Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

Hibar, Derrek P.; Stein, Jason L.; Jahanshad, Neda; Kohannim, Omid; Hou, Xue; Toga, Arthur W.; McMahon, Katie L.; Zubicaray, Greig I. de; Martin, Nicholas G.; Wright, Margaret J.; Weiner, Michael W.; Thompson, Paul M.

doi:10.1016/j.neurobiolaging.2014.02.033

Cited by 24 publications

(22 citation statements)

References 53 publications

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“…Although we have only considered single quantitative secondary phenotype–single SNP associations, we anticipate that our conclusions will be likely to hold for other cases, such as for binary secondary phenotypes (Wang and Shete 2010; Chen et al 2013), multiple secondary phenotypes (Lin et al 2012; Zhang et al 2014; Zhu et al 2014), longitudinal secondary phenotypes (Skup et al 2012; Xu et al 2014), or for gene-gene or gene-environment interactions (Ge et al 2015; Hibar et al 2015b), though further studies are needed.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…On the other hand, surprisingly, to our knowledge, all the publications on analyses of secondary phenotypes for the ADNI data have relied on standard linear regression without any adjustment to or even any discussion on possible problems with the biased ADNI sample (e.g. Shen et al 2010; Stein et al 2010a; Meda et al 2012; Hibar et al 2015b). Biased inference may lead to not only biased parameter estimates, but also inflated Type I error rates and reduced power.…”

Section: Introductionmentioning

confidence: 99%

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A cautionary note on using secondary phenotypes in neuroimaging genetic studies

Kim

Pan

2015

NeuroImage

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Almost all genome-wide association studies (GWASs), including Alzheimer’s Disease Neuroimaging Initiative (ADNI), are based on the case-control study design, implying that the resulting case-control data are likely a biased, not random, sample of the target population. Although association analysis of the disease (e.g. Alzheimer’s disease in the ADNI) can be conducted using a standard logistic regression by ignoring the biased case-control sampling, a standard linear regression analysis on a secondary phenotype (e.g. any neuroimaging phenotype in the ADNI) may in general lead to biased inference, including biased parameter estimates, inflated Type I errors and reduced power for association testing. Despite of this well known result in genetic epidemiology, to our surprise, all the published studies on secondary phenotypes with the ADNI data have ignored this potential problem. Here we aim to answer whether such a standard analysis of a secondary phenotype is valid or problematic with the ADNI data. Through both real data analyses and simulation studies, we found that, strikingly, such an analysis was generally valid (with only small biases or slightly inflated Type I errors) for the ADNI data, though cautions must be taken when analyzing other data. We also illustrate applications and possible problems of two methods specifically developed for valid analysis of secondary phenotypes.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A cautionary note on using secondary phenotypes in neuroimaging genetic studies

Kim

Pan

2015

NeuroImage

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“…A review of several early studies did not find compelling statistical evidence validating the vast majority of reported interactions [6], and more recent studies support this conclusion [14], [15], [16]. A study including thousands of patients with breast cancer and control participants revealed no significant interactions among 2.5 billion possible two-SNP combinations [16].…”

Section: Combinations Of Genetic Variants In Clinical Studiesmentioning

confidence: 97%

Combinations of Genetic Variants Occurring Exclusively in Patients

Mellerup

Møller

2017

Computational and Structural Biotechnology Journal

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In studies of polygenic disorders, scanning the genetic variants can be used to identify variant combinations. Combinations that are exclusively found in patients can be separated from those combinations occurring in control persons. Statistical analyses can be performed to determine whether the combinations that occur exclusively among patients are significantly associated with the investigated disorder. This research strategy has been applied in materials from various polygenic disorders, identifying clusters of patient-specific genetic variant combinations that are significant associated with the investigated disorders. Combinations from these clusters are found in the genomes of up to 55% of investigated patients, and are not present in the genomes of any control persons.

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“…It assumes additivity alone (which is supported(111)) and neglects potential epistatic effects, which while observed in imaging genetics studies (112,113) have yet to be widely replicated (114). Also, by aggregating across the genome, when used in isolation, PRS provide no insight into potential underlying molecular mechanisms.…”

Section: Polygenic Approachesmentioning

confidence: 99%

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential

Bogdan¹,

Salmeron

Carey³

et al. 2017

Biological Psychiatry

147

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Imaging genetics and genomics research has begun to provide insight into the molecular and genetic architecture of neural phenotypes and the neural mechanisms through which genetic risk for psychopathology may emerge. As it approaches its third decade, imaging genetics is confronted by many challenges including the proliferation of studies using small sample sizes and diverse designs, limited replication, problems with harmonization of neural phenotypes for meta-analysis, unclear mechanisms, and evidence that effect sizes may be more modest than originally posited, with increasing evidence of polygenicity. These concerns have encouraged the field to grow in many new directions including the development of consortia and large scale data collection projects as well as the use of novel methods (e.g., polygenic approaches, machine learning), which enhance the quality of imaging genetic studies, but also introduce new challenges. Here, we critically review progress in imaging genetics and offer suggestions and highlight potential pitfalls of novel approaches. Ultimately, the strength of imaging genetics and genomics lies in its translational and integrative potential with other research approaches (e.g., non-human animal models, psychiatric genetics, pharmacologic challenge) to elucidate brain-based pathways that give rise to the vast individual differences in behavior as well as risk for psychopathology.

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Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

Cited by 24 publications

References 53 publications

A cautionary note on using secondary phenotypes in neuroimaging genetic studies

A cautionary note on using secondary phenotypes in neuroimaging genetic studies

Combinations of Genetic Variants Occurring Exclusively in Patients

Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential

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