Literature DB >> 11121189

Implications of comorbidity and ascertainment bias for identifying disease genes.

J W Smoller1, K L Lunetta, J Robins.   

Abstract

Comorbidity, the co-occurrence of disorders, is frequently observed to occur at higher rates in clinically ascertained samples than in population-based samples. An explanation for this finding is that subjects suffering from multiple illnesses are more likely to seek medical care and receive a diagnostic evaluation. We refer to the component of the comorbidity between illnesses due to such ascertainment bias as "spurious comorbidity." When spurious comorbidity is present, an apparent association between a candidate locus and the phenotype of interest may actually be attributable to an association between the locus and a comorbid phenotype. This phenomenon, which we call "spurious comorbidity bias," could thus produce misleading association findings. In this article, we describe this phenomenon and demonstrate that it may produce marked bias in the conclusions of family-based association studies. Because of the extremely high rates of comorbidity among psychiatric disorders in clinical samples, this problem may be particularly salient for genetic studies of neuropsychiatric disorders. We conclude that ascertainment bias may contribute to the frequent difficulty in replicating candidate gene study findings in psychiatry. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 96:817-822, 2000. Copyright 2000 Wiley-Liss, Inc.

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Mesh:

Year:  2000        PMID: 11121189     DOI: 10.1002/1096-8628(20001204)96:6<817::aid-ajmg25>3.0.co;2-a

Source DB:  PubMed          Journal:  Am J Med Genet        ISSN: 0148-7299


  12 in total

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2.  Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics.

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Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-11-17       Impact factor: 4.254

3.  Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene-region aggregation techniques.

Authors:  Lauren L Schmitz; Arianna M Gard; Erin B Ware
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4.  Examining and interpreting the female protective effect against autistic behavior.

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Journal:  Proc Natl Acad Sci U S A       Date:  2013-02-19       Impact factor: 11.205

Review 5.  Bringing a developmental perspective to anxiety genetics.

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Journal:  Dev Psychopathol       Date:  2012-11

6.  On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects.

Authors:  Stijn Vansteelandt; Sylvie Goetgeluk; Sharon Lutz; Irwin Waldman; Helen Lyon; Eric E Schadt; Scott T Weiss; Christoph Lange
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Review 7.  Pleiotropy in complex traits: challenges and strategies.

Authors:  Nadia Solovieff; Chris Cotsapas; Phil H Lee; Shaun M Purcell; Jordan W Smoller
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Review 8.  Pleiotropy and Cross-Disorder Genetics Among Psychiatric Disorders.

Authors:  Phil H Lee; Yen-Chen A Feng; Jordan W Smoller
Journal:  Biol Psychiatry       Date:  2020-10-10       Impact factor: 13.382

9.  Comorbidity in psychiatry: Way forward or a conundrum?

Authors:  Nimesh G Desai
Journal:  Indian J Psychiatry       Date:  2006-04       Impact factor: 1.759

Review 10.  Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine.

Authors:  Can Yang; Cong Li; Qian Wang; Dongjun Chung; Hongyu Zhao
Journal:  Front Genet       Date:  2015-06-30       Impact factor: 4.599

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