QTL Mapping 1&2
Recombination
- independent assortment produces a recombination frequency of 50% meaning genes are very far apart on a chromosome or even on another chromosome. if it was closer to 0% the closure the 2 genes are, therefore recombination frequency is much lower - We can apply this rational to QTL mapping - whereby instead of looking at two genes inherited together we look at the segregation of markers (genotype) and phenotype.
What is needed?
- selection of parental strains displaying variation of the genotype / phenotype of interest - sequenced genomes - Comparison of genomes and determine polymorphisms that display genotypic differences that could be used for RFLP, VNTR/STR or SNP typing. - recombination progeny ( to find recombination frequency ). they need to be genotypical unique - all different from each other
What is needed for QTL Analysis?
2- Variable number of tandem repeats (VNTRs), in which the repeat unit is up to 25 bp in length 3- Short tandem repeats (STRs), usually dinucleotide, trinucleotide or tetranucleotide units
Physical vs Genetic Maps
Both maps are a collection of genetic markers and gene loci. Genetic map distances are based on the genetic linkage information measured in Centi-Morgans, while physical maps use actual physical distances usually measured in number of base pairs. MapMaker is the program used. Physical map could be a more "accurate" representation of the genome. Genetic maps often offer insights into the nature of different regions of the chromosome, e.g. the genetic distance to physical distance ratio varies greatly at different genomic regions which reflects different recombination rates.
Construction of a Genetic Linkage Map
Construction of the genetic map is the first step in the genetic dissection of a trait of interest thus critical for identifying the location of genes that cause genetic diseases. A genetic linkage map shows the position of known genes or genetic markers relative to each other in terms of recombination frequency, rather than a specific physical distance along each chromosome genetic distances are based on recombination frequency
Phenotypes and Genotypes
Finally, the phenotypes of the population need to be determined. All the genotype and phenotype data is entered into a QTL mapping program e.g., S MapManager/QTX. • Markers that are genetically linked to a QTL influencing the trait of interest will segregate more frequently with trait values whereas unlinked markers will not show significant association with phenotype.
QTL mapping
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 putative functions of genes by their similarity to genes with known function, usually in other genomes. This can be done using BLAST, 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[17][18] 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. This can provide information on how the phenotype may be evolving.[citation needed] In a recent development, classical QTL analyses were combined with gene expression profiling i.e. by DNA microarrays. Such expression QTLs (eQTLs)describe cis- and trans-controlling elements for the expression of often disease-associated genes.[19] Observed epistatic effects have been found beneficial to identify the gene responsible by a cross-validation of genes within the interacting loci with metabolic pathway- and scientific literature databases.
QTL Mapping Requires
Good marker coverage and construction of a Genetic map. Parental strains differing in genotype/phenotype of interest. Generation and selection of genotypically unique recombinant progeny. Isolation and purification of DNA and genotyping of all progeny. Phenotyping of all progeny. Analysis of phenotypic and genotypic data. Construction of QTL map using MapManager QTX software. Lod score ("log of the odds") - what are the odds of observing the family marker data if the marker is linked to the disease (less recombination than expected) compared to if the marker is not linked to the disease
Genetic map: specific markers spaced across the genome.
Ideally markers should be spaced every 10- 20cM and span the whole genome.
What is needed for QTL Analysis?
In order to begin a QTL analysis - You need to construct a genetic map. Generate progeny from parental lines - need two or more strains of organisms that differ genetically. Generate genetic markers that distinguish between these parental lines. Several types of markers are used, including RFLPs, SNPs and STRs RFLP allows individuals to be identified based on unique patterns of restriction enzymes cutting in specific regions of DNA
How Is QTL Analysis Conducted?
In order to begin a QTL analysis, scientists require two things. First, they need two or more strains of organisms that differ genetically with regard to the trait of interest. For example, they might select lines fixed for different alleles influencing egg size (one large and one small). Second, researchers also require genetic markers that distinguish between these parental lines. Molecular markers are preferred for genotyping, because these markers are unlikely to affect the trait of interest. Several types of markers are used, including single nucleotide polymorphisms (SNPs), simple sequence repeats(SSRs, or microsatellites), restriction fragment length polymorphisms (RFLPs), and transposable element positions (Casa et al., 2000; Vignal et al., 2002; Gupta & Rustgi, 2004; Henry, 2006). Then, to carry out the QTL analysis, the parental strains are crossed, resulting in heterozygous (F1) individuals, and these individuals are then crossed using one of a number of different schemes (Darvasi, 1998). Finally, the phenotypes and genotypes of the derived (F2) population are scored. Markers that are genetically linked to a QTL influencing the trait of interest will segregate more frequently with trait values (large or small egg size in our example), whereas unlinked markers will not show significant association with phenotype (Figure 1). For traits controlled by tens or hundreds of genes, the parental lines need not actually be different for the phenotype in question; rather, they must simply contain different alleles, which are then reassorted by recombination in the derived population to produce a range of phenotypic values. Consider, for example, a trait that is controlled by four genes, wherein the upper-case alleles increase the value of the trait and the lower-case alleles decrease the value of the trait. Here, if the effects of the alleles of the four genes are similar, individuals with the AABBccdd and aabbCCDD genotypes might have roughly the same phenotype. The members of the F1 generation (AaBbCcDd) would be invariant and would have an intermediate phenotype. However, the F2generation, or the progeny from a backcross of an F1 individual with either parent, would be variable. The F2 offspring would have anywhere from zero to eight upper-case alleles; the backcross progeny would have anywhere from four to eight upper-case alleles. A principal goal of QTL analysis has been to answer the question of whether phenotypic differences are primarily due to a few loci with fairly large effects, or to many loci, each with minute effects. It appears that a substantial proportion of the phenotypic variation in many quantitative traits can be explained with few loci of large effect, with the remainder due to numerous loci of small effect (Remington & Purugganan, 2003; Mackay, 2004; Roff, 2007). For example, in domesticated rice (Oryza sativa), studies of flowering time have identified six QTL; the sum of the effects of the top five QTL explains 84% of the variation in this trait (Yano et al., 1997; Yamamoto et al., 1998, 2000). Once QTL have been identified, molecular techniques can be employed to narrow the QTL down to candidate genes (a process described later in this article). One important emerging trend in these analyses is the prominent role of regulatory genes, or genes that code for transcription factors and other signaling proteins. For instance, in rice, three flowering time QTL have been identified at the molecular level, and all of these loci encode regulatory proteins known from studies of Arabidopsis thaliana (Remington & Purugganan, 2003). A meta-analysis of extensive data in pigs and dairy found that QTL effects were skewed towards fewer QTL with large effects (Hayes and Goddard 2001). Orr (2001) addresses the question of defining and distinguishing between "large" and "small" effects. As with all statistical analyses, sample size is a critical factor. Small sample sizes may fail to detect QTL of small effect and result in an overestimation of effect size of those QTL that are identified (Beavis 1994, 1997). This is known at the "Beavis effect". Otto and Jones (2000) suggested a method for comparing detected QTL to a distribution of expected values in order to estimate how many loci might have been missed. Recent studies have taken these biases into account (e.g., Albert et al. 2007). Another consistent trend in looking at QTL across traits and taxa is that phenotypes are frequently affected by a variety of interactions (e.g., genotype-by-sex, genotype-by-environment, and epistaticinteractions between QTL), although not all QTL studies are designed to detect such interactions. Indeed, several complex traits in the fruit fly Drosophila melanogaster have been extensively analyzed, and this research has indicated that the effects of such interactions are common (Mackay, 2001, 2004). For example, detailed examination of life span in D. melanogaster has revealed that many genes influence longevity (Nuzhdin et al., 2005; Wilson et al., 2006). In addition, significant dominance, epistatic, and genotype-by-environment effects have also been reported for life span (Leips & Mackay, 2002; Forbes et al., 2004). Similarly, QTL studies examining plant architecture differences between maize and teosinte have repeatedly shown significant epistatic interactions (Doebley et al., 1995; Lauter & Doebley, 2002). These same types of interactions have additionally been demonstrated in soybeans (Lark et al., 1995). It is also possible to perform QTL analysis on unmanipulated natural populations using hybrids, sibships (half-sibling or full-sibling families), and/or pedigree information (Lynch & Walsh, 1998; Mott et al., 2000; Slate, 2005). Diverse ecological and evolutionary questions have been addressed using these tools. For example, Shaw and colleagues (2007) identified multiple QTL associated with differences in male calling song between two closely related species of the Hawaiian cricket, a trait involved in rapid speciation. Similarly, Baack and colleagues (2008) addressed the question of possible gene flow between domesticated crops and their wild relatives in contrasting environments using crop-sunflower hybrids. Environmental and conservation questions have also been explored. For instance, Weinig and colleagues (2007) examined various loci that influence invasive success by exotic species, while Pauwels and colleagues (2008) reviewed questions surrounding QTL for tolerance to heavy metal exposure in plants that could contribute to phytoremediation of polluted soils.
Interval mapping
Lander and Botstein developed interval mapping, which overcomes the three disadvantages of analysis of variance at marker loci.[20] Interval mapping is currently the most popular approach for QTL mapping in experimental crosses. The method makes use of a genetic map 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 odds ratio (LOD score) is calculated for the model that the given locus is a true QTL. The odds ratio is related to the Pearson correlation coefficient between the phenotype and the marker genotype for each individual in the experimental cross.[21] 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. 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 are highest. 3) A significance threshold can be established by permutation testing.[22] 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[23] the likelihood for a single putative QTL is assessed at each location on the genome. However, QTLs 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.[24] 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 composite interval mapping 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.
Caveats and Qualifications of QTL Analysis
Like most methods, QTL analysis is not without limitations. For instance, QTL studies require very large sample sizes, and they can only map those differences that are captured between the initial parental strains. Because these strains are unlikely to contain segregatingalleles of large effect at every locus contributing to variation in natural populations, some loci will remain undetected. Furthermore, the specific alleles that do segregate, particularly in inbred lines, may not be relevant to natural populations. Other alleles at these same loci are likely to be of interest, however. Thus, the goal for many studies is to identify loci rather than particular alleles. (One notable area of exception involves applied studies in medicine and agriculture, which are often interested in specific segregating alleles). The number of times that individual genes have been identified following a QTL mapping study remains small. Indeed, Roff (2007) lists examples of quantitative traits in which single genes have major effects and their molecular basis has been studied, and he notes that this number is modest relative to the effort invested in QTL studies. One reason for this discrepancy is that many QTL map to regions of the genome of perhaps 20 centimorgans (cM) in length, and these regions often contain multiple loci that influence the same trait (see, however, Price, 2006). Moreover, identifying the actual loci that affect a quantitative trait involves demonstrating causality using techniques like positional cloning (see Clee et al., 2006) followed by targeted gene replacement (see Sullivan et al., 1997). Frequently, the quest for individual genes within a QTL is assisted by the identification of a priori candidate genes using classical reverse genetics or bioinformatics. A functional relationship between the candidate gene and the QTL must then be demonstrated, such as by using functional complementation (the addition of wild-type complementary DNA from the gene in question into the nucleus to rescue a loss-of function mutation or to produce an alternative phenotype; see, for example, Frary et al., 2000). Other techniques, such as deficiency mapping (deletion mapping), are available for specific organisms, including Drosophila (Mackay, 2001).
The Power of Fine Mapping
Mapped to a region with just 21 genes. LOD score of 7. This is for the mortality phenotype of virulence What about migration, transmigration, growth and serum?
From QTL to candidate gene/s Fine Mapping/Positional Cloning
Mapping results in large loci associated with the phenotype Mapping a QTL that explains the phenotypic variance 19 progeny yielded a region approximately 1.3 -1.7Mb in size (over 200 genes) Strategies for getting to causal loci: Identify additional markers if possible Generate additional recombinants to fine map QTL - Effect sizes of QTL can be overestimated- Often one large QTL is composed of many tightly linked QTLs of small effect 2. Identify candidate genes from those known to cause same/similar phenotype in other organisms or that may have a function that could explain your phenotype of interest.
The Future of QTL Mapping
New permutations of QTL mapping build upon the utility of the original premise: locus discovery by co-segregation of traits with markers. Now, however, the definition of a trait can be broadened beyond whole-organism phenotypes to phenotypes such as the amount of RNAtranscript from a particular gene (expression or eQTL; Schadt et al., 2003) or the amount of protein produced from a particular gene (protein QTL or PQL; Damerval et al., 1994). QTL mapping works in these contexts because these phenotypes are polygenic, just like more traditional organismal phenotypes, such as yield in corn. For example, transcript abundance is controlled not just by cis-acting sequences like the promoter, but also by potentially unlinked, trans-acting transcription factors. Similarly, protein abundance is controlled by "local" variation at the coding gene itself, and by "distant" variation mapping to other regions of the genome. Local variationis likely to be composed of cis variants controlling transcript levels (though the correlation between transcript level and protein abundance is often quite low, so this may represent a minority of cases; see Foss et al., 2007). Other local mechanisms might include polymorphisms for the stability or regulation of the protein. In contrast, distant variation could include upstream regulation control regions. Beyond these examples, further extension of QTL analysis includes mapping the contribution of imprinting to size-related traits (Cheverud et al., 2008), and other adaptations of QTL mapping will no doubt follow. Historically, the availability of adequately dense markers (genotypes) has been the limiting step for QTL analysis. However, high-throughput technologies and genomics have begun to overcome this barrier. Thus, the remaining limitations in QTL analysis are now predominantly at the level of phenotyping, although the use of genomic and proteomic data as phenotypes circumvents this challenge to some extent. Genome-wide association studies (GWAS) are becoming increasingly popular in genetic research, and they are an excellent complement to QTL mapping. Whereas QTL contain many linked genes, which are then challenging to separate, GWAS produce many unlinked individual genes or even nucleotides, but these studies are riddled with large expected numbers of false positives. Though GWAS remain limited to organisms with genomic resources, combining the two techniques can make the most of both approaches and help provide the ultimate deliverable: individual genes or even nucleotides that contribute to the phenotype of interest. Indeed, combining different QTL techniques and technologies has great promise. For example, Hubner and colleagues (2005) used data on gene expression in fat and kidney tissue from two previously generated, recombinant rat strains to study hypertension. Alternatively, samples adapted to different environments may be compared, or other populations of interest might be selected for expression analysis. This approach permits measurement of hundreds or even thousands of traits simultaneously. Differences in expression may be co-localized with phenotypic QTL that have been previously determined to create manageable lists of positional candidate genes (Wayne & McIntyre, 2002). Other interesting questions concerning gene regulation can be addressed by combining eQTL and QTL, such as the relative contributions of cis-regulatory elements versus trans-regulatory elements. Regarding hypertension, Hubner et al. (2005) identified 73 candidate genes deemed suitable for testing in human populations, and many of the most highly linked eQTL were regulated in cis. These integrated approaches will become more common, and they promise a deeper understanding of the genetic basis of complex traits, including disease (Hubner et al., 2006). Integrating phenotypic QTL with protein QTL can also give investigators a more direct link between genotype and phenotype via co-localization of candidate protein abundance with a phenotypic QTL (De Vienne et al., 1999). Still more kinds of data can be integrated with QTL mapping for a "total information" genomics approach (e.g., eQTL, proteomics, and SNPs) (Stylianou et al., 2008). QTL studies have a long and rich history and have played important roles in gene cloning and characterization; however, there is still a great deal of work to be done. Existing data on model organisms need to be expanded to the point at which meta-analysis is feasible in order to document robust trends regarding genetic architecture. Data generated by lab-based QTL studies can also be used to direct and inform other efforts, such as population genomics, wherein a large number of molecular markers are scored in the attempt to identify targets of selection and thus genes underlying ecologically important traits (Stinchcombe & Hoekstra, 2008). Furthermore, QTL studies can inform functional genomics, in which the goal is to characterize allelic variation and how it influences the fitness and function of whole organisms. Thus, although the map between genotype and phenotype remains difficult to read, QTL analysis and a variety of associated innovations will likely continue to provide key landmarks.
Linkage and QTL Mapping
Quantitative Trait Loci (QTL) analysis • Correlate segregation of the quantitative trait with that of the qualitative trait, i.e., markers. QTL is a locus ( section of DNA ) that correlates with variation of a quantitative trait in the phenotype of the population of organisms. QTL's are mapped by identifying which SNPs/ RFLP/ Tandem repeats correlate with an observed trait. this is an early step in identifying and sequencing the actual genes that cause the trait variation
Human Chromosome 1 Map
The more DNA markers there are on a genetic map, the more likely it is that one will be closely linked to a disease gene - and the easier it will be to zero-in on that gene. One of the first major achievements of the HGP was to develop dense genetic maps of markers spaced evenly across the entire collection of human DNA.
Analysis of variance
The simplest method for QTL mapping is analysis of variance (ANOVA, sometimes called "marker regression") at the marker loci. In this method, in a backcross, one may calculate a t-statistic to compare the averages of the two marker genotype 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 F-statistic. 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 recombination 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.
Lod scores
This stands for logarithm of the odds that the genes are linked- what are the odds of observing the family marker data if the marker is linked to the disease (less recombination than expected) compared to if the marker is not linked to the disease. MapManagerQTX >3.0 evidence for linkage <-2.0 can rule out linkage In between - inconclusive, collect more data. Multipoint lod scores for the genome-wide scan. Individuals in 137 sibships demonstrating exceptional longevity were genotyped at 400 marker loci throughout the genome. Scores are plotted as a function of specific markers. Chromosome number is designated at the top of each plot. The horizontal threshold lines on each graph represent MLS = 2.0, a score slightly higher than the average maximum score expected by chance once in a genome scan.
Goals of QTL Mapping
To identify the loci that contribute to phenotypic variation. 1. Cross two parents with extreme phenotypes 2. Genotype the progeny at markers across the genome 3. Score the progeny for the phenotype 4. Associate the observed phenotypic variation with the genotypic variation 5. Ultimate goal: identify causal polymorphisms that explain the phenotypic variation.
Aims of QTL Mapping
To obtain a crude chromosomal location of the gene or genes associated with a phenotype of interest. Examples:Cysticfibrosis, Diabetes, Alzheimer, and Blood pressure. Leads towards a fundamental understanding of individual gene actions and interactions. ( develop drug targets )
Caveats of Mapping
You need a large sample size. Lots of markers required. The resolution of the of a genetic map depends on the number of crossovers that have been scored. Genetic maps can have limited accuracy. Lab crosses vs natural population. Laborious.
QTL mapping
a statistical study of the alleles that occurs in a locus and the phenotypes they produce - the process of constructing linkage maps and conducting QTL analysis to identify genomic regions associated with traits is known as QTL mapping linking genotypes with phenotypes and searching for inheritance to linkup A quantitative trait locus (QTL) is a locus (section of DNA) that correlates with variation of a quantitative trait in the phenotype of a population of organisms.[1] QTLs are mapped by identifying which molecular markers (such as SNPs or AFLPs) correlate with an observed trait. This is often an early step in identifying and sequencing the actual genes that cause the trait variation A quantitative trait locus (QTL) is a region of DNA which is associated with a particular phenotypic trait, which varies in degree and which can be attributed to polygenic effects, i.e., the product of two or more genes, and their environment.[2] These QTLs are often found on different chromosomes. The number of QTLs which explain variation in the phenotypic trait indicates the genetic architecture 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. Typically, QTLs underlie continuous traits (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 Mendel in his experiments). Moreover, a single phenotypic trait is usually determined by many genes. Consequently, many QTLs are associated with a single trait. Another use of QTLs is to identify candidate genes underlying a trait. Once a region of DNA is identified as contributing to a phenotype, it can be sequenced. 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, being this task fundamental for marker-assisted crop improvement
Quantitative trait locus mapping.
a) Quantitative trait locus (QTL) mapping requires parental strains (red and blue plots) that differ genetically for the trait, such as lines created by divergent artificial selection. b) The parental lines are crossed to create F1 individuals (not shown), which are then crossed among themselves to create an F2, or crossed to one of the parent lines to create backcross progeny. Both of these crosses produce individuals or strains that contain different fractions of the genome of each parental line. The phenotype for each of these recombinant individuals or lines is assessed, as is the genotype of markers that vary between the parental strains. c) Statistical techniques such as composite interval mapping evaluate the probability that a marker or an interval between two markers is associated with a QTL affecting the trait, while simultaneously controlling for the effects of other markers on the trait. The results of such an analysis are presented as a plot of the test statistic against the chromosomal map position, in recombination units (cM). Positions of the markers are shown as triangles. The horizontal line marks the significance threshold. Likelihood ratios above this line are formally significant, with the best estimate of QTL positions given by the chromosomal position corresponding to the highest significant likelihood ratio. Thus, the figure shows five possible QTL, with the best-supported QTL around 10 and 60 cM.
linkage = co-segregation
genes that are inherited together will lie very closely on a chromosome and so recombination won't occur frequently - deviation from menders independent assortment law
Outbred populations: Complications
• QTL not segregating in all families• Association between marker and QTL at the family rather than population level• (i.e. linkage phase differs between families) • Additional variance between families due to other loci
SNP Detection
•Under highly stringent hybridization conditions, a stable hybrid occurs only if the oligonucleotide is able to form a completely base-paired structure with the target DNA. •If there is a single mismatch then the hybrid does not form. To achieve this level of stringency, the incubation temperature must be just below the melting temperature of the oligonucleotide •The oligonucleotide probe has two end- labels. One of these is a fluorescent dye and the other is a quenching compound. The two ends of the oligonucleotide base- pair to one another, so the fluorescent signal is quenched. •When the probe hybridizes to its target DNA, the ends of the molecule become separated, enabling the fluorescent dye to emit its signal. The two labels are called 'molecular beacons'.
