A single-nucleotide polymorphism tagging set for human drug metabolism and transport.

Interindividual variability in drug response, ranging from no therapeutic benefit to life-threatening adverse reactions, is influenced by variation in genes that control the absorption, distribution, metabolism and excretion of drugs. We genotyped 904 single-nucleotide polymorphisms (SNPs) from 55 such genes in two population samples (European and Japanese) and identified a set of tagging SNPs that represents the common variation in these genes, both known and unknown. Extensive empirical evaluations, including a direct assessment of association with candidate functional SNPs in a new, larger population sample, validated the performance of these tagging SNPs and confirmed their utility for linkage-disequilibrium mapping in pharmacogenetics. The analyses also suggest that rare variation is not amenable to tagging strategies.

Large-scale pathways-based association study in amyotrophic lateral sclerosis.

Sporadic amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease, most likely results from complex genetic and environmental interactions. Although a number of association studies have been performed in an effort to find genetic components of sporadic ALS, most of them resulted in inconsistent findings due to a small number of genes investigated in relatively small sample sizes, while the replication of results was rarely attempted. Defects in retrograde axonal transport, vesicle trafficking and xenobiotic metabolism have been implicated in neurodegeneration and motor neuron death both in human disease and animal models. To assess the role of common genetic variation in these pathways in susceptibility to sporadic ALS, we performed a pathway-based candidate gene case-control association study with replication. Furthermore, we determined reliability of whole genome amplified DNA in a large-scale association study. In the first stage of the study, 1277 putative functional and tagging SNPs in 134 genes spanning 8.7 Mb were genotyped in 822 British sporadic ALS patients and 872 controls using whole genome amplified DNA. To detect variants with modest effect size and discriminate among false positive findings 19 SNPs showing a trend of association in the initial screen were genotyped in a replication sample of 580 German sporadic ALS patients and 361 controls. We did not detect strong evidence of association with any of the genes investigated in the discovery sample (lowest uncorrected P-value 0.00037, lowest permutation corrected P-value 0.353). None of the suggestive associations was replicated in a second sample, further excluding variants with moderate effect size. We conclude that common variation in the investigated pathways is unlikely to have a major effect on susceptibility to sporadic ALS. The genotyping efficiency was only slightly decreased ( approximately 1%) and genotyping quality was not affected using whole genome amplified DNA. It is reliable for large scale genotyping studies of diseases such as ALS, where DNA sample collections are limited because of low disease prevalence and short survival time.

Failure to replicate effect of Kibra on human memory in two large cohorts of European origin.

It was recently suggested that the Kibra polymorphism rs17070145 has a strong effect on multiple episodic memory tasks in humans. We attempted to replicate this using two cohorts of European genetic origin (n = 319 and n = 365). We found no association with either the original SNP or a set of tagging SNPs in the Kibra gene with multiple verbal memory tasks, including one that was an exact replication (Auditory Verbal Learning Task, AVLT). These results suggest that Kibra does not have a strong and general effect on human memory.

Selection and evaluation of tagging SNPs in the neuronal-sodium-channel gene SCN1A: implications for linkage-disequilibrium gene mapping.

Association studies are widely seen as the most promising approach for finding polymorphisms that influence genetically complex traits, such as common diseases and responses to their treatment. Considerable interest has therefore recently focused on the development of methods that efficiently screen genomic regions or whole genomes for gene variants associated with complex phenotypes. One key element in this search is the use of linkage disequilibrium to gain maximal information from typing a selected subset of highly informative single-nucleotide polymorphism (SNP) markers, now often called "tagging SNPs" (tSNPs). Probably the most common approach to linkage-disequilibrium gene mapping involves a three-step program: (1) characterization of the haplotype structure in candidate genes or genomic regions of interest, (2) identification of tSNPs sufficient to represent the most common haplotypes, and (3) typing of tSNPs in clinical material. Early definitions of tSNPs focused on the amount of haplotype diversity that they explained. To select tSNPs that would have maximal power in a genetic association study, however, we have developed optimization criteria based on the r2 measure of association and have compared these with other criteria based on the haplotype diversity. To evaluate the full program and to assess how well the selected tags are likely to perform, we have determined the haplotype structure and have assessed tSNPs in the SCN1A gene, an important candidate gene for sporadic epilepsy. We find that as few as four tSNPs are predicted to maintain a consistently high r2 value with all other common SNPs in the gene, indicating that the tags could be used in an association study with only a modest reduction in power relative to direct assays of all common SNPs. This implies that very large case-control studies can be screened for variation in hundreds of candidate genes with manageable experimental effort, once tSNPs are identified. However, our results also show that tSNPs identified in one population may not necessarily perform well in another, indicating that the preliminary study to identify tSNPs and the later case-control study should be performed in the same population. Our results also indicate that tSNPs will not easily identify discrepant SNPs, which lie on importantly discriminating but apparently short genealogical branches. This could significantly complicate tagging approaches for phenotypes influenced by variants that have experienced positive selection.