Mohammadi et al. 2017 [1] defined allele fold change (aFC) as the ratio in the number of gene transcripts under the alternative allele with respect to that of the reference allele. Consequently, it is a parameter that quantifies the effect of any one regulatory variant with its target gene. The authors of [1] showed mathematically how to combine individual phased genotypes and aFC values to predict observed gene expression. While this definition and model is general, the authors used it to specifically study cis-regulatory effects and RNA sequencing data. To find out more on the model checkout the More on the model section below.

This software package for the Python programming language can be used to:

• predict gene expression abundances from genotype data under the model of [1].

from either the command line or within a Python script.

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To begin, let's define the model variables:

• $Y\in\mathbb{R_{>=0}}$ the abundance of a gene,
• $x_h\in{0,1}^{n\times 1}$ encoding $n$ biallelic cis-regulatory, reference (0) and alternative (1) allele, variants for haplotype $h\in{1,2}$,
• $\alpha\in\mathbb{R}$ the log abundance under the reference haplotype
• $\beta\in\mathbb{R}^{n\times 1}$ the log allele fold change

then it follows that

$$ \log(Y) = \log 2 \left(2^{\alpha + x_1^{T}\beta} + 2^{\alpha + x_2^{T}\beta}\right) + \epsilon $$

where $\epsilon\sim\mathcal{N}(0,\sigma^2)$.

[1] Mohammadi et al. Genome Research 2017

@article{Mohammadi2017GenomeResearch,
  title={Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change},
  author={Mohammadi, Pejman and Castel, Stephane E and Brown, Andrew A and Lappalainen, Tuuli},
  journal={Genome research},
  volume={27},
  number={11},
  pages={1872--1884},
  year={2017},
  publisher={Cold Spring Harbor Lab}
}

[2] Ehsan et al. BioRxiv

@article {Ehsan2022BioRxiv,
	author = {Nava Ehsan and Bence M. Kotis and Stephane E. Castel and Eric J. Song and Nicholas Mancuso and Pejman Mohammadi},
	title = {Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data},
	year = {2022},
	doi = {10.1101/2022.01.28.478116},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/01/28/2022.01.28.478116},
	journal = {bioRxiv}
}