Allele frequency matching between SNPs reveals an excess of linkage disequilibrium in genic regions of the human genome

Widespread ultraconservation divergence in primates

The distribution and evolution of ultraconserved elements (UCEs, DNA stretches that are perfectly identical in primates and rodents) were examined in genomes of 3 primate species (human, chimpanzee, and rhesus macaque). It was found that the number of UCEs has decreased throughout primate evolution. At least 26% of ancestral UCEs have diverged in hominoids, whereas an additional 17% have accumulated one or more single nucleotide polymorphisms in the human genome. Sequence polymorphism analyses indicate that mutation fixation within an UCE can trigger a relaxation in the selective constraint on that element. Homogeneous mutation accumulations in UCEs served as a template by which purifying selection acted more effectively on protein-coding UCEs. Gene ontology annotation suggests that UCE sequence variation, primarily occurring in noncoding regions, might be linked to the reprogramming of the expression pattern of transcription factors and developmentally important genes. Many of these genes are expressed in the central nervous system. Finally, UCE sequence variability within human populations has been identified, including population-specific nonsynonymous changes in protein-coding regions.

Linkage disequilibrium between STRPs and SNPs across the human genome

Patterns of linkage disequilibrium (LD) reveal the action of evolutionary processes and provide crucial information for association mapping of disease genes. Although recent studies have described the landscape of LD among single nucleotide polymorphisms (SNPs) from across the human genome, associations involving other classes of molecular variation remain poorly understood. In addition to recombination and population history, mutation rate and process are expected to shape LD. To test this idea, we measured associations between short-tandem-repeat polymorphisms (STRPs), which can mutate rapidly and recurrently, and SNPs in 721 regions across the human genome. We directly compared STRP-SNP LD with SNP-SNP LD from the same genomic regions in the human HapMap populations. The intensity of STRP-SNP LD, measured by the average of D', was reduced, consistent with the action of recurrent mutation. Nevertheless, a higher fraction of STRP-SNP pairs than SNP-SNP pairs showed significant LD, on both short (up to 50 kb) and long (cM) scales. These results reveal the substantial effects of mutational processes on LD at STRPs and provide important measures of the potential of STRPs for association mapping of disease genes.

Genotype, haplotype and copy-number variation in worldwide human populations

Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups. Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected--including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas--the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.

Fine haplotype structure of a chromosome 17 region in the laboratory and wild mouse

Extensive linkage disequilibrium among classical laboratory strains represents an obstacle in the high-resolution haplotype mapping of mouse quantitative trait loci (QTL). To determine the potential of wild-derived mouse strains for fine QTL mapping, we constructed a haplotype map of a 250-kb region of the t-complex on chromosome 17 containing the Hybrid sterility 1 (Hst1) gene. We resequenced 33 loci from up to 80 chromosomes of five mouse (sub)species. Trans-species single-nucleotide polymorphisms (SNPs) were rare between Mus m. musculus (Mmmu) and Mus m. domesticus (Mmd). The haplotypes in Mmmu and Mmd differed and therefore strains from these subspecies should not be combined for haplotype-associated mapping. The haplotypes of t-chromosomes differed from all non-t Mmmu and Mmd haplotypes. Half of the SNPs and SN indels but only one of seven longer rearrangements found in classical laboratory strains were useful for haplotype mapping in the wild-derived M. m. domesticus. The largest Mmd haplotype block contained three genes of a highly conserved synteny. The lengths of the haplotype blocks deduced from 36 domesticus chromosomes were in tens of kilobases, suggesting that the wild-derived Mmd strains are suitable for fine interval-specific mapping.

Cluster analysis of genetic and epidemiological data in molecular epidemiology

Current molecular epidemiological studies of complex diseases include a large number of genetic and epidemiological variables. Clustering approaches are a useful tool to detect patterns in data sets and generate hypothesis regarding potential relationships in complex data situations. In this article similarity coefficients are presented for a hierarchical cluster analysis of single-nucleotide polymorphisms (SNPs) and epidemiological data to gain insight into the relationship of variables and detect potential differences between diseased and control individuals in case-control studies. This approach was applied to two subsets of data from the GENICA study of sporadic breast cancer, a molecular epidemiological population-based case-control study conducted in the greater Bonn region between 2000 and 2004. Separate cluster analyses for cases and controls using flexible matching coefficients for SNPs, Pearson's corrected coefficient of contingency for categorical epidemiological variables, and Spearman's correlation coefficient for quantitative epidemiological variables as measures of similarity revealed small subgroups of SNPs usually of the same gene, as well as clusters of genetic and of epidemiological variables with minor differences between cases and controls. In addition to recent and well-known findings, the joint cluster analysis of SNPs and epidemiological variables provides further insight into the relationship of these variables.

[Toward a non-empirical treatment for rheumatoid arthritis based on its molecular pathology]

Rheumatoid arthritis (RA) is a chronic, disabbling disease that affects individuals during the productive years of their lives. Modern treatment for RA includes the so called "biologic" therapy, which is based on recombinant proteins that modify the biologic processes. These agents have potent therapeutic effects and different mechanisms of action. Nevertheless, therapeutic failure still prevails. Treatment that prevents disability in RA must be started in an early manner, before the development of complications and, ideally, with a minimum possibility of therapeutic failure. As yet, there are no clinical or laboratory criteria to identify those patients with a higher probability of responding to particular types of therapy, delaying control of RA ad affecting the prevention of incapacity. Research into gene diversity through single-nucleotide polymorphisms (SNPs) by means of microarray systems, allows the detailed analysis of gene factors associated to a given disease. SNPs have been recently applied to the study of RA, where the major polymorphisms associated to RA occur primarily in genes that code for proteins related to the initiation of an immune response and/or the control of cellular activity in the immune system, in addition to genes related to tissue repair. The specific meaning of these findings is in its initial stages of research. On the other hand, proteomics relate to the analysis of protein expression profiles at multiple levels. Both types of studies will contribute to the knowledge of patterns of gene expression in RA compared to the general population, and will allow an understanding of the pathogenesis of RA. Moreover, proteomic and genomic profiles can be employed to designs probes that identify individuals with the risk of developing RA, individually predict the response to different therapeutic modalities (pharmacogenomics) and for the follow-up of the biologic response to therapy.

From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions

A brief history of human genetics and genomics is provided, comparing recent progress in those fields with that in pharmacogenetics and pharmacogenomics, which are subsets of genetics and genomics, respectively. Sequencing of the entire human genome, the mapping of common haplotypes of single-nucleotide polymorphisms (SNPs), and cost-effective genotyping technologies leading to genome-wide association (GWA) studies - have combined convincingly in the past several years to demonstrate the requirements needed to separate true associations from the plethora of false positives. While research in human genetics has moved from monogenic to oligogenic to complex diseases, its pharmacogenetics branch has followed, usually a few years behind. The continuous discoveries, even today, of new surprises about our genome cause us to question reviews declaring that "personalized medicine is almost here" or that "individualized drug therapy will soon be a reality." As summarized herein, numerous reasons exist to show that an "unequivocal genotype" or even an "unequivocal phenotype" is virtually impossible to achieve in current limited-size studies of human populations. This problem (of insufficiently stringent criteria) leads to a decrease in statistical power and, consequently, equivocal interpretation of most genotype-phenotype association studies. It remains unclear whether personalized medicine or individualized drug therapy will ever be achievable by means of DNA testing alone.

Association of haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 to alcohol dependence in independent case–control and family samples

There have been many conflicting reports concerning the association of the DRD2 locus with alcohol dependence (AD). To investigate whether these findings could be reconciled by considering the genomic region of DRD2 in greater detail, we conducted two separate association studies of AD in 1220 European-American subjects using family-based (488 subjects) and case-control (318 cases and 414 controls) designs, and 43 single nucleotide polymorphisms mapped to the gene cluster of NCAM1, TTC12, ANKK1 and DRD2. We used a generalized linear model and haplotype score tests for the case-control sample, and the family-based association test for the family sample. Haplotype associations centered on TTC12 exon 3 [rs1893699-rs723077; optimal individual haplotype simulated P-value (P(oihs)) = 0.00021] in both independent samples (family and case-control). Additional AD-associated haplotypes centered around NCAM1 exon 12 in the family sample (P(oihs) = 0.0032), and at exons 2 and 5 of ANKK1 in the case-control sample (P(oihs) = 0.00058). LD contrasts between cases and controls support selection at TTC12 exon 3 and ANKK1 exon 2. The armadillo repeat domains encoded by TTC12 and dopamine interact in the Wnt pathway and may have effects on dopamine cell development in the ventral midbrain. We conclude that risk for AD is attributable in part to variants in four regions within this cluster: exon 3 of TTC12, exon 12/intron13 of NCAM1 and exons 2 and 5 of ANKK1. The complexity of these relationships, many of which replicate between our independent samples, may explain prior inconsistent results.

On the utility of linkage disequilibrium as a statistic for identifying targets of positive selection in nonequilibrium populations

A critically important challenge in empirical population genetics is distinguishing neutral nonequilibrium processes from selective forces that produce similar patterns of variation. We here examine the extent to which linkage disequilibrium (i.e., nonrandom associations between markers) improves this discrimination. We show that patterns of linkage disequilibrium recently proposed to be unique to hitchhiking models are replicated under nonequilibrium neutral models. We also demonstrate that jointly considering spatial patterns of association among variants alongside the site-frequency spectrum is nonetheless of value. Through a comparison of models of equilibrium neutrality, nonequilibrium neutrality, equilibrium hitchhiking, nonequilibrium hitchhiking, and recurrent hitchhiking, we evaluate a linkage disequilibrium (LD) statistic (omega(max)) that appears to have power to identify regions recently shaped by positive selection. Most notably, for demographic parameters relevant to non-African populations of Drosophila melanogaster, we demonstrate that selected loci are distinguishable from neutral loci using this statistic.

Evaluation of the SNP tagging approach in an independent population sample--array-based SNP discovery in Sami

Significant efforts have been made to determine the correlation structure of common SNPs in the human genome. One method has been to identify the sets of tagSNPs that capture most of the genetic variation. Here, we evaluate the transferability of tagSNPs between populations using a population sample of Sami, the indigenous people of Scandinavia. Array-based SNP discovery in a 4.4 Mb region of 28 phased copies of chromosome 21 uncovered 5,132 segregating sites, 3,188 of which had a minimum minor allele frequency (mMAF) of 0.1. Due to the population structure and consequently high LD, the number of tagSNPs needed to capture all SNP variation in Sami is much lower than that for the HapMap populations. TagSNPs identified from the HapMap data perform only slightly better in the Sami than choosing tagSNPs at random from the same set of common SNPs. Surprisingly, tagSNPs defined from the HapMap data did not perform better than selecting the same number of SNPs at random from all SNPs discovered in Sami. Nearly half (46%) of the Sami SNPs with a mMAF of 0.1 are not present in the HapMap dataset. Among sites overlapping between Sami and HapMap populations, 18% are not tagged by the European American (CEU) HapMap tagSNPs, while 43% of the SNPs that are unique to Sami are not tagged by the CEU tagSNPs. These results point to serious limitations in the transferability of common tagSNPs to capture random sequence variation, even between closely related populations, such as CEU and Sami.