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

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

Current evidence for a modulation of low back pain by human genetic variants

The manifestation of chronic back pain depends on structural, psychosocial, occupational and genetic influences. Heritability estimates for back pain range from 30% to 45%. Genetic influences are caused by genes affecting intervertebral disc degeneration or the immune response and genes involved in pain perception, signalling and psychological processing. This inter-individual variability which is partly due to genetic differences would require an individualized pain management to prevent the transition from acute to chronic back pain or improve the outcome. The genetic profile may help to define patients at high risk for chronic pain. We summarize genetic factors that (i) impact on intervertebral disc stability, namely Collagen IX, COL9A3, COL11A1, COL11A2, COL1A1, aggrecan (AGAN), cartilage intermediate layer protein, vitamin D receptor, metalloproteinsase-3 (MMP3), MMP9, and thrombospondin-2, (ii) modify inflammation, namely interleukin-1 (IL-1) locus genes and IL-6 and (iii) and pain signalling namely guanine triphosphate (GTP) cyclohydrolase 1, catechol-O-methyltransferase, μ opioid receptor (OPMR1), melanocortin 1 receptor (MC1R), transient receptor potential channel A1 and fatty acid amide hydrolase and analgesic drug metabolism (cytochrome P450 [CYP]2D6, CYP2C9)