Genetic variation and pharmacogenomics: concepts, facts, and challenges

The analysis of genetic variation in candidate genes is an issue of central importance in pharmacogenomics. The specific approaches taken will have a critical impact on the successful identification of disease genes, the molecular correlates of drug response, and the establishment of meaningful relationships between genetic variants and phenotypes of biomedical and pharmaceutical importance in general. Against a historical background, this article distinguishes different approaches to candidate gene analysis, reflecting different stages in human genome research. Only recently has it become feasible to analyze genetic variation systematically at the ultimate level of resolution, ie, the DNA sequence. In this context, the importance of haplotype-based approaches to candidate gene analysis has at last been recognized; the determination of the specific combinations of variants for each of the two sequences of a gene defined as a haplotype is essential. An up-to-date summary of such maximum resolution data on the amount, nature, and structure of genetic variation in candidate genes will be given. These data demonstrate abundant gene sequence and haplotype diversity. Numerous individually different forms of a gene may exist. This presents major challenges to the analysis of relationships between genetic variation, gene function, and phenotype. First solutions seem within reach. The implications of naturally occurring variation for pharmacogenomics and “personalized” medicine are now evident. Future approaches to the identification, evaluation, and prioritization of drug targets, the optimization of clinical trials, and the development of efficient therapies must be based on in-depth knowledge of candidate gene variation as an essential prerequisite

Fosmid-based whole genome haplotyping of a HapMap trio child: evaluation of Single Individual Haplotyping techniques

Determining the underlying haplotypes of individual human genomes is an essential, but currently difficult, step toward a complete understanding of genome function. Fosmid pool-based next-generation sequencing allows genome-wide generation of 40-kb haploid DNA segments, which can be phased into contiguous molecular haplotypes computationally by Single Individual Haplotyping (SIH). Many SIH algorithms have been proposed, but the accuracy of such methods has been difficult to assess due to the lack of real benchmark data. To address this problem, we generated whole genome fosmid sequence data from a HapMap trio child, NA12878, for which reliable haplotypes have already been produced. We assembled haplotypes using eight algorithms for SIH and carried out direct comparisons of their accuracy, completeness and efficiency. Our comparisons indicate that fosmid-based haplotyping can deliver highly accurate results even at low coverage and that our SIH algorithm, ReFHap, is able to efficiently produce high-quality haplotypes. We expanded the haplotypes for NA12878 by combining the current haplotypes with our fosmid-based haplotypes, producing near-to-complete new gold-standard haplotypes containing almost 98% of heterozygous SNPs. This improvement includes notable fractions of disease-related and GWA SNPs. Integrated with other molecular biological data sets, this phase information will advance the emerging field of diploid genomics

Multiple haplotype-resolved genomes reveal population patterns of gene and protein diplotypes

To fully understand human biology and link genotype to phenotype, the phase of DNA variants must be known. Here we present a comprehensive analysis of haplotype-resolved genomes to assess the nature and variation of haplotypes and their pairs, diplotypes, in European population samples. We use a set of 14 haplotype-resolved genomes generated by fosmid clone-based sequencing, complemented and expanded by up to 372 statistically resolved genomes from the 1000 Genomes Project. We find immense diversity of both haploid and diploid gene forms, up to 4.1 and 3.9 million corresponding to 249 and 235 per gene on average. Less than 15% of autosomal genes have a predominant form. We describe a ‘common diplotypic proteome’, a set of 4,269 genes encoding two different proteins in over 30% of genomes. We show moreover an abundance of cis configurations of mutations in the 386 genomes with an average cis/trans ratio of 60:40, and distinguishable classes of cis- versus trans-abundant genes. This work identifies key features characterizing the diplotypic nature of human genomes and provides a conceptual and analytical framework, rich resources and novel hypotheses on the functional importance of diploidy

Meta-Analysis of the INSIG2 Association with Obesity Including 74,345 Individuals: Does Heterogeneity of Estimates Relate to Study Design?

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI≥30 kg/m2) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status (‘healthy population’, HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I2 measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I2 measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI≥32.5, 35.0, 37.5, 40.0 kg/m2 versus BMI<25 kg/m2) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far

Meta-Analysis of the INSIG2 Association with Obesity Including 74,345 Individuals: Does Heterogeneity of Estimates Relate to Study Design?

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI≥30 kg/m2) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status (‘healthy population’, HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I2 measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I2 measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI≥32.5, 35.0, 37.5, 40.0 kg/m2 versus BMI<25 kg/m2) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far

Haplotypes and the systematic analysis of genetic variation in genes and genomes

Haplotypes have been used in various fields of genetics for a long time, in a variety of contexts, and for different purposes. Now, haplotype-based approaches to the analysis of candidate genes and genome-wide linkage disequilibrium (LD) mapping have gained center stage. It is time to explicitly distinguish the different concepts implied in the present 'haplotype' approaches: 'haplotypes' are not 'haplotypes', after all. The distinction of three different categories, 'ancestral, common haplotypes' or 'haplotype blocks', 'gene-based haplotypes as complex genetic markers' and 'gene-based functional haplotypes', is proposed. These categories serve as framework to review and analyze in particular the recent work suggesting evidence for a haplotype block structure of the human genome and the body of comparative sequencing studies addressing haplotype and LD structures at the gene level. Haplotype approaches will be evaluated along the dimensions 'preselection of variants versus complete DNA sequence information', 'role of LD' and 'stages in the process of disease gene identification'. Overall, the 'content' of 'haplotypes' is conceived as a function of available technologies to evaluate genetic variation and general advances in human genome research