European Journal of Human Genetics

Advances in high-throughput sequencing and genetic research have expanded the role of genetics in medicine and society. Population-based screening programs, including neonatal and preconception testing, are increasingly implemented globally, alongside the rise of direct-to-consumer (DTC) genetic testing. The "Genetics and the General Public" Ethics Working Group of the French Federation of Human Genetics (FFGH) assessed knowledge and awareness of genetics within the French population through a nationally representative survey (n=3,013) conducted by the polling firm Ipsos bva. Results indicated that 69% of respondents report an interest in genetics, although their level of knowledge remains limited. Most respondents expressed positive attitudes toward genetics, perceiving it as a major source of hope in healthcare. While a majority indicated willingness to undergo genetic testing for medical purposes, they also reported legitimate concerns regarding the potential results. Despite legal restrictions, 12% reported having ordered a DTC genetic test (5% for genealogical; 5% for medical and 2% for both purposes), and 45% of non-users expressed strong interest in this type of test. Notably, there is a substantial lack of awareness regarding the limitations of these tests and the French legal framework governing their use. These findings highlight critical gaps in public knowledge, emphasizing the need for improved genetic education, including incorporating genetics into school curricula and launching targeted awareness campaigns. These initiatives should help clarify the distinctions between clinically validated genetic tests and DTC genetic testing services, addressing both their benefits and their ethical, legal, and scientific limitations, in order to promote informed decision-making.

Finnish gelsolin amyloidosis (AGel amyloidosis) is an autosomal dominant systemic amyloidosis caused by GSN c.640G>A p.D187N (rs121909715) founder variant. The disease was first described in 1969, and it was hypothesized that the Finnish patients share a common ancestor dating back to the 14th century. The link between two Finnish regions with high AGel incidence (Kanta-Hame and Kymenlaakso) has been hypothesized to have occurred in 1365 by a settler moving from Kanta-Hame to Kymenlaakso. Here, we used haplotype sharing tree (HST) to analyze Finnish AGel amyloidosis haplotypes to trace the geographic origin of the variant. We also estimated the time from the most recent common ancestor (MRCA) using single nucleotide polymorphism and short tandem repeat data. The HST -based analyses leveraging AGel amyloidosis cohorts from different Finnish geographic regions indicated, that the variant more likely appeared first in Kymenlaakso, not Kanta-Hame, contrary to the original hypothesis. The MRCA estimates for Finnish AGel ranged from 15 to 40 generations using four different methods, the mean of all estimates (27 generations) dated back to the 14th century. Thus, the data supports the original hypothesis on the variants spreading temporally, but not geographically. These results illustrate the use of HSTs in the analysis of haplotype structures and in tracing the ancestry of a founder variant.

Unlike other European countries, the human population genetics and demographic history of Metropolitan France is surprisingly understudied. In this work, we combined newly genotyped samples from various zones in France with publicly available data and applied both allele frequency and haplotype-based methods in order to describe the internal structure of this country, by using genome-wide single nucleotide polymorphism (SNP) array genotypes. We found out that French Basques are genetically distinct from all other populations in the Hexagone and that the populations from southwest France (namely the Gascony region) share a large proportion of their ancestry with Basques. Otherwise, the genetic makeup of the French population is relatively homogeneous and mostly related to Southern and Central European groups. However, a fine-grained, haplotype-based analysis revealed that Bretons slightly separated from the rest of the groups, due mostly to gene flow from the British Isles in a time frame that coincides both historically attested Celtic population movements to this area between the 3th and the 9th centuries CE, but also with a more ancient genetic continuity between Brittany and the British Isles related to the shared drift with hunter-gatherer populations. Haplotype-based methods also unveiled subtle internal structures and connections with the surrounding modern populations, particularly in the periphery of the Hexagone.

Non-invasive prenatal diagnosis for single-gene disorders (SGD-NIPD) has been widely adopted by patients, but is mostly limited to the exclusion of paternal or de novo mutations. Indeed, it is still difficult to infer the inheritance of maternal allele from cell free DNA (cfDNA) analysis. Based on the study of maternal haplotypes imbalance in cfDNA, relative haplotype dosage (RHDO) was developed to address this challenge. Although RHDO has proven to be reliable, robust control of statistical error and explicit delineation of critical parameters for assessing the quality of analysis have not been fully considered yet. Here we propose a universal and adaptable enhanced-RHDO procedure (eRHDO) through an automated bioinformatics pipeline with a didactical visualization of results that aims to be applied for any SGD-NIPD in routine care. A training cohort of 43 families carriers for CFTR, NF1, DMD, or F8 mutations allowed the characterization and optimal setting of several adjustable data variables, such as minimal sequencing depth and type 1 and type 2 statistical errors, as well as the quality assessment for intermediate steps and final result through block score and concordance score. Validation was successfully carried out on 56 pregnancies of the test cohort. Finally, computer simulations were used to estimate the effect of fetal-fraction, sequencing depth and number of informative SNPs on the quality of results. Our workflow proved to be robust, as we obtained 94.9% conclusive and correctly inferred fetal genotypes, without any false negative or false positive result. By standardizing data generation and analysis, we fully describe a turnkey protocol for laboratories wishing to offer eRHDO-based non-invasive prenatal diagnosis for single-gene disorders as an alternative to conventional prenatal diagnosis.

The imputation of single nucleotide polymorphisms (SNPs) provides a low-cost alternative to augment the size of genotyped SNP panels. Genotype imputation is commonly applied to study genotype-phenotype correlations in medical and population genetics, and has a great - yet unexplored - potential in a forensic context. Forensic DNA phenotyping, i.e., the prediction of phenotypic traits based on SNPs, can greatly benefit from imputing missing DNA markers necessary for utilising available prediction models and implementing novel prediction models. Currently however, most imputation studies investigate the performance of random SNPs with limited focus on SNPs involved in phenotypic expression or association. In the current study, individuals from the 1000 Genomes Project with high predicted trait diversity were used to explore the imputation accuracy of SNPs leveraged in phenotype prediction models and SNPs associated with facial traits compared to all SNPs. Further, the performance of the HIrisPlex-S prediction model for phenotypic traits was investigated using different imputed datasets. Firstly, we were able to corroborate that the number and selection of SNPs in the genotype dataset and the minor allele frequency (MAF) are major drivers of imputation call and error rates. Secondly, we explored increased imputation errors for phenotypic SNPs compared to randomly selected SNPs due to MAF differences. Further, we corroborated findings on lower imputation error rates for SNPs in coding regions due to increased linkage compared to non-coding regions. When investigating the impact of imputation on the performance of trait prediction using the HIrisPlex-S prediction model, we observed that datasets with more genotyped SNPs and phenotypes with more observations in the reference panel improved the prediction of these phenotypes. Finally, we showed novel insights into the improved trait prediction when applying more lenient calling thresholds for SNP imputation due to the detrimental impact of missing genotypes on trait prediction accuracy compared to imputation errors. Our findings, which show different imputation performances for general compared to phenotype-associated and prediction-model SNPs, highlight the importance of investigating imputation performances for the SNPs of interest. Further, we reported optimal trait predictions using lenient calling threshold of imputed SNP genotypes paired with a SNP panel with high linkage, which shows the high applicability of SNP imputation for phenotypic trait predictions. We recommend imputation tests for the prediction models of interest due to the differences between prediction models. HighlightsO_LINumber and selection of SNPs in imputation input and MAF impact call and error rate C_LIO_LILower imputation accuracy of phenotype-associated SNPs compared to random SNPs C_LIO_LILower imputation error rates for SNP in coding over non-coding regions C_LIO_LIHigh abundance of phenotype in reference panel favours its prediction C_LIO_LIMost accurate trait predictions for lenient SNP calling thresholds for imputation C_LI

BackgroundTwo decades of genome-wide association studies (GWAS) have led to the fast-growing application of polygenic risk prediction (PRS). However, due to population structure and evolutionary path differences, the PRS substrate derived mostly from studies of European ancestry does not work equally well for other ancestries. There is an association between prediction accuracy decay and individual genetic distance (GD) to the genetic centers (GC) of various populations. ObjectivesTo develop a new PRS method and software that utilizes individual GD to improve PRS risk prediction accuracy, especially for non-European populations. MethodWe hypothesize that adding a GD-based weight into PRS methods would enhance its risk prediction performance, particularly for minority groups. We explore the GD first by principal components (PC) and then by phylogenetic tree structures. Building on top of an emerging software (PRS-CSx) that achieves high prediction accuracy across multiple-ancestries, we present PGS-GRID, where "GRID" stands for "Genetic Reference based on Individual Distance". ResultsWe developed a preliminary version of PRS-GRID and pilot tested its prediction performance for a classic quantitative trait (e.g., height) and a disease trait (e.g., type-2 diabetes). We found slight but noticeable improvement of risk prediction in minority populations. We further explored a random forest approach so that the performance of PRS-GRID could be clearly explained, which is a key step for PRS to be used in clinical and public health practice. ConclusionsThe PRS-GRID philosophy and method represent an innovative and significant advancement in the field of polygenic risk prediction. Our work provides a foundation for future research and clinical applications aimed at reducing health disparities and improving population health through personalized medicine.

BackgroundEven with the introduction of new genetic techniques that enable accurate genomic characterization, knowledge about the phenotypic spectrum of rare chromosomal disorders is still limited, both in literature and existing databases. Yet this clinical information is of utmost importance for health professionals and the parents of children with rare diseases. Since existing databases are often hampered by the limited time and willingness of health professionals to input new data, we collected phenotype data directly from parents of children with a chromosome 6 disorder. These parents were reached via social media, and the information was collected via the online Chromosome 6 Questionnaire, which includes 115 main questions on congenital abnormalities, medical problems, behaviour, growth and development. Here, we assess data consistency by comparing parent-reported phenotypes to phenotypes based on copies of medical files for the same individual and data availability by comparing the data available on specific characteristics reported by parents to data available in existing literature. ResultsThe reported answers to the main questions on phenotype characteristics were 85-95% consistent, and the consistency of answers to subsequent more detailed questions was 77-96%. For all but two main questions, significantly more data was collected from parents via the Chromosome 6 Questionnaire than was currently available in literature. For the topics developmental delay and brain abnormalities, no significant difference in the amount of available data was found. The only feature for which significantly more data was available in literature was a sub-question on the type of brain abnormality present. ConclusionsThis is the first study to compare phenotype data collected directly from parents to data extracted from medical files on the same individuals. We found that the data was highly consistent, and phenotype data collected via the online Chromosome 6 Questionnaire resulted in more available information on most clinical characteristics when compared to phenotypes reported in literature reports thus far. We encourage active patient participation in rare disease research and have shown that parent-reported phenotypes are very reliable and contribute to our knowledge of the phenotypic spectrum of rare chromosomal disorders.

Background As part of Singapore's effort towards precision medicine tailored to Asian diversity, we describe the implementation of a nationwide reproductive carrier screening program. Using a customised 112-gene panel, incorporating population-specific recessive genetic diseases, we outline the overall program design, and initial efforts of community and stakeholder engagement, to inform culturally appropriate implementation. Methods Participants receive culturally tailored online education regarding our reproductive screening program and are provided results with genetic counselling and reproductive options. Community and stakeholder perspectives were assessed through questionnaires and consultations with religious leaders. Results Recruitment is nation-wide, and since initiation of our pilot phase in September 2024, 1,619 couples have registered interest, with 60% uptake of those deemed eligible. Among the 456 couples that have received results to date, four couples (0.9%) were identified to be at increased risk. Community questionnaire responses (n=1002), involving couples who participated in the program as well as the general public, indicated interest is high (59%) across the cohort but awareness, intent to participate and implications for reproductive options differed by sociodemographic factors such as ancestry and religion. Healthcare professional respondents (n=113) acknowledged carrier screening will be routine in medical care, but report limited confidence and resources. Engagement with religious leaders indicated support for the program. Conclusion These early program outcomes and community engagement are guiding the implementation of expanding population-based carrier screening in Singapore, contingent on addressing practical challenges through equitable outreach and professional training.

Axenfeld-Rieger Syndrome (ARS) is an autosomal dominant condition with both ocular and non-ocular manifestations. ARS is primarily caused by coding variants at the PITX2 or FOXC1 loci, yet many cases still remain undiagnosed. Here we used whole-genome sequencing to identify two non-coding structural variants associated with a typical presentation of PITX2-associated ARS: one with a 450 kb deletion removing a series of conserved enhancer elements distal to PITX2, and the second with a 12.5 Mb inversion displacing the PITX2 gene from these same enhancer elements. Neither variant disrupted the PITX2 gene itself, and therefore both were expected to reduce PITX2 expression by disrupting its proximity or access to enhancer elements. Enhancer-disrupting intergenic inversions therefore represent a unique genetic mechanism for the development of ARS, which should be carefully considered in the context of ARS and other conditions without a conclusive genetic diagnosis.

Clinical genetics is the prime application of genetics in healthcare, providing highly advanced and reliable diagnostics for patients with (mostly rare) disease of genetic origin. Whereas many novel technologies have expanded the genetic toolkit, integration or alignment with other areas of healthcare is often challenging. We hypothesise that this is due to the characteristics inherent to the regimes in which the genetic technologies were to be implemented. In order to facilitate integration of genetic applications in a rebooting and perhaps transforming healthcare system, we here provide insights in discrepancies between clinical genetics and four of its adjacent regimes; public health, human genetic research, non-genetic healthcare, and society. We conducted twelve semi-structured group interviews and a focus group to collect information on overlapping and distinctive elements of each regime. We identified three aspects in which the adjacent regimes differed considerably compared to clinical genetics; perception of data, expectations from technologies, and compartimentalisation units. Strikingly, divergence within each of these aspects was determined by elements of culture, and not - as is often thought - by elements of structure, e.g. regulation and policy. We conclude that implementation of genetics requires transdisciplinary empathy - understanding of the way of organizing, thinking and doing in adjacent regimes.

The genetic variation of the European population at a macro-geographic scale follows genetic gradients which reflect main migration events. However, less is known about factors affecting mating choices at a micro-geographic scale. In this study we have analyzed 726,718 autosomal SNPs in 435 individuals from the Catalan Pyrenees covering around 200 km of a vast and abrupt region in the north of the Iberian Peninsula, for which we have information about the geographic origin of all grand-parents and parents. At a macro-geographic scale, our analyses recapitulate the genetic gradient observed in Spain. However, we also identified the presence of micro-population substructure among the sampled individuals. Such micro-population substructure does not correlate with geographic barriers such as the expected by the orography of the considered region, but by the bishoprics present in the covered geographic area. These results support that, on top of main human migrations, long ongoing socio-cultural factors have also shaped the genetic diversity observed at rural populations.

Study questionHow does serial sperm donation impact genetic risk in the donor-conceived children and their descendants? Summary answerIn addition to the psychological effects of serial sperm donation, donor-conceived children are at risk of unintentional inbreeding. This risk is compounded by the hard-to-quantify effect of social proximity between mothers. Such inbreeding would cause children to have up to 15% excess risk of childhood mortality or congenital morbidities. The risk to descendants after many generations is spread across many individuals and remains low as long as the number of donor-conceived children does not increase appreciably. What is known alreadyInbreeding increases the risk for a range of diseases among the offspring, with the risk increasing with the degree of inbreeding. Sperm donation increases the risk of accidental inbreeding, and thus likely increases disease risk. Study design, size, durationWe performed a literature review of risks associated with consanguinity across a range of traits, together with a model-based mathematical analysis to estimate the short- and long-term risk associated with serial sperm donation. Participants/materials, setting, methodsWe used whole-genome sequencing and imputed sequence data from the CARTaGENE longitudinal study to estimate population prevalence of relevant risk alleles. We performed mathematical modelling based on these results on published estimates of the risk associated with inbreeding. Main results and the role of chanceWith over 600 children conceived in this serial sperm donation event, 0.1 consanguineous unions would be expected under the simplest model of random mating by generation within the province of Quebec. Preferential mating due to geographic and social proximity among the mothers could increase this rate appreciably, so that accidental inbreeding is not unlikely. Since the likelihood of inbreeding events increases quadratically with the number of children, active inbreeding avoidance by the offspring and interventions to reduce continued serial donation can reduce risk. Over generations, more distant inbreeding is unavoidable, but inbreeding coefficients are reduced. Our model predicts that the long-term excess number of serious adverse events will be fewer than one per generation. The short- and long-term rates of specific diseases may be affected, however, given public information about the donor carrier status, we expect an excess of 0.84 children per generation [95% CI: 0,3] affected by Hereditary Tyrosinemia of type 1. Large scale dataCARTaGENE is a biobank based in Quebec, Canada, that is accessible following an independent data access protocol and can be found at: https://cartagene.qc.ca/en/ Limitations, reasons for cautionOur analysis relies on uncertain estimates of the burden associated with inbreeding. We also rely on simplifying assumptions about future events, including migrations, social interactions between mothers, and future sperm donation events. As a result, our estimates should be seen as coarse estimates. Wider implications of the findingsSerial sperm donation is not uncommon. Each documented instance has raised questions about the genetic burden associated with the practice. By quantifying this risk, this study will help inform the public health and genetic counselling response to these situations, in addition to being of interest from a population genetics perspective. Study funding/competing interest(s)This research was supported by the Canadian Institute for Health Research (CIHR) project grant 437576, NSERC grant RGPIN-2017-04816, the Canada Research Chair program to S.G., and the Canada Foundation for Innovation. T.M.Z was supported by the QLS Grad and Grad Excellence Award. The authors report no competing interests. ConsanguinityThe degree of relatedness between individuals, as measured by inheritance from recent ancestors. For example, second cousins share on average 3.125% of their DNA from their great-grandparents. InbreedingThe production of offspring from individuals with high consanguinity. Runs of Homozygosity (ROH)Stretches of the genome where identical alleles were received from both parents. The fraction of the genome in ROH is a measure of inbreeding. Donor-Conceived Child (DCC)Child born following sperm donation. DCC(X) refers to a child born following sperm donation by individual X. Congenital MorbidityDiseases or medical conditions present from birth, including physical, intellectual, or developmental. Specifically, does not include any diseases or conditions that arise from exposure to medications or chemicals during gestation or infections during pregnancy.

Recent genome wide association studies (GWAS) have identified genetic risk factors for developing severe COVID-19 symptoms. The first published study reported a 1bp insertion rs11385942 on chromosome 3 (1) and subsequent studies single nucleotide variants (SNVs) such as rs35044562, rs67959919 (2) and rs13078854 (3), all highly correlated with each other. Zeberg and Paabo (4) subsequently traced them back to Neanderthal origin. They found that a 49.4 kb genomic region including the risk allele of rs35044562 is inherited from Neanderthals of Vindija in Croatia. Here we add a differently focused evaluation of this major genetic risk factor to these recent analyses. We show that (i) COVID-19-related genetic factors of three previously assessed Neanderthals deviate from those of modern humans and that (ii) they differ among world-wide human populations, which compromises risk prediction in non-Europeans. Currently, caution is thus advised in the genetic risk assessment of non-Europeans during this world-wide COVID-19 pandemic.

For complex disorders, estimating the age-related penetrance associated with rare variants of strong effect is essential before a putative use for genetic counseling or disease prevention. However, rarity and co-occurrence with other risk factors make such estimations difficult. In the context of Alzheimer disease, we present a survival model to estimate the penetrance of SORL1 rare (allele frequency< 1%) Loss-of-Function variants (LoF) while accounting for APOE-{varepsilon}4, the main risk factor (allele frequency[~] 14% in Caucasians). We developed an efficient strategy to compute penetrance estimates accounting for both common and rare genetic variants based on available penetrance curves associated with common risk factors and using incomplete pedigree data to quantify the additional risk conferred by rare variants. Our model combines: (i) a baseline for non-carriers of SORL1 LoF variants, stratified by APOE genotypes derived from the Rotterdam study and (ii) an age-dependent proportional hazard effect for SORL1 LoF variants estimated from pedigrees with a proband carrying such a variant. We embed this model into an Expectation-Maximisation algorithm to accommodate for missing genotypes. Confidence intervals were computed by bootstraps. To correct for ascertainment bias, proband phenotypes were omitted. We obtained penetrance curves associated with SORL1 LoF variants at the digenic level. By age 70, we estimate a 100% penetrance of SORL1 LoF variants only among APOE-{varepsilon}4{varepsilon}4 carriers, while penetrance is 56%[40% - 72%] among {varepsilon}4 heterozygous carriers and 37%[26% - 51%] among {varepsilon}4 non-carriers. We conclude that rare SORL1 LoF variants should not be used for genetic counseling regardless of the APOE status.

46,XY gonadal dysgenesis (46,XY GD) results from disruptions in the genetic program that governs testicular differentiation during gonadal sex determination, presenting as either complete (46,XY CGD) or partial (46,XY PGD) forms. While monogenic defects account for approximately 50% of cases, recent evidence suggests an oligogenic basis for some 46,XY GD cases. In this study, we investigated a case of 46,XY CGD and performed whole-exome sequencing (WES) on the patient and her parents to explore the genetic basis of the patients condition. Although no pathogenic variants were identified in known 46,XY GD-associated genes, we detected rare variants in the STARD9 and CDK5RAP2 genes. Previous study in mice indicate that the orthologues of these genes are highly expressed in Sertoli cells during gonadal sex determination, with Cdk5rap2 playing a critical role in Sertoli cell polarization. Notably, the human STARD9 and CDK5RAP2 proteins interact with each other. Structural analysis suggests that the variants in STARD9 and CDK5RAP2 may alter their protein-protein interactions. Based on these findings, we propose that STARD9 and CDK5RAP2 variants may act together to impair Sertoli cell function, leading to 46,XY CGD, consistent with an oligogenic mode of inheritance. These results suggest that STARD9 and CDK5RAP2 should be considered as candidate genes for 46,XY GD and included in genetic panels for this condition.

Subfertility and recurrent pregnancy loss (RPL) affect a significant proportion of couples worldwide. Genetic causes can be seen in up to 30% of these individuals but require multiple genetic tests, which often impede a comprehensive work up. Newer genomic technologies, such as PacBio HiFi long read sequencing (LRS) can detect most subclasses of variations (such as structural rearrangement, monogenic disorders) through one single test. In this multicenter study, we enrolled couples with unexplained subfertility and/or RPL and performed HiFi LRS to determine the underlying genetic etiology. Participants were recruited using a standardized inclusion/ exclusion criteria to rule out other known causes of subfertility and/or RPL. 96 individuals were recruited across the 5 sites. Average age of participants was 36 years (range 30-46 years). Among the 84 individuals who completed sequencing, 4.8% were identified with a likely genetic diagnosis and variants of uncertain significance were identified in another 14.2% of individuals. One individual was identified with an ACMG secondary finding, and while multiple carriers for recessive genetic disorders were identified, none of the couples were identified to be at increased risk. This study highlights the utility of performing genomic sequencing in couples with unexplained subfertility and/or RPL, with 1 in 10 couples harboring a clinically significant variant. In addition, use of HiFi LRS allowed for characterization of different subclasses of genomic variations through a single test. Future studies, including exploring the cost effectiveness and resource utilization of LRS as first line test, will help in optimizing care for such couples. TWEETABLE STATEMENTA single long-read genome sequencing test can consolidate multiple genetic investigations and uncover clinically relevant causes in couples with unexplained subfertility and recurrent pregnancy loss. AT A GLANCEO_LIWhy was this study conducted? O_LIMany couples with subfertility and recurrent pregnancy loss remain undiagnosed after multiple conventional genetic tests C_LIO_LIExisting workflows require sequential testing and may miss complex genomic variants C_LI C_LIO_LIWhat are the key findings? O_LILong-read genome sequencing identified clinically relevant variants in [~]1 in 10 couples with unexplained subfertility or recurrent pregnancy loss C_LIO_LIA single assay enabled detection of multiple variant types, including structural and sequence variants C_LI C_LIO_LIWhat does this study add to what is already known? O_LIDemonstrates feasibility of a unified genomic testing approach in a real-world multicenter cohort C_LIO_LISupports a potential shift from fragmented testing toward a single comprehensive genomic workflow C_LI C_LI

Variants of uncertain significance (VUS) are genetic variations with unclear clinical implications, often complicating clinical management in genetic testing. The analysis of co-segregation of the variant with the disease in families has been shown to be a powerful tool for the classification of these variants. We present CAL-Leiden (Co-segregation Analysis via Likelihood ratio analysis-Leiden), a comprehensive co-segregation model facilitating the classification of variants in BRCA1, BRCA2 and PALB2 genes, which can be used as an important component of the ACMG/AMP classification guideline. CAL-Leiden includes an expanded range of cancer types, including pancreatic cancer, in addition to breast and ovarian cancer. It also considers contralateral breast cancer. The model integrates population incidence rates from the Netherlands and the United Kingdom, along with penetrance data from the latest literature. A web-based platform has been developed, making the model accessible and practical for use in diagnostic labs: https://bioexp.net/cosegregation/. We demonstrate the functionality of the tool with multiple pedigrees and compare its performance with alternative approaches. These features in CAL-Leiden collectively contribute to a more comprehensive and accurate assessment of variant pathogenicity, helping lab specialists in classification of the variants of uncertain significance.

Polygenic risk scores (PRS) for breast cancer (BC) have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium (LD) and differences in variant frequency (AF-diff). Thus, locally-sourced population-based phenotypic and genomic datasets are essential to assess the validity of PRS derived from signals detected across populations. Here, assess the transferability of a BC PRS composed of 313 risk variants (313-PRS) in two Brazilian tri-hybrid admixed ancestries (European, African and Native American) whole-genome sequenced cohorts. We computed 313-PRS in both cohorts (n=753 and n=853) versus the UK Biobank (UKBB, n=264,307) as reference. We show that although the Brazilian cohorts have a high European (EA) component, with AF-diff and to a lesser extent LD patterns like those found in EA populations, the 313-PRS distribution is inflated when compared to that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. Interestingly, we find that case-controls lead to equivalent predictive power when compared to UKBB-EA samples with AUROC values of 0.66-0.62 compared to 0.63 for UKBB.

To date, a disease-causing mutation can be found in 15-30% of families with hereditary breast and ovarian cancer (HBOC) and it is believed that more than half of the cases still remain unsolved. Usually it is intended to perform genetic analyses in the family member with the most severe phenotype, which, however, may not always be possible. Moreover, no standard criteria have been established to define the person who is most suitable for genetic testing within a family: the best index case. We therefore established clinical criteria to identify the best index case in HBOC and analyzed the impact on genetic testing. 130 patients who presented at our department from 2016 to 2018 were divided into two groups. In group A (N = 98) genetic analyses were performed in the best index case based on our criteria. In group B (N = 32) at least one other family member was considered a better index case. The detection rate of expected mutations was significantly higher for group A (64.3% vs. 32.0%, p = 0.034) while there was no significant difference of calculated mutation carrier risks between these groups. We conclude that the mutation detection rate in families with HBOC is notably higher after identifying the best index case for genetic testing according to the clinical selection criteria reported here.

PurposeArtificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and management as well as automating administrative tasks, thus enhancing and potentially enabling clinician/patient interactions. While the introduction of AI into clinical genetics is increasing, there remain unclear questions about risks and benefits, and the readiness of the workforce. MethodsTo assess the current clinical genetics workforces use, knowledge, and attitudes toward available medical AI applications, we conducted a survey involving 215 US-based genetics clinicians and trainees. ResultsOver half (51.2%) of participants report little to no knowledge of AI in clinical genetics and 64.3% reported no formal training in AI applications. Formal training directly correlated with self-reported knowledge of AI in clinical genetics, with 69.3% of respondents with formal training reporting intermediate to extensive knowledge of AI vs. 37.5% without formal training. Most participants reported that they lacked sufficient knowledge of clinical AI (83.4%) and agreed that there should be more education in this area (97.6%) and would take a course if offered (89.3%). The majority (51.6%) of clinician participants said they never used AI applications in the clinic. However, after a tutorial describing clinical AI applications, 75.8% reported some use of AI applications in the clinic. When asked specifically about clinical AI application usage, the majority of clinician participants used facial diagnostic applications (54.9%) and AI-generated genomic testing results (62.1%), whereas other applications such as chatbots, large language models (LLMs), pedigree or medical summary generators, and risk assessment were only used by a fraction of the clinicians, ranging from 11.1 to 12.5%. Nearly all participants (94.6%) reported clinical genetics professionals as being overburdened. ConclusionFurther clinician education is both desired and needed to optimally utilize clinical AI applications with the potential to enhance patient care and alleviate the current strain on genetics clinics.