Individuals who belong to the same family or the same population are related because of their shared ancestry. Population and quantitative genetics theory is built with parameters that describe relatedness, and the estimation of these parameters from genetic markers enables progress in fields as disparate as plant breeding, human disease gene mapping and forensic science. The large number of multiallelic microsatellite loci and biallelic SNPs that are now available have markedly increased the precision with which relationships can be estimated, although they have also revealed unexpected levels of genomic heterogeneity of relationship measures.

Estimates of genetic population structure (F ST ) were constructed from all autosomes in two large SNP data sets. The Perlegen data set contains genotypes on ∼1 million SNPs segregating in all three samples of Americans of African, Asian, and European descent; and the Phase I HapMap data set contains genotypes on ∼0.6 million SNPs segregating in all four samples from specific Caucasian, Chinese, Japanese, and Yoruba populations. Substantial heterogeneity of F ST values was found between segments within chromosomes, although there was similarity between the two data sets. There was also substantial heterogeneity among population-specific F ST values, with the relative sizes of these values often changing along each chromosome. Population-structure estimates are often used as indicators of natural selection, but the analyses presented here show that individual-marker estimates are too variable to be useful. There is inherent variation in these statistics because of variation in genealogy even among neutral loci, and values at pairs of loci are correlated to an extent that reflects the linkage disequilibrium between them. Furthermore, it may be that the best indications of selection will come from population-specific F ST values rather than the usually reported population-average values.Publication of the Perlegen SNP data set (Hinds et al. 2005) and completion of Phase I of the International HapMap Project (The International HapMap Consortium 2005) have allowed a new perspective on the genetic structure of human populations. These two whole-genome data sets allow population genetic analyses at an unprecedented scale: Previous estimates of genetic population structure (for review, see Garte 2003) have been based on a limited number of loci and provided only average figures of quantities such as F ST (Wright 1951) across the whole genome. The precision of previous estimates is not high, and they relate only to specific genes rather than to the region in which the markers are located. We can expect there to be some diversity in the magnitude of population structure between regions of the genome because the precise genealogy is not the same for each chromosome or part thereof, with values becoming increasingly similar the more closely linked are the regions. The genealogy can differ both by random events and by non-random events such as selection. Strong selection at a locus will induce hitchhiking of nearby regions (Maynard Smith and Haigh 1974), leading to both a reduction in heterozygosity within populations and an increase in diversity between populations as measured by F ST . Examination of the differences in diversity between regions therefore provides an opportunity to identify those that cannot be explained solely in terms of random sampling of the genealogy due to Mendelian segregation, variation in family size, migration, and recombination between genetic sites.Methods for estimating F ST from samples of a group of populations are well established (e.g., Weir and Cockerham 1984).More recently they have been ...

A maximum-likelihood estimator for pairwise relatedness is presented for the situation in which the individuals under consideration come from a large outbred subpopulation of the population for which allele frequencies are known. We demonstrate via simulations that a variety of commonly used estimators that do not take this kind of misspecification of allele frequencies into account will systematically overestimate the degree of relatedness between two individuals from a subpopulation. A maximum-likelihood estimator that includes F ST as a parameter is introduced with the goal of producing the relatedness estimates that would have been obtained if the subpopulation allele frequencies had been known. This estimator is shown to work quite well, even when the value of F ST is misspecified. Bootstrap confidence intervals are also examined and shown to exhibit close to nominal coverage when F ST is correctly specified. The above methods generally assume that allele frequencies are known without error, but Wang (2002) also considered the case in which allele frequencies were estimated from a sample of size N from the population in which relatedness is to be estimated. In this work, we consider a different situation: the case in which the individuals examined belong to a subpopulation of the population to which known allele frequencies apply. An example of this situation would be when ''European'' allele frequencies are used in estimating the relatedness between two individuals who happen to be Italian.The effect of using allele frequencies from the overall population is to shift the reference from which we measure relatedness back in time. As an illustration, consider the case in which two individuals share copies of an allele that is common in their subpopulation, but very rare in the population as a whole. Using the subpopulation allele frequencies, the fact that both individuals have this allele provides little evidence for relatedness. When the population allele frequencies are used, however, the evidence for relatedness becomes strong. The difference is that, when the population allele frequencies are used, relatedness is implicitly measured with respect to the time when the population allele frequencies held in the subpopulation, that is, before the subpopulation split from the ancestral population (which may be assumed to have allele frequencies similar to that of the current overall population-there is an implicit assumption here that the overall population is so large that its allele frequencies remain roughly unchanged over time). In this scenario, the relatedness estimate using the population allele frequencies is affected by the generations during which the allele frequencies in the subpopulation were diverging via drift from that in the overall population. From that perspective, the abundant copies of the allele in the subpopulation may all be copies of one allele in the ancestral population and these two individuals both have copies because they share common ancestry. Hence, even though the indiv...

Adrenergic receptor β 2 (ADRB2) is a primary target for epinephrine. It plays a critical role in mediating physiological and psychological responses to environmental stressors. Thus, functional genetic variants of ADRB2 will be associated with a complex array of psychological and physiological phenotypes. These genetic variants should also interact with environmental factors such as physical or emotional stress to produce a phenotype vulnerable to pathological states. In this study, we determined whether common genetic variants of ADRB2 contribute to the development of a common chronic pain condition that is associated with increased levels of psychological distress and low blood pressure, factors which are strongly influenced by the adrenergic system. We genotyped 202 female subjects and examined the relationships between three major ADRB2 haplotypes and psychological factors, resting blood pressure, and the risk of developing a chronic musculoskeletal pain condition -Temporomandibular Joint Disorder (TMD). We propose that the first haplotype codes for lower levels of ADRB2 expression, the second haplotype codes for higher ADRB2 expression, and the third haplotype codes for higher receptor expression and rapid agonist-induced internalization. Individuals who carried one haplotype coding for high and one coding for low ADRB2 expression displayed the highest positive psychological traits, had higher levels of resting arterial pressure, and were about 10 times less likely to develop TMD. Thus, our data suggest that either positive or negative imbalances in ADRB2 function increase the vulnerability to chronic pain conditions such as TMD through different etiological pathways that imply the need for tailored treatment options.