The Genom of Homo sapiens.pdf

STRATIFICATION OF LINKAGE DISEQUILIBRIUM 83Table 1. Linkage Disequilibrium Analyses Performed in Worldwide SamplesSex-averageCrom.rate DeCodeN pop N ind band Kb cM/Mb ReferenceCD4 42 1600 12p13.31 10 3.5 Tishkoff et al. (1996)DRD2 28 1342 11q23.2 25 0.8 Kidd et al. (1998)DM 25 1231 19q13.32 20 1.8 Tishkoff et al. (1998)SLC6A4 7 242 17q11.2 15 0.3 Gelernter et al. (1999)PLAT 30 1287–1420 8p11.21 22 0.6 Tishkoff et al. (2000)PAH 29 1475 12q23.2 75 1.5 Kidd et al. (2000)CFTR 18 972 7q31.2 163 0.4 Mateu et al. (2001)G6PD 15 605 Xq28 23 2.0 Tishkoff et al. (2001)DRD4 5 300 11p15.5 5 1.2 Ding et al. (2002)CAPN10 11 561 2q37.3 12 1.6 Fullerton et al. (2002)COMT 32 1,701 22q11.21 28 2.6 DeMille et al. (2002)PKLR-GBA 17 896 1q22 70 1.3 Mateu et al. (2002)ADH 41 2,134 4q23 112 1.5 Osier et al. (2002)out of Africa is not a fixed quantity but has a very largestochastic variation. Therefore, the different geographicalpatterns observed in different loci can be interpreted asthe distribution of a stochastic variable, in which CD4and CFTR would be opposite extremes. It is possible,when sufficient data from a larger number of regions haveaccumulated, that the size and duration of the bottleneckmight be derived from the average and variance of the increaseof LD in non-Africans compared to Africans. Certainly,a more detailed knowledge of locus-specific recombinationrates should be factored in this calculation.In addition to these different results in the analyzed locidue to genetic drift, several unusual LD patterns in globalanalyses have been interpreted as being the result of theeffects of positive selection in specific loci. The high frequencyof some haplotypes in groups of populations andthe low LD decay over chromosomal distance cannot beexplained by genetic drift alone. This is true in the case ofG6PD (Tishkoff et al. 2001), DRD4 (Ding et al. 2002),CAPN10 (Fullerton et al. 2002), and ADH (Osier et al.2002). Interestingly, the LD analysis and its decay alongthe chromosomal region have been shown to be a powerfulapproach to detect positive selection in human populations(Sabeti et al. 2002). Therefore, global LD surveyswill allow detection of population differences in selectivepressures due to different environmental factors, such asexposure to certain pathogens, without prior knowledgeof a specific variant or selective advantage. No doubt thisapproach will lead to the understanding of differentialadaptation among human populations.Besides the different patterns observed in Africancompared to non-African populations, other subtle populationaspects might be detected. The unusual LD patternobserved for the American populations (e.g., PLATand PAH loci) might draw our attention to specific populationsin order to score all the human LD diversitypresent beyond major continental groups. The foundereffect experienced by the ancestors of Native Americanpopulations during the colonization of the continentmight have left its effect in the LD pattern of extant populations.Strong founder effects are also detected in Pacificpopulations, such as the Micronesian Nasioi. Therefore,beyond the main demographic effects detected inthe LD pattern of the human species, such as the expansionout of Africa of modern humans, other minor demographicprocesses that might have left their footprintin extant LD population patterns could be traced by LDanalysis.BLOCK STRUCTURE OF HUMAN LDRecently, several studies have proposed that, despitetheir obvious complexity and variability, LD patterns inthe human genome can be described by means of a quitesimple framework. According to these models, thegenome is divided in a series of “haplotype blocks” (Dalyet al. 2001; Patil et al. 2001; Dawson et al. 2002; Gabrielet al. 2002; Stumpf 2002), sets of consecutive sites invery strong LD between each other which only constitutea few haplotypes. Contiguous blocks are separated by regionsshowing strong evidence of historical recombination.These models are currently the subject of great interest,because, if proven true, it would becomestraightforward to choose SNPs in whole-genome associationstudies. Once the haplotypes forming a block regionwere defined, only a few haplotype-tagging SNPs wouldbe necessary to screen that region for association (Zhanget al. 2002; Meng et al. 2003).Different authors have detected haplotype blocks usingdifferent data sets, different methods, and, crucially, verydifferent definitions of a block (Wall and Pritchard2003b). In general, block definitions are operational andmay change within the same study to fit a priori knowledgeabout the involved populations. For example, whenstudying African populations, Daly et al. (2001) decreasetheir LD threshold for a pair of markers to be consideredpart of the block, because of the known lower LD levels ofthese populations. Thus, assessing the real “blockiness” ofour genome is a difficult issue. In a couple of recentworks, Wall and Pritchard (2003a,b) tackle this problem.They reanalyze several data sets, both real and simulated,using a variation of the block definition of Gabriel et al.(2002), which defines blocks as long stretches of extremelyhigh pair-wise LD (measured as ⎪D´⎪) separatedby regions of extensive recombination. The amount ofblockiness of real and simulated data is assessed by meansof three ad hoc statistics: the amount of sequence coveredby blocks, the internal consistency of blocks, and the de-