Omnigenic model

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The Omnigenic Model, first proposed by Evan A. Boyle, Yang I. Li, and Jonathan K. Pritchard, describes a hypothesis regarding the heritability of complex traits. [1] Expanding beyond polygenes, the authors propose that all genes expressed within a cell affect the expression of a given trait. In addition, the model states that the peripheral genes, ones that do not have a direct impact on expression, explain more heritability of traits than core genes, ones that have a direct impact on expression. The process that the authors propose that facilitates this effect is called “network pleiotropy”, in which peripheral genes can affect core genes, not by having a direct effect, but rather by virtue of being mediated within the same cell. [2]

Contents

History

The proposed Omnigenic Model is type of non-mendellian inheritance that builds off of previous research regarding the Polygenic Model and Fisher's Infinitesimal Model. Under the Polygenic Model, for traits, like height, to be continuous in a population there must be many genes that code for the trait. Otherwise, the expression of the trait is limited by the number of possible combinations of alleles. The many genes which code for the continuous trait are also further modified by environmental conditions. [3]

Similar to Fisher's Infinitesimal Model, the Omnigenic Model proposes that as the number of genes that code for a trait increases, the total amount of heritability each gene explains decreases. [4] However, in the case of the Omnigenic Model, genes are organized into two groups – core and peripheral genes. Core genes are the relatively few genes that directly impact trait expression and peripheral genes have non-direct effects on core genes. Core genes may have large detectable effects on trait heritability; however, core genes explain less heritability than peripheral genes, as their small effects greatly outnumber the core genes. The identification and distinctions between core and peripheral genes are still being investigated. [5]

The Omnigenic hypothesis has broad implications for the field of genetics, especially regarding the effectiveness of Genome-wide Association Studies (GWAS) in detecting genetic variants that are predictive of disease. [6] GWAS detect genetic variants that predict the incidence of a disease. For example, GWAS have identified genetic variants that are responsible for 10% of the heritability of Type II diabetes. [7] Under the Omnigenic Model, these detected variants may not be as important as other peripheral gene effects. [8] The Omnigenic hypothesis may also explain why the significant genetic variants detected by GWAS vary between populations, which complicates the usefulness of GWAS in clinical settings. [9] Identification of core genes is still considered important to provide biological insights. [5]

In 1999, Autism Spectrum Disorder (ASD) was determined to be a highly polygenic trait with more than 15 traits associated with its expression. [10] This conclusion has been revisited and challenged in light of the formation of the Omnigenic Model. [11] The current understanding of the disease has identified thousands of possible genes that affect the expression and severity of ASD; however, these genes act through similar pathways such as a deficit in neural development early in life. [12]

Evidence

The initial evidence proposed in support for the Omnigenic Model comes from two main components: the widespread effect of traits across the genome and the inability of cell-specific disease pathways to fully explain heritability. [1] Genes that encode for continuous traits are found widely across the genome, with a gene that has a significant effect on trait expression occurring every 10,000-100,000 base pairs. The distance between these significant effects within the genome implies that these significant genes are not tied to similar regulatory pathways. Likewise, within some diseases, genes that are broadly found in all cells typically have stronger effects on heritability than genes expressed within cell-specific disease pathways.

Since this initial evidence, support for the Omnigenic Model has grown, especially with the documentation of trans-regulatory elements on gene expression. [8] Trans-regulatory elements are DNA sequences that modify and regulate the expression of many distant genes. Evidence of trans-regulatory elements significantly impacting heritability builds support for a mechanism that which peripheral genes can impact trait expression and heritability.

Beyond humans, evidence for Omnigenic traits have been found across animals and plants. [13] [14] For example, the Eurasian Aspen, Populus tremula , has high variability in leaf shape across its range, but core genes that determine leaf shapes are unable to be identified using genome-wide association studies, which suggests that leaf shape is determined by many genes with small statistically insignificant effect sizes.

Evolutionary Implications

The Omnigenic Model challenges modern efforts within evolutionary biology to identify traits that are responsible for adaption. [15] Under the Omnigenic Model, trait adaptations that are a result of changes in a single to a few genes may be rare, instead the majority of trait adaptations may be driven by small changes in allele frequency over the whole genome. These small changes across the genome would add up to have profound effects on trait expression. Methods for detecting widespread gene expression adaptation have been developed and provide evidence that many adaptations are highly polygenic. [16] [17] [18]

Related Research Articles

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In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome that is present in a sufficiently large fraction of considered population.

A quantitative trait locus (QTL) is a locus that correlates with variation of a quantitative trait in the phenotype of a population of organisms. QTLs are mapped by identifying which molecular markers correlate with an observed trait. This is often an early step in identifying the actual genes that cause the trait variation.

<span class="mw-page-title-main">Pleiotropy</span> Influence of a single gene on multiple phenotypic traits

Pleiotropy occurs when one gene influences two or more seemingly unrelated phenotypic traits. Such a gene that exhibits multiple phenotypic expression is called a pleiotropic gene. Mutation in a pleiotropic gene may have an effect on several traits simultaneously, due to the gene coding for a product used by a myriad of cells or different targets that have the same signaling function.

<span class="mw-page-title-main">Genome-wide association study</span> Study of genetic variants in different individuals

In genomics, a genome-wide association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

Gene–environment correlation is said to occur when exposure to environmental conditions depends on an individual's genotype.

<span class="mw-page-title-main">Neurogenetics</span>

Neurogenetics studies the role of genetics in the development and function of the nervous system. It considers neural characteristics as phenotypes, and is mainly based on the observation that the nervous systems of individuals, even of those belonging to the same species, may not be identical. As the name implies, it draws aspects from both the studies of neuroscience and genetics, focusing in particular how the genetic code an organism carries affects its expressed traits. Mutations in this genetic sequence can have a wide range of effects on the quality of life of the individual. Neurological diseases, behavior and personality are all studied in the context of neurogenetics. The field of neurogenetics emerged in the mid to late 20th century with advances closely following advancements made in available technology. Currently, neurogenetics is the center of much research utilizing cutting edge techniques.

In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.

Behavioural genetics, also referred to as behaviour genetics, is a field of scientific research that uses genetic methods to investigate the nature and origins of individual differences in behaviour. While the name "behavioural genetics" connotes a focus on genetic influences, the field broadly investigates the extent to which genetic and environmental factors influence individual differences, and the development of research designs that can remove the confounding of genes and environment. Behavioural genetics was founded as a scientific discipline by Francis Galton in the late 19th century, only to be discredited through association with eugenics movements before and during World War II. In the latter half of the 20th century, the field saw renewed prominence with research on inheritance of behaviour and mental illness in humans, as well as research on genetically informative model organisms through selective breeding and crosses. In the late 20th and early 21st centuries, technological advances in molecular genetics made it possible to measure and modify the genome directly. This led to major advances in model organism research and in human studies, leading to new scientific discoveries.

Expression quantitative trait loci (eQTLs) are genomic loci that explain variation in expression levels of mRNAs.

The missing heritability problem is the fact that single genetic variations cannot account for much of the heritability of diseases, behaviors, and other phenotypes. This is a problem that has significant implications for medicine, since a person's susceptibility to disease may depend more on the combined effect of all the genes in the background than on the disease genes in the foreground, or the role of genes may have been severely overestimated.

Predictive genomics is at the intersection of multiple disciplines: predictive medicine, personal genomics and translational bioinformatics. Specifically, predictive genomics deals with the future phenotypic outcomes via prediction in areas such as complex multifactorial diseases in humans. To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in genome-wide association (GWA) studies. The identification of associated single-nucleotide polymorphisms underpin GWA studies in complex diseases that have ranged from Type 2 Diabetes (T2D), Age-related macular degeneration (AMD) and Crohn's disease.

Genome-wide complex trait analysis (GCTA) Genome-based restricted maximum likelihood (GREML) is a statistical method for heritability estimation in genetics, which quantifies the total additive contribution of a set of genetic variants to a trait. GCTA is typically applied to common single nucleotide polymorphisms (SNPs) on a genotyping array and thus termed "chip" or "SNP" heritability.

A human disease modifier gene is a modifier gene that alters expression of a human gene at another locus that in turn causes a genetic disease. Whereas medical genetics has tended to distinguish between monogenic traits, governed by simple, Mendelian inheritance, and quantitative traits, with cumulative, multifactorial causes, increasing evidence suggests that human diseases exist on a continuous spectrum between the two.

<span class="mw-page-title-main">Polygenic score</span> Numerical score aimed at predicting a trait based on variation in multiple genetic loci

In genetics, a polygenic score (PGS) is a number that summarizes the estimated effect of many genetic variants on an individual's phenotype. The PGS is also called the polygenic index (PGI) or genome-wide score; in the context of disease risk, it is called a polygenic risk score or genetic risk score. The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated alleles.

Polygenic adaptation describes a process in which a population adapts through small changes in allele frequencies at hundreds or thousands of loci.

<span class="mw-page-title-main">Complex traits</span>

Complex traits, also known as quantitative traits, are traits that do not behave according to simple Mendelian inheritance laws. More specifically, their inheritance cannot be explained by the genetic segregation of a single gene. Such traits show a continuous range of variation and are influenced by both environmental and genetic factors. Compared to strictly Mendelian traits, complex traits are far more common, and because they can be hugely polygenic, they are studied using statistical techniques such as quantitative genetics and quantitative trait loci (QTL) mapping rather than classical genetics methods. Examples of complex traits include height, circadian rhythms, enzyme kinetics, and many diseases including diabetes and Parkinson's disease. One major goal of genetic research today is to better understand the molecular mechanisms through which genetic variants act to influence complex traits.

In statistical genetics, linkage disequilibrium score regression is a technique that aims to quantify the separate contributions of polygenic effects and various confounding factors, such as population stratification, based on summary statistics from genome-wide association studies (GWASs). The approach involves using regression analysis to examine the relationship between linkage disequilibrium scores and the test statistics of the single-nucleotide polymorphisms (SNPs) from the GWAS. Here, the "linkage disequilibrium score" for a SNP "is the sum of LD r2 measured with all other SNPs".

The infinitesimal model, also known as the polygenic model, is a widely used statistical model in quantitative genetics and in genome-wide association studies. Originally developed in 1918 by Ronald Fisher, it is based on the idea that variation in a quantitative trait is influenced by an infinitely large number of genes, each of which makes an infinitely small (infinitesimal) contribution to the phenotype, as well as by environmental factors. In "The Correlation between Relatives on the Supposition of Mendelian Inheritance", the original 1918 paper introducing the model, Fisher showed that if a trait is polygenic, "then the random sampling of alleles at each gene produces a continuous, normally distributed phenotype in the population". However, the model does not necessarily imply that the trait must be normally distributed, only that its genetic component will be so around the average of that of the individual's parents. The model served to reconcile Mendelian genetics with the continuous distribution of quantitative traits documented by Francis Galton.

Personality traits are patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances.

Transcriptome-wide association study (TWAS) is a genetic methodology that can be used to compare the genetic components of gene expression and the genetic components of a trait to determine if an association is present between the two components. TWAS are useful for the identification and prioritization of candidate causal genes in candidate gene analysis following genome-wide association studies. TWAS looks at the RNA products of a specific tissue and gives researchers the abilities to look at the genes being expressed as well as gene expression levels, which varies by tissue type. TWAS are valuable and flexible bioinformatics tools that looks at the associations between the expressions of genes and complex traits and diseases. By looking at the association between gene expression and the trait expressed, genetic regulatory mechanisms can be investigated for the role that they play in the development of specific traits and diseases.

References

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  2. ↑ Yong E (2017-06-16). "What If (Almost) Every Gene Affects (Almost) Everything?". The Atlantic. Retrieved 2023-03-29.
  3. ↑ "Polygenic Trait". Genome.gov. Retrieved 2023-04-13.
  4. ↑ Turelli M (December 2017). "Commentary: Fisher's infinitesimal model: A story for the ages". Theoretical Population Biology. 118: 46–49. doi:10.1016/j.tpb.2017.09.003. PMID   28987627. S2CID   11797539.
  5. 1 2 Boyle EA, Li YI, Pritchard JK (2017-10-12). "The Omnigenic Model: Response from the Authors". Journal of Psychiatry and Brain Science. 2 (5). doi: 10.20900/jpbs.20170014S8 .
  6. ↑ "This Study is Forcing Scientists to Rethink the Human Genome". Gizmodo. 2017-06-16. Retrieved 2023-03-29.
  7. ↑ Billings LK, Florez JC (November 2010). "The genetics of type 2 diabetes: what have we learned from GWAS?". Annals of the New York Academy of Sciences. 1212 (1): 59–77. doi:10.1111/j.1749-6632.2010.05838.x. PMC   3057517 . PMID   21091714.
  8. 1 2 Liu X, Li YI, Pritchard JK (May 2019). "Trans Effects on Gene Expression Can Drive Omnigenic Inheritance". Cell. 177 (4): 1022–1034.e6. doi:10.1016/j.cell.2019.04.014. PMC   6553491 . PMID   31051098.
  9. ↑ Mathieson I (September 2021). "The omnigenic model and polygenic prediction of complex traits". American Journal of Human Genetics. 108 (9): 1558–1563. doi:10.1016/j.ajhg.2021.07.003. PMC   8456163 . PMID   34331855.
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  11. ↑ Fóthi Á, Pintér C, Pollner P, Lőrincz A (August 2022). "Peripheral gene interactions define interpretable clusters of core ASD genes in a network-based investigation of the omnigenic theory". npj Systems Biology and Applications. 8 (1): 28. doi:10.1038/s41540-022-00240-x. PMC   9365765 . PMID   35948596.
  12. ↑ Chen JA, Peñagarikano O, Belgard TG, Swarup V, Geschwind DH (2015-01-24). "The emerging picture of autism spectrum disorder: genetics and pathology". Annual Review of Pathology. 10 (1): 111–144. doi: 10.1146/annurev-pathol-012414-040405 . PMID   25621659.
  13. ↑ Davoodi P, Ehsani A, Vaez Torshizi R, Masoudi AA (2022-12-31), "596. Chicken quantitative traits follow the omnigenic model", Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP), Wageningen Academic Publishers, pp. 2467–2470, doi:10.3920/978-90-8686-940-4_596, ISBN   978-90-8686-940-4, S2CID   256754979
  14. ↑ Mähler N, Schiffthaler B, Robinson KM, Terebieniec BK, Vučak M, Mannapperuma C, et al. (November 2020). "Leaf shape in Populus tremula is a complex, omnigenic trait". Ecology and Evolution. 10 (21): 11922–11940. doi:10.1002/ece3.6691. PMC   7663049 . PMID   33209260.
  15. ↑ Vitti JJ, Grossman SR, Sabeti PC (2013-11-23). "Detecting natural selection in genomic data". Annual Review of Genetics. 47 (1): 97–120. doi:10.1146/annurev-genet-111212-133526. PMID   24274750. S2CID   52831046.
  16. ↑ Fraser HB, Babak T, Tsang J, Zhou Y, Zhang B, Mehrabian M, Schadt EE (March 2011). "Systematic detection of polygenic cis-regulatory evolution". PLOS Genetics. 7 (3): e1002023. doi: 10.1371/journal.pgen.1002023 . PMC   3069120 . PMID   21483757.
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  18. ↑ Field Y, Boyle EA, Telis N, Gao Z, Gaulton KJ, Golan D, et al. (November 2016). "Detection of human adaptation during the past 2000 years". Science. 354 (6313): 760–764. Bibcode:2016Sci...354..760F. doi:10.1126/science.aag0776. PMC   5182071 . PMID   27738015.
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