Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Zhu, Carrie; Ming, Matthew J.; Cole, Jared M.; Kirkpatrick, Mark; Harpak, Arbel

doi:10.2139/ssrn.4124638

Cited by 22 publications

(52 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we plan to expand the MELD framework to also study confounding stemming from factors such as gene-by-environment (G×E) or gene-by-sex (G×Sex) interactions. We can do this by computing a new set of scores which encode how loci interact with one or more environmental instruments [66][67][68] . Lastly, we have only focused on analyzing one phenotype at a time in this study.…”

Section: Discussionmentioning

confidence: 99%

Discovering non-additive heritability using additive GWAS summary statistics

Darnell

Smith

Ramachandran

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The inflation of test statistics in genome-wide association (GWA) studies due to confounding factors such as cryptic relatedness, population stratification, and spurious non-zero genetic effects driven by linkage disequilibrium (LD) has been well characterized in the literature. The key theoretical contribution of this work is that epistasis (i.e., the interaction between multiple loci and/or genes) can also lead to bias in GWA summary statistics. To address this challenge, we develop marginal epistatic LD score regression and the accompanying software package MELD: an extended framework which takes in GWA test statistics and accurately partitions true additive genetic variation from non-additive genetic variation, as well as other biases. By re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals of European ancestry in the UK Biobank and up to 159,095 individuals in BioBank Japan, we illustrate that nonlinear effects are a significant source of signal in reported GWA summary statistics and provide evidence that epistasis is more widespread in human phenotypes than previously reported. Of the 25 complex traits we analyzed in the UK Biobank, 23 phenotypes have a significant amount of epistatic confounding captured within additive variation, including height, urate level, and cholesterol levels. The MELD software and its application to these biobanks represent a significant step towards resolving the true contribution of epistasis to human complex traits.

show abstract

Section: Discussionmentioning

confidence: 99%

Discovering non-additive heritability using additive GWAS summary statistics

Darnell

Smith

Ramachandran

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, predictions from the massive height study mentioned above accounted for only 10-20% of the variance in height outside people of European ancestries (Yengo et al, 2022), a pattern that has been observed repeatedly in analogous contexts (Martin et al, 2017(Martin et al, , 2019Thompson et al, 2022). The causes of these problems with generalizability are not fully understood, but evidence points to contributions from worldwide differences in the frequencies and correlations of genetic variants (Wang et al, 2020) as well as genetic interactions (Mostafavi et al, 2020;Zhu, Ming, Cole, Kirkpatrick, & Harpak, 2022;Patel et al, 2022), in which the effect of a genetic variant on a trait depends on the environment (gene-by-environment interaction) or on genotypes at other positions in the genome (gene-by-gene interaction, or epistasis).…”

Section: Blair Underwoodmentioning

confidence: 96%

GATTACA AS A LENS ON CONTEMPORARY GENETICS Marking 25 years into the film’s “not-too-distant” future

Ogbunugafor¹,

Edge²

2022

Preprint

View full text Add to dashboard Cite

The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern contro-versies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the“completion” of the human genome (2000), as the science of human genetics was developing a renewedsense of its social implications. The story is set in a near-future world in which parents can, with tech-nological assistance, influence the genetic composition of their offspring on the basis of predicted lifeoutcomes. This moment—25 years after the film’s release—offers an opportunity to reflect on where so-ciety currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss severalactive areas of genetic research—genetic prediction, embryo selection, forensic genetics, and others–thatinterface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattacaremains an important reflection of society’s expectations and fears with respect to the ways that geneticscience has manifested in the real world. In an accompanying appendix, we offer some thought questionsto guide group discussions inside and outside of the classroom.

show abstract

“…Recent work in personality science suggests that most replicable interaction effects are in fact smaller than this (Vize et al, 2022). Similarly, recent work in human statistical genetics has found that while interactions are pervasive, they are largely orders of magnitude smaller than maineffects (Zhu et al, 2022). These and related observations have led to prominent commentaries suggesting that researchers should plan on interaction effects that are at least half the size of main effects (i.e., an attenuated effect) (Gelman, 2018), as well as theoretical work suggesting that most observable interactions are necessarily quite small (Tosh et al, 2021).…”

Section: Selecting An Appropriate Effect Sizementioning

confidence: 99%

Tutorial: Power analyses for interaction effects in cross-sectional regressions

Baranger¹,

Finsaas²,

Goldstein³

et al. 2022

Preprint

View full text Add to dashboard Cite

Interaction analyses (also termed ‘moderation’ analyses or ‘moderated multiple regression’) are a form of linear regression analysis designed to test whether the association between two variables changes when conditioned on a third variable. It can be challenging to perform a power analysis for interactions with existing software, particularly when variables are correlated and continuous. Moreover, while power is impacted by variable parameters that are always present in cross-sectional observational studies, such as reliability and skew, it can be unclear how to incorporate these effects into a power analysis. The R package InteractionPoweR and associated Shiny app allow researchers with minimal or no programming experience to perform analytic and simulation-based power analyses for interactions. At minimum, these analyses require the Pearson’s correlation between variables and sample size, and parameters including reliability, skew, and the number of discrete levels that a variable takes (e.g., binary or likert scale) can optionally be specified. In this Tutorial we demonstrate how to perform power analyses using our package and give examples of how power can be impacted by main effects, correlations between main effects, reliability, and variable distributions. We close with a brief discussion of how researchers may select an appropriate interaction effect size when performing a power analysis.

show abstract

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Cited by 22 publications

References 73 publications

Discovering non-additive heritability using additive GWAS summary statistics

Discovering non-additive heritability using additive GWAS summary statistics

GATTACA AS A LENS ON CONTEMPORARY GENETICS Marking 25 years into the film’s “not-too-distant” future

Tutorial: Power analyses for interaction effects in cross-sectional regressions

Contact Info

Product

Resources

About