Complex interactions in Parkinson's disease: a two-phased approach.

The identification of pathogenic mutations in the three genes alpha-synuclein, parkin, and ubiquitin carboxy-terminal hydrolase L1 (UCHL1) has elucidated the ubiquitin proteasome system (UPS) and its potential role as a causal pathway in Parkinson's disease (PD). In addition, polymorphisms of these three genes have been shown to be independently associated with PD. In a sample of 298 unrelated PD cases and 185 controls, we applied a two-phased approach of recursive partitioning and logistic regression analyses to explore complex interactions. For women only, we observed an epistatic interaction of UCHL1 and alpha-synuclein genotypes with significant effects on PD risk (odds ratio = 2.42; P = 0.003). Our findings are consistent with the hypothesis that PD is a multigenic disorder of the UPS.     

A biometrical genetic approach to chromosome analysis inDrosophila: Detection of epistatic interactions in geotaxis

Chromosome analysis has been widely used as a first step in eclucidating the genetic architecture of several behaviors ofDrosophila melanogaster. These chromosome studies have generally used incomplete designs or fairly simple statistical analyses. Here I reanalyze two data sets on geotaxis from Pyle (1978) and Ksander (1966) using a biometrical genetic design. Results from the biometrical genetic reanalysis suggest that individual differences in geotaxis might be due to genes on all three major chromosomes which show extensive epistatic interactions.     

Turning point in the design of linkage studies of schizophrenia

Despite extensive genomic scans, linkage studies of multiplex pedigrees have been unable to produce replicable evidence of genes predisposing to schizophrenia. This indicates that it is unlikely that a single gene accounts for a majority of cases of schizophrenia, even in multiplex pedigrees. It is most likely that schizophrenia is caused by the nonlinear interaction of multiple genetic and environmental factors influencing brain development and function. This conclusion has strong implications for the design of linkage and association studies. Recently designed linkage studies involve several improvements to deal with extensive locus heterogeneity and multiplicative interaction. These improvements include much larger samples of pedigrees, systematic ascertainment and sequential extension rules, and standardized procedures at multiple sites to facilitate collaboration and replication. Future improvements are likely to require advances in the assessment of clinical and neurobiological variability in multiplex pedigrees, more systematic environmental assessment, and advances in analytic methods to deal with multiplicative interaction. Rather than focusing only on schizophrenia as one or more discrete disorders, future linkage efforts should also consider the etiology of individual clinical syndromes or dimensional components of risk that interact to cause the complex pattern of syndromal comorbidity observed within schizophrenics and their families. © 1994 Wiley-Liss, Inc.     

LEAP: Biomarker Inference Through Learning and Evaluating Association Patterns

Single nucleotide polymorphism (SNP) high‐dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000‐SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high‐dimensional datasets and determining their probability.     

Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

Among the single-nucleotide polymorphisms (SNPs) previously reported to be associated with body mass index (BMI) and obesity, we focus on a common risk variant rs7566605 upstream of the insulin-induced gene 2 (INSIG2) gene and a rare protective variant rs2229616 on the melanocortin-4 receptor (MC4R) gene. INSIG2 is involved in adipogenesis and MC4R effects hormonal appetite control in response to the amount of adipose tissue. The influence of rs2229616 (MC4R) on BMI and obesity has been confirmed repeatedly and insight into the underlying mechanism provided. However, a main effect of rs7566605 (INSIG2) is under debate because of inconsistent replications of association. Interaction of rs7566605 with age may offer an explanation. SNP–age and SNP–SNP interaction models were tested on independent individuals from three population-based longitudinal cohorts, restricting the analysis to an observed age of 25–74 years. KORA S3/F3, KORA S4/F4 (Augsburg, Germany, 1994–2005, 1999–2008), and Framingham-Offspring data (Framingham, USA, 1971–2001) were analysed, with a total sample size of N=6926 in the joint analysis. The effect of interaction between rs7566605 and age on BMI and obesity status is significant and consistent across studies. This new evidence for rs7566605 (INSIG2) complements previous research. In addition, the interaction effect of rs7566605 with the MC4R variant rs2229616 on BMI was observed. This effect size was three times larger than that in a previously reported single-locus main effect of rs2229616. This leads to the conclusion that SNP–age or SNP–SNP interactions can mask genetic effects for complex diseases if left unaccounted for.     

Phenotypic expression of the targeted null-mutation in the dopamine transporter gene varies as a function of the genetic background

The dopamine transporter (DAT) plays a critical role in calibrating the duration and intensity of dopamine (DA) neurotransmission. Mice in which the DAT gene has been genetically deleted exhibit constitutively high levels of extrasynaptic DA and spontaneous hyperactivity. Numerous studies have characterized the adaptive molecular, physiological, and behavioural consequences of abnormal DA neurotransmission in these mice. In order to determine the genetic background contribution to these phenotypes, the DAT mutation was transferred on C57BL/6JOrl (B6) or DBA/2JOrl (D2) inbred backgrounds for more than ten generations of back-crossing to derive three B6-, D2-, and B6xD2(F(1))-DAT strains. We observed that the genetic background dramatically affects phenotypes previously reported on DAT knockout (KO) mice. Depending on the genetic background, it was possible to restore survival, growth rate and ability to lactate. Interactions with the genetic background were found to modulate both quantitative and qualitative patterns of novelty-driven spontaneous hyperactivity. The paradoxical calming effect of cocaine was observed for all DAT-KO mice. However, the genetic background influenced individual threshold responses to both locomotor and rewarding effects of cocaine. These findings reveal the extent of phenotypic variation associated with the DAT mutation. They also provide concrete arguments against the assumption that the normal function of a gene can be inferred directly from its mutant phenotype.     

Properties of case/pseudocontrol analysis for genetic association studies: Effects of recombination, ascertainment, and multiple affected offspring

The case/pseudocontrol approach is a general framework for family-based association analysis, incorporating several previously proposed methods such as the transmission/disequilibrium test and log-linear modelling of parent-of-origin effects. In this report, I examine the properties of methods based on a case/pseudocontrol approach when applied to a linked marker rather than (or in addition to) the true disease locus or loci, and when applied to sibships that have been ascertained on, or that may simply contain, multiple affected sibs. Through simulations and analytical calculations, I show that the expected values of the observed relative risk parameters (estimating quantities such as effects due to a child's own genotype, maternal genotype, and parent-of-origin) depend crucially on the ascertainment scheme used, as well as on whether there is non-negligible recombination between the true disease locus and the locus under study. In the presence of either recombination or ascertainment on multiple affected offspring, methods based on conditioning on parental genotypes are shown to give unbiased genotype relative risk estimates at the true disease locus (or loci) but biased estimates of population genotype relative risks at a linked marker, suggesting that the resulting estimates may be misleading when used to predict the power of future studies. Methods that allow for exchangeability of parental genotypes are shown (in the presence of either recombination or ascertainment on multiple affected offspring) to produce false-positive evidence of maternal genotype effects when there are true parent-of-origin or mother-child interaction effects, even when analyzing the true locus. These results suggest that care should be taken in both the interpretation and application of parameter estimates obtained from family-based genetic association studies.     

Inferring the effect of genomic variation in the new era of genomics

Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene‐variant–disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next‐generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as “synergistic heterozygosity,” digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large‐scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.