Curses-winner's and otherwise-in genetic epidemiology
ABSTRACT The estimated effect of a marker allele from the initial study reporting the marker-allele association is often exaggerated relative to the estimated effect in follow-up studies (the "winner's curse" phenomenon). This is a particular concern for genome-wide association studies, where markers typically must pass very stringent significance thresholds to be selected for replication. A related problem is the overestimation of the predictive accuracy that occurs when the same data set is used to select a multilocus risk model from a wide range of possible models and then estimate the accuracy of the final model ("over-fitting"). Even in the absence of these quantitative biases, researchers can over-state the qualitative importance of their findings--for example, by focusing on relative risks in a context where sensitivity and specificity may be more appropriate measures. Epidemiologists need to be aware of these potential problems: as authors, to avoid or minimize them, and as readers, to detect them.
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ABSTRACT: Performing a genome-wide association study (GWAS) might add to a better understanding of the development of claw disorders and the need for trimming. Therefore, the aim of the current study was to perform a GWAS on claw disorders and trimming status and to validate the results for claw disorders based on an independent data set. Data consisted of 20,474 cows with phenotypes for claw disorders and 50,238 cows with phenotypes for trimming status. Recorded claw disorders used in the current study were double sole (DS), interdigital hyperplasia (IH), sole hemorrhage (SH), sole ulcer (SU), white line separation (WLS), a combination of infectious claw disorders consisting of (inter-)digital dermatitis and heel erosion, and a combination of laminitis-related claw disorders (DS, SH, SU, and WLS). Of the cows with phenotypes for claw disorders, 1,771 cows were genotyped and these cow data were used for the GWAS on claw disorders. A SNP was considered significant when the false discovery rate ≤ 0.05 and suggestive when the false discovery rate ≤ 0.20. An independent data set of 185 genotyped bulls having at least 5 daughters with phenotypes (6,824 daughters in total) for claw disorders was used to validate significant and suggestive SNP detected based on the cow data. To analyze the trait "trimming status" (i.e., the need for claw trimming), a data set with 327 genotyped bulls having at least 5 daughters with phenotypes (18,525 daughters in total) was used. Based on the cow data, in total 10 significant and 45 suggestive SNP were detected for claw disorders. The 10 significant SNP were associated with SU, and mainly located on BTA8. The suggestive SNP were associated with DS, IH, SU, and laminitis-related claw disorders. Three of the suggestive SNP were validated in the data set of 185 bulls, and were located on BTA13, BTA14, and BTA17. For infectious claw disorders, SH, and WLS, no significant or suggestive SNP associations were detected. For trimming status, 1 significant and 1 suggestive SNP were detected, both located close to each other on BTA15. Some significant and suggestive SNP were located close to SNP detected in studies on feet and leg conformation traits. Genes with major effects could not be detected and SNP associations were spread across the genome, indicating that many SNP, each explaining a small proportion of the genetic variance, influence claw disorders. Therefore, to reduce the incidence of claw disorders by breeding, genomic selection is a promising approach. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.Journal of Dairy Science 12/2014; 98(2). DOI:10.3168/jds.2014-8302 · 2.55 Impact Factor
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ABSTRACT: Psychiatric treatment relies on a solid armamentarium of pharmacologic and nonpharmacologic treatment modalities that perform reasonably well for many patients but leave others in a state of chronic disability or troubled by problematic side effects. Treatment planning in psychiatry remains an art that depends on considerable trial and error. Thus, there is an urgent need for better tools that will provide a means for matching individual patients with the most effective treatments while minimizing the risk of adverse events. This review will consider the current state of the science in predicting treatment outcomes in psychiatry. Genetic and other biomarkers will be considered alongside clinical diagnostic, and family history data. Problems inherent in prediction medicine will also be discussed, along with recent developments that support the hope that psychiatry can do a better job in quickly identifying the best treatments for each patient.Dialogues in clinical neuroscience 12/2014; 16(4):455-64.
Critical Care Medicine 01/2015; 43(1):242-3. DOI:10.1097/CCM.0000000000000664 · 6.15 Impact Factor