Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy

Salinas, Yasmmyn D.; Wang, Zuoheng; DeWan, Andrew T.

doi:10.1093/aje/kwx296

Cited by 20 publications

(12 citation statements)

References 75 publications

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“…Our results agree with this previous work, as the observed ability of multivariate GWAS to identify QTNs was generally high for all scenarios particularly in soybean. The fact that the multivariate GWAS was able to detect non-pleiotropic QTNs is not surprising because the null hypothesis for most multivariate tests of association, including those used for the multivariate MLM, is H 0 : No association between the tested SNP and any trait (Schaid et al, 2016 ; Salinas et al, 2018 ). Thus, the multivariate MLM's detection of non-pleiotropic QTN, and more specifically spurious pleiotropy under the “QTNs in linkage” scenario, should not be regarded as false positives because these events technically occur in the alternative hypothesis.…”

Section: Discussionmentioning

confidence: 99%

“…The X-axis displays the narrow-sense heritability for Trait 1 (bottom value) and Trait 2 (top value). (A) Inputted minor allele frequency (MAF) of 0.05; (B) MAF of 0.4. hypothesis for most multivariate tests of association, including those used for the multivariate MLM, is H 0 : No association between the tested SNP and any trait (Schaid et al, 2016;Salinas et al, 2018). Thus, the multivariate MLM's detection of non-pleiotropic QTN, and more specifically spurious pleiotropy under the "QTNs in linkage" scenario, should not be regarded as false positives because these events technically occur in the alternative hypothesis.…”

Section: High Spurious Pleiotropy Detection Rates From Multivariate Gmentioning

confidence: 99%

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How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

et al. 2021

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Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.

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Section: Discussionmentioning

confidence: 99%

Section: High Spurious Pleiotropy Detection Rates From Multivariate Gmentioning

confidence: 99%

How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

et al. 2021

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“…Notably, when mediated pleiotropy is discussed, it is usually in the context of one trait that mediates a genetic effect on another trait, such as the association between a genetic locus on chromosome 15 and both nicotine dependence and lung cancer (genetic variant → nicotine dependence → lung cancer; e.g., Gage, Smith, Ware, Flint, & Munafò, 2016; Salinas, Wang, & DeWan, 2017; Solovieff et al, 2013). Another form of mediated pleiotropy, which has been largely overlooked, is environmentally mediated pleiotropy (Fig.…”

Section: The Various Faces Of Pleiotropymentioning

confidence: 99%

“…All of these complexities create challenges for estimating genetic influences and for correctly identifying the directly involved genetic variants. The statistical methods that attempt to deal with these challenges have been discussed elsewhere (e.g., Avinun & Knafo-Noam, 2015; Gage et al, 2016; Hackinger & Zeggini, 2017; Pingault et al, 2018; Salinas et al, 2017).…”

Section: The Complexity Of Human Behaviormentioning

confidence: 99%

The E Is in the G: Gene–Environment–Trait Correlations and Findings From Genome-Wide Association Studies

Avinun

2019

Perspect Psychol Sci

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Genome-wide association studies (GWASs) have shown that pleiotropy is widespread (i.e., the same genetic variants affect multiple traits) and that complex traits are polygenic (i.e., affected by many genetic variants with very small effect sizes). However, despite the growing number of GWASs, the possible contribution of gene–environment correlations (rGEs) to pleiotropy and polygenicity has been mostly ignored. rGEs can lead to environmentally mediated pleiotropy or gene–environment–trait correlations (rGETs), given that an environment that is affected by one genetically influenced phenotype, can in turn affect a different phenotype. By adding correlations with environmentally mediated genetic variants, rGETs can contribute to polygenicity. Socioeconomic status (SES) and the experience of stressful life events may, for example, be involved in rGETs. Both are genetically influenced and have been associated with a myriad of physical and mental disorders. As a result, GWASs of these disorders may find the genetic correlates of SES and stressful life events. Consequently, some of the genetic correlates of physical and mental disorders may be modified by public policy that affects environments such as SES and stressful life events. Thus, identifying rGETs can shed light on findings from GWASs and have important implications for public health.

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“…To distinguish biological pleiotropy from mediated pleiotropy, we performed mediation analysis (Salinas, Wang, & DeWan, 2017). An example causal diagram used to conceptualize the COPD disease process is presented in Figure 2.…”

Section: Mediation Analysesmentioning

confidence: 99%

Assessing pleiotropy and mediation in genetic loci associated with chronic obstructive pulmonary disease

Parker

Lutz

Hobbs

et al. 2019

Genetic Epidemiology

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Genetic association studies have increasingly recognized variant effects on multiple phenotypes. Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with environmental and genetic causes. Multiple genetic variants have been associated with COPD, many of which show significant associations to additional phenotypes. However, it is unknown if these associations represent biological pleiotropy or if they exist through correlation of related phenotypes (“mediated pleiotropy”). Using 6,670 subjects from the COPDGene study, we describe the association of known COPD susceptibility loci with other COPD‐related phenotypes and distinguish if these act directly on the phenotypes (i.e., biological pleiotropy) or if the association is due to correlation (i.e., mediated pleiotropy). We identified additional associated phenotypes for 13 of 25 known COPD loci. Tests for pleiotropy between genotype and associated outcomes were significant for all loci. In cases of significant pleiotropy, we performed mediation analysis to test if SNPs had a direct association to phenotype. Most loci showed a mediated effect through the hypothesized causal pathway. However, many loci also had direct associations, suggesting causal explanations (i.e., emphysema leading to reduced lung function) are incomplete. Our results highlight the high degree of pleiotropy in complex disease‐associated loci and provide novel insights into the mechanisms underlying COPD.

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Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy

Cited by 20 publications

References 75 publications

How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

The E Is in the G: Gene–Environment–Trait Correlations and Findings From Genome-Wide Association Studies

Assessing pleiotropy and mediation in genetic loci associated with chronic obstructive pulmonary disease

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