12€¦ · Web view12 - 12 - GENE422/522 Genetic Evaluation and Breeding ©2009 The Australian Wool Education Trust licensee for educational activities University of New England

12. Gene Discovery

Karen Marshall

Learning objectivesBy the end of this topic, you should have an understanding of:

the basic steps to gene discovery for a quantitative trait the limitations of linkage and linkage disequilibrium analysis in relation to refining a QTL

position the term ‘positional candidate gene’, and how these genes can be identified how the identification of functional mutations can be used to prove the candidate gene is

actually the gene of interest

Key terms and concepts Gene discovery, linkage mapping, linkage disequilbrium (LD) mapping, combined linkage disequilibrium – linkage (LDLA) mapping, targeted recombinant progeny (TRP) mapping, positional candidates, differential expression, microarrays, functional mutations.

Introduction to the topicGene discovery refers to the identification of gene(s) controlling a trait of interest, and the associated mutation(s) responsible for the altered phenotype. Livestock geneticists are interested in gene discovery as it allows for the application of genetic assisted selection (GAS). Gene discovery can also lead to structural / functional studies on the gene product and knowledge of the biochemical pathway controlling the trait of interest.

This topic overviews the general strategies in gene discovery for a quantitative trait. The main focus is the ‘positional candidate’ approach, where QTL mapping is used to narrow the gene position to a small interval, with candidate genes subsequently identified within this region. Some revision of QTL mapping is given in the context of resolution of the QTL map position, before addressing topics more specific to gene discovery. The final section of the lecture describes the first successful report of gene discovery for a quantitative trait in an outbred mammal: the DGAT1 gene affecting milk yield and composition (Grisart et al., 2002).

12.1 Steps to gene discoveryThe steps to gene discovery for quantitative traits can be (somewhat arbitrarily) broken down as follows:

unknown location to an approximately 20-30cM region, by broad-scale QTL mapping using linkage analysis (LA),

20-30cM region to a <3cM region, by fine-scale QTL mapping often using linkage disequilibrium (LD) analysis, or multi-generational QTL mapping approaches,

<3cM to gene and functional mutation generally using the ‘positional candidate’ approach.

More detail on each of these steps is given below. In practice there are many variations to that described, depending on factors such as prior knowledge of the gene, size of the QTL effect, and resources available for gene mapping.

GENE422/522 Genetic Evaluation and Breeding - 12 - 1©2009 The Australian Wool Education Trust licensee for educational activities University of New England

12.2 Location of the QTL to a broad (20-30cM) regionBroad-scale linkage analysisThe first step to gene discovery is to find the rough location of the QTL on the genome. This is often achieved by linkage analysis, as described in Topic 10. For revision purposes a diagram showing the basis of linkage mapping is given in Figure 12.1.

Figure 12.1 Basis of linkage analysis for a half-sib mapping design and single marker analysis. Shown are trait distributions for progeny inheriting either a M1 or M2 marker allele

from their sire. Source: Marshall, (2006).

12.1 A. Progeny inheriting an M1 allele have a significantly different trait mean to those inheriting an M2 allele, indicating that the marker is linked to a QTL. For example, consider a sire heterozygous for a QTL for weight with M1 linked to a QTL allele (q) of negative effect and M2 linked to a QTL allele (Q) of positive effect. Progeny inheriting M1 will mostly inherit q and progeny inheriting M2 will mostly inherit Q, and thus the average weight of progeny inheriting M1 is lower than the average weight of progeny inheriting M2. The actual difference between the means of the M1 and M2 groups will depend on the size of the QTL effect (a bigger QTL corresponds to a bigger mean difference) and the recombination frequency (r) between the marker and QTL (the larger the r the smaller the mean difference as recombination disrupts the association between QTL and marker). Note that in single marker analysis the size of the QTL effect and recombination distance are confounded.

12.1B. Progeny inheriting the M1 and M2 alleles do not have a significantly different trait mean, indicating that the marker is not linked to a QTL. For example, consider a sire heterozygous for a QTL for weight (Qq) but with the marker locus located on a different chromosome (i.e. unlinked). In this case progeny inheriting M1 will equally inherit the Q and q alleles, as will progeny inheriting M2. The average weight of progeny inheriting M1 and M2 therefore does not differ.

In practice, QTL mapping experiments often start with a genome scan, with markers spread over the genome at intervals of around 20cM. Interval mapping is performed, with the resultant test statistic plotted against map distance to form a ‘QTL profile’. The highest peak in this profile corresponds to the most likely QTL position. Depending on other factors of the experimental design (such as family size and marker informativeness), the confidence interval* around the most likely QTL position can be in the range of 20-30cM (Figure 12.2). Given such a region can contain 20-30 million base pairs and 200-300 genes�, it clearly requires further refinement before moving to a more directed gene discovery approach.

*when performing QTL mapping using interval mapping via maximum likelihood, a 96.8% confidence interval can be defined as the map positions corresponding to a one LOD or 2.3 LRT decrease either side of the best QTL position �using the ‘rule of thumb’ that 1cM equates to 1 million base pairs containing 10 genes, see Topic 9.

12 - 2 - GENE422/522 Genetic Evaluation and Breeding©2009 The Australian Wool Education Trust licensee for educational activities University of New England

Figure 12.2 Example of a QTL mapping profile from an initial genome scan. Plotted is LRT versus map distance in Morgans. A broad confidence interval surrounding the most likely

QTL position can be observed. Source: Marshall, (2006).

Single half-sib family of approximately 60 individuals, markers located at approximately 0.2M intervals (except for 0.2 to 1.0M where no informative markers exist), analysed via the interval mapping approach. The test statistic is the likelihood ratio test (LRT), the position corresponding to the highest LRT is the most likely QTL positions. The 96.8% confidence interval around this position is shown (as the region corresponding to a 2.3 LRT drop-off from the most likely position). Horizontal lines refer to 5% and 1% chromosome-wide significance levels.

12.3 Refinement of the QTL position to <3cM

Finer-scale linkage analysisFiner-scale linkage analysis involves additional genotyping within the region of interest (Figure 12.3), and aims to reduce the confidence interval of the position estimate. At some point (often 5-10cM), however, increasing the marker density will not further refine the QTL position (Darvasi et al., 1993; Long et al., 1995). This is because recombinations within the region of interest are required to refine the QTL position, thus for small marker distances very large family sizes are required. For example, for markers spaced 2cM apart only 2 out of 100, or 20 out of 1000, progeny are expected to be recombinant. (Recall from Topic 9 that 1cM corresponds to 1 recombination event between two loci per 100 meiosis).

Figure 12.3 Finer-scale linkage analysis – additional markers (represented by arrows) are analysed within the region of interest. Source: Marshall, (2006).


Linkage disequilibrium (LD) mappingFirst some revision on the term ‘linkage disequilibrium’. Two loci located on separate chromosomes are said to be unlinked and in linkage equilibrium, as they segregate independently (for example for A and B loci located on different chromosomes, an A1A2, B1B2 individual will produce equal numbers of A1B1, A1B2, A2B1 and A2B2 gametes). In contrast, two loci located on the same chromosome are said to be linked and in linkage disequilibrium: these loci do not segregate independently.

The terms, linkage and linkage disequilibrium mapping, can be confusing as they both rely on the presence of linkage disequilibrium between the marker and the QTL. Linkage mapping considers the linkage disequilibrium that exists within families, whereas linkage disequilibrium (LD) mapping considers the linkage disequilibrium that occurs across the entire population.

In its purest form linkage disequilibrium mapping disregards pedigree structure. Essentially the method groups animals into those containing haplotypes that are identical by descent (IBD), and then ascertains whether different haplotypes are associated with different trait means. If this is the case, evidence for a QTL exists. The haplotypes under consideration represent small chromosomal regions (often <1 cM) that are in LD. Creation of these regions is depicted in Figure 12.4. If the mutation creating the QTL allele occurred many generations ago, the ancestral haplotype is likely to be disrupted due to recombination, leaving a small IBD region surrounding the mutation..

Figure 12.4 The basis of linkage disequilibrium mapping. Source: The Wellcome Trust (2005). www.wellcome.ac.uk/genome.

A new mutation (X) arises in the proximity of six single nucleotide polymorphisms (SNPs), with the ancestral haplotype signature TATCAT. Over several generations, the haplotype signature may be eroded by recombination. For example, contemporary haplotype 1 was produced by recombination between the first and second SNPs. The smallest conserved haplotype signature in all individuals carrying X places the gene between SNPs 3 and 4. (Text slightly adapted)

Linkage disequilibrium can result from selection, non-random mating, small population size, migration and mutation (Lander and Schork, 1994). In livestock populations, finite population size is often the key cause. Significant LD in livestock has been reported for regions spanning up to 10cM (Farnir et al., 2000; McRae et al., 2001; Nsengimana et al., 2004). In humans, LD is often reported to extend for shorter distances (e.g. <0.1cM), largely attributable to the greater effective population size (Dunning et al., 2000, Kruglyak, 1999; Reich et al., 2001).

For LD mapping to be effective the spacing between the markers must be compatible with the distance that LD extends. Consider an example where regions spanning up to 2cM show complete LD, regions spanning 2cM to 5cM show lower but still significant LD, and regions longer than this show little LD. A marker spacing of say 20cM would not be effective, as this spacing is much greater than the extent of significant LD. On the other hand a marker spacing of 0.1cM would be wasteful, as the QTL position can only be resolved to 2cM. Thus the longer extent of LD in


livestock compared to humans is seen to be both advantageous and disadvantageous: the advantage is that very closely linked markers are not required, the disadvantage is that the resolution of the QTL position is limited. Under simulation studies relevant to livestock populations LD mapping was demonstrated to position a QTL to within 1-3cM for marker spacings of 1cM or less (Meuwissen and Goddard, 2000).

In practice an analysis combining both linkage and linkage disequilibrium information is often used, termed linkage-linkage disequilibrium analysis (LDLA) (Meuwissen et al., 2002; Lee and van der Werf, 2004; Lund et al., 2003). This has the reported advantage of removing spurious peaks, as the data has to conform with both LD and LA principles (Meuwissen et al., 2002) LDLA mapping was used to fine-map a QTL for twinning rate in cattle to <1cM (Meuwissen et al., 2002).

The experimental designs for LD or LDLA mapping usually involve historical or industry data, allowing the analysis to be performed over generations and families. The data must provide a sufficient number of haplotypes, and sufficient records per haplotype to allow an accurate estimation of the haplotype effect.

Targeted recombinant progeny (TRP) approachThe targeted recombinant (TRP) approach (Heifetz et al., 2002) is based on the LD mapping concept, but shows how QTL mapping designs can be optimised to reduce the required number of genotypes and phenotypes. The steps are:

produce many sons from a sire heterozygous for the QTL (‘Qq’),

identify those sons that are recombinant within the gene region of interest by genotyping the two most outer markers. Obtain further genotypes throughout the region on only these recombinant sons.

determine whether the recombinant sons inherited a ‘Q’ or ‘q’ allele from their sire. For most quantitative traits this will involve a progeny test,

determine the location of the QTL via ‘breakpoint analysis’, as shown in Figure 12.5

This is a useful method to refine the QTL position, but requires sufficient recombinant sons and time to allow for the progeny testing. To date, this approach has been mostly used for discrete traits where the need to progeny test is avoided (as individuals can be classed as carrying ‘Q’ or ‘q’ on their own phenotype).

Figure 12.5 Use of ‘break-point’ analysis to refine a QTL position.Source: Marshall, (2006).

In this example the sire has allele ‘A’ at markers M1 to M9 in phase with the QTL allele ‘q’, and allele ‘B’ at markers M1 to M9 in phase with the QTL allele ‘Q’. Individuals recombinant within the region of interest, which for M1-M9 are either A-B or B-A, are classed as inheriting the sires Q or q allele based on a progeny test. The QTL position is narrowed to the region M6-M7 as all ‘Q’ individuals inherited a B allele at these loci, and all ‘q’ individuals inherited an ‘a’ allele at these loci. A ‘-‘ refers to an unknown marker genotype.


12.4 From <3cM to a candidate gene

General gene identification strategiesGene identification strategies include:

the positional approach, which uses knowledge of the mapped location of the gene,

the functional approach, which uses knowledge of the protein encoded by the gene,

and the candidate gene approach, where candidate genes are identified based on sequence, literature, or experimental data.

Positional-candidate approachFor quantitative traits the candidate gene approach is generally applied after the position of the gene has been refined by QTL mapping to 3cM or preferably less. Combining the positional and candidate approaches in this way is termed the positional-candidate approach.

One method to identify candidate genes is on the basis of sequence data. If the genome is sequenced, all genes within the region of interest can be identified by bioinformatics-based approaches. If the genome is not sequenced, genes can be predicted from comparative maps to another sequenced species. For example, sheep genes could be predicted on the basis of the cattle or even human sequence. Around 10 genes could be expected in a 1cM region. Predicted gene function is then used to identify which of these genes are the best candidates. This may be based on gene function in other species, gene function of the broader gene family, and other data available through the literature. This approach has a higher success if there is a small confidence interval around the QTL – preferably 1cM or less, if the comparative maps are well defined, and if there is a reasonable understanding of the gene function and trait biology.

An alternate method to identify candidate genes is on the basis of expression data. In this approach genes that are differentially expressed in ‘Q’ and ‘q’ animals (where ‘Q’ and ‘q’ animals refer to the alternate QTL alleles) are detected by various methods. Differentially expressed genes that map to the region of interest are the positional candidates.

One method to detect differentially expressed genes is via the use of microarrays. In this technique, mRNA is first extracted from the two tissue sources in which differential expression is to be established (e.g. tissue from ‘Q’ and ‘q’ animals). mRNA (or cDNA) from one tissue is labelled with a green fluorescent dye, and mRNA from the other tissue with a red dye. Equals amounts of mRNA from the two tissues are combined and used to probe a microarray, which comprises thousands of representative gene sequences attached to a slide (one ‘spot’ or position on the slide for each gene sequence). Following hybridisation and washing to remove unbound material, the level of gene expression is deduced by colour. A green colour for a particular microarray position indicates the gene was over-expressed in the tissue labelled with green dye, a red colour indicates over-expression in the alternate tissue, and a yellow colour indicates equal expression in both tissues (Figure 12.6).

Microarrays generally produce numerous candidates, a number of which may also be positional. The technique has a greater chance of success if all possible candidate genes are represented on the array, which may not be the case for new or novel genes. Further, the genes identified by microarray analysis may be ‘downstream’ of that identified in the QTL mapping experiment. For example, if a mutation in gene A (detected by QTL mapping) causes a change in the expression level of gene B (detected in the microarray analysis).


Figure 12.6 Microarray analysis. In this example the two tissue sources are ‘normal’ and ‘tumour’. Alternatively, the tissues could be sourced from individuals harbouring the

different QTL alleles. Source: Courtesy of National Human Genome Research Institute, (2005).

Positional approachIf genes within a region of interest are unknown, only a positional (rather than positional-candidate) approach can be employed. In this case, clones of genomic DNA representing the region of interest are obtained and screened for gene-containing sequences. This can be achieved by techniques such as zoo blots, exon trapping and cDNA selection (refer to the appropriate reference under readings for more information). The following step is to determine which of the gene-containing clones represents the gene of interest. This can be achieved by, for example, testing which of the clones hybridise to mRNA or cDNA derived from cells known to express the gene of interest.

12.5 Establishing whether the candidate gene is actually the gene of interest

Identification of the functional mutationIdentification of the functional or causative mutation(s) is generally required as proof that the candidate gene is actually the gene of interest (i.e. that the gene has a direct effect on the trait, rather than an effect due to linkage with another gene). Functional mutations are identified by sequencing the broader gene region of a number of ‘Q’ and ‘q’ individuals and comparing the sequences. Functional mutations could result in an altered gene-product (i.e. protein) or a difference in the expression levels of its own or even another gene-product. Putative functional mutations are usually confirmed using a population independent to that used in the initial study. Alternatively, putative mutations can be confirmed by transgenic studies where an altered form of the gene is introduced into mice or other host species, and the resultant phenotype observed.

Note that functional mutations can be located within regulatory sequences as well as protein-coding sequences. Further note that not all mutations within a protein-coding sequence will be functional. Some may be silent (code for the same amino-acid), cause a conserved amino-acid substitution, or alter a non-critical part of the protein. The extract below shows the complexity of functional mutations affecting the double muscling gene in beef cattle (Georges et al., 1998).

“The gene causing muscular hypertrophy (double muscling) in beef cattle has been cloned. It is the myostatin gene. The complete myostatin coding sequence from double-muscle cattle representing ten breeds was examined. Seven DNA sequence polymorphisms were found. Of these, five polymorphisms are predicted to alter the function of the protein, one caused a conserved amino-acid substitution, and one was a silent mutation. Four additional DNA sequence polymorphisms


were identified in intronic sequences. In eight out of the ten breeds all double-muscled animals were either homozygous or compound heterozygous for one of the five predicted functional mutations. The lack of obvious functional mutations in the remaining two breeds indicates additional mutations in other, as yet unexamined, regions of the gene or locus heterogeneity of double muscling.”

12.6 Success of gene-discoveryNumerous genes controlling discrete or semi-quantitative traits of interest to livestock geneticists have been identified. Examples include the Booroola and Inverdale genes affecting fecundity in sheep (Galloway et al., 2000; Wilson et al., 2001), the myostatin gene responsible for double-muscling in cattle (Grobert et al., 1997; Kambadur et al., 1997), the dwarf gene in poultry (Guillaume, 1976), and the Halothane gene in pigs which gives increased lean percent but also increased stress susceptibility (Milan et al., 2000).

Whilst it is easier to identify genes for discrete traits in comparison to quantitative traits, the process can still take numerous years. Identification of the Inverdale gene was described by Galloway et al. (2001) as “The finding was the culmination of many years of research involving breeding and segregation studies, genetic linkage mapping, physiology, molecular biology and comparative links to studies in human and mice”.

First success for a quantitative trait in outbred mammalsThe first successful example of positional-candidate cloning of a QTL in an outbred mammal was reported by Grisart et al. (2002). It is the DGAT1 gene affecting milk yield and composition. Key events in this process were:

Identification of a QTL affecting milk yield on chromosome 14 and with a confidence interval of 20-40cM, by genome-wide linkage analysis (Georges et al., 1995)

Refinement of the QTL position to a 3cM region by LD and LDLA mapping (Riquet et al., 1999 and Farnir et al. 2002)

Identification of a candidate gene within the 3cM region (the DGAT1 gene), with subsequent sequence analysis identifying a functional mutation within this gene (Grisart et al., 2002 and Grisart et al., 2004)

An extract from the discussion of the Grisart et al. (2002) paper is given below.

“We herein report one of the first successful positional cloning efforts of a QTL in an outbred species, including humans. This success was undoubtedly enabled by two factors that facilitate fine-mapping of the QTL: the magnitude of its effect and the fact that it was attributable to a single mutation in one gene. In addition, the identified mutation proved to be a more easily interpretable missense mutation (rather than for instance a regulatory promoter mutation) in a very strongly supported candidate gene. Forthcoming QTL cloning experiments are likely to be more complicated, because all of these conditions will in general not apply. Our present results, however, demonstrate the feasibility of positional cloning as an approach to identify QTL and should encourage further efforts along these lines.”

The expectation is that numerous other genes underlying quantitative traits will be identified over the next decade.


Readings ³The following readings are available on CD1. Crawford, A.M. 2003, 'The use of genetic modification technologies in the discovery of

genes affecting production traits and disease resistance in animals', New Zealand Veterinary Journal, vol. 51, pp. 52-57. (General overview of gene discovery for quantitative traits, New Zealand focus).

2. Rothschild, M.F. 2003, 'Advances in pig genomics and functional gene discovery', Comparative and Functional Genomics, vol. 4, pp. 266-270. (Pig as a livestock example).

Further reading not on CD.1. (Optional) Gene422/522 lecture notes from 2003: Gene cloning and direct markers

identifying the clones of interest (descriptions of zoo blots, exon trapping and cDNA selection).

ActivitiesAvailable on WebCT

Multi-Choice QuestionsSubmit answers via WebCT

Useful Web LinksAvailable on WebCT

Assignment QuestionsChoose ONE question from ONE of the

topics as your assignment. Short answer questions appear on WebCT. Submit your answer via WebCt

Summary ³Summary Slides are available on CDGene discovery allows for the application of genotypic assisted selection, and for functional studies on the gene product. For quantitative traits a suitable strategy for gene discovery is the positional cloning approach, where the position of the gene is first narrowed to <2cM (or thereabouts) by QTL mapping, and candidate genes then identified within this region. Identification of candidate genes is generally based on sequence or expression analysis, in combination with knowledge of the predicted gene function. Once a candidate gene is identified it must be shown to be the actual gene of interest, rather than having an effect due to linkage with another gene. This is usually achieved by identifying the functional or causative mutation(s). The first report of positional cloning of a QTL in an outbred mammal was reported in 2002 by Grisart et al. proving the feasibility of this approach.


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Glossary of termsBreakpoint analysis Analysis of marker genotypes of individuals recombinant within

the region of interest to narrow a QTL positioncDNA Complimentary DNA, derived from reverse-transcribing mRNAcM Centi-Morgan: A unit of distance on a genetic or linkage mapComparative mapping

Alignment of chromosomal regions of related species

Confidence interval of QTL position

The region surrounding the most likely QTL position: a 96.8% confidence interval corresponding to a one LOD score decrease either side of the most likely position

DGAT1 gene Gene affecting milk yield and composition identified by the positional cloning approach (Grisart et al. 2002)


http://www.accessexcellence.org/RC/VL/GG/microArray.html

Differential expression

Refers to the expression of genes at different levels in different tissues

Direct marker The DNA mutation responsible for the altered phenotype, i.e. the functional mutation

Discrete trait A trait that falls into distinct classes (e.g. eye colour, number of progeny born)

Functional mutation The DNA mutation responsible for the altered phenotype, also termed the causative or causal mutation

GAS Genotypic assisted selection: uses direct markers to aid selection decisions

Gene discovery Identification of gene(s) controlling a trait of interestIBD Identical by descent: alleles are said to be identical by descent if

they are derived from a common ancestorLD Linkage disequilbrium: two loci are said to be in LD when they

are linked (located on the same chromosome) and thus do not segregate independently

LDLA analysis Linkage disequilbrium – linkage analysisLinkage analysis Type of QTL mapping analysis which uses linkage disequilibrium

(LD) between the QTL and marker on a within family basisLinkage disequilibrium analysis

Type of QTL mapping analysis which uses linkage disequilibrium (LD) between the QTL and marker on a population wide basis

Marker Used in this lecture in the sense of a molecular marker: molecular markers reveal variations or polymorphisms at the DNA level

Microarray An array or grid of thousands of representative gene sequences attached to a slide

Microarray analysis A procedure used to detect differentially expressed genesmRNA Messenger RNAMissense mutation Mutation resulting in a codon producing a different and

functionally non-equivalent amino acidPositional candidate A candidate gene whose position was first determined from QTL

mapping based approachesQuantitative trait A trait that shows a continuous distribution (e.g. weight, muscle,

fat)TRP Targeted Recombinant Progeny: a multi-generational approach

to QTL mapping that uses breakpoint analysis


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