Association mapping

Association mapping methods attempt to get past the difficulty of low resolution by relying on information on a population level rather than on just the individual family level. The idea is that population history can provide information about historical recombination events occurring far prior to the current generation of individuals. To understand association mapping methods, it is necessary to understand a little about the quantity linkage disequilibrium. 

There are a number of ways linkage disequilibrium can arise between loci in a population. One possibility is selection If alleles at two (or more) loci confer an advantage to an individual when the alleles occur together, then chromosomes containing these alleles will start to become more frequent in the population than they would otherwise. This increase in this combination of alleles creates linkage disequilibrium. If two populations in different environments experience different levels of selection, the amount of linkage disequilibrium will also probably differ between the populations. This illustrates an important concept While the distance between two loci is constant across populations, the amount of linkage 

While the creation of linkage disequilibrium in a population is important, for genetic mapping purposes, it is the decay of linkage disequilibrium that is of primary interest. This is because there is a connection between how this decay occurs and the distance between loci. For mapping purposes, we are interested in distances between loci in order to map our unknown genes to nearby markers. 

The more frequently that specific alleles occur together on a chromosome, the stronger the linkage disequilibrium between those loci. At the same time, the further apart two loci are, the more likely that alleles will be separated by recombination when gametes are created. Because of this, once linkage disequilibrium is created in a population, recombination between loci will act to reduce it. The amount it will be reduced depends on the amount of recombination between the loci, which in turn depends on how close the loci are. 

---

One should compare capabilities to the electron beam X-ray emission methods of Chapter 3. The major difference is the higher lateral resolution with electron beams and the associated mapping capabilides. Another difference is the shorter probing depth possible with electrons, except when compared to the specialized TXRF method. Comparing electron-beam EDS to X-ray/particle EDS or electron-beam WDS to X-ray/particle WDS, the electron beams have poorer detection limits because of the greater X-ray bacl ound associated with electron... 

Pritchard JK, Stephens M, Rosenberg NA, Donnelly P. Association mapping in structured populations. Am J Hum Genet 2000 67 170-181. 

Association mapping is appropriate for monogenic and complex diseases, and may always be preferable to linkage analyses for late onset diseases where it is difficult to obtain nuclear families, rare diseases for which multiplex pedigrees may not be available, e.g., type-1 diabetes and multiple sclerosis in China, and infectious diseases. Study design can incorporate disease heterogeneity and interaction effects between loci (Sec. 7.2). 

Escamilla et al. (86), in a study of bipolar mood disorder in an isolated population from Costa Rica, using micro satellite markers spaced 6cM intervals across chromosome 18, concluded that LD methods will be useful in this case in a larger sample. The Finnish and Costa Rican populations are considered ideal, since they are relatively homogeneous and show LD over a wider recombination distance than other populations. However, LD is routinely seen for closely linked loci and around disease genes in all populations. With sufficiently closely linked markers, including haplotype level analyses (61,62), association mapping should be a powerful and informative approach in many, and possibly most, populations (57). 

Barcellos LF, Klitz W, Field LL, Tobias R, Bowcock AM, Wilson R, Nelson MP, Nagatomi J, Thomson G. Association mapping of disease loci, by use of a pooled DNA genomic screen. Am J Hum Genet 1997 61 734—747. 

Figure 8.7 Factor scores association maps from soils of the metropolitan area of Napoli, Italy (Cicchella et al., 2008b).

For each sampled site, radioactivity was measured by a portable scintillometer (Lima et al., 2005). The data set was used to produce various types of geochemical maps, including dot maps, baseline maps, factor analysis association maps, risk, partial and total radioactivity maps. 

Unlike iconicity and associations, mappings of conceptual and spatial schemas based on polarity do not rely on perceptual resemblance, nor on previously experienced pairings between attributes and objects. Instead, polarity is based on the organizational structure underlying many perceptual and conceptual dimensions. Polarity constrains mappings of spatial and conceptual schemas when a spatial representation shares oppositional structure or directionality of dimension with the concept being represented. A simple way to envision this oppositional structure is as a continuum with asymmetrically weighted ends. 

In general, each class corresponds to a type in an XML schema with an element and a key. Attributes correspond to nested elements, while associations map to key references. The refactoring operation above therefore results in removing the nested element from the Employee type, creating a new type and element with a key for Department, and a key reference between the two types. An XSLT stylesheet is also generated to migrate data to ensure Department data is not lost. 

This corresponds to using the adjoint symplectic Euler scheme to solve the Newtonian part of the Langevin dynamics SDE, followed by an exact OU solve. An alternative is to use velocity Verlet for the Hamiltonian part, resulting in a scheme denoted BABO] with associated map... 

Kang, H.M., Zaitlen, N.A., Wade, C.M., Kirby, A., Heckerman, D., Daly, M.J., Eskin, E., 2008. Efficient control of population structure in model organism association mapping. Genetics 178, 1709-1723. 

---