Back

Identifying Inheritance Patterns of Allelic Imbalance, using Integrative Modeling and Bayesian Inference

Hoyt, S. H.; Reddy, T. E.; Gordan, R.; Allen, A. S.; Majoros, W. H.

2026-03-31 bioinformatics
10.64898/2026.03.28.714974 bioRxiv
Show abstract

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis-regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis-regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
The American Journal of Human Genetics
206 papers in training set
Top 0.2%
18.2%
2
PLOS Computational Biology
1633 papers in training set
Top 3%
9.9%
3
PLOS Genetics
756 papers in training set
Top 2%
8.2%
4
Genetics
225 papers in training set
Top 0.5%
8.2%
5
Bioinformatics
1061 papers in training set
Top 4%
6.7%
50% of probability mass above
6
Nature Communications
4913 papers in training set
Top 34%
4.7%
7
GENETICS
189 papers in training set
Top 0.2%
3.9%
8
Nucleic Acids Research
1128 papers in training set
Top 6%
3.5%
9
Genome Biology
555 papers in training set
Top 2%
3.5%
10
Cell Systems
167 papers in training set
Top 4%
3.5%
11
NAR Genomics and Bioinformatics
214 papers in training set
Top 1.0%
3.0%
12
eLife
5422 papers in training set
Top 37%
2.0%
13
Genome Research
409 papers in training set
Top 2%
1.8%
14
Frontiers in Genetics
197 papers in training set
Top 5%
1.7%
15
BMC Bioinformatics
383 papers in training set
Top 5%
1.4%
16
Cell Genomics
162 papers in training set
Top 5%
1.2%
17
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 38%
1.2%
18
PLOS ONE
4510 papers in training set
Top 65%
0.9%
19
Genetic Epidemiology
46 papers in training set
Top 0.7%
0.9%
20
Bioinformatics Advances
184 papers in training set
Top 4%
0.8%
21
Molecular Biology and Evolution
488 papers in training set
Top 4%
0.7%
22
The Annals of Applied Statistics
15 papers in training set
Top 0.1%
0.7%
23
Nature Genetics
240 papers in training set
Top 8%
0.7%
24
Scientific Reports
3102 papers in training set
Top 76%
0.7%
25
Biometrics
22 papers in training set
Top 0.2%
0.7%
26
Genome Medicine
154 papers in training set
Top 9%
0.7%
27
BMC Genomics
328 papers in training set
Top 6%
0.7%