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Cheverud, James M.; Churchill, Gary A.; Cook, Melloni; Crabbe, John C.; Crusio, Wim E.; Darvasi, Ariel; de Haan, Gerald; Demant, Peter; Doerge, R. W.; Elliott, Rosemary W.; Farber, Charles R.; Flaherty, Lorraine; Flint, Jonathan; Gershenfeld, Howard; Gibson, John P.; Gu, Jing; Gu, Weikuan; Himmelbauer, Heinz; Hitzemann, Robert; Hsu, Hui-Chen; Hunter, Kent; Iraqi, Fuad A.; Jansen, Ritsert C.; Johnson, Thomas E.; Jones, Byron C.; Kempermann, Gerd; Lammert, Frank; Lu, Lu; Manly, Kenneth F.; Matthews, Douglas B.; Medrano, Juan F.; Mehrabian, Margarete; Mittleman, Guy; Mock, Beverly A.; Mogil, Jeffrey S.; Montagutelli, Xavier; Morahan, Grant; Mountz, John D.; Nagase, Hiroki; Nowakowski, Richard S.; O'Hara, Bruce F.; Osadchuk, Alexander V.; Paigen, Beverly; Palmer, Abraham A.; Peirce, Jeremy L.; Pomp, Daniel; Rosemann, Michael; Rosen, Glenn D.; Schalkwyk, Leonard C.; Seltzer, Ze'ev; Settle, Stephen; Shimomura, Kazuhiro; Shou, Siming; Sikela, James M.; Siracusa, Linda D.; Spearow, Jimmy L.; Teuscher, Cory; Threadgill, David W.; Toth, Linda A.; Toye, Ayo A.; Vadasz, Csaba; Van Zant, Gary; Wakeland, Edward; Williams, Robert W.; Zhang, Huang-Ge; Zou, Fei (2003).
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lab and that show
Mendelian inheritance patterns reflect a large deviation from the wild type, and Castle believed that acquisition of such features is the basis of "discontinuous variation" that characterizes speciation. Darwin discussed the inheritance of similar mutant features but did not invoke them as a requirement of speciation. Instead Darwin used the emergence of such features in breeding populations as evidence that mutation can occur at random within breeding populations, which is a central premise of his model of selection in nature. Later in his career, Castle would refine his model for speciation to allow for small variation to contribute to speciation over time. He also was able to demonstrate this point by selectively breeding laboratory populations of rats to obtain a hooded phenotype over several generations.
196:, a graduate student who trained under Castle, summarized contemporary thinking about the genetic basis of quantitative natural variation: "As genetic studies continued, ever smaller differences were found to mendelize, and any character, sufficiently investigated, turned out to be affected by many factors." Wright and others formalized population genetics theory that had been worked out over the preceding 30 years explaining how such traits can be inherited and create stably breeding populations with unique characteristics. Quantitative trait genetics today leverages Wright's observations about the statistical relationship between genotype and phenotype in families and populations to understand how certain genetic features can affect variation in natural and derived populations.
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located elsewhere on the genome can have an interfering effect. As a consequence, the power of detection may be compromised, and the estimates of locations and effects of QTLs may be biased (Lander and
Botstein 1989; Knapp 1991). Even nonexisting so-called "ghost" QTLs may appear (Haley and Knott 1992; Martinez and Curnow 1992). Therefore, multiple QTLs could be mapped more efficiently and more accurately by using multiple QTL models. One popular approach to handle QTL mapping where multiple QTL contribute to a trait is to iteratively scan the genome and add known QTL to the regression model as QTLs are identified. This method, termed
599:), involves multiple families instead of a single family. Family-based QTL mapping has been the only way for mapping of genes where experimental crosses are difficult to make. However, due to some advantages, now plant geneticists are attempting to incorporate some of the methods pioneered in human genetics. Using family-pedigree based approach has been discussed (Bink et al. 2008). Family-based linkage and association has been successfully implemented (Rosyara et al. 2009)
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suitable marker loci to serve as covariates; once these have been chosen, CIM turns the model selection problem into a single-dimensional scan. The choice of marker covariates has not been solved, however. Not surprisingly, the appropriate markers are those closest to the true QTLs, and so if one could find these, the QTL mapping problem would be complete anyway.
522:. The ANOVA approach for QTL mapping has three important weaknesses. First, we do not receive separate estimates of QTL location and QTL effect. QTL location is indicated only by looking at which markers give the greatest differences between genotype group averages, and the apparent QTL effect at a marker will be smaller than the true QTL effect as a result of
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differences in expression between them? Generally, what makes the two individuals different are likely to be environmental factors. Due to the involved nature of genetic investigations needed to determine such inheritance patterns, this is not usually the first avenue of investigation one would choose to determine etiology.
336:, this binomial distribution will begin to resemble a normal distribution. From this viewpoint, a disease state will become apparent at one of the tails of the distribution, past some threshold value. Disease states of increasing severity will be expected the further one goes past the threshold and away from the
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himself observed that inbred features of fancy pigeons were inherited in accordance with Mendel's laws (although Darwin did not actually know about Mendel's ideas when he made the observation), it was not obvious that these features selected by fancy pigeon breeders can similarly explain quantitative
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Another interest of statistical geneticists using QTL mapping is to determine the complexity of the genetic architecture underlying a phenotypic trait. For example, they may be interested in knowing whether a phenotype is shaped by many independent loci, or by a few loci, and do those loci interact.
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In this method, one performs interval mapping using a subset of marker loci as covariates. These markers serve as proxies for other QTLs to increase the resolution of interval mapping, by accounting for linked QTLs and reducing the residual variation. The key problem with CIM concerns the choice of
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The principle for QTL mapping is: 1) The likelihood can be calculated for a given set of parameters (particularly QTL effect and QTL position) given the observed data on phenotypes and marker genotypes. 2) The estimates for the parameters are those where the likelihood is highest. 3) A significance
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to unify the laws of
Mendelian inheritance with Darwin's theory of speciation invoked the idea that species become distinct from one another as one species or the other acquires a novel Mendelian factor. Castle's conclusion was based on the observation that novel traits that could be studied in the
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If multifactorial inheritance is indeed the case, then the chance of the patient contracting the disease is reduced only if cousins and more distant relatives have the disease. It must be stated that while multifactorially-inherited diseases tend to run in families, inheritance will not follow the
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If it is shown that the brothers and sisters of the patient have the disease, then there is a strong chance that the disease is genetic and that the patient will also be a genetic carrier. This is not quite enough as it also needs to be proven that the pattern of inheritance is non-Mendelian. This
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A mutation resulting in a disease state is often recessive, so both alleles must be mutant in order for the disease to be expressed phenotypically. A disease or syndrome may also be the result of the expression of mutant alleles at more than one locus. When more than one gene is involved, with or
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The paradigm of polygenic inheritance as being used to define multifactorial disease has encountered much disagreement. Turnpenny (2004) discusses how simple polygenic inheritance cannot explain some diseases such as the onset of Type I diabetes mellitus, and that in cases such as these, not all
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in organisms, especially human organisms such as: height, skin color, and body mass. All of these phenotypes are complicated by a great deal of give-and-take between genes and environmental effects. The continuous distribution of traits such as height and skin color described above, reflects the
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Complex Trait
Consortium —; Abiola, Oduola; Angel, Joe M.; Avner, Philip; Bachmanov, Alexander A.; Belknap, John K.; Bennett, Beth; Blankenhorn, Elizabeth P.; Blizard, David A.; Bolivar, Valerie; Brockmann, Gudrun A.; Buck, Kari J.; Bureau, Jean-Francois; Casley, William L.; Chesler, Elissa J.;
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Conventional methods for the detection of quantitative trait loci (QTLs) are based on a comparison of single QTL models with a model assuming no QTL. For instance in the "interval mapping" method the likelihood for a single putative QTL is assessed at each location on the genome. However, QTLs
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For organisms whose genomes are known, one might now try to exclude genes in the identified region whose function is known with some certainty not to be connected with the trait in question. If the genome is not available, it may be an option to sequence the identified region and determine the
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If a genetic cause is suspected and little else is known about the illness, then it remains to be seen exactly how many genes are involved in the phenotypic expression of the disease. Once that is determined, the question must be answered: if two people have the required genes, why are there
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The assumption of polygenic inheritance is that all involved loci make an equal contribution to the symptoms of the disease. This should result in a normal (Gaussian) distribution of genotypes. When it does not, the idea of polygenetic inheritance cannot be supported for that illness.
463:, an online tool that allows users to enter a primary sequence and search for similar sequences within the BLAST database of genes from various organisms. It is often not the actual gene underlying the phenotypic trait, but rather a region of DNA that is closely linked with the gene
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The term 'interval mapping' is used for estimating the position of a QTL within two markers (often indicated as 'marker-bracket'). Interval mapping is originally based on the maximum likelihood but there are also very good approximations possible with simple regression.
535:
Lander and
Botstein developed interval mapping, which overcomes the three disadvantages of analysis of variance at marker loci. Interval mapping is currently the most popular approach for QTL mapping in experimental crosses. The method makes use of a
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between the marker and the QTL. Second, we must discard individuals whose genotypes are missing at the marker. Third, when the markers are widely spaced, the QTL may be quite far from all markers, and so the power for QTL detection will decrease.
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Castle's was perhaps the first attempt made in the scientific literature to direct evolution by artificial selection of a trait with continuous underlying variation, however the practice had previously been widely employed in the development of
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Castle's work was among the first to attempt to unify the recently rediscovered laws of
Mendelian inheritance with Darwin's theory of evolution. Still, it would be almost thirty years until the theoretical framework for evolution of
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determine both the location and effects size of QTL more accurately than single-QTL approaches, especially in small mapping populations where the effect of correlation between genotypes in the mapping population may be problematic.
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Rosyara U.R., J.L. Gonzalez-Hernandez, K.D. Glover, K.R. Gedye and J.M. Stein. 2009. Family-based mapping of quantitative trait loci in plant breeding populations with resistance to
Fusarium head blight in wheat as an illustration
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action of genes that do not manifest typical patterns of dominance and recessiveness. Instead the contributions of each involved locus are thought to be additive. Writers have distinguished this kind of inheritance as
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underlying a trait. The DNA sequence of any genes in this region can then be compared to a database of DNA for genes whose function is already known, this task being fundamental for marker-assisted crop improvement.
124:(those traits which vary continuously, e.g. height) as opposed to discrete traits (traits that have two or several character values, e.g. red hair in humans, a recessive trait, or smooth vs. wrinkled peas used by
1293:
Daware, Anurag; Parida, Swarup K.; Tyagi, Akhilesh K. (2020), Vaschetto, Luis M. (ed.), "Integrated
Genomic Strategies for Cereal Genetic Enhancement: Combining QTL and Association Mapping",
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Daware, Anurag; Parida, Swarup K.; Tyagi, Akhilesh K. (2020), Vaschetto, Luis M. (ed.), "Integrated
Genomic Strategies for Cereal Genetic Enhancement: Combining QTL and Association Mapping",
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variation. Several genes factor into determining a person's natural skin color, so modifying only one of those genes can change skin color slightly or in some cases, such as for
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of the typed markers, and, like analysis of variance, assumes the presence of a single QTL. In interval mapping, each locus is considered one at a time and the logarithm of the
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groups. For other types of crosses (such as the intercross), where there are more than two possible genotypes, one uses a more general form of ANOVA, which provides a so-called
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Multifactorially inherited diseases are said to constitute the majority of genetic disorders affecting humans which will result in hospitalization or special care of some kind.
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would require studying dozens, even hundreds of different family pedigrees before a conclusion of multifactorial inheritance is drawn. This often takes several years.
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Traits controlled both by the environment and by genetic factors are called multifactorial. Usually, multifactorial traits outside of illness result in what we see as
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Rosyara, U. R.; Maxson-stein, K.L.; Glover, K.D.; Stein, J.M.; Gonzalez-hernandez, J.L. (2007). "Family-based mapping of FHB resistance QTLs in hexaploid wheat".
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to obtain livestock or plants with favorable features from populations that show quantitative variation in traits like body size or grain yield.
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The above are well-known examples of diseases having both genetic and environmental components. Other examples involve atopic diseases such as
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Bink MCAM, Boer MP, ter Braak CJF, Jansen J, Voorrips RE, van de Weg WE: Bayesian analysis of complex traits in pedigreed plant populations.
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trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait. Another use of QTLs is to identify
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distribution. This shows that multifactorial inheritance is polygenic, and genetic frequencies can be predicted by way of a polyhybrid
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putative functions of genes by their similarity to genes with known function, usually in other genomes. This can be done using
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of a trait. It may indicate that plant height is controlled by many genes of small effect, or by a few genes of large effect.
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have been found beneficial to identify the gene responsible by a cross-validation of genes within the interacting loci with
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Mapping
Mendelian factors underlying quantitative traits using RFLP linkage maps. ES Lander and D Botstein. Genetics. 1989
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1790:– a software for genome-wide interaction analysis (GWIA) of case-control SNP data and analysis of quantitative traits
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Garnier, Sophie, Truong, Vinh, Genome-Wide Haplotype Analysis of Cis Expression Quantitative Trait Loci in Monocytes
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without the presence of environmental triggers, we say that the disease is the result of multifactorial inheritance.
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cross. Phenotypic frequencies are a different matter, especially if they are complicated by environmental factors.
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510:, sometimes called "marker regression") at the marker loci. In this method, in a backcross, one may calculate a
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159:. For early geneticists, it was not immediately clear that the smooth variation in traits like body size (i.e.,
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304:. Polygenic inheritance can be explained as Mendelian inheritance at many loci, resulting in a trait which is
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and numerous others. Most phenotypic characteristics are the result of the interaction of multiple genes.
237:(discrete categories). Instead, their phenotypes typically vary along a continuous gradient depicted by a
1977:
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Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits edn 1 (Sinauer Associates, 1998).
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Jannink, J; Bink, Mc; Jansen, Rc (August 2001). "Using complex plant pedigrees to map valuable genes".
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Thus, due to the nature of polygenic traits, inheritance will not follow the same pattern as a simple
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In a recent development, classical QTL analyses were combined with gene expression profiling i.e. by
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would be widely formalized. In an early summary of the theory of evolution of continuous variation,
1634:"Inclusive Composite Interval Mapping of QTL by Environment Interactions in Biparental Populations"
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548:) is calculated for the model that the given locus is a true QTL. The odds ratio is related to the
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155:'s ideas spread, geneticists began to connect Mendel's rules of inheritance of single factors to
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between the phenotype and the marker genotype for each individual in the experimental cross.
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refers to polygenic inheritance that also includes interactions with the environment. Unlike
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1392:"Systematic identification of trans eQTLs as putative drivers of known disease associations"
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70:) correlate with an observed trait. This is often an early step in identifying the actual
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486:-controlling elements for the expression of often disease-associated genes. Observed
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113:. The number of QTLs which explain variation in the phenotypic trait indicates the
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1441:"Mapping mendelian factors underlying quantitative traits using RFLP linkage maps"
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Birth Defects Genetics Centre, University of South Dakota School of Medicine.
696:"The nature and identification of quantitative trait loci: a community's view"
1971:
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Bloom J. S.; Ehrenreich I. M.; Loo W. T.; Lite T.-L. V.; Kruglyak L. (2013).
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1297:, Methods in Molecular Biology, vol. 2072, Springer US, pp. 15–25,
838:, Methods in Molecular Biology, vol. 2072, Springer US, pp. 15–25,
787:"Map-Based Cloning of the Gene Associated With the Soybean Maturity Locus E3"
407:, no characteristic genetic markers have been determined with any certainty.
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Watanabe, Satoshi; Hideshima, Rumiko; Xia, Zhengjun; et al. (2009).
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329:
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110:
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A list of computer programs for genetic analysis including QTL analysis
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479:
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The more genes involved in the cross, the more the distribution of the
987:"Variation in the Hooded Pattern of Rats, and a New Allele of Hooded"
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584:(ICIM) has also been proposed as a potential method for QTL mapping.
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132:
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163:) was caused by the inheritance of single genetic factors. Although
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This can provide information on how the phenotype may be evolving.
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1515:
1117:"Human Genetics for 1st Year Students: Multifactorial Inheritance"
62:. QTLs are mapped by identifying which molecular markers (such as
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109:, and their environment. These QTLs are often found on different
1803:
1150:. University of South Dakota School of Medicine. Archived from
1148:
Clinical Genetics: A Self-Study Guide for Health Care Providers
261:
257:
1813:
1741:
1499:"Finding the sources of missing heritability in a yeast cross"
1818:
1245:. Children's Hospital of the King's Daughters. Archived from
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The simplest method for QTL mapping is analysis of variance (
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is the number of involved loci, then the coefficients of the
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Li, Shanshan; Wang, Jiankang; Zhang, Luyan (10 July 2015).
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characteristic (trait) that is attributable to two or more
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27:
DNA locus associated with variation in a quantitative trait
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was rediscovered at the beginning of the 20th century. As
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Proud, Virginia & Roberts, Helen (31 December 2005).
91:
43:
101:, which varies in degree and which can be attributed to
1809:
A Statistical Framework for Quantitative Trait Mapping
1575:"Interval mapping of multiple quantitative trait loci"
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threshold can be established by permutation testing.
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is widely believed to be multifactorially genetic by
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1744:Proceedings of National Fusarium Head Blight Forum
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368:genes are thought to make an equal contribution.
324:) will give the frequency of distribution of all
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595:, or Family-pedigree based mapping (Linkage and
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344:Heritable disease and multifactorial inheritance
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1243:"Medical Genetics: Multifactorial Inheritance"
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233:, polygenic traits do not follow patterns of
1794:Precision Mapping of Quantitative Trait Loci
1783:Plant Breeding and Genomics on eXtension.org
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1341:Grisel, Judith E.; Crabbe, John C. (1995).
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1766:Theoretical Applied Genetics 118:1617–1631
1269:"BLAST: Basic Local Alignment Search Tool"
1174:"Definition of Multifactorial inheritance"
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514:to compare the averages of the two marker
105:effects, i.e., the product of two or more
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672:"Quantitative trait locus (QTL) analysis"
630:Expression quantitative trait loci (eQTL)
451:Example of a genome-wide scan for QTL of
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1834:Quantitative Trait Locus (QTL) Analysis
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1295:Cereal Genomics: Methods and Protocols
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912:: CS1 maint: archived copy as title (
836:Cereal Genomics: Methods and Protocols
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94:which is associated with a particular
46:) that correlates with variation of a
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1231:
1217:Emery's Elements of Medical Genetics
1039:"Evolution in Mendelian Populations"
582:Inclusive composite interval mapping
225:and can be measured quantitatively.
120:Typically, QTLs underlie continuous
1439:Lander, E.S.; Botstein, D. (1989).
1178:MedicineNet.com MedTerms Dictionary
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244:An example of a polygenic trait is
24:
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332:. For sufficiently high values of
252:, moderately. Many disorders with
25:
2009:
1899:Concepts in Quantitative Genetics
1848:What are Quantitative Trait Loci?
1776:
1347:Alcohol Health and Research World
1343:"Quantitative Trait Loci Mapping"
1180:. MedicineNet.com. Archived from
1573:Jansen, R C (1 September 1993).
573:Composite interval mapping (CIM)
276:Multifactorial traits in general
74:that cause the trait variation.
1843:Mapping Quantitative Trait Loci
1625:
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1390:Westra HJ, et al. (2013).
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1037:Wright, Sewall (1 March 1931).
550:Pearson correlation coefficient
1092:, McGraw-Hill Higher Education
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978:
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778:
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442:
13:
1:
1713:10.1016/S1360-1385(01)02017-9
1075:– via www.genetics.org.
1023:– via www.genetics.org.
656:
588:Family-pedigree based mapping
77:
1659:10.1371/journal.pone.0132414
1144:"Multifactorial Inheritance"
985:Castle, W. E. (1 May 1951).
7:
1759:Euphytica 2008, 161:85–96.
1303:10.1007/978-1-4939-9865-4_3
844:10.1007/978-1-4939-9865-4_3
803:10.1534/genetics.108.098772
670:Miles, C; Wayne, M (2008).
602:
375:
217:refers to inheritance of a
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2014:
1594:10.1093/genetics/135.1.205
1457:10.1093/genetics/121.1.185
956:10.1126/science.18.456.396
932:"Mendel's Law of Heredity"
640:Nested association mapping
566:composite interval mapping
438:on the human chromosome 20
282:continuous characteristics
227:Multifactorial inheritance
203:
143:
1934:
1926:Effective population size
1898:
1219:(12th ed.). Elsevier
1208:Turnpenny, Peter (2004).
1003:10.1093/genetics/36.3.254
415:same pattern as a simple
256:are polygenic, including
1916:Quantitative trait locus
1804:Complex Trait Consortium
1055:10.1093/genetics/16.2.97
615:Family-based QTL mapping
593:Family-based QTL mapping
291:quantitative inheritance
84:quantitative trait locus
32:quantitative trait locus
1988:Quantitative trait loci
1701:Trends in Plant Science
701:Nature Reviews Genetics
476:expression QTLs (eQTLs)
1273:blast.ncbi.nlm.nih.gov
650:Genetic susceptibility
635:Genetic predisposition
455:
439:
210:Oligogenic inheritance
1998:Quantitative genetics
1892:Quantitative genetics
1852:University of Warwick
1089:Multifactorial Traits
496:scientific literature
450:
433:
235:Mendelian inheritance
215:Polygenic inheritance
206:Monogenic inheritance
173:William Ernest Castle
168:variation in nature.
149:Mendelian inheritance
128:in his experiments).
1993:Genetic epidemiology
1983:Statistical genetics
1952:Evolutionary biology
1824:QTL discussion forum
1086:Ricki Lewis (2003),
625:Dominance (genetics)
502:Analysis of variance
306:normally-distributed
171:An early attempt by
161:incomplete dominance
115:genetic architecture
1942:Population genetics
1650:2015PLoSO..1032414L
1533:10.1038/nature11867
1525:2013Natur.494..234B
1184:on 17 December 2013
1154:on 30 December 2006
948:1903Sci....18..396C
610:Association mapping
597:association mapping
358:normal, or Gaussian
200:Quantitative traits
157:Darwinian evolution
131:Moreover, a single
1978:Classical genetics
1249:on 15 October 2006
930:Castle WE (1903).
762:has generic name (
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392:(open spine), and
314:binomial expansion
254:genetic components
48:quantitative trait
1965:
1964:
1509:(7436): 234–237.
1402:(10): 1238–1243.
894:on 3 October 2013
492:metabolic pathway
488:epistatic effects
90:) is a region of
16:(Redirected from
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405:biopsychiatrists
356:will resemble a
246:human skin color
231:monogenic traits
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1906:Heritability
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1615:. Retrieved
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1182:the original
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1152:the original
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1088:
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896:. Retrieved
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42:(section of
35:
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1814:GeneNetwork
1278:18 February
1210:"Chapter 9"
712:: 911–916.
538:genetic map
520:F-statistic
512:t-statistic
498:databases.
443:QTL mapping
394:anencephaly
182:agriculture
111:chromosomes
1972:Categories
657:References
542:odds ratio
434:A QTL for
417:monohybrid
386:dermatitis
298:monohybrid
239:bell curve
219:phenotypic
204:See also:
133:phenotypic
96:phenotypic
78:Definition
56:population
1911:Dominance
1721:1360-1385
1668:1932-6203
1516:1208.2865
1396:Nat Genet
1359:0090-838X
1329:202711099
1253:6 January
1223:6 January
1188:6 January
1158:6 January
1122:6 January
870:202711099
773:195367115
620:Epistasis
546:LOD score
478:describe
362:Mendelian
354:genotypes
287:polygenic
103:polygenic
60:organisms
52:phenotype
1957:Heredity
1947:Genomics
1888:Genetics
1838:Scitable
1788:INTERSNP
1729:11495765
1686:26161656
1638:PLOS ONE
1582:Genetics
1551:23376951
1445:Genetics
1426:24013639
1377:31798043
1321:31541435
1073:17246615
1043:Genetics
1021:14840647
991:Genetics
972:11670642
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908:cite web
862:31541435
821:19474204
791:Genetics
744:27285742
736:14634638
645:Oncogene
603:See also
516:genotype
376:Examples
266:diabetes
1819:GridQTL
1677:4498613
1646:Bibcode
1617:1 March
1612:8224820
1603:1205619
1542:4001867
1521:Bibcode
1475:2563713
1466:1203601
1417:3991562
1368:6875759
1064:1201091
1012:1209518
944:Bibcode
936:Science
812:2728863
727:2063446
474:. Such
328:allele
250:SLC24A5
144:History
50:in the
38:) is a
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482:- and
399:While
382:eczema
262:cancer
258:autism
165:Darwin
153:Mendel
126:Mendel
122:traits
1578:(PDF)
1511:arXiv
1325:S2CID
1213:(PDF)
968:S2CID
866:S2CID
769:S2CID
740:S2CID
508:ANOVA
484:trans
461:BLAST
308:. If
289:, or
223:genes
107:genes
99:trait
72:genes
68:AFLPs
54:of a
40:locus
1725:PMID
1717:ISSN
1682:PMID
1664:ISSN
1619:2023
1608:PMID
1547:PMID
1471:PMID
1422:PMID
1373:PMID
1355:ISSN
1317:PMID
1307:ISBN
1280:2018
1255:2007
1225:2007
1190:2007
1160:2007
1124:2007
1069:PMID
1017:PMID
960:PMID
914:link
900:2013
858:PMID
848:ISBN
817:PMID
764:help
732:PMID
682:(1).
338:mean
316:of (
208:and
64:SNPs
1709:doi
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1590:doi
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1059:PMC
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999:doi
952:doi
840:doi
807:PMC
799:doi
795:182
722:PMC
714:doi
480:cis
419:or
384:or
300:or
92:DNA
88:QTL
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58:of
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