The genetic architecture of common diseases is central to the scientific and clinical goals of human genetics because it directly impacts biology, disease screening, diagnosis, prognosis and treatment.
Genetic architecture is currently assessed by exploiting the differences in types of genetic variants ascertained through genome-wide association studies, whole-exome sequencing studies and whole-genome sequencing studies. Each of these has its own merits and disadvantages, but all are subject to the limitations of sample size. Gene mapping studies should thus be tailored to the unique contributions of each of these technologies.
To date, the observed genetic architecture of highly heritable diseases and traits differs markedly and cannot be reliably predicted. Where large sample sizes are available, differences in detectable architecture still exist.
The concept of variance explained is not always relevant to individual-level risk prediction or drug development, whereas the genetic architecture of a given trait or disease can be more pertinent.
Genetic architecture is variable in time and place and can be theoretically influenced by phenotypic measurement, selection and decanalization.
Interactions between genetic determinants of a trait or environmental influences contribute to genetic architecture. To date, few such interactions have been identified for most common diseases and traits, but this will likely change with increasing sample sizes.
Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.
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The authors wish to acknowledge V. Forgetta for his help drawing the figures. N.J.T. is a Wellcome Trust Investigator (202802/Z/16/Z), is a programme lead in the Medical Research Council (MRC) Integrative Epidemiology Unit (MC_UU_12013/3) and works within the University of Bristol National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). C.M.T.G. has received funding from the Natural Sciences and Engineering Research Council (NSERC) and the Canadian Institutes of Health Research (CIHR). D.J.L. is funded by the Wellcome Trust under grant number WT104125MA. J.B.R. receives support from the CIHR, the Lady Davis Institute of the Jewish General Hospital and the Fonds de Recherche Santé Québec.
The authors declare no competing financial interests.
- Broad-sense phenotypic heritability
The proportion of trait variance that is due to all genetic factors, including dominant and recessive factors, as well as the interactions between genetic factors. Narrow-sense heritability is the proportion of trait variance that is due to additive genetic factors.
A measurable characteristic of an individual.
- Complex traits
Traits that do not follow Mendelian inheritance patterns and are derived from any combination of multiple genetic factors, environmental factors and their interactions.
- Single nucleotide variants
(SNVs). Single base pair positions in the genome where there is variation across individuals. SNVs need not be biallelic or common.
- Genome-wide association studies
(GWAS). Studies that test the association of all measured genetic variation across the genome with a trait or disease. GWAS usually test the association of a phenotype with genetic variants that have a minor allele frequency (MAF) ≥1%, but deep imputation methods allow GWAS to test associations with variants at a lower MAF.
- Whole-exome sequencing studies
Studies that test the association between genetic variation (usually single nucleotide variants) across the measured coding sequence of the genome with a trait or disease. Whole-exome sequencing studies can measure most coding genetic variants, regardless of minor allele frequency.
- Whole-genome sequencing studies
Studies that test the association of genetic variation across the entire variable genetic sequence of the genome with a trait or disease. Whole-genome sequencing studies can measure most genetic variants present in the genome, regardless of minor allele frequency. However, certain regions are not usually measurable via sequencing, such as highly repetitive regions.
- Minor allele frequency
(MAF). The frequency of the less frequent allele at a genetic variant in a population. The less frequent allele is referred to as the minor allele.
- Deep imputation
The use of large imputation reference panels to accurately estimate most low-frequency (minor allele frequency (MAF) ≥1% but ≤5%) and some rare (MAF <1%) unobserved genetic variation in individuals who have undergone genome-wide genotyping.
- Single nucleotide polymorphisms
(SNPs). Single base pair positions in the genome where two or more nucleotides occur commonly in the population. 'Common' is usually defined as at least 1% of the population carrying an alternative allele. Most often, SNPs are biallelic, which means that the nucleotide will be one of two different alleles.
A characteristic or trait that has a portion of variability that is accounted for by genetic factors.
Sections of commonly varying or linked chromosomal material said to be in gametic phase, that is, not punctuated by recombination at an appreciable population-based frequency.
- Imputation reference panel
A data set containing genetic information on a large number of individuals who have undergone whole-genome sequencing and had their haplotypes reconstructed. These haplotype panels enable accurate imputation of non-genotyped genetic variants in individuals who have undergone genome-wide genotyping.
- Single SNV association test
A genetic association test that tests variation at a single nucleotide variant with variation in a phenotype. This is the most common genetic association test and is frequently used for genome-wide genotyping data.
- Region-based testing
A single test of association between many genetic variants in a chosen region of the genome and a phenotype.
- Burden test
A class of region-based testing that collapses genetic variation into a single genetic score by measuring the total number of minor alleles across a genomic region.
- Variance component test
A single test of whether the phenotypic variance explained by a set of chosen genetic variants across a genomic region is zero. For example, a variance component test could be used to test whether all single nucleotide variants in a gene contribute to the variability in a phenotype.
Genetic variants that are observed twice within the population studied.
- Variance explained
The proportion of variance in a phenotype that is explained by a mathematical model.
- Linkage disequilibrium
The non-random association of alleles in a population.
- Receiver operator curve
(ROC). A method to evaluate the performance of a diagnostic test for a binary outcome that plots the sensitivity of the test (the true positive rate) against one minus the specificity of the test (the false positive rate).
- Phenotypic variance
The variance in a phenotype, which is often assumed to be a function of environmental and genetic factors as well as their interactions.
When the association between an exposure and an outcome is distorted by their associations with a third variable. A confounding variable is a variable that is associated with both the exposure and the outcome but is not in the causal pathway between the two. A confounding variable could include a common cause of both the exposure and the outcome.
- Reverse causation
The phenomenon whereby the outcome influences the exposure.
- Horizontal pleiotropy
When the genetic variant in a Mendelian randomization study influences the outcome in a manner independent of the risk factor. This is a violation of Mendelian randomization assumptions.
- Vertical pleiotropy
When the genetic variant in a Mendelian randomization study influences the outcome through multiple biomarkers in the same pathway. This is not a violation of Mendelian randomization assumptions.
- Founder effect
The reduced genetic diversity that occurs when a population is descended from a small number of founders.
- Lactase persistence
The continued activity of the enzyme lactase in adulthood in humans.
Genetic variant that is observed only once within the population studied.
- Admixture mapping
A method of genetic association testing that relies on the admixture of populations, which occurs when individuals from two or more historically isolated populations interbreed.
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Cite this article
Timpson, N., Greenwood, C., Soranzo, N. et al. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat Rev Genet 19, 110–124 (2018). https://doi.org/10.1038/nrg.2017.101
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