scholarly journals Novel pedigree analysis implicates DNA repair and chromatin remodeling in Multiple Myeloma risk

2017 ◽  
Author(s):  
Rosalie G. Waller ◽  
Todd M. Darlington ◽  
Xiaomu Wei ◽  
Michael J. Madsen ◽  
Alun Thomas ◽  
...  

ABSTRACTThe high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance – a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691*, p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly, p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.AUTHOR SUMMARYAlthough family-based studies demonstrate inherited variants play a role in many common and complex diseases, finding the genes responsible remains a challenge. High-risk pedigrees, or families with more disease than expected by chance, have been helpful in the discovery of variants responsible for less complex diseases, but have not reached their potential in complex diseases. Here, we describe a method to utilize high-risk pedigrees to discover risk-genes in complex diseases. Our method is appropriate for complex diseases because it allows for genetic-heterogeneity, or multiple causes of disease, within a pedigree. This method allows us to identify shared segments that likely harbor disease-causing variants in a family. We apply our method in Multiple Myeloma, a heritable and complex cancer of plasma cells. We identified two genes USP45 and ARID1A that fall within shared segments with compelling statistical evidence. Exome sequencing of these genes revealed likely-damaging variants inherited in Myeloma high-risk families, suggesting these genes likely play a role in development of Myeloma. Our Myeloma findings demonstrate our high-risk pedigree method can identify genetic regions of interest in large high-risk pedigrees that are also relevant to smaller nuclear families and overall disease risk. In sum, we offer a strategy, applicable across phenotypes, to revitalize high-risk pedigrees in the discovery of the genetic basis of common and complex disease.

2016 ◽  
Vol 370 (2) ◽  
pp. 302-312 ◽  
Author(s):  
Alyssa L. Smith ◽  
Najmeh Alirezaie ◽  
Ashton Connor ◽  
Michelle Chan-Seng-Yue ◽  
Robert Grant ◽  
...  

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 2-2
Author(s):  
Anil Aktas-Samur ◽  
Mariateresa Fulciniti ◽  
Sanika Derebail ◽  
Raphael Szalat ◽  
Giovanni Parmigiani ◽  
...  

Multiple Myeloma is preceded by precursor states of monoclonal gammopathy of undermined significance (MGUS) and smoldering multiple myeloma (SMM). Studies have shown that progression to symptomatic MM five years after diagnosis is 1% for MGUS and 10% for SMM. However, based on the genomic background, this rate is highly variable, especially for SMM patients. Recent studies have evaluated the high-risk genomic features for SMM, but the genomic background of SMM patients who do not progress to MM after long-term follow-up (>= 5 years) has not been described. Here, we evaluated genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within a short time (N=71) as well as newly diagnosed Myeloma (N=192). We also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to Myeloma. We report a similar large-scale CN alteration in non-progressor SMM compared to progressor SMMs or MM at diagnosis. However, whole-genome sequencing data showed that the overall mutational load for non-progressor SMM samples was lower than Progressor MGUS/SMM (median SNV 5460 vs. 7018). This difference significantly increased for mutations affecting the coding regions. NP samples at diagnosis had 26% and 53% less coding mutations (missense, nonsense, and frameshift mutations) compared to progressor MGUS/SMM (p=0.008) and newly diagnosed MM (p < 0.001) respectively. We observed very low NRAS (3%, OR=8.86) and BRAF (3%, OR=2.17), mutation frequency in non-progressor SMM samples compared to newly diagnosed MM. We did not observe driver mutations in FAM46C, TTN, CYLD, TP53, KMT2C, IRF4, HIST1H1E that are otherwise frequently mutated in high-risk SMM or symptomatic MM. None of the non-progressor SMM samples had MYC alteration. We observed t(11;14), t(4;14), and t(14;16) translocations at similar frequency compared to newly diagnosed MM samples. We also observed a significant difference in non-recurrent focal deletions. Based on our recent data in newly-diagnosed MM, we quantified genomic scar score, and observed that non-progressor SMM have lower GSS (median=3,IQR=[1-9]) compared to progressor MGUS/SMM (median=11,IQR=[5-15] / median=9,IQR=[9-15], respectively) as well as MM samples at diagnosis (median=9, IQR= [5-16],p=0.002). We further validated this observation in an independent cohort with 69 SMM samples in whom progressor SMM patients had high GSS (median =4, IQR=[2-7.75]), compared to delayed progressor (> four years) or non-progressor SMM (median =1.5, IQR= [0-3.5]; p=0.029). Moreover, non-progressor SMM had significantly low utilization of APOBEC and DNA repair mutational processes. Next, we compared non- progressor SMM with progressor SMM using RNAseq data. We identified 1653 differentially expressed genes (DEG) (762 up-regulated and 891 down-regulated). Genes that were upregulated in non-progressor SMM samples were enriched in IL6/JAK/STAT3 and IL2/STAT5 signaling and the regulation of cytokine secretion. Whereas genes up-regulated in progressor SMM were enriched in MYC targets, DNA repair, and mTOR pathways. Moreover, genes that control the translational initiation, translational elongation, mitochondrial translation, and ATP control were among the top highly expressed genes in progressor SMM. We used our MGUS/SMM to MM paired samples and showed that the E2F target, MYC target, and G2/M checkpoint pathways are more active at MM. We measured the distance between progressor and non-progressor SMM as well as MM and found that non-progressor SMM is less similar to MM compared to progressor SMM. In conclusion, the global CNA and translocations are similar between progressor and non-progressor SMM and symptomatic MM and confirm their role in the development of precursor condition but not adequate for progression to MM, which requires additional hits. On the other hand, lower GSS score reflecting genomic stability along with lower SNVs, low DNA damage and APOBEC mutational processes, down-regulated MYC target genes, and low DNA repair activation define low-risk SMM. These results now provide the basis to develop a genomic definition of SMM. Disclosures Fulciniti: NIH: Research Funding. Parmigiani:Phaeno Biotehnologies: Current equity holder in publicly-traded company; CRA Health: Current equity holder in publicly-traded company; Foundation Medicine Institute: Consultancy; Delphi Diagnostics: Consultancy; BayesMendel Laboratory: Other: Co-lead. Munshi:Janssen: Consultancy; Adaptive: Consultancy; Legend: Consultancy; Amgen: Consultancy; AbbVie: Consultancy; Karyopharm: Consultancy; Takeda: Consultancy; C4: Current equity holder in private company; BMS: Consultancy; OncoPep: Consultancy, Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 3346-3346 ◽  
Author(s):  
Aneta Mikulasova ◽  
Brian A Walker ◽  
Christopher P Wardell ◽  
Eileen M Boyle ◽  
Alexander Murison ◽  
...  

Abstract Introduction: Malignant transformation of normal to tumour cells is a multistep process followed by sequential aggregation of hits at different molecular levels. Genetic events including single nucleotide variants (SNVs), insertion-deletion changes (indels) as well as copy number variants (CNVs) affect the phenotype of the tumour population and consequently patient prognosis. Transformation from a symptomless state, monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) can be used as a unique model for cancer development studies. To date, there is very little data regarding the mechanisms leading to disease progression at molecular level. In our study, we performed exome sequencing together with SNP array analysis on 33 MGUS patients to describe the premalignant phenotype and compared these to advanced tumour cells at the DNA level. We hypothesised that increased genetic instability indicated MGUS patients with a high risk of progression to MM. Methods: 33 MGUS patients (M:F 1.5:3; median age 61, range: 35-86) were included in this study. Plasma cells were isolated from bone marrow by FACSAria (BD Biosciences) system using CD138, CD19 and CD56 markers to obtain a pure abnormal plasma cell population with a purity >90%. Tumour DNA was isolated using Gentra Puregene Kit and amplified using REPLI-g Midi Kit (both Qiagen); control DNA was gained from peripheral white blood cells by MagNA Pure System (Roche Diagnostics). For exome sequencing, NEBNext kit (NEB) and SureSelect Human All Exon V5 (Agilent Technologies) were used and samples were sequenced by HiSeq2000 (Illumina) using 76-bp paired end reads. Unbalanced CNVs were tested by SurePrint G3 CGH+SNP, 4x180K (Agilent Technologies). Results were compared to 463 MM patients. Results: In our analysis, we found acquired SNVs in 100% (33/33) MGUS patients with a median of 89 (range 9-315) SNVs per patient. Non-synonymous SNVs (NS-SNVs) were present in 97% (32/33) cases with a median 19 (range 0–70) NS-SNVs per patient. Overall, 42 genes were recurrently mutated in at least 2 patients and 6 genes were mutated in at least 3 cases including MUC16, IGK, TTN, KLHL6, AKAP9 and NPIPL2. We identified 7 genes which were significantly mutated in MM in our previous study including KRAS (n=2), HIST1H1E (n=2) and NRAS, DIS3, EGR1, LTB, PRKD2 (all n=1). IGH translocations were identified in 27% (9/33) of patients: t(11;14) in 12% (4/33), t(4;14) in 9% (3/33), t(14;16) in 3% (1/33) and t(14;20) in 3% (1/33). We did not find any translocations involving MYC (8q24.21) or the light chain loci IGK (2p12) and IGL (22q11.2). Using SNP arrays, unbalanced CNVs were presented in 67% (22/33) of MGUS patients and detected CNVs showed similarity to MM across the cohort. As previously described in MM, only one type of IGH translocation was found per patient and all 9 cases with IGH translocation did not have additional hyperdiploidy. Furthermore, we identified a patient with two CCND1 (p.K50T, p.E51D) mutations and a t(11;14), a case with a DIS3 (p.D488N) mutation and a 13q loss. Moreover, we noticed a co-segregation of cases t(4;14) and t(14;16) who all had a 13q loss (100%, 4/4). In contrast none of the patients (0/5) with a t(11;14) or a t(14;20) had a 13q loss. Of note 29% (7/24) patients without any IGH translocation had a 13q loss. Sixty seven percent (2/3) of patients with a t(4;14) and the one case with a t(14;16) also had a 1q gain. In comparison, none of patients with a t(11;14) (0%; 0/4) had a 1q gain. Unlike what has previously been described in MM, neither of the 2 MGUS patients with a KRAS (p.Q61L and p.A146T) mutations had a t(11;14). We also identified a patient with both a KRAS (p.Q61L) and an NRAS (p.G13R) mutation which are although not mutually exclusive, negatively correlated in MM. Importantly, we did not find any mutations in TP53, ATM, ATR and ZFHX4 genes involved in DNA repair pathway alterations which were identified as unfavourable factors in survival of MM patients. Summary: We have performed the first comprehensive analysis of 33 MGUS patients using exome sequencing together with SNP arrays and described the main genetic events that are already present in this premalignant state. We found similarities to MM in terms of SNVs, CNVs and their correlations. We identified 6 MGUS cases with NS-SNVs in potential key genes that could indicate a potential high risk to progression. Support: IGA MH CZ NT13492, OPVK CZ.1.07/2.3.00/20.0183. Disclosures Walker: Onyx Pharmaceuticals: Consultancy, Honoraria.


2021 ◽  
Author(s):  
María Ortiz-Estévez ◽  
Mehmet Samur ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
...  

AbstractDespite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features.Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval<1×10−6)], which uniquely presents a broad genomic loss (>9% of entire genome, t.test pval<1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways.Statement of SignificanceUsing multi-omics unsupervised clustering we discovered a new high-risk multiple myeloma patient segment. We linked its diverse genetic markers (previously known, and new including genomic loss) to transcriptional dysregulation (cell cycle, DNA repair and DNA damage) and identified master regulators that control these key biological pathways.


2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Stephen F. Kingsmore ◽  
Ingrid E. Lindquist ◽  
Joann Mudge ◽  
William D. Beavis

Novel, comprehensive approaches for biomarker discovery and validation are urgently needed. One particular area of methodologic need is for discovery of novel genetic biomarkers in complex diseases and traits. Here, we review recent successes in the use of genome wide association (GWA) approaches to identify genetic biomarkers in common human diseases and traits. Such studies are yielding initial insights into the allelic architecture of complex traits. In general, it appears that complex diseases are associated with many common polymorphisms, implying profound genetic heterogeneity between affected individuals.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
María Ortiz-Estévez ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
In Sock Jang ◽  
...  

Abstract Background Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10−6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Conclusion Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.


PLoS Genetics ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. e1007111 ◽  
Author(s):  
Rosalie G. Waller ◽  
Todd M. Darlington ◽  
Xiaomu Wei ◽  
Michael J. Madsen ◽  
Alun Thomas ◽  
...  

2021 ◽  
Author(s):  
María Ortiz-Estévez ◽  
Mehmet Samur ◽  
Fadi Towfic ◽  
Erin Flynt ◽  
Nicholas Stong ◽  
...  

Abstract Background Despite significant therapeutic advances in improving lives of Multiple Myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features. Results Our integrative approach let us identify ndMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank pval < 1x10− 6)], which uniquely presents a broad genomic loss (> 9% of entire genome, t.test pval < 1e-5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways. Conclusion Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 30-30
Author(s):  
Matteo Marchesini ◽  
Paola Storti ◽  
Yamini Ogoti ◽  
Marianna D'Anca ◽  
Luigi Nezi ◽  
...  

Abstract In the last decade, significant effort has been directed toward the stratification of multiple myeloma (MM) patients for targeted therapy, and many studies have shown that some genetic alterations, especially t(4;14) translocation, loss of the short arm of chromosome 17, and amplification of chromosome 1q21, are associated with a poor outcome. The 1q21 amplicon spans a region of 10-15 Mb and contains a large number of possible candidate genes; it is among the most frequent chromosomal aberrations in patients with MM and is associated with poor prognosis, disease progression, and drug resistance. Therefore, the identification of critical 1q21 genes may yield potential therapeutic targets for this high-risk MM subgroup and provide a rationale for patient stratification. In an effort to accomplish this goal, we first identified a high-priority list of 78 copy number-driven 1q21 MM-relevant genes by integrating high-resolution array comparative genomic hybridization (aCGH) and matched expression profiling of the 254 MM samples deposited in the Multiple Myeloma Research Consortium (MMRC) database. Then, we performed a high-throughput systematic shRNA screen in vitroto identify 1q21 genes whose loss of function resulted in the selective death and/or growth inhibition of MM cells carrying the 1q21 amplification. We used shRNA targeting (excluding shRNAs that displayed cytotoxic activity regardless of 1q21 amplification) and a GFP competitive growth assay to identify 1q21-resident targets whose downregulation significantly decreased the percentage of GFP-positive MM cells with 1q21 amplification over a time of 7 days. These assays identified UBAPL2, INTS3, LASS2, KRTCAP2, and ILF2 as key targets for further analysis. Secondary validation experiments in the MM cell lines JJN3 and H929 confirmed that the downregulation of all of our top five candidate genes induced significant levels of apoptosis, inhibition of proliferation, and cell cycle arrest. Integration of copy number analysis, expression profiling, and clinical outcome indicated that only UBAPL2 and ILF2 were highly significant prognostic genes, and target validation in NOD-SCID mice showed that ILF2, but not UBAPL2, downregulation had a significant impact on in vivo survival. Therefore, we sought to further characterize ILF2’s role in 1q21-amplified MM. ILF2 encodes NF45, the regulatory subunit of NF90/NF110 complexes, which are involved in mitotic control, DNA break repair, and RNA splicing regulation. Downregulation of ILF2 in MM cells with 1q21 amplification resulted in multinucleated phenotypes and abnormal nuclear morphologies (nucleoplasmic bridges and buds and micronuclei) that were associated with a significant accumulation of phospho-H2AX foci and DNA damage response activation, increased sensitivity to the DNA damaging agent melphalan, and impaired activation of DNA repair pathways. Experiments of immunoprecipitation combined with mass spectometry showed that ILF2 interacts with numerous RNA binding proteins directly implicated in DNA repair or regulation of DNA damage response by modulating alternative splicing and stability of specific pre-mRNAs. Accordingly, RNA-seq analysis of ILF2-depleted MM cells, when compared to cells carrying scrambled shRNAs, identified specific changes in RNA splicing patterns both before and after treatment with melphalan. In conclusion, our studies have revealed an unanticipated link between 1q21 amplification, DNA damage response, and RNA splicing. We identified ILF2 as a key driver of this interaction, and our findings support the development of strategies designed to modulate ILF2 expression in patients with high-risk MM carrying 1q21 amplification. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
S Carmi ◽  
D Backenroth ◽  
A Green ◽  
O Weissbrod ◽  
O Zuk ◽  
...  

Abstract Study question It is now feasible to screen human IVF embryos with “polygenic risk scores” for predicting complex disease risk. What is the expected risk reduction? Summary answer Under some conditions, prioritizing embryos based on polygenic risk scores can lead to substantial disease risk reductions. However, only excluding high-risk embryos is less effective. What is known already Recent genetic studies have identified numerous mutations associated with complex diseases, leading to the development of accurate polygenic risk scores (PRSs) for disease risk prediction. Given that genomes of human IVF embryos can now be sequenced with relative ease, it has become technically feasible to use PRSs for prioritization of embryos for transfer. Clearly, such use is associated with ethical and social concerns, from inequality to eugenics. Nevertheless, polygenic embryo screening is already offered to consumers, with little research so far on expected outcomes. Our previous evaluation of screening IVF embryos for polygenic traits showed little current utility. Study design, size, duration This is a theoretical/computational study based on statistical genetics theory and simulations. Participants/materials, setting, methods We used the liability threshold model to estimate the disease risk given the PRS. We considered screening for a single disease (with known prevalence and PRS accuracy), and assumed that n viable embryos are available. We calculated the risk of the child given these parameters and the prioritization strategy, either when parents are random or when their disease status is known. We also used simulations based on genomic data from a schizophrenia case-control study. Main results and the role of chance We modeled the disease risk of a hypothetical future child when the PRSs of embryos are used for prioritization, relative to random selection. When selecting an embryo at random among those who do not have an extremely high risk (typically, top 2% of the PRS distribution), the relative risk reduction (RRR) is limited, and is under 10% for currently realistic scenarios. In contrast, selecting the lowest risk embryo for implantation results in substantial RRRs of ∼20-50% already with n = 5 embryos and realistic disease parameters. For example, the RRR for schizophrenia is &gt; 40% with current PRSs, a result we validated with simulated genomes of parents and children based on genotypes from a schizophrenia study. When one of the parents is known to be affected, selecting the lowest risk embryo out of n = 5 may restore the risk of the future child to nearly normal levels. Limitations, reasons for caution Our analytical modeling is based on several simplifying assumptions regarding the dependence of the risk on the PRS and the accuracy of the PRS. Further, the estimated risk reductions depend on the availability of n = 5 embryos that could lead to a live birth. Wider implications of the findings Under some conditions, prioritizing embryos for transfer based on polygenic risk scores could lead to substantial disease risk reductions. However, predicted outcomes vary considerably with prioritization strategies and disease and PRS parameters. The emerging ethical and social concerns and the challenges in communicating these results need to be urgently discussed. Trial registration number Not applicable


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