Consent and privacy in pharmacogenetic testing.

The clinical use of pharmacogenetic drugs will require that a sample of a patient's DNA be tested before a drug is prescribed. Although pharmacogenetic tests pose fewer risks than genetic tests for disease mutations, they might still reveal personal information that could be used adversely to a patient's interests. Informed consent and privacy of pharmacogenetic test results may be essential in most clinical uses of pharmacogenetic drugs.     

Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis

Multiple sclerosis is a chronic, often disabling, disease of the central nervous system affecting more than 1 in 1,000 people in most western countries. The inflammatory lesions typical of multiple sclerosis show autoimmune features and depend partly on genetic factors. Of these genetic factors, only the HLA gene complex has been repeatedly confirmed to be associated with multiple sclerosis, despite considerable efforts. Polymorphisms in a number of non-HLA genes have been reported to be associated with multiple sclerosis, but so far confirmation has been difficult. Here, we report compelling evidence that polymorphisms in IL7R, which encodes the interleukin 7 receptor alpha chain (IL7Ralpha), indeed contribute to the non-HLA genetic risk in multiple sclerosis, demonstrating a role for this pathway in the pathophysiology of this disease. In addition, we report altered expression of the genes encoding IL7Ralpha and its ligand, IL7, in the cerebrospinal fluid compartment of individuals with multiple sclerosis.     

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.     

A common polymorphism of the growth hormone receptor is associated with increased responsiveness to growth hormone

Growth hormone is used to increase height in short children who are not deficient in growth hormone, but its efficacy varies largely across individuals. The genetic factors responsible for this variation are entirely unknown. In two cohorts of short children treated with growth hormone, we found that an isoform of the growth hormone receptor gene that lacks exon 3 (d3-GHR) was associated with 1.7 to 2 times more growth acceleration induced by growth hormone than the full-length isoform (P < 0.0001). In transfection experiments, the transduction of growth hormone signaling through d3-GHR homo- or heterodimers was approximately 30% higher than through full-length GHR homodimers (P < 0.0001). One-half of Europeans are hetero- or homozygous with respect to the allele encoding the d3-GHR isoform, which is dominant over the full-length isoform. These observations suggest that the polymorphism in exon 3 of GHR is important in growth hormone pharmacogenetics.     

A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.     

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.     

The sex-specific genetic architecture of quantitative traits in humans

Mapping genetically complex traits remains one of the greatest challenges in human genetics today. In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomplete penetrance make thorough genetic dissection of complex traits difficult, if not impossible. Sex could be considered an environmental factor that can modify both penetrance and expressivity of a wide variety of traits. Sex is easily determined and has measurable effects on recognizable morphology; neurobiological circuits; susceptibility to autoimmune disease, diabetes, asthma, cardiovascular and psychiatric disease; and quantitative traits like blood pressure, obesity and lipid levels, among others. In this study, we evaluated sex-specific heritability and genome-wide linkages for 17 quantitative traits in the Hutterites. The results of this study could have important implications for mapping complex trait genes.     

Genetic similarity and quality interact in mate choice decisions by female mice

Females express mate preferences for genetically dissimilar males, especially with respect to the major histocompatibility complex, MHC, and for males whose sexually selected signals indicate high genetic quality. The balance of selection pressure on each trait will depend on how females weight these desirable qualities under different conditions, but this has not been tested empirically. Here we show in mice that although MHC dissimilarity and a 'good genes' indicator (investment in scent-marking) both have a role in determining female preference, their relative influence can vary depending on the degree of variability in each trait among available males. Such interactions between condition-dependent and disassortative mate choice criteria suggest a mechanism by which female choice can contribute to maintenance of additive genetic variance in both the MHC and condition-dependent traits, even under consistent directional selection.     

The new date, new format, new goals and new sponsor of the Archon Genomics X PRIZE competition

The Archon Genomics X PRIZE presented by MEDCO is a $10 million prize for whole human genome sequencing that will test 100 samples and establish a defined method for determining quality and cost per genome. The contest will entail a head-to-head competition starting on 3 January 2013. The $10 million grand prize will be awarded to the first team to meet all the quality standards. If there is no grand prize winner, lesser awards will be made for single achievements in the three categories of accuracy, completeness and haplotype phasing. Similar to the open comment period offered earlier this year for the validation protocol, the X PRIZE Foundation seeks commentary from the scientific community on the revised competition format and judging criteria prior to publishing final rules as part of a master agreement with competing teams next year. The official validation protocol will make the competition judging transparent and fair using multiple standard techniques and robust bioinformatics. With sponsorship from Medco Health Solutions (MEDCO), the 100 samples will be derived from genomes of appropriately consented centenarian subjects. At the conclusion of the contest, the data from the redundantly sequenced genomes will be deposited into a scientific database and provide a heretofore unrealized depth of sequence data based on multiple technologies. The sample set and the bioinformatic pipeline will also be made available to the scientific community.     

Common deletion polymorphisms in the human genome

The locations and properties of common deletion variants in the human genome are largely unknown. We describe a systematic method for using dense SNP genotype data to discover deletions and its application to data from the International HapMap Consortium to characterize and catalogue segregating deletion variants across the human genome. We identified 541 deletion variants (94% novel) ranging from 1 kb to 745 kb in size; 278 of these variants were observed in multiple, unrelated individuals, 120 in the homozygous state. The coding exons of ten expressed genes were found to be commonly deleted, including multiple genes with roles in sex steroid metabolism, olfaction and drug response. These common deletion polymorphisms typically represent ancestral mutations that are in linkage disequilibrium with nearby SNPs, meaning that their association to disease can often be evaluated in the course of SNP-based whole-genome association studies.     

Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.     

A truncating mutation of HDAC2 in human cancers confers resistance to histone deacetylase inhibition

Disruption of histone acetylation patterns is a common feature of cancer cells, but very little is known about its genetic basis. We have identified truncating mutations in one of the primary human histone deacetylases, HDAC2, in sporadic carcinomas with microsatellite instability and in tumors arising in individuals with hereditary nonpolyposis colorectal cancer syndrome. The presence of the HDAC2 frameshift mutation causes a loss of HDAC2 protein expression and enzymatic activity and renders these cells more resistant to the usual antiproliferative and proapoptotic effects of histone deacetylase inhibitors. As such drugs may serve as therapeutic agents for cancer, our findings support the use of HDAC2 mutational status in future pharmacogenetic treatment of these individuals.     

Genome-wide strategies for detecting multiple loci that influence complex diseases

After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.     

Current issues in mouse genome engineering

The mouse is the foremost vertebrate experimental model because its genome can be precisely and variously engineered. Now that the mouse genome has been sequenced and annotated, the task of mutating each gene is feasible, and an international cooperation is providing mutated embryonic stem cells and mice as readily available resources. Because these resources will change biomedical research, decisions about their nature will have far-reaching effects. It is therefore timely to consider topical issues for mouse genome engineering, such as the background genotype; homologous, site-specific and transpositional recombination; conditional mutagenesis; RNA-mediated interference; and functional genomics with embryonic stem cells.     

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

Stochastic switching as a survival strategy in fluctuating environments

A classic problem in population and evolutionary biology is to understand how a population optimizes its fitness in fluctuating environments. A population might enhance its fitness by allowing individual cells to stochastically transition among multiple phenotypes, thus ensuring that some cells are always prepared for an unforeseen environmental fluctuation. Here we experimentally explore how switching affects population growth by using the galactose utilization network of Saccharomyces cerevisiae. We engineered a strain that randomly transitions between two phenotypes as a result of stochastic gene expression. Each phenotype was designed to confer a growth advantage over the other phenotype in a certain environment. When we compared the growth of two populations with different switching rates, we found that fast-switching populations outgrow slow switchers when the environment fluctuates rapidly, whereas slow-switching phenotypes outgrow fast switchers when the environment changes rarely. These results suggest that cells may tune inter-phenotype switching rates to the frequency of environmental changes.     

Trans-dominant inhibition of RNA viral replication can slow growth of drug-resistant viruses

The high error rates of viral RNA-dependent RNA polymerases create heterogeneous viral populations whose disparate RNA genomes affect each other's survival. We systematically screened the poliovirus genome and identified four sets of dominant mutations. Mutated alleles in capsid- and polymerase-coding regions resulted in dominant negative phenotypes, probably due to the proteins' oligomeric properties. We also identified dominant mutations in an RNA element required for priming RNA synthesis (CRE) and in the protein primer (VPg), suggesting that nonproductive priming intermediates are inhibitory. Mutations that inhibit the activity of viral proteinase 2A were dominant, arguing that inhibition of its known intramolecular activity creates a toxic product. Viral products that, when defective, dominantly interfere with growth of nondefective viruses will probably be excellent drug targets because drug-sensitive viruses should be dominant over drug-resistant variants. Accordingly, a virus sensitive to anticapsid compound WIN51711 dominantly inhibited the intracellular growth of a drug-resistant virus. Therefore, dominant inhibitor screening should validate or predict targets for antiviral therapy with reduced risk for drug resistance.