complex traits

{{See also|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.{{cn|date=February 2023}} 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.{{Cite journal |last1=Fisher |first1=R. A. | date=1919|title=XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.|journal=Earth and Environmental Science Transactions of the Royal Society of Edinburgh|volume=52|issue=2|pages=399–433|doi=10.1017/S0080456800012163 |s2cid=181213898 | url=https://zenodo.org/record/1428666}} 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.{{cite journal | vauthors = Gibson G | title = Rare and common variants: twenty arguments | journal = Nature Reviews. Genetics | volume = 13 | issue = 2 | pages = 135–45 | date = January 2012 | pmid = 22251874 | pmc = 4408201 | doi = 10.1038/nrg3118 }} 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.{{cite journal | vauthors = Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM | display-authors = 6 | title = Finding the missing heritability of complex diseases | journal = Nature | volume = 461 | issue = 7265 | pages = 747–53 | date = October 2009 | pmid = 19812666 | pmc = 2831613 | doi = 10.1038/nature08494 | bibcode = 2009Natur.461..747M }} 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.{{cite journal | vauthors = Shi H, Kichaev G, Pasaniuc B | title = Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data | journal = American Journal of Human Genetics | volume = 99 | issue = 1 | pages = 139–53 | date = July 2016 | pmid = 27346688 | pmc = 5005444 | doi = 10.1016/j.ajhg.2016.05.013 }}{{cite journal | vauthors = Krumm N, Turner TN, Baker C, Vives L, Mohajeri K, Witherspoon K, Raja A, Coe BP, Stessman HA, He ZX, Leal SM, Bernier R, Eichler EE | title = Excess of rare, inherited truncating mutations in autism | journal = Nature Genetics | volume = 47 | issue = 6 | pages = 582–8 | date = June 2015 | pmid = 25961944 | pmc = 4449286 | doi = 10.1038/ng.3303 }} 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.{{cite journal | vauthors = Boyle EA, Li YI, Pritchard JK | title = An Expanded View of Complex Traits: From Polygenic to Omnigenic | journal = Cell | volume = 169 | issue = 7 | pages = 1177–1186 | date = June 2017 | pmid = 28622505 | pmc = 5536862 | doi = 10.1016/j.cell.2017.05.038 }}

Quantitative traits have phenotypes that are expressed on continuous ranges.{{cite book |last1=Klug |first1=William S. |title=Concepts of Genetics |date=2012 |publisher=Pearson Education |isbn=978-0-321-72412-0 }}{{pn|date=May 2024}} They have many different genes that impact the phenotype, with differing effect sizes.{{Cite web |last=DiCOTATO |first=ALLESSANDRA |date=October 14, 2022 |title=Scientists Uncover Nearly All Genetic Variants Linked to Height |url=https://hms.harvard.edu/news/scientists-uncover-nearly-all-genetic-variants-linked-height |access-date=May 10, 2024 |website=Harvard Medical School}} Many of these traits are somewhat heritable. For example, height is estimated to be 60-80% heritable; however, other quantitative traits have varying heritability.{{Cite web |title=Is height determined by genetics?: MedlinePlus Genetics |url=https://medlineplus.gov/genetics/understanding/traits/height/ |access-date=2024-05-10 |website=medlineplus.gov |language=en}}

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. The environment can also impact expression, as chickens will not lay as many eggs depending on the time of year.{{Cite web |last=EZMFrdmHtchy |date=2022-12-05 |title=How to Keep Chickens Laying Eggs in the Winter {{!}} Freedom Ranger Hatchery |url=https://www.freedomrangerhatchery.com/blog/how-to-keep-your-chickens-laying-eggs-in-the-winter-the-right-way/ |access-date=2024-05-10 |language=en-us}}

Threshold traits have phenotypes that have limited expressions (usually two). It is a complex trait because multiple genetic and environmental factors impact the phenotype.{{cite web |title=threshold trait / threshold traits |url=https://www.nature.com/scitable/definition/threshold-traits-121/ |website=Scitable }}{{Cite book |last=Pierce |first=Benjamin A. |title=Genetics: a conceptual approach |date=2012 |publisher=Palgrave |isbn=978-1-4292-3252-4 |edition=4 |location=Basingstoke |pages=662}} 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. An example of this is type 2 diabetes, the phenotype is either normal/healthy or lethal/diseased.{{cite journal |last1=Rosales-Gómez |first1=Roberto Carlos |last2=López-Jiménez |first2=José de Jesús |last3=Núñez-Reveles |first3=Nelly Yazmine |last4=González-Santiago |first4=Ana Elizabeth |last5=Ramírez-García |first5=Sergio Alberto |title=Nefropatía por diabetes mellitus tipo 2: un rasgo multifactorial con umbral y su mapa mórbido cromosómico |trans-title=Type 2 diabetes nephropathy: a thresholds complex trait and chromosomal morbid map |language=es |journal=Revista Medica del Instituto Mexicano del Seguro Social |date=2010 |volume=48 |issue=5 |pages=521–530 |pmid=21205501 |url=https://www.medigraphic.com/cgi-bin/new/resumen.cgi?IDARTICULO=37383 }}

{{See also|Quantitative genetics|Quantitative trait locus|Genome-wide association study}}

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.{{cite journal |last1=Rowe |first1=Suzanne J. |last2=Tenesa |first2=Albert |title=Human Complex Trait Genetics: Lifting the Lid of the Genomics Toolbox - from Pathways to Prediction |journal=Current Genomics |date=2012 |volume=13 |issue=3 |pages=213–224 |doi=10.2174/138920212800543101 |pmid=23115523 |pmc=3382276 }}

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. 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.{{Cite web |title=Quantitative Trait Locus (QTL) Analysis {{!}} Learn Science at Scitable |url=http://www.nature.com/scitable/topicpage/quantitative-trait-locus-qtl-analysis-53904 |access-date=2024-05-15 |website=www.nature.com |language=en}} They can either reproduce with their siblings, with themselves (different from asexual reproduction), or backcross.{{Cite book |last1=Klug |first1=William S. |title=Concepts of genetics |last2=Cummings |first2=Michael R. |last3=Spencer |first3=Charlotte A. |last4=Palladino |first4=Michael Angelo |date=2015 |publisher=Pearson |isbn=978-0-321-94891-5 |edition=Eleventh |location=Boston}} 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.{{Cite web |date=2016-06-06 |title=10.5: Quantitative Trait Locus (QTL) Analysis |url=https://bio.libretexts.org/Bookshelves/Genetics/Online_Open_Genetics_(Nickle_and_Barrette-Ng)/10%3A__Molecular_Markers_and_Quantitative_Traits/10.05%3A_Quantitative_Trait_Locus_(QTL)_Analysis |access-date=2024-05-15 |website=Biology LibreTexts |language=en}} 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.

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.{{cite book |last1=Griffiths |first1=Anthony J. F. |last2=Wessler |first2=Susan R. |last3=Carroll |first3=Sean B. |last4=Doebley |first4=John |title=An Introduction to Genetic Analysis |date=2015 |publisher=Macmillan Learning |isbn=978-1-4641-0948-5 }}{{pn|date=May 2024}} 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.{{fact|date=May 2024}} 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.File:Manhattan Plot.pngStatistical 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.{{Cite web |title= |url=https://visa.pharmacy.wsu.edu/bioinformatics/documents/chi-square-tests.pdf |access-date=2024-05-10 |archive-url=https://web.archive.org/web/20180629131548/https://visa.pharmacy.wsu.edu/bioinformatics/documents/chi-square-tests.pdf |archive-date=2018-06-29 }}{{cite journal |last1=Feldman |first1=Igor |last2=Rzhetsky |first2=Andrey |last3=Vitkup |first3=Dennis |title=Network properties of genes harboring inherited disease mutations |journal=Proceedings of the National Academy of Sciences |date=18 March 2008 |volume=105 |issue=11 |pages=4323–4328 |doi=10.1073/pnas.0701722105 |doi-access=free |pmid=18326631 |pmc=2393821 |bibcode=2008PNAS..105.4323F }}

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.{{cite book |last1=Griffiths |first1=Anthony J. F. |last2=Wessler |first2=Susan R. |last3=Carroll |first3=Sean B. |last4=Doebley |first4=John |title=An Introduction to Genetic Analysis |date=2015 |publisher=Macmillan Learning |isbn=978-1-4641-0948-5 }}{{pn|date=May 2024}} 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.{{cite journal |last1=Timpson |first1=Nicholas J. |last2=Greenwood |first2=Celia M. T. |last3=Soranzo |first3=Nicole |last4=Lawson |first4=Daniel J. |last5=Richards |first5=J. Brent |title=Genetic architecture: the shape of the genetic contribution to human traits and disease |journal=Nature Reviews Genetics |date=February 2018 |volume=19 |issue=2 |pages=110–124 |doi=10.1038/nrg.2017.101 |pmid=29225335 |url=https://research-information.bris.ac.uk/en/publications/af2c1b78-3cca-42ab-b973-717e01938cc4 }}

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.{{cite journal | vauthors = Frazer KA, Murray SS, Schork NJ, Topol EJ | title = Human genetic variation and its contribution to complex traits | journal = Nature Reviews. Genetics | volume = 10 | issue = 4 | pages = 241–51 | date = April 2009 | pmid = 19293820 | doi = 10.1038/nrg2554 | s2cid = 19987352 }} 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.{{cite journal | vauthors = Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA, Golan D, Gilad Y, Pritchard JK | title = RNA splicing is a primary link between genetic variation and disease | journal = Science | volume = 352 | issue = 6285 | pages = 600–4 | date = April 2016 | pmid = 27126046 | pmc = 5182069 | doi = 10.1126/science.aad9417 | bibcode = 2016Sci...352..600L }} 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.

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.{{Cite journal|last=Callaway|first=Ewen | name-list-style = vanc |date=2017-06-15|title=New concerns raised over value of genome-wide disease studies |journal=Nature|volume=546|issue=7659|pages=463|doi=10.1038/nature.2017.22152 |doi-access=free}} For example, one study hypothesizes that there exist rate-limiting genes pivotal to the function of gene regulatory networks.{{cite journal | vauthors = Chakravarti A, Turner TN | title = Revealing rate-limiting steps in complex disease biology: The crucial importance of studying rare, extreme-phenotype families | journal = BioEssays | volume = 38 | issue = 6 | pages = 578–86 | date = June 2016 | pmid = 27062178 | doi = 10.1002/bies.201500203 | s2cid = 3813041 }} Others studies have identified the functional impacts of key genes and mutations on disorders, including autism and schizophrenia.{{cite journal | vauthors = Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, Tooley K, Presumey J, Baum M, Van Doren V, Genovese G, Rose SA, Handsaker RE, Daly MJ, Carroll MC, Stevens B, McCarroll SA | title = Schizophrenia risk from complex variation of complement component 4 | journal = Nature | volume = 530 | issue = 7589 | pages = 177–83 | date = February 2016 | pmid = 26814963 | pmc = 4752392 | doi = 10.1038/nature16549 | bibcode = 2016Natur.530..177. }} 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. 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. 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.{{cite journal | vauthors = Preininger M, Arafat D, Kim J, Nath AP, Idaghdour Y, Brigham KL, Gibson G | title = Blood-informative transcripts define nine common axes of peripheral blood gene expression | journal = PLOS Genetics | volume = 9 | issue = 3 | pages = e1003362 | date = 2013-03-14 | pmid = 23516379 | pmc = 3597511 | doi = 10.1371/journal.pgen.1003362 | doi-access = free }}{{cite journal | first = Xin | last = He | name-list-style = vanc | date = October 2017 | title = Comment on: An Expanded View of Complex Traits: From Polygenic to Omnigenic | journal = Journal of Psychiatry and Brain Science | volume = 2 | issue = 5 | doi = 10.20900/jpbs.20170014s2 | doi-access = free }}

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Category:Quantitative genetics