Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE)

Brecca R Miller; Alison M Morse; Jacqueline E Borgert; Zihao Liu; Kelsey Sinclair; Gavin Gamble; Fei Zou; Jeremy R B Newman; Luis G León-Novelo; Fabio Marroni; Lauren M McIntyre

doi:10.1093/g3journal/jkab096

Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE)

G3 (Bethesda). 2021 May 7;11(5):jkab096. doi: 10.1093/g3journal/jkab096.

Authors

Brecca R Miller^{1

2}, Alison M Morse^{1

3}, Jacqueline E Borgert^{1

4}, Zihao Liu^{1

3}, Kelsey Sinclair^{1

3}, Gavin Gamble¹, Fei Zou^{4

5}, Jeremy R B Newman^{1

6}, Luis G León-Novelo⁷, Fabio Marroni⁸, Lauren M McIntyre^{1

3}

Affiliations

¹ Genetics Institute, University of Florida, Gainesville, FL 32608, USA.
² NYU Langone Health, New York University, New York, NY 10013, USA.
³ Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32608, USA.
⁴ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27515, USA.
⁵ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27515, USA.
⁶ Department of Pathology, University of Florida, Gainesville, FL 32608 USA.
⁷ Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston-University of Texas School of Public Health, Houston, TX 7703, USA.
⁸ Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Udine, 33100, Italy.

Abstract

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? The approach described applies to any technology generating allele-specific sequence counts, for example for chromatin accessibility and can be applied generally including to comparisons between tissues or environments for the same genotype. Tests of allelic effect are generally performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between nontester alleles, allowing n alleles to be compared with n crosses. Using a mouse data set where both testcrosses and direct comparisons have been performed, we show that the predicted differences between nontester alleles are validated at levels of over 90% when a parent-of-origin effect is present and of 60%-80% overall. Power considerations for a testcross, are similar to those in a reciprocal cross. In all applications, the testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BayesASE has been packaged in Galaxy, made available in Nextflow and as a collection of scripts for the SLURM workload manager on github (https://github.com/McIntyre-Lab/BayesASE).

Keywords: cis-regulatory variation; Bayesian analysis; allele specific expression; allelic imbalance.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Alleles
Allelic Imbalance*
Bayes Theorem
Genotype
Polymorphism, Single Nucleotide*

Associated data

figshare/10.25387/g3.14174291

Grants and funding

R01 GM128193/GM/NIGMS NIH HHS/United States