measurements, PrediXcan can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environment.
The researchers said that, with PrediXcan, validation studies need to test a few thousand genes, instead of millions of potential single mutations. In addition, the method can be used to re-analyze existing genomic datasets, with a focus on mechanism, in a high-throughput manner.
“This integrates what we know about consequences of genetic variation in the transcriptome in order to discover genes, instead of just looking at mutations,” Dr Im said. “In a way, we’re modeling one mechanism through which genes affect disease or traits, which is the regulation of gene expression level.”
Dr Im noted that, because PrediXcan creates estimates based on genome sequence data, it is most accurate for strongly heritable traits. However, almost every complex trait or disease has a genetic component. The method can be used to predict the influence of that component, reducing the complexity of follow-up studies.
Dr Im is now working to improve the prediction of PrediXcan and applying it to mental health disorders. In addition, she is working to expand it beyond gene expression levels, to predict the links between diseases or traits and protein levels, epigenetics, and other measurements that can be estimated based on genomic data.
“GWAS studies have been incredibly successful at finding genetic links to disease, but they have been unable to account for mechanism,” Dr Im said. “We now have a computational method that allows us to understand the consequences of GWAS studies.”
Photo by Darren Baker
A new computational method improves the detection of genes linked to complex diseases and biological traits, according to researchers.
The method, PrediXcan, estimates gene expression levels across the whole genome and integrates this data with genome-wide association study (GWAS) data.
Researchers say PrediXcan has the potential to identify gene targets for therapeutic applications faster and with greater accuracy than traditional methods.
“PrediXcan tells us which genes are more likely to affect a disease or trait by learning the relationship between genotype, gene expression levels from large-scale transcriptome studies, and disease associations from GWAS studies,” said Hae Kyung Im, PhD, of the University of Chicago in Illinois.
“This is the first method that accounts for the mechanisms of gene regulation and can be applied to any heritable disease or phenotype.”
Dr Im and her colleagues described the method in Nature Genetics.
They said PrediXcan uses computational algorithms to learn how genome sequence influences gene expression, based on large-scale transcriptome datasets. This can then be used to create computational estimates of gene expression levels from any whole-genome sequence or chip dataset.
Genomes that have been sequenced as part of a GWAS can be run through PrediXcan to generate a gene expression level profile, which is then analyzed to determine the association between gene expression levels and the disease states or the trait of interest being studied.
The researchers said this method can reveal potentially causal genes and determine directionality—whether high or low levels of expression might cause the disease or trait.
As calculations are based on DNA sequence data and not physical measurements, PrediXcan can tease apart the genetically determined component of gene expression from the effects of the trait itself (avoiding reverse causality) and other factors such as environment.
The researchers said that, with PrediXcan, validation studies need to test a few thousand genes, instead of millions of potential single mutations. In addition, the method can be used to re-analyze existing genomic datasets, with a focus on mechanism, in a high-throughput manner.
“This integrates what we know about consequences of genetic variation in the transcriptome in order to discover genes, instead of just looking at mutations,” Dr Im said. “In a way, we’re modeling one mechanism through which genes affect disease or traits, which is the regulation of gene expression level.”
Dr Im noted that, because PrediXcan creates estimates based on genome sequence data, it is most accurate for strongly heritable traits. However, almost every complex trait or disease has a genetic component. The method can be used to predict the influence of that component, reducing the complexity of follow-up studies.
Dr Im is now working to improve the prediction of PrediXcan and applying it to mental health disorders. In addition, she is working to expand it beyond gene expression levels, to predict the links between diseases or traits and protein levels, epigenetics, and other measurements that can be estimated based on genomic data.
“GWAS studies have been incredibly successful at finding genetic links to disease, but they have been unable to account for mechanism,” Dr Im said. “We now have a computational method that allows us to understand the consequences of GWAS studies.”