Major heart disease genes prove elusive.

Science (Impact Factor: 31.48). 06/2010; 328(5983):1220-1. DOI: 10.1126/science.328.5983.1220
Source: PubMed
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    ABSTRACT: BackgroundA decline in hemoglobin (Hb) concentration during antiviral therapy in chronic hepatitis C (CHC) is a serious side effect. It may compel to dose reduction or even termination of antiviral treatment. The activation of erythropoietin (EPO) synthesis as a physiological response to anemia and its relation to a genetic variation within the EPO gene has not been evaluated yet.MethodsData of 348 CHC patients were reviewed retrospectively. Samples were genotyped for EPO rs1617640 and inosine triphosphatase (ITPA) rs1127354. Serum EPO concentrations were determined before and during therapy. Primary endpoints were set as Hb decline >3 g/dl at weeks 4 and 12.ResultsEPO rs1617640 G homozygotes showed a significantly lower rise of serum EPO level over time than T allele carriers (p < 0.001). The cumulative frequency of a significant Hb reduction added up to 40%. Multivariate analysis revealed that besides age, ribavirin starting dose and baseline Hb also EPO rs1617640 G homozygosity associates with Hb reduction at week 4 (p = 0.025) and 12 (p = 0.029), while ITPA C homozygotes are at risk for Hb decline particularly early during treatment. Furthermore, EPO rs1617640 G homozygotes were more frequently in need for blood transfusion, epoetin-α supplementation, or ribavirin dose reduction (p < 0.001).ConclusionsOur data suggest that EPO rs1617640 genotype, the rise of serum EPO concentration as well as ITPA rs1127354 genotype are promising parameters to evaluate the Hb decline during antiviral therapy. A rational adjustment of therapy with epoetin-α supplementation might prevent serious adverse events or the need to terminate treatment.
    BMC Infectious Diseases 09/2014; 14(1):503. DOI:10.1186/1471-2334-14-503 · 2.56 Impact Factor
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    BMC Medical Informatics and Decision Making 03/2014; 14(1):15. DOI:10.1186/1472-6947-14-15 · 1.50 Impact Factor
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    ABSTRACT: Quantitative genetics (QG) analyses variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. "Classical" QG, where the analysis of variation does not involve data on measurable genetic or environmental entities or factors, is reformulated in this article using models that are free of hypothetical, idealized versions of such factors, while still allowing for defined degrees of relatedness among kinds of individuals or "varieties." The gene-free formulation encompasses situations encountered in human QG as well as in agricultural QG. This formulation is used to describe three standard assumptions involved in classical QG and provide plausible alternatives. Several concerns about the partitioning of trait variation into components and its interpretation, most of which have a long history of debate, are discussed in light of the gene-free formulation and alternative assumptions. That discussion is at a theoretical level, not dependent on empirical data in any particular situation. Additional lines of work to put the gene-free formulation and alternative assumptions into practice and to assess their empirical consequences are noted, but lie beyond the scope of this article. The three standard QG assumptions examined are: (1) partitioning of trait variation into components requires models of hypothetical, idealized genes with simple Mendelian inheritance and direct contributions to the trait; (2) all other things being equal, similarity in traits for relatives is proportional to the fraction shared by the relatives of all the genes that vary in the population (e.g., fraternal or dizygotic twins share half of the variable genes that identical or monozygotic twins share); (3) in analyses of human data, genotype-environment interaction variance (in the classical QG sense) can be discounted. The concerns about the partitioning of trait variation discussed include: the distinction between traits and underlying measurable factors; the possible heterogeneity in factors underlying the development of a trait; the kinds of data needed to estimate key empirical parameters; and interpretations based on contributions of hypothetical genes; as well as, in human studies, the labeling of residual variance as a non-shared environmental effect; and the importance of estimating interaction variance.
    Acta Biotheoretica 09/2012; DOI:10.1007/s10441-012-9164-2 · 1.23 Impact Factor