qtl mapping

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Slide 1:

Quantitative Trait Loci MAPPING Non Credit Seminar NISHAT 2009BS122M

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CONTENT Advantages and limitation of QTL mapping Methods of QTL mapping and processing of this Data using computer Software Mapping of QTL and different Approaches QTL & its Trait identification Future Aspects: a wish from current research

Slide 3:

QTLs are genes whose phenotypic effects show a continuous range of variation in a population and is more or less normally distributed A quantitative trait locus/loci (QTL) is the location of individual locus or multiple loci in the genome that affects a trait that is measured on a quantitative (linear) scale. Examples of quantitative traits are: - Plant height (measured on a ruler) - Grain yield (measured on a balance) Definition of QTL

Slide 4:

Trait effects In this example genome have 3 loci, one associated with decreased yield, and one associated with higher yield. The phenotype, depending on the size of the effect of each QTL and how they work together, may be low yield. A variety may have some QTL that increase a trait (for example, increase yield) and others that decrease the trait. These work together to create the phenotype of the plant.

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Finding “good” QTL So the key is identifying the “good” QTL – those that affect the trait in the direction you want, and then separating those from the negative ones. This is where QTL identification techniques are important. e.g. Positive QTL : Grain Yield, Disease resistance, Oil content, Protein or Mineral linked. Negative QTL : Plant Height, Environment effected traits. Note that these techniques are simply statistical correlations, just like genetic mapping and any marker-trait correlations; however, because we are looking for many markers that correlate with a single trait, it is somewhat more complex statistically.

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QTL Mapping QTL mapping is the statistical study of the alleles that occur in a locus and the phenotypes (physical forms or traits) that they produce. The process of constructing linkage maps and conducting QTL analysis–to identify genomic regions associated with traits–is known as QTL mapping (McCouch & Doerge, 1995)

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QTL mapping’ is based on the segregation of genes and markers via chromosome recombination (called crossing-over) during meiosis (i.e. sexual reproduction), thus allowing their analysis in the progeny (Paterson, 1996). Partition of mapping population into different genotypic classes based on genotype Marker and apply correlative statistics to determine the significant difference of one genotype with another with respect to trait being measured. Principles of QTL Mapping

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Prerequisites for QTL mapping Availability of a good linkage map (this can be done at the same time the QTL mapping) A segregating population derived from parents that differ for the trait(s) of interest, and which allow for replication of each segregant, so that phenotype can be measured with precision (such as RILs or DHs ) A good assay for the trait(s) of interest Software available for analyses

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The purpose of the phenotyping experiment is to assign a trait value to each mapping population member. This value is then combined with the allele score at the set of marker loci distributed throughout the genome. A data file is then created which includes all the trait data and all the marker data for the entire population. various software applications can be applied to this data file to identify  statistical associations between the presence of alternative alleles and the trait value . Overview of QTL mapping

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Approaches to mapping Experimental crosses (Segregating Population) - Backcrosses - F2 intercrosses Recombinant inbred (RI) lines Double Haploids Pedigree analysis Association studies (Linkage disequilibrium, LD mapping) - With candidate genes (direct approach) - Localized association studies (chromosomal region) - Whole-genome association studies

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Need of Segregating Population In natural population consistent association between QTL and Marker genotype will not usually exists (Except where marker is completely linked to QTL, which is very rare). So to study the recombination b/w QTL and marker segregating population is useful.

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Types and Size of population A segregating population using parental lines that are highly contrasting phenotypic ally for the trait. The parent lines should be genetically divergent. The size of mapping population depend upon type of mapping population employed for analysis, genetic nature of target trait. Note: The choice of mapping population could vary based upon the objectives of experiment and timeframe as well as resources available for undertaking QTL analysis.

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Backcross (BC) Advantages : It is easier to identify QTL as there are less epistatic and linkage drag effects; especially useful for crosses with wild species. Disadvantages : Difficult or impossible in species that are highly heterozygous and outcrossing. Use: best when inbred lines are available Huang et al.( 2003)

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Advantage : Fast and easy to construct Disadvantage : F3 families are still very heterozygous, so the precision of the estimates can be low (because of the high standard error); can’t be replicated F2 populations Jampatong et al. (2002)

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AA x aa Aa x Aa AA, Aa, aa AA x aa AA x Aa AA, Aa F 2 design is more powerful in cases of partial dominance, since heterozygous effects can be identified Backcross design is more efficient in cases of complete dominance . Comparison …..

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True breeding or homozygous Immortal collection Replicate experiments in different environments Molecular Marker database can be updated Recombinant inbred (RI) lines Advantages: fixed lines so can be replicated across many locations and/or years; can eliminate problem of background heterozygosity Disadvantages : Can take a long time to produce. (Some species are not amenable). He P et al.(2001)

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Recombinant inbred (RI) lines AA BB CC DD EE FF aa bb cc dd ee ff X Aa Bb Cc Dd Ee Ff Aa Bb Cc Dd Ee Ff Aa Bb Cc Dd Ee Ff Aa Bb Cc Dd Ee Ff Aa Bb Cc Dd Ee Ff Aa Bb Cc Dd Ee Ff AA BB cc dd ee FF aa bb CC DD ee ff aa bb cc DD ee FF aa BB cc dd EE FF AA bb cc dd EE FF AA BB CC dd ee ff Basic method Start with inbred parental lines, homozygous at every locus Make F1s, heterozygous at every locus Inbreed different F1 lines These recombinant inbred lines are homozygous at each locus

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Advantages : 1)Spontaneous chromosome doubling of Haploid microspores in in vitro culture 2) Homozygosity achieved in a single step Plants. Disadvantages: Less recombination between linked markers Not all systems are amenable to in vitro culture Doubled haploid Lines(DHL)

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Advantage : Very precise and statistically strong, as background is constant; especially useful for validation experiments Disadvantage : Can take time to construct; only useful for specific target QTL Near Isogenic Lines (NILs) Szalma SJ et al .

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Single Marker approach The simplest, and probably most common method. Distribution of trait values is examined separately for each marker locus . Each marker-trait association test is performed independent of information from all other markers, so that a chromosome with n markers offers n separate single marker tests . Uses: This technique is good choice when the goal is simple detection of a QTL linked to a marker, rather than estimation of its position and effects . Limitations : This method can not Determine Whether the marker are associated with one or more QTLs. The effect of QTL are likely to be under estimated because they are confound with recombination frequencies. Methods to detect QTLs

With Interval Mapping :

This technique use the two Flanking marker. A separate analysis is performed for each pair of adjacent marker loci. The use of such two-locus marker genotypes results in n ¡-1 separate tests of marker-trait associations for a chromosome with n markers (one for each marker interval). Both single-marker and interval mapping approaches are biased when multiple QTLs are linked to the marker/interval being considered. Methods simultaneously using three or more marker loci attempt to reduce or remove such bias. Advantages: Interval mapping offers increased of power of detection and more precise estimates of QTL effects and position. With Interval Mapping Lander and Botstein 1989 Flanking marker analysis

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It evaluates the association between the trait values and the expected contribution of a QTL (the target QTL) at multiple analysis points between each pair of adjacent marker loci (the target interval). The expected QTL genotype is estimated from the genotypes of flanking marker loci and their distance from the QTL. Since there is usually uncertainty in the QTL genotype, the like- lihood is a sum of terms, one for each possible QTL genotype, weighted by the probability of that genotype given the genotypes of the flanking markers. The analysis point that yields the most significant association may be taken as the location of a putative QTL. Simple Interval Mapping (SIM)

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CIM evaluates the possibility of a target QTL at multiple analysis points across each inter-marker interval (same as SIM). However at each point it also includes the effect of one or more background markers. composite interval mapping Background markers: That have been shown to be associated with the trait and therefore lie close to other QTLs (background QTLs) affecting the trait. Multiple QTL mapping (Jansen 1993) The inclusion of a background marker in the analysis helps in one of two ways, Based upon the linkage of Background marker and the target interval If they are linked , inclusion of the background marker may help to separate the target QTL from other linked QTLs. If they are not linked , inclusion of the background marker makes the analysis more sensitive to the presence of a QTL in the target interval. (Zeng 1993, 1994).

Interval mapping:

Interval mapping Advantages Takes proper account of missing data Interpolate positions between markers Provide a support interval Provide more accurate estimate of QTL effect Disadvantages Intense computation Rely on a genetic map with good quality Difficult to incorporate covariate

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The power of a QTL-detection experiment, defined as the probability of detecting a QTL at a given level of statistical significance, depends upon the strength of the QTL and the number of progeny in the population. If we consider the strength of the QTL in terms of the fraction of the total trait variance that it explains, we can define three categories of QTLs. Those which explain over 20% of the variance are strong QTLs; traits controlled by such QTLs can be considered almost Mendelian. Strong QTLs can be detected with a power greater than 80% even with the AXB/BXA set of recombinant inbred strains. At the other extreme, weak QTLs, which explain 1% or less of the trait variance, require at least a thousand progeny to detect them with high power. Detection of such QTLs is not routinely feasible. The number of progeny required to detect a QTL is, roughly speaking, proportional to the variance of the nongenetic (environmental) contributions and inversely proportional to the square of the strength of the QTL.

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Name of Software URL of the site Mapmaker/QTL ftp://genome.wi.mit.edu/pub/mapmaker3 QTL Cartographer http://statgen.ncsu.edu/qtlcart/ Map Manager QT http://mcbio.med.buffalo.edu/mapmgr.html QGene ™ qgene@clarityconnect.com MapQTL ™ http://www.cpro.dlo.nl/cbw/ PLABQTL http://www.unihohenheim.de/~ipspwww/soft.html MQTL ftp://gnome.agrenv.mcgill.ca/pub/genetics/software/MQTL/ Multimapper http://www.RNI.Helsinki.FI/~mjs/ The QTL Cafe http://sun1.bham.ac.uk/g.g.seaton/ Epistat http://www.larklab.4biz.net/epistat.htm Manly et al. (1999)

Multiple regression analysis:

Multiple regression analysis To obtain the final estimates of the effects of the QTL detected with CIM proportion of phenotypic variation accounted for by an individual QTL

Slide 28:

The mapping approaches described so far require crosses. Mapping with experimental crosses requires organisms that can be manipulated and that have sufficiently short generation times to be tractable. Humans cannot be crossed for ethical reasons, and many other organisms (perhaps most other organisms?) cannot be studied this way for practical reasons (e.g. elephants, whales, giant sequoias, many insects, many rodents,.....). Pedigree Analysis

Slide 29:

The basic idea in pedigree mapping is to follow affected individuals and markers in related families. Markers that are co-inherited with disease status are linked to the causative gene. Mapping with pedigrees Two unrelated pedigrees Haplotypes in each pedigree are identified by numbers (different for each family), and determined from five linked markers Dark blue - affected White - unaffected Light blue - status uncertain In each case one haplotype, in red, co-segregates with the disease

Slide 30:

Linkage Disequilibrium (LD) Non random association of alleles (nucleotides) at different genes (sites) The non-independence of alleles at different loci. This occurs when certain combinations of alleles across loci occur more often than expected by chance alone, based on their respective allele frequencies in the population.

Complete Linkage Disequilibrium:

1 2 Complete Linkage Disequilibrium Adapted from Rafalski (2002) Curr Opin Plant Biol 5:94-100. D’  =1 r 2 =1 6 6 Locus 1 Locus 2 Same mutational history and no recombination. No resolution

Linkage Disequilibrium:

1 2 Linkage Disequilibrium D’  =1 r 2 =0.33 3 6 Locus 1 Locus 2 Different mutational history and no recombination. Some resolution 3

Linkage Equilibrium:

1 2 Linkage Equilibrium D’  =0 r 2 =0 3 3 Locus 1 Locus 2 Same mutational history with recombination . Resolution 3 3

Slide 34:

Markers are used to partition the mapping population into different genotypic groups and to determine whether significant differences exist between groups with respect to the trait being measured. A significant difference between phenotypic means and marker system in mapping population indicates that the marker locus being used to partition the mapping population is linked to a QTL controlling the trait. Therefore, the QTL and marker will be usually be inherited together in the progeny, and the mean of the group with the tightly-linked marker will be significantly different ( P <0.05) to the mean of the group without the marker. Detection of LD

Slide 35:

A difference in mean phenotype indicates that linkage disequilibrium present in the population and marker is linked to a QTL. But this does not mean that every marker that is linked to a QTL is expected to show a mean difference in phenotype. This vary with the extent of LD Linkage between markers is usually calculated using odds ratios logarithm of odds :- the ratio of linkage versus no linkage This ratio is generally called LOD score. LOD values of >3 are typically used to construct linkage maps. A LOD value of 3 between genes and marker indicates that linkage is 1000 times more likely (i.e. 1000:1) than no linkage (null hypothesis). LOD values may be lowered in order to detect a greater level of linkage or to place additional markers within maps constructed at higher LOD value. Extent of LD Risch et al. 1992

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Mutation Drift Population structure (admixture) Gene flow Population contraction Selection (hitchhiking or epistasis ) What causes LD?

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Recombination Gene conversion Recurrent mutation What reduces LD?

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The amount and extent of LD that exists in the populations that are used for genetic improvement is the net result of all the forces that create and break-down LD and is therefore, the result of the breeding and selection history of each population, along with random sampling. On this basis, populations that have been closed for many generations are expected to be in linkage equilibrium, except for closely linked loci. Thus, in those populations, only markers that happen to be tightly linked to QTL may show an association with phenotype, and even then there is no guarantee because of the chance effects of random sampling. Implications of the extent of LD

Slide 39:

With candidate genes Candidate genes is the genes that are associated with the trait of interest. This technique evaluate markers that are inside the genes or close to genes that are thought to be associated with the trait of interest. The candidate gene approach utilizes knowledge from species that are rich in genome information (e.g. human, mouse), effects of mutations in other species, previously identified QTL regions, and/or knowledge of the physiological basis of traits to identify genes that are thought to play a role in the physiology of the trait. Following mapping and identification of polymorphisms within the gene in the plants, the association of genotype at the candidate gene with phenotype can be estimated in a closed breeding population. child et al. (2003)

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Whole Genome scan The whole genome scan approach to QTL detection uses random genetic markers spread over the genome to identify genomic regions that harbor QTL. The QTL regions are detected by following the co-segregation of markers with phenotype in structured populations using interval mapping . (Haley et al. 1994)

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Summary of QTL analysis Recombinant Inbred Lines (RILs,F2,F3,Doubled Haploid Lines) Genotype with molecular markers Analyse trait data for each line Link trait data with marker data - Mapping software Trait QTL mapped at bottom of small chromosome Parent 1 Parent 2 QTL Create a Linkage map with molecularmarkers

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Merits of QTL Mapping Where mutant approaches fail to detect genes with phenotypic functions , QTL mapping can help Good alternative when mutant screening is laborious and expensive e.g circadium rhythm screens Can identify New functional alleles of known function genes e.g.Flowering time QTL,EDI was the CRY2 gene Natural variation studies provide insight into the origins of plant evolution Identification of novel genes

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Limitations.... Mainly identifies loci with large effects Less strong ones can be hard to pursue No. of QTLs detected, their position and effects are subjected to statistical error Small additive effects / epistatic loci are not detected and may require further analyses Cloning can be challenging but not impossible

Future Prospects:

Future Prospects Constant improvements of Molecular platforms New Types of genetic materials( e.g. introgression lines: small effect QTLs can be detected) Advances in Bioinformatics

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