Complex traits
Complex traits are phenotypes that are controlled by two or more genes and do not follow Mendel's Law of Dominance.
Modern understanding has 3 categories of complex traits: quantitative, meristic, and threshold.
[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.
[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.
[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.
[9] The environment can also impact expression, as chickens will not lay as many eggs depending on the time of year.
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.
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.
A Genome-Wide Association Study (GWAS) is a technique used to find gene variants linked to complex traits.
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.
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.
[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.
[27] Others studies have identified the functional impacts of key genes and mutations on disorders, including autism and schizophrenia.
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.
