Analysis of Genetic Ancestry from NGS Data Using EthSEQ

Dalfovo, Davide; Romanel, Alessandro

doi:10.1002/cpz1.663

Cited by 4 publications

References 27 publications

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Germline determinants of aberrant signaling pathways in cancer

Dalfovo,

Scandino,

Paoli

et al. 2024

npj Precis. Onc.

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Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits’ oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients’ genetic liability to develop specific cancer molecular and clinical profiles.

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Germline determinants of aberrant signaling pathways in cancer

Dalfovo,

Scandino,

Paoli

et al. 2024

npj Precis. Onc.

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SNVstory: A dockerized algorithm for rapid and accurate inference of sub-continental ancestry

Bollas,

Rajkovic,

Ceyhan

et al. 2023

Preprint

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Knowing a patient's genetic ancestry is crucial in clinical settings, providing benefits such as tailored genetic testing, targeted health screening based on ancestral disease-predisposition rates, and personalized medication dosages. However, self-reported ancestry can be subjective, making it difficult to apply consistently. Moreover, existing approaches utilize genome sequencing data to infer ancestry at the continental level, creating the need for methods optimized for individual ancestry assignment. We present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We apply SNVstory to a clinical dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry. SNVstory represents a significant advance in methods to assign genetic ancestry, predicting ancestry across 36 different populations with high accuracy.

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SNVstory: inferring genetic ancestry from genome sequencing data

Bollas,

Rajkovic,

Ceyhan

et al. 2024

BMC Bioinformatics

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Background Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level. Results To address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy. Conclusions SNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory.

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Analysis of Genetic Ancestry from NGS Data Using EthSEQ

Cited by 4 publications

References 27 publications

Germline determinants of aberrant signaling pathways in cancer

Germline determinants of aberrant signaling pathways in cancer

SNVstory: A dockerized algorithm for rapid and accurate inference of sub-continental ancestry

SNVstory: inferring genetic ancestry from genome sequencing data

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