A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach

Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies

<p>Abstract</p> <p>Background</p> <p>Several methods have been presented for the analysis of complex interactions between genetic polymorphisms and/or environmental factors. Despite the available methods, there is still a need for alternative methods, because no single method will perform well in all scenarios. The aim of this work was to evaluate the performance of three selected rule based classifier algorithms, RIPPER, RIDOR and PART, for the analysis of genetic association studies.</p> <p>Methods</p> <p>Overall, 42 datasets were simulated with three different case-control models, a varying number of subjects (300, 600), SNPs (500, 1500, 3000) and noise (5%, 10%, 20%). The algorithms were applied to each of the datasets with a set of algorithm-specific settings. Results were further investigated with respect to a) the Model, b) the Rules, and c) the Attribute level. Data analysis was performed using WEKA, SAS and PERL.</p> <p>Results</p> <p>The RIPPER algorithm discovered the true case-control model at least once in >33% of the datasets. The RIDOR and PART algorithm performed poorly for model detection. The RIPPER, RIDOR and PART algorithm discovered the true case-control rules in more than 83%, 83% and 44% of the datasets, respectively. All three algorithms were able to detect the attributes utilized in the respective case-control models in most datasets.</p> <p>Conclusions</p> <p>The current analyses substantiate the utility of rule based classifiers such as RIPPER, RIDOR and PART for the detection of gene-gene/gene-environment interactions in genetic association studies. These classifiers could provide a valuable new method, complementing existing approaches, in the analysis of genetic association studies. The methods provide an advantage in being able to handle both categorical and continuous variable types. Further, because the outputs of the analyses are easy to interpret, the rule based classifier approach could quickly generate testable hypotheses for additional evaluation. Since the algorithms are computationally inexpensive, they may serve as valuable tools for preselection of attributes to be used in more complex, computationally intensive approaches. Whether used in isolation or in conjunction with other tools, rule based classifiers are an important addition to the armamentarium of tools available for analyses of complex genetic association studies.</p

The Future of Precision Medicine : Potential Impacts for Health Technology Assessment

Objective Precision medicine allows health care interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information or treatment response. We analyse what developments are expected in precision medicine over the next decade and consider the implications for health technology assessment (HTA) agencies. Methods We perform a pragmatic review of the literature on the health economic challenges of precision medicine, and conduct interviews with representatives from HTA agencies, research councils and researchers from a variety of fields, including digital health, health informatics, health economics and primary care research. Results Three types of precision medicine are highlighted as likely to emerge in clinical practice and impact upon HTA agencies: complex algorithms, digital health applications and ‘omics’-based tests. Defining the scope of an evaluation, identifying and synthesizing the evidence and developing decision analytic models will more difficult when assessing more complex and uncertain treatment pathways. Stratification of patients will result in smaller subgroups, higher standard errors and greater decision uncertainty. Equity concerns may present in instances where biomarkers correlate with characteristics such as ethnicity, whilst fast-paced innovation may reduce the shelf-life of guidance and necessitate more frequent reviewing. Discussion Innovation in precision medicine promises substantial benefits to patients, but will also change the way in which some health services are delivered and evaluated. As biomarker discovery accelerates and AI-based technologies emerge, the technical expertise and processes of HTA agencies will need to adapt if the objective of value for money is to be maintained

Evidence synthesis and guideline development in genomic medicine: current status and future prospects

Purpose: With the accelerated implementation of genomic medicine, health-care providers will depend heavily on professional guidelines and recommendations. Because genomics affects many diseases across the life span, no single professional group covers the entirety of this rapidly developing field. Methods: To pursue a discussion of the minimal elements needed to develop evidence-based guidelines in genomics, the Centers for Disease Control and Prevention and the National Cancer Institute jointly held a workshop to engage representatives from 35 organizations with interest in genomics (13 of which make recommendations). The workshop explored methods used in evidence synthesis and guideline development and initiated a dialogue to compare these methods and to assess whether they are consistent with the Institute of Medicine report "Clinical Practice Guidelines We Can Trust." Results: The participating organizations that develop guidelines or recommendations all had policies to manage guideline development and group membership, and processes to address conflicts of interests. However, there was wide variation in the reliance on external reviews, regular updating of recommendations, and use of systematic reviews to assess the strength of scientific evidence. Conclusion: Ongoing efforts are required to establish criteria for guideline development in genomic medicine as proposed by the Institute of Medicine