Biology subjects

Rebbeck, T. R.

Publications and source records attributed to Rebbeck, T. R..

6 recordsLinked to original sources

bioRxiv2026

A Common Pathogenic Founder Variant in Rwandan Breast Cancer Cases

Germline data from African populations remain sparse, limiting characterization of population-specific BRCA1/2 pathogenic variants. In a study of 175 Rwandan women with breast cancer, 7 unrelated carriers (4% of cases; 22% of pathogenic variant carriers) harbored the same BRCA1 frameshift variant, c.4065_4068del (p.Asn1355Lysfs*10), which is extremely rare in gnomAD yet recurrent in European, Asian, and Middle Eastern cohorts. Whole-exome sequencing and haplotype analysis of all 7 carriers revealed a shared ancestral block of approximately 581 kb surrounding the variant, and extended haplotype homozygosity and network analyses confirmed a common founder origin. Coalescent-based age estimation placed the founder event approximately 4,000--10,000 years ago. Comparison with 1000 Genomes Project data showed the founder haplotype is absent or exceedingly rare outside African and South Asian populations. These findings strongly suggest the c.4065_4068del variant as a pre-historical BRCA1 founder variant in Rwanda, with implications for targeted genetic testing, cascade screening, and cancer prevention in the region.

genetics↗

bioRxiv2024

Uncovering the genetic architecture and evolutionary roots of androgenetic alopecia in African men

Androgenetic alopecia is a highly heritable trait. However, much of our understanding about the genetics of male pattern baldness comes from individuals of European descent. Here, we examined a novel dataset comprising 2,136 men from Ghana, Nigeria, Senegal, and South Africa that were genotyped using a custom array. We first tested how genetic predictions of baldness generalize from Europe to Africa, finding that polygenic scores from European GWAS yielded AUC statistics that ranged from 0.513 to 0.546, indicating that genetic predictions of baldness in African populations performed notably worse than in European populations. Subsequently, we conducted the first African GWAS of androgenetic alopecia, focusing on self-reported baldness patterns at age 45. After correcting for present age, population structure, and study site, we identified 266 moderately significant associations, 51 of which were independent (p-value < 10-5, r2 < 0.2). Most baldness associations were autosomal, and the X chromosomes does not appear to have a large impact on baldness in African men. Finally, we examined the evolutionary causes of continental differences in genetic architecture. Although Neanderthal alleles have previously been associated with skin and hair phenotypes, we did not find evidence that European-ascertained baldness hits were enriched for signatures of ancient introgression. Most loci that are associated with androgenetic alopecia are evolving neutrally. However, multiple baldness-associated SNPs near the EDA2R and AR genes have large allele frequency differences between continents. Collectively, our findings illustrate how evolutionary history contributes to the limited portability of genetic predictions across ancestries.

genomics↗

bioRxiv2023

E2EGraph: An End-to-end Graph Learning Model for Interpretable Prediction of Pathlogical Stages in Prostate Cancer

Prostate cancer is one of the deadliest cancers worldwide. An accurate prediction of pathological stages using the expressions and interactions of genes is effective for clinical assessment and treatment. However, identification of interactions using biological procedure is time consuming and prohibitively expensive. A graph is a powerful representation for the complex interactome of genes, their transcripts, and proteins. Recently, Graph Neural Networks (GNNs) have gained great attention in machine learning due to their capability to capture the graphical interactions among data entities. To leverage GNNs for predicting pathological stage stages, we developed an end-to-end graph representation and learning model, namely E2EGraph, which can automatically generate a graph representation using gene expression data and a multi-head graph attention network to learn the strength of interactions among genes and make the prediction. To ensure the reliability of model prediction, we identify critical components of graph representation and GNN model to interpret prediction results from multiple perspectives at gene and patient levels. We evaluated E2EGraph to predict pathological stages of prostate cancer using The Cancer Genome Atlas (TCGA) data. Our experimental results demonstrate that E2EGraph reaches the state-of-art prediction performance while being effective in identifying marker genes indicated by interpretability. Our results point to a direction where adaptive graph construction and attention based GNNs can be leveraged for various prediction tasks and interpretation of model prediction in a variety of data domains including disease prediction.

bioinformatics↗

bioRxiv2023

Variant-specific Mendelian Risk Prediction Model

Many pathogenic sequence variants (PSVs) have been associated with increased risk of cancers. Mendelian risk prediction models use Mendelian laws of inheritance to predict the probability of having a PSV based on family history, as well as specified PSV frequency and penetrance (agespecific probability of developing cancer given genotype). Most existing models assume penetrance is the same for any PSVs in a certain gene. However, for some genes (for example, BRCA1/2), cancer risk does vary by PSV. We propose an extension of Mendelian risk prediction models to relax the assumption that risk is the same for any PSVs in a certain gene by incorporating variant-specific penetrances and illustrating these extensions on two existing Mendelian risk prediction models, BRCAPRO and PanelPRO. Our proposed BRCAPRO-variant and PanelPRO-variant models incorporate variant-specific BRCA1/2 PSVs through the region classifications. Due to the sparsity of the variant information we classify BRCA1/2 PSVs into three regions; the breast cancer clustering region (BCCR), the ovarian cancer clustering region (OCCR), and an other region. Simulations were conducted to evaluate the performance of the proposed BRCAPRO-variant model compared to the existing BRCAPRO model which assumes the penetrance is the same for any PSVs in BRCA1 (and respectively BRCA2). Simulation results showed that the BRCAPRO-variant model was well calibrated to predict region-specific BRCA1/2 carrier status with high discrimination and accuracy on the region-specific level. In addition, we showed that the BRCAPRO-variant model achieved performance gains over the existing risk prediction models in terms of calibration without loss in discrimination and accuracy. We also evaluated the performance of the two proposed models, BRCAPRO-variant and PanelPRO-variant, on a cohort of 1,961 families from the Cancer Genetics Network (CGN). We showed that our proposed models provide region-specific PSV carrier probabilities with high accuracy, while the calibration, discrimination and accuracy of gene-specific PSV carrier probabilities were comparable to the existing gene-specific models. As more variant-specific PSV penetrances become available, we have shown that Mendelian risk prediction models can be extended to integrate the additional information, providing precise variant or region-specific PSV carrier probabilities and improving future cancer risk predictions.

genetics↗

bioRxiv2023

Split-Transformer Impute (STI): Genotype Imputation Using a Transformer-Based Model

MotivationDespite recent advances in sequencing technologies, genome-scale datasets continue to have missing bases and genomic segments. Such incomplete datasets can undermine downstream analyses, such as disease risk prediction and association studies. Consequently, the imputation of missing information is a common pre-processing step for which many methodologies have been developed. However, the imputation of genotypes of certain genomic regions and variants, including large structural variants, remains a challenging problem. ResultsHere, we present a transformer-based deep learning framework, called a split-transformer impute (STI) model, for accurate genome-scale genotype imputation. Empowered by the attention-based transformer model, STI can be trained for any collection of genomes automatically using self-supervision. STI handles multi-allelic genotypes naturally, unlike other models that need special treatments. STI models automatically learned genome-wide patterns of linkage disequilibrium (LD), evidenced by much higher imputation accuracy in high LD regions. Also, STI models trained through sporadic masking for self-supervision performed well in imputing systematically missing information. Our imputation results on the human 1000 Genomes Project show that STI can achieve high imputation accuracy, comparable to the state-of-the-art genotype imputation methods, with the additional capability to impute multi-allelic structural variants and other types of genetic variants. Moreover, STI showed excellent performance without needing any special presuppositions about the patterns in the underlying data when applied to a collection of yeast genomes, pointing to easy adaptability and application of STI to impute missing genotypes in any species.

genomics↗

bioRxiv2019

A custom genotyping array reveals population-level heterogeneity for the genetic risks of prostate cancer and other cancers in Africa

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. We find that samples from Ghana and Nigeria cluster together, while samples from Senegal and South Africa yield distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores were also generated for each genome in the MADCaP pilot dataset, and we found that predicted risks of CaP are lower in Senegal and higher in Nigeria.\n\nSignificanceWe have developed an Africa-specific genotyping array which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

genomics↗