Complex traits

Summary

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. History of genetics 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. 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. 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. 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.

Official source

https://en.wikipedia.org/wiki/Complex_traits

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Related publications (3)

Cancer is the second leading cause of death worldwide. Cancer develops through multiple hallmark functions including apoptosis evasion, unlimited replicative potential, metastasis, and immune avoidance. Over the past few decades, researchers have reported a substantial amount of information about the role of gene expression dysregulation in cancer, whereas transposable elements (TEs) have been overlooked despite the fact they constitute nearly half of the human genome. TEs are DNA repetitive sequences that spread across the genome of living organisms throughout the evolution of species, contributing to genomic diversity and shaping the epigenomic landscape. The vast majority of TEs appear to be transcriptionally silent in adult tissues, thus avoiding deleterious mutational insertions and recombination events in somatic tissues. Due to the global epigenetic dysregulation that occurs during tumorigenesis, some TEs lose repressive marks and become transcriptionally active in cancer cells. Consequently, TE insertions into oncogenes and tumor suppressor genes may occur, thereby contributing to cancer development and disease progression. It is also widely accepted that some TEs harbor transcription factor recognition sites and have regulatory functions on gene expression. Some transcriptionally active TEs can give rise to alternative TE-derived chimeric gene products. Given these facts, integrative transcriptome analysis of TEs and genes may contribute to a better understanding of complex traits of cancer and help to better classify cancer subtypes with clinical implications for patients.To gain insights into the underlying mechanisms of cancer pathogenesis, we constructed a co-expression network based on the similarity in expression levels of TEs and genes. We focused on colorectal cancer (CRC), a heterogeneous disease with different genetic and molecular backgrounds, contributing to patient outcomes and response to therapy. We investigated the coordinated activity of TEs and genes in view of recurrent CRC chromosomal aberrations, genome organization, and functional significance. We further examined co-expression changes in network structure under the contrasting conditions of cancerous versus matched healthy colic mucosal tissues. We found that the cancer network was associated with a dramatic decrease in the number of gene interactions as opposed to an increase in TE interactions. Physical chromosomal distance affects the degree of co-expression, where the proximal distance effect of TE is contrasted with the distant effect of the gene. Our study sheds light on the complex interplay between TEs and genes and how they coordinately shape cancer and normal co-expression networks. Furthermore, we integrated gene and TE expression to develop a new consensus molecular subtype (CMS) classifier derived from multiple layers of TE and gene signatures. We demonstrated that our new approach led to better identification of CRC patients of the former CMS3 subtype, the most heterogeneous subtype with mixed biological characteristics, and a global reduction of the expected misclassification of other CMS subtypes. We further optimized our classifier for potential clinical use and obtained a set of 50 genes and 20 TE integrants with the best ability to identify CMS subtypes. Our integrative model could be a major add on for the development of future clinical trials by improving patient stratification over current gene-restricted molecular classifications.

EPFL2022

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021

Large-scale variational inference for Bayesian joint regression modelling of high-dimensional genetic data

Hélène Ruffieux

Genetic association studies have become increasingly important in understanding the molecular bases of complex human traits. The specific analysis of intermediate molecular traits, via quantitative trait locus (QTL) studies, has recently received much attention, prompted by the advance of high-throughput technologies for quantifying gene, protein and metabolite levels. Of great interest is the detection of weak trans-regulatory effects between a genetic variant and a distal gene product. In particular, hotspot genetic variants, which remotely control the levels of many molecular outcomes, may initiate decisive functional mechanisms underlying disease endpoints. This thesis proposes a Bayesian hierarchical approach for joint analysis of QTL data on a genome-wide scale. We consider a series of parallel sparse regressions combined in a hierarchical manner to flexibly accommodate high-dimensional responses (molecular levels) and predictors (genetic variants), and we present new methods for large-scale inference. Existing approaches have limitations. Conventional marginal screening does not account for local dependencies and association patterns common to multiple outcomes and genetic variants, whereas joint modelling approaches are restricted to relatively small datasets by computational constraints. Our novel framework allows information-sharing across outcomes and variants, thereby enhancing the detection of weak trans and hotspot effects, and implements tailored variational inference procedures that allow simultaneous analysis of data for an entire QTL study, comprising hundreds of thousands of predictors, and thousands of responses and samples. The present work also describes extensions to leverage spatial and functional information on the genetic variants, for example, using predictor-level covariates such as epigenomic marks. Moreover, we augment variational inference with simulated annealing and parallel expectation-maximisation schemes in order to enhance exploration of highly multimodal spaces and allow efficient empirical Bayes estimation. Our methods, publicly available as packages implemented in R and C++, are extensively assessed in realistic simulations. Their advantages are illustrated in several QTL applications, including a large-scale proteomic QTL study on two clinical cohorts that highlights novel candidate biomarkers for metabolic disorders.

EPFL2019

Related concepts (10)

Complex traits

Quantitative genetics

Quantitative genetics deals with quantitative traits, which are phenotypes that vary continuously (such as height or mass)—as opposed to discretely identifiable phenotypes and gene-products (such as eye-colour, or the presence of a particular biochemical). Both branches use the frequencies of different alleles of a gene in breeding populations (gamodemes), and combine them with concepts from simple Mendelian inheritance to analyze inheritance patterns across generations and descendant lines.

Gene–environment interaction

Gene–environment interaction (or genotype–environment interaction or G×E) is when two different genotypes respond to environmental variation in different ways. A norm of reaction is a graph that shows the relationship between genes and environmental factors when phenotypic differences are continuous. They can help illustrate GxE interactions. When the norm of reaction is not parallel, as shown in the figure below, there is a gene by environment interaction. This indicates that each genotype responds to environmental variation in a different way.

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