Recombination affects allele-specific expression of deleterious variants in human populations

Harwood, Michelle P; Alves, Isabel; Edgington, Hilary A.; Agbessi, Mawussé; Bruat, Vanessa; Soave, David; Lamaze, Fabien C.; Favé, Marie-Julie; Awadalla, Philip

doi:10.1126/sciadv.abl3819

Cited by 5 publications

(8 citation statements)

References 75 publications

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“…More generally, these results give empirical support to many theoretical studies about the accumulation of slightly deleterious mutations in non-recombining regions [28,30]. Similarly, increased prevalence of deleterious mutations in low recombining regions have been reported in plants [57] and in human populations [32,58,59]. In particular, recent work in human populations have shown that both variation in demographic history (i.e.…”

Section: Discussionsupporting

confidence: 78%

“…In particular, recent work in human populations have shown that both variation in demographic history (i.e. change in effective population size) and recombination rate variation are affecting allele specific expression of harmful mutations [32]. These authors showed that allele specific expression more efficiently cause under expression of harmful mutations in normally and highly recombining regions compared to low recombining regions and that this varies among human populations with different demographic histories.…”

Section: Discussionmentioning

confidence: 99%

“…Such a process will be exacerbated in the absence of recombination [28,31]. How demographic factors and recombination rate jointly affect selection efficacy and the deleterious load has been rarely investigated (see [32]).…”

Section: Introductionmentioning

confidence: 99%

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Allele surfing causes maladaptation in a Pacific salmon of conservation concern

Rougemont

Leroy

Rondeau

et al. 2022

Preprint

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The Anthropocene threatens worldwide biodiversity, with a potential to induce species decline and range contraction. In this context, it is important to quantify species adaptive potential and how demographic changes may impact selection efficacy and the burden of deleterious mutations in different populations. Here we show that key evolutionary processes, including variation in effective population size through postglacial change in demography and recombination rates have affected the efficacy of selection and impacted the load in Coho salmon (Oncorhynchus kisutch), a widely distributed salmonid species on the west coast of North America. Using whole genome resequencing (30x coverage) data from populations at different latitudinal distances from their southern glacial refugium, we found support for postglacial gene surfing, with reduced Ne at the range recolonization front, thus inducing both a reduction of the adaptive potential and a surf of deleterious alleles in populations evolving at low Ne. This inference was robust to various proxy of the load, namely ratios of non-synonymous to synonymous diversity, the number of missense and loss of function mutations. In addition, comparing residual tetrasomic and re-diploidizing regions of the salmon genome, we found support for a prime role of recombination rates in shaping the within-genome variation of the load. Overall, our empirical results are remarkably consistent with expectations under the nearly neutral theory of molecular evolution. We discuss the fundamental and applied implications of these findings for evolutionary and conservation genomics.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

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Allele surfing causes maladaptation in a Pacific salmon of conservation concern

Rougemont

Leroy

Rondeau

et al. 2022

Preprint

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show abstract

“…Ascertainment bias from unrepresentative sampling of ancestries is an acute unsolved challenge in major spheres of human disease genomics, including GWAS [24][25][26] and transcription models [27][28][29]. While big data genomic resources have revolutionized the study of cancer and other diseases, the systemic imbalance in cancer genomic data collection across human populations is severe.…”

Section: Global Population Diversity Impacts Disease Genomicsmentioning

confidence: 99%

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

Graim

Smith

Cahill

2023

Preprint

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Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1–8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address this significant gap, we present PhyloFrame, the first-ever machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating big data tissue-specific functional interaction networks, global population variation data, and disease-relevant transcriptomic data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. These results demonstrate how AI can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

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“…allele-specific expression (ASE) 5,6 , with high-impact applications in e.g. plant genetics 7 , autoimmunity 8 , cancer 9 and evolutionary studies 10 . ASE can be attributed to genetic variation of alleles themselves, consistently manifesting as unequal expression between allelic variants, here termed Allelic Bias (AB).…”

Section: Introductionmentioning

confidence: 99%

MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

Stroobandt

Goovaerts

Coussement

et al. 2022

Preprint

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Decreasing sequencing costs have instigated large-scale RNAseq experiments, yet genetic polymorphisms in such data remain poorly exploited. Currently, allele-specific expression (ASE) studies focus almost exclusively on genetic variants explaining expression differences (cis-eQTLs), largely ignoring other ASE effects. The latter are typically associated with higher variance in expression of both copies of a gene, here called Allelic Divergence (AD). We therefore developed an RNAseq-driven population-level beta-binomial mixture model for (differential) AD detection. The model simultaneously enables RNAseq-driven genotyping, which outperforms alternative RNA genotyping methods when applied on healthy kidney data from The Cancer Genome Atlas. Moreover, we identify well-known non-cis-eQTL ASE, e.g. random monoallelic expression of HLA and immunoglobulin genes in healthy kidney, as well as allele-specific aberrations in clear cell kidney carcinoma, including long-range epigenetic silencing of protocadherins, copy-number alterations, and loss of imprinting. These methods are available as the Modeller of Allelic Gene Expression (MAGE) tool suite: https://biobix.github.io/MAGE/.

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Recombination affects allele-specific expression of deleterious variants in human populations

Cited by 5 publications

References 75 publications

Allele surfing causes maladaptation in a Pacific salmon of conservation concern

Allele surfing causes maladaptation in a Pacific salmon of conservation concern

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

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