Musings on genome medicine: the value of family history
TL;DR: It is argued that the routine availability of genome sequence information on individuals will not render family history information obsolete, both because the taking of a family history has other uses for the health professional, and because genome sequence data on their own omit the effects of numerous factors important for modifying risks of disease.
Abstract: Will the routine availability of genome sequence information on individuals render family history information obsolete? I argue that it will not, both because the taking of a family history has other uses for the health professional, and because genome sequence data on their own omit the effects of numerous factors important for modifying risks of disease. These include information derived from factors downstream of genetic variants and from upstream epigenetic effects. Further difficulties arise with uncertainties relating to gene-gene and gene-environment interactions, which may take decades to resolve if their resolution is even possible.
Citations
More filters
TL;DR: Using Alzheimer’s disease as an illustrative example, it is shown how population databases of AD cases on which individual risk estimates are based are faulty due to confusion about the AD phenotype.
Abstract: As genetic tests become cheaper and more readily available, pressure is increasing to routinely test individuals for susceptibility genes for complex common disorders. Using Alzheimer’s disease (AD) as an illustrative example, it is shown how population databases of AD cases on which individual risk estimates are based are faulty due to confusion about the AD phenotype. Furthermore, the APOEe4 genotype associated with increased risk of AD is neither necessary nor sufficient to cause AD. The article concludes with ethnographic findings that result from interviews with individuals who have been tested for their APOE status.
40 citations
TL;DR: The question remains how future genetic testing and genomic profiling may be of aid in the therapeutic algorithms related to idiopathic scoliosis.
Abstract: Idiopathic scoliosis is one of the most common complex genetic disorders of the musculoskeletal system. The clinical parameters relating to onset, curve progression, and severity in relation to clinical prognosis and current treatment modalities have been defined, but do not address the cause of this disorder. In an effort to define causative genetic elements, multiple studies have delineated potential genetic loci that are statistically related to idiopathic scoliosis in a variety of populations. The question remains how future genetic testing and genomic profiling may be of aid in the therapeutic algorithms related to this disorder.
29 citations
Cites background from "Musings on genome medicine: the val..."
...In cases of known disease, such as Hodgkins lymphoma and cervical cancer, genomic profiling has had some success in relation to disease prognosis and therapeutic treatment options in those who were earlier diagnosed with the disease.(39,40)...
[...]
TL;DR: Although participants thought collecting FHH information was important and had positive reactions to both tools, the majority did not use the tools to write down information and instead collected FHH informally, underline the importance of separating the components of FHH collection behaviors to analyze the steps used in FHH creation.
Abstract: Little is known about African American women’s collection of family health history (FHH) information and use of FHH tools. Most FHH research has investigated tools that use a biomedical paradigm, but other kinds of tools, such as those that include information about family social context, have been developed for use in diverse populations. Using mixed methods, we interviewed 32 African American women about behavioral steps to collecting FHH, family communication about health, and reactions to a biomedical FHH tool. Participants chose one of two FHH tools to take home. A follow-up call three weeks later assessed tool use. Many participants expressed support for writing down FHH information, but at baseline few had done so; most participants who had collected FHH information had done so verbally. Participants reacted positively to the biomedical FHH tool used during the interview, with many saying it allowed them to see patterns in their FHH. At follow-up, 67 % reported using their FHH tool, primarily to promote discussion among family members; only 32 % used the tool to write down FHH information. Although participants thought collecting FHH information was important and had positive reactions to both tools, the majority did not use the tools to write down information and instead collected FHH informally. These findings underline the importance of separating the components of FHH collection behaviors to analyze the steps used in FHH creation. Practitioners should consider additional methods of encouraging patients to create written FHHs in order to share the information with health care providers.
20 citations
Cites background from "Musings on genome medicine: the val..."
...Tools using a biomedical approach, however, may be difficult for lay individuals without specialized genetics knowledge or with limited health literacy (Wang et al. 2011; Kelly and Sweet 2007), due in part to unfamiliar terms and concepts (Clarke 2009; Fuller et al. 2010; Wallace et al. 2009)....
[...]
...2011; Kelly and Sweet 2007), due in part to unfamiliar terms and concepts (Clarke 2009; Fuller et al. 2010; Wallace et al. 2009)....
[...]
TL;DR: This paper will review the setting before the guidelines were published, and empiric research and discussion that has occurred since, to best approach the current state of secondary findings in genomic medicine.
Abstract: The American College of Medical Genetics and Genomics (ACMG) recommendations for reporting of incidental (now "secondary") findings in clinical exome and genome sequencing (Green et al., Genet Med 15:565, 2013) is an often cited and sometimes misapplied professional guideline. To best approach the current state of secondary findings (SFs) in genomic medicine, and consider their impact, it is helpful to understand how and why the guideline was created. Of particular importance is the context - the state of the science and clinical practice during 2011-2012 when the guideline were initially developed. This paper will review the setting before the guidelines were published, and empiric research and discussion that has occurred since.
19 citations
Additional excerpts
...…genet‐ ics research and clinical practice (Bick & Dimmock, 2011; Majewski, Schwartzentruber, Lalonde, Montpetit, & Jabado, 2011; Singleton, 2011) and skeptical realism regarding potential challenges (Brunham & Hayden, 2012; Clarke, 2009; Li, 2011; Ormond et al., 2010; Schrijver & Galli, 2012)....
[...]
TL;DR: In this paper, the authors compared the completeness and accuracy of medical history data collected and recorded by physicians in electronic health records (EHR) with data collected by computerized history-taking (CHT) for patients presenting to an emergency room with acute chest pain.
Abstract: Patients' medical histories are the salient dataset for diagnosis. Prior work shows consistently, however, that medical history-taking by physicians generally is incomplete and not accurate. Such findings suggest that methods to improve the completeness and accuracy of medical history data could have clinical value. We address this issue with expert system software to enable automated history-taking by computers interacting directly with patients, i.e. computerized history-taking (CHT). Here we compare the completeness and accuracy of medical history data collected and recorded by physicians in electronic health records (EHR) with data collected by CHT for patients presenting to an emergency room with acute chest pain. Physician history-taking and CHT occurred at the same ED visit for all patients. CHT almost always preceded examination by a physician. Data fields analyzed were relevant to the differential diagnosis of chest pain and comprised information obtainable only by interviewing patients. Measures of data quality were completeness and consistency of negative and positive findings in EHR as compared with CHT datasets. Data significant for the differential of chest pain was missing randomly in all EHRs across all data items analyzed so that the dimensionality of EHR data was limited. CHT files were near complete for all data elements reviewed. Separate from the incompleteness of EHR data, there were frequent factual inconsistencies between EHR and CHT data across all data elements. EHR data did not contain representations of symptoms that were consistent with those reported by patients during CHT. Trial registration: This study is registered at https://www.clinicaltrials.gov (unique identifier: NCT03439449).
5 citations
References
More filters
TL;DR: The authors quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines and observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes.
Abstract: Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits. Natural populations harbor a wide range of phenotypic variation for all aspects of morphology, physiology, behaviors and disease susceptibility. Knowledge of the genetic basis of this variation is important for understanding adaptive evolution, deriving elite domestic crop and animal strains and improving human health. However, determining the genetic architecture of natural phenotypic variation is challenging because most phenotypic variation is attributable to segregating alleles at many interacting genes with environmentally sensitive effects 1,2 .
524 citations
TL;DR: There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention.
Abstract: Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention.
273 citations
TL;DR: Family history reports of common, chronic disease are prevalent among the population at large, and collection and interpretation of comprehensive family history data is a feasible, initial method for risk stratification for many preventable, chronic conditions.
Abstract: Targeting individuals with increased risk for common, chronic disease can improve the efficiency and efficacy of preventive efforts by improving the predictability of screening tests and participant compliance. Individuals with the greatest risk for these disorders are those with a genetic susceptibility. The purpose of this study was to determine the feasibility of using a single, comprehensive family history as a method for stratifying risk for many preventable, common genetic disorders. Family histories obtained in a prenatal diagnostic clinic were reviewed regarding cardiovascular diseases, diabetes and several cancers; 42.5% of individuals reported a family history for at least one of the disorders under study. Familial coronary artery disease was most commonly reported (29% of participants), followed by noninsulin-dependent diabetes (14%). Qualitative characterization of disease susceptibility was also accomplished using family history data. For example, occurrence of different cancers within pedigrees was suggestive of familial cancer syndromes, and clustering of noninsulin-dependent diabetes and cardiovascular disease suggested an insulin resistance syndrome. Depending on the specific disease, 5 to 15% of at-risk individuals had a moderately increased risk (2 to 5 times the population risk), and approximately 1 to 10% had a high risk (absolute risks approaching 50%). Family history reports of common, chronic disease are prevalent among the population at large, and collection and interpretation of comprehensive family history data is a feasible, initial method for risk stratification for many preventable, chronic conditions. These findings may have important implications for disease prevention and management.
258 citations
TL;DR: Studies in Drosophila have revealed large numbers of pleiotropic genes that interact epistatically to regulate quantitative traits, and large number of QTLs with sex-, environment- and genotype-specific effects, which offer valuable lessons for understanding the genetic basis of variation for complex traits in other organisms, including humans.
213 citations
TL;DR: This review discusses methodological issues involved in investigating gene–environment (G × E) interactions in genetic–epidemiological studies of complex diseases and their potential relevance for clinical application and attempts to clarify conceptual differences of the term ‘interaction’ in the statistical and biological sciences.
Abstract: Genetic and environmental risk factors and their interactions contribute to the development of complex diseases In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified These issues as well as a critical appraisal of common methodological concerns are finally discussed
181 citations