Complex traits

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The size of a tomato is one example of a complex trait. 2013 09 10 Tomate.jpg
The size of a tomato is one example of a complex trait.

Complex traits are phenotypes that are controlled by two or more genes and do not follow Mendel's Law of Dominance. They may have a range of expression which is typically continuous. Both environmental and genetic factors often impact the variation in expression. Human height is a continuous trait meaning that there is a wide range of heights. There are an estimated 50 genes that affect the height of a human. Environmental factors, like nutrition, also play a role in a human's height. Other examples of complex traits include: crop yield, plant color, 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. Complex traits are also known as polygenic traits and multigenic traits. [1] [2]

Contents

The existence of complex traits, which are far more common than Mendelian traits, represented a significant challenge to the acceptance of Mendel's work. Modern understanding has 3 categories of complex traits: quantitative, meristic, and threshold. These traits have been studied on a small scale with observational techniques like twin studies. They are also studied with statistical techniques like quantitative trait loci (QTL) mapping, and genome-wide association studies (GWAS) on a large scale. The overall goal of figuring out how genes interact with each other and the environment and how those interactions can lead to variation in a trait is called genetic architecture.

History

When Mendel's work on inheritance was rediscovered in 1900, scientists debated whether Mendel's laws could account for the continuous variation observed for many traits.[ citation needed ] One group known as the biometricians argued that continuous traits such as height were largely heritable, but could not be explained by the inheritance of single Mendelian genetic factors. Work published by Ronald Fisher in 1919 mostly resolved debate by demonstrating that the variation in continuous traits could be accounted for if multiple such factors contributed additively to each trait. [1] However, the number of genes involved in such traits remained undetermined; until recently, genetic loci were expected to have moderate effect sizes and each explain several percent of heritability. [3] After the conclusion of the Human Genome Project in 2001, it seemed that the sequencing and mapping of many individuals would soon allow for a complete understanding of traits' genetic architectures. However, variants discovered through genome-wide association studies (GWASs) accounted for only a small percentage of predicted heritability; for example, while height is estimated to be 80-90% heritable, early studies only identified variants accounting for 5% of this heritability. [4] Later research showed that most missing heritability could be accounted for by common variants missed by GWASs because their effect sizes fell below significance thresholds; a smaller percentage is accounted for by rare variants with larger effect sizes, although in certain traits such as autism, rare variants play a more dominant role. [5] [6] [7] While many genetic factors involved in complex traits have been identified, determining their specific contributions to phenotypes—specifically, the molecular mechanisms through which they act—remains a major challenge. [8]

Types of complex traits

Quantitative traits

Quantitative traits have phenotypes that are expressed on continuous ranges. [9] They have many different genes that impact the phenotype, with differing effect sizes. [10] Many of these traits are some what heritable. For example, height is estimated to be 60-80% heritable; however, other quantitative traits have varying heritability. [11]

Meristic traits

Meristic traits have phenotypes that are described by whole numbers. An example is the rate chickens lay eggs. A chicken can lay one, two, or five eggs a week, but never half an egg. [9] The environment can also impact expression, as chickens will not lay as many eggs depending on the time of year. [12]

Threshold traits

Threshold traits have phenotypes that have limited expressions (usually two). It is a complex trait because multiple genetic and environmental factors impact the phenotype. [13] [14] The phenotype before the threshold is referred to as normal or absent, and after the threshold as lethal or present. These traits are often examined in a medical context, because many diseases exhibit this pattern or similar. [9] An example of this is type 2 diabetes, the phenotype is either normal/healthy or lethal/diseased. [15]

Methods for finding complex traits

Twin studies

Twin studies is an observational test using monozygotic twins and dizygotic twins, preferably same sex. They are used to figure out the environmental influence on complex traits. Monozygotic twins in particular are estimated to share 100% of their DNA with each other so any phenotypic differences should be caused by environmental influences. [2]

QTL mapping

Many complex traits are genetically determined by quantitative trait loci (QTL). A Quantitative Trait Loci analysis can be used to find regions on the genome sequence that are associated with a complex trait. [16] To find these regions, researchers will select a trait of interest and take a group of individuals of a species with varying expressions of this trait. They will label the individuals as founding parents and attempt to measure the trait. This can be difficult as most traits do not have a direct cut off point. Researchers will then genotype the parents using molecular markers such as SNPs or RFLPs. These act as signposts pointing to an area of where the genes associated with a trait are. From there, the parents are crossed to produce offspring. These offspring are then made to produce new offspring, but who they breed with can vary. [17] They can either reproduce with their siblings, with themselves (different from asexual reproduction), or backcross. [18] After this, a new generation is produced that are more genetically diverse. This is due to recombination. The genotype and phenotype of this new generation are measured and compared with the molecular markers to identify which alleles are associated with the trait. [19] This does not mean there is a direct causal relationship between these regions and the trait, but it does give insight that there are genes that do have some relationship with the trait and reveals where to look in future research.

GWAS

A Genome-Wide Association Study (GWAS) is a technique used to find gene variants linked to complex traits. A GWAS is done with populations that mate randomly because all the genetic variants are tested at once. Then researchers can compare the different alleles at a locus. It is similar to QTL mapping. [20] The most common set-up for a GWAS is a case study which creates two populations one with the trait we are looking at and one without the trait. With the two populations researchers will map every subject's genome and compare them to find different variance in the SNPs between the two populations.[ citation needed ] Both populations should have similar environmental backgrounds. GWAS is only looking at the DNA and does not include differences that would be caused by environmental factors. [2]

A manhattan plot showing genome-association with microcirculation. Manhattan Plot.png
A manhattan plot showing genome-association with microcirculation.

Statistical test, such as a chi squared is used to find if there is association with the trait and each of the SNPs tested. The statistical test produces a p-value which the researcher will use to conclude if the SNP is significant. This p-value cut off can range from being a higher number or a lower number at the researcher's discretion. The data can then be visualized in a Manhattan plot which takes the -log (p-value) so all the significant SNPs are at the top of the graph. [21] [22]

Genetic architecture

Genetic architecture is an overall explanation of all the genetic factors that play a role in a complex trait and exists as the core foundation of quantitative genetics. With the use of mathematical models and statistical analysis, like GWAS, researchers can determine the number of genes affecting a trait as well as the level of influence each gene has on the trait. This is not always easy as the architecture of one trait can be different between two separate populations of the same species. [16] This can be due to the fact that both populations live in different environments. Differing environments can lead to different interactions between genes and the environment, changing the architecture of both populations. [23]

Recently, with rapid increases in available genetic data, researchers have begun to characterize the genetic architecture of complex traits better. One surprise has been the observation that most loci identified in GWASs are found in noncoding regions of the genome; therefore, instead of directly altering protein sequences, such variants likely affect gene regulation. [24] To understand the precise effects of these variants, QTL mapping has been employed to examine data from each step of gene regulation; for example, mapping RNA-sequencing data can help determine the effects of variants on mRNA expression levels, which then presumably affect the numbers of proteins translated. A comprehensive analysis of QTLs involved in various regulatory steps—promotor activity, transcription rates, mRNA expression levels, translation levels, and protein expression levels—showed that high proportions of QTLs are shared, indicating that regulation behaves as a “sequential ordered cascade” with variants affecting all levels of regulation. [25] Many of these variants act by affecting transcription factor binding and other processes that alter chromatin function—steps which occur before and during RNA transcription. [25]

To determine the functional consequences of these variants, researchers have largely focused on identifying key genes, pathways, and processes that drive complex trait behavior; an inherent assumption has been that the most statistically significant variants have the greatest impact on traits because they act by affecting these key drivers. [8] [26] For example, one study hypothesizes that there exist rate-limiting genes pivotal to the function of gene regulatory networks. [27] Others studies have identified the functional impacts of key genes and mutations on disorders, including autism and schizophrenia. [7] [28] However, a 2017 analysis by Boyle et al. argues that while genes which directly impact complex traits do exist, regulatory networks are so interconnected that any expressed gene affects the functions of these "core" genes; this idea is called the "omnigenic" hypothesis. [8] While these "peripheral" genes each have small effects, their combined impact far exceeds the contributions of core genes themselves. To support the hypothesis that core genes play a smaller than expected role, the authors describe three main observations: the heritability for complex traits is spread broadly, often uniformly, across the genome; genetic effects do not appear to be mediated by cell-type specific function; and genes in the relevant functional categories only modestly contribute more to heritability than other genes. [8] One alternative to the omnigenic hypothesis is the idea that peripheral genes act not by altering core genes but by altering cellular states, such as the speed of cell division or hormone response. [29] [30]

Related Research Articles

<span class="mw-page-title-main">Heritability</span> Estimation of effect of genetic variation on phenotypic variation of a trait

Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"

<span class="mw-page-title-main">Single-nucleotide polymorphism</span> Single nucleotide in genomic DNA at which different sequence alternatives exist

In genetics and bioinformatics, a single-nucleotide polymorphism is a germline substitution of a single nucleotide at a specific position in the genome. Although certain definitions require the substitution to be present in a sufficiently large fraction of the population, many publications do not apply such a frequency threshold.

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.

Genetic architecture is the underlying genetic basis of a phenotypic trait and its variational properties. Phenotypic variation for quantitative traits is, at the most basic level, the result of the segregation of alleles at quantitative trait loci (QTL). Environmental factors and other external influences can also play a role in phenotypic variation. Genetic architecture is a broad term that can be described for any given individual based on information regarding gene and allele number, the distribution of allelic and mutational effects, and patterns of pleiotropy, dominance, and epistasis.

The candidate gene approach to conducting genetic association studies focuses on associations between genetic variation within pre-specified genes of interest, and phenotypes or disease states. This is in contrast to genome-wide association studies (GWAS), which is a hypothesis-free approach that scans the entire genome for associations between common genetic variants and traits of interest. Candidate genes are most often selected for study based on a priori knowledge of the gene's biological functional impact on the trait or disease in question. The rationale behind focusing on allelic variation in specific, biologically relevant regions of the genome is that certain alleles within a gene may directly impact the function of the gene in question and lead to variation in the phenotype or disease state being investigated. This approach often uses the case-control study design to try to answer the question, "Is one allele of a candidate gene more frequently seen in subjects with the disease than in subjects without the disease?" Candidate genes hypothesized to be associated with complex traits have generally not been replicated by subsequent GWASs or highly powered replication attempts. The failure of candidate gene studies to shed light on the specific genes underlying such traits has been ascribed to insufficient statistical power, low prior probability that scientists can correctly guess a specific allele within a specific gene that is related to a trait, poor methodological practices, and data dredging.

Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait more often than would be expected by chance occurrence.

<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.

<span class="mw-page-title-main">Neurogenetics</span> Study of role of genetics in the nervous system

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.

In genetics, association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link phenotypes to genotypes, uncovering genetic associations.

The missing heritability problem refers to the difference between heritability estimates from genetic data and heritability estimates from twin and family data across many physical and mental traits, including 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.

<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.

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 Omnigenic Model, first proposed by Evan A. Boyle, Yang I. Li, and Jonathan K. Pritchard, describes a hypothesis regarding the heritability of complex traits. 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.

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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