Browsing by Issue Date, starting with "2024-09-28"
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- Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysisPublication . Gonçalves de Gouveia Maia Xavier, Joana; Magno, Ramiro; Russell, Roslin; Almeida, Bernardo P. de; Jacinta-Fernandes, Ana; Duarte, André; Besouro-Duarte, André; Dunning, Mark; Samarajiwa, Shamith; O’Reilly, Martin; MARQUES MAIA DE ALMEIDA, JOSE ANTONIO; Rocha, Cátia L.; Rosli, Nordiana; Ponder, Bruce A. J.; Maia, Ana-TeresaUnderstanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease’s tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.
- Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 facultiesPublication . Busch, Felix; Hoffmann, Lena; Truhn, Daniel; Ortiz-Prado, Esteban; Makowski, Marcus R.; Bressem, Keno K.; Adams, Lisa C.; COMFORT consortiumBackground The successful integration of artifcial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional diferences in these perceptions? Methods An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students’ AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-feld comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann–Whitney U-test, Kruskal–Wallis test, and Dunn-Bonferroni post hoc test. Results The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3–4) and a desire for more AI teaching (median: 4, IQR: 4–5). However, they had limited AI knowledge (median: 2, IQR: 2–2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1–3). Subgroup analyses revealed signifcant diferences between the Global North and South (r=0.025 to 0.185, all P<.001) and across continents (r=0.301 to 0.531, all P<.001), with generally small efect sizes. Conclusions This large-scale international survey highlights medical, dental, and veterinary students’ positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifes a need for integrating AI education into medical curricula, considering regional diferences in perceptions and educational needs. Trial registration Not applicable (no clinical trial)