Genetics influence gut microbiota function: Study
Their paper, “The Collective Impact of Human Genome Variation on Microbiome Function”, was published in the journal
The research is a collaboration between universities, combining Brito’s knowledge of the microbiome with the faculty’s expertise in genetic variation and statistical methods, respectively, from Andrew Clark, Jacob Gould Schurman Professor of Population Genetics at the College of Arts and Sciences; and Martin Wells, Charles A. Alexander Professor of Statistical Science in the Department of Information Science.
“When a disease or phenotype is caused by a single genetic mutation, it can be a relatively simple process to find the responsible gene,” says Brito. But often, whole genomes can interact to lead to disease or other phenotypic expression, a much more complex mechanism. In the human genome there are many sequential variations from person to person and even within paired chromosomes of the same person.
When a variant is produced by the substitution of a single nucleotide, this is called a single nucleotide polymorphism (SNP). Using a unique computational and modeling approach, Brito’s team was able to identify SNPs that correlate with microbiome-associated traits, disorders, and cancers. In other words, they were able to show direct effects of the human genome on gut microbiota functions.
“Linking variation in the human genome to variation in the gut microbiome is difficult, because human genomic variations are correlated, and may have functions,” says Clark. involved, and so are the species of bacteria in the gut. are not independent of each other. ”
The novelty of the present study is the use of this structure in the data. It focuses on the function of the gut microbiota rather than the genetic makeup of individual species in the agglomeration of organisms that make up the microbiome; it looked at collections of human genes and their influence on microbiome functions as opposed to examining single genes; and it used a new type of strategy to model the distribution of functions and species in the human gut.
Previous models do not fit well with the general characteristics of the measurement system sequencing datasets. Wells came up with the idea of using the Tweedie distribution – a type of probabilistic model – to explain these characteristics.
“My research group has previously applied the Tweedie modeling strategy to natural language processing,” says Wells. “Here also seems to be very relevant. We found that the Tweedie modeling method is flexible enough to capture the mean power-variance relationship in coefficient classification and gene abundance and excess outperform standard approaches.”
The first author of the paper was Felicia New, Ph.D. ’21, formerly part of Brito’s lab group and second author Dr. Benjamin Baer. ’21, a Wells advice.
“Felicia brought expertise about these bacteria and their function and human genetics, and Benjamin brought the statistical background and they worked together to synthesize that knowledge,” says Brito. your expertise and see which particular approach makes sense,” says Brito. “It’s through this partnership that we’ve accomplished some outstanding work.”
The study was supported by the National Institutes of Health.
Source: Eurekalert