Alan R. Sanders

Background: We previously developed a tier‐based germline classification using the National Comprehensive Cancer Network (NCCN)‐recommended DNA damage repair (DDR) genes and KLK3 I179T to predict prostate cancer (PCa)‐specific mortality (PCSM). To provide an easier‐to‐use single inherited risk score while preserving gene‐specific effects, we developed GenProb‐PCSM. Methods: We analyzed 14,644 men with incident PCa from the UK Biobank. The cohort was randomly divided into training (60%) and independent testing (40%) datasets. GenProb‐PCSM was developed in the training cohort by integrating pathogenic variants in ten NCCN‐recommended DDR genes and the KLK3 I179T variant using gene‐specific weights derived from Fine–Gray competing‐risk models. Performance was evaluated in the independent testing cohort by discrimination, calibration, and risk stratification. Secondary analyzes evaluated metastatic progression and the composite endpoint of metastatic progression and/or PCSM. Results: Among 14,644 men with incident PCa, 1,581 died from PCa. GenProb‐PCSM remained significantly associated with PCSM in the independent testing cohort (HR per SD, 1.18; 95% CI, 1.12–1.24; p < 0.001). Using predefined risk thresholds derived from the training cohort, patients in the intermediate‐ and high‐risk groups had significantly increased risks of PCSM compared with the low‐risk group (HR 1.49, 95% CI 1.22–1.84; and HR 4.04, 95% CI 2.58–6.33, respectively). GenProb‐PCSM also predicted independent metastatic progression and the composite endpoint of metastatic progression and/or PCSM. Conclusions: GenProb‐PCSM transforms complex germline findings into a single inherited risk score for PCSM and metastatic progression. Its simplicity and preservation of gene‐specific effects may facilitate clinical implementation of germline prognostic assessment in PCa.

Most polygenic scores (PS) for type 1 diabetes were developed using European (EUR) ancestry datasets, limiting performance in non-European (non-EUR) populations. We evaluated novel type 1 diabetes PS across diverse populations to assess whether newer models improve prediction in underrepresented populations. Seven type 1 diabetes PS (T1D GRS2, T1D GRS2′, T1D GRS _HLA , T1GRS, TA-PS, TA-PS (S), T1D MAPS) were evaluated in All of Us and Mass General Brigham Biobank. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). T1D GRS2 and T1D MAPS showed the highest AUC in All of Us, while other scores demonstrated comparatively lower performance. These highest-performing scores were then evaluated in a meta-analysis across three additional biobanks (Genomic Health Initiative at Endeavor Health, Penn Medicine BioBank, Geisinger MyCode). Among 2,782 individuals with type 1 diabetes and 546,577 controls, T1D MAPS showed comparable performance to T1D GRS2 in EUR populations but significantly improved discrimination in non-EUR populations (meta-analysis ΔAUC=0.049, p =1.7×10 ⁻⁷ ). Overall, T1D MAPS improves prediction in non-EUR populations, highlighting the importance of multi-ancestry approaches for equitable genetic risk prediction. These findings could inform precision medicine in diverse populations.

Thyroid cancer is the most common endocrine malignancy, yet its biological underpinnings remain incompletely understood. Here we show that common risk alleles for thyroid cancer point to distinct biological pathways underlying disease susceptibility. We perform a multi-ancestry genome-wide association meta-analysis of thyroid cancer (16,167 cases and 2,430,374 controls), identifying 51 independent loci, including 21 not previously reported. By integrating these loci with genetic associations for 151 thyroid-cancer-related traits, we identify pleiotropic mechanistic clusters linked to thyroid function, oncogenic pathways, and mixed physiological function. Two thyroid-specific clusters, associated with thyroid stimulating hormone or thyroid growth and function, are enriched in thyroid tissues. Oncogenic clusters include DNA repair ( ATM , CHEK2 , TP53 ) and telomere maintenance ( TERT ) genes, implicating shared cancer mechanisms. Cluster-specific polygenic scores are associated with thyroid disease, cancer, and metabolic traits across ancestry groups, suggesting distinct genetic subtypes of thyroid cancer risk and supporting pleiotropy-based approaches to genetic risk stratification.

Most causal variants for neuropsychiatric disorders (NPD) remain unknown. A major hurdle is that disease variants may act in specific contexts, such as during neuronal activation, which is difficult to study in vivo at the population level. We profiled single-nucleus neuron-activation multiomics in human induced pluripotent stem cell–derived neurons from 100 donors, revealing the NPD-relevant transcriptomic and epigenomic landscape of neuronal activation. We identified abundant genetic variants associated with activity-dependent gene expression and chromatin accessibility, the latter explaining larger proportions of NPD heritability. Integrating multiomics data with genome-wide association studies further revealed NPD risk variants and genes with effects detected only upon stimulation, such as activity-dependent cholesterol metabolism. Our work highlights the power of cell stimulation to reveal context-specific “hidden” genetic effects.

SUMMARY: Human induced pluripotent stem cells (hiPSC) and iPSC-differentiated neural cells, in combination with CRISPR editing, are commonly used for studying neurodevelopmental and other brain disorders. Female iPSCs undergo random X-chromosome inactivation (XCI) via epigenetic silencing by noncoding X inactive specific transcript ( XIST ). It is known that female iPSCs may lose XIST expression, leading to XCI erosion that affects both X-linked and autosomal gene expression. However, the effects of CRSIPR editing and neural differentiation on XCI erosion in iPSC-derived neurons and how this may confound a real-world transcriptomic analysis of differentially expressed genes (DEGs) are poorly understood. Here, leveraging bulk RNA-seq of hundreds of CRISPR-edited female iPSC lines from four donor lines for 66 genes and single-cell RNA-seq of iPSC-derived neurons of a subset of 42 edited genes, we investigated the effects of XCI erosion during CRISPR editing and in iPSC-derived neurons. We found that XCI erosion was variable in CRISPR-edited female iPSCs and largely preserved in iPSC-derived neurons. Like in iPSCs, XIST in neurons predominately influenced the expression of X-linked genes; however, its effect on autosomal genes was more pronounced in single neurons. Mechanistically, XIST epigenetically causes allelic imbalance of both X-linked and autosomal genes, with the former showing stronger allele-specific expression (ASE) bias. Notably, XIST- induced ASE bias exhibited a conserved positional pattern at loci affecting neurodevelopmental genes across different female lines and cell types. Finally, we demonstrated a confounding effect of XCI erosion on DEG analyses in iPSC-derived neurons. These results have significant implications in hiPSC modeling of neurodevelopmental and other brain disorders.

Thyroid diseases are common and highly heritable. We performed a meta-analysis of genome-wide association studies from 19 biobanks for five thyroid diseases: thyroid cancer (ThC), benign nodular goiter, Graves’ disease, lymphocytic thyroiditis and primary hypothyroidism. We analyzed genetic association data from ~2.9 million genomes and identified 313 known and 570 new independent loci linked to thyroid diseases. We discovered genetic correlations between ThC, benign nodular goiter and autoimmune thyroid diseases ( rg = 0.16–0.97). Telomere maintenance genes contributed to benign and malignant thyroid nodular disease risk, whereas cell cycle, DNA repair and damage response genes were associated with ThC. We propose a paradigm that explains genetic predisposition to benign and malignant thyroid nodules. We found polygenic risk score associations with ThC risk of structural disease recurrence, tumor size, multifocality, lymph node metastases and extranodal extension. Polygenic risk scores identified individuals with aggressive ThC in a biobank, creating an opportunity for genetically informed population screening.

We report the findings of a genome-wide association study (GWAS) meta-analysis of endometriosis across 14 biobanks worldwide, including 32% non-European patient participants, as part of ​the ​Global Biobank Meta-Analys​i​s Initiative​ (GBMI)​. Out of 58 total loci (29 previously unreported), the largest meta-analysis accounted for 46 (20 previously unreported). We detected the first genome-wide significant loci (2p13.3 and 20q13.2) uniquely driven by the African-ancestry meta-analysis. Our imaging- and surgery-confirmed phenotypes yielded six additional previously unreported loci. Leveraging our large and diverse study population, we observed SNP heritability estimates of 9-13% for all ancestry groups, and 13 loci had at least one variant in the credible set after fine-mapping. Investigating the complex array of endometriosis comorbidities and risk factors revealed 135 genetically correlated phenotypes and 95 with evidence of vertical pleiotropy, including triglycerides and anxiety disorders. We prioritized 35 disease-relevant cellular contexts from the endometrial cell atlas and found 322 examples of differentially expressed genes in cells from donors with endometriosis. Further high-throughput multi-omic analyses implicated a total of 282 genes in endometriosis pathogenesis. Our diverse, comprehensive GWASs, with downstream analyses spanning molecular to phenotypic scales, provide detailed evidence for aspects of endometriosis including the role of immune cell types, Wnt signaling, and cellular proliferation. These interconnected pathways and risk factors underscore the complex, multi-faceted etiology of endometriosis, suggesting multiple targets for precise and effective therapeutic interventions.

Despite genome-wide association studies (GWAS) of late-onset Alzheimer’s disease (LOAD) having identified many genetic risk loci^1,2,3, the underlying disease mechanisms remain largely unclear. Determining causal disease variants and their LOAD-relevant cellular phenotypes has been a challenge. Here, using our approach for identifying functional GWAS risk variants showing allele-specific open chromatin, we systematically identified putative causal LOAD-risk variants in human induced pluripotent stem (iPS)-cell-derived neurons, astrocytes and microglia, and linked a PICALM LOAD-risk allele to a microglial-specific role of PICALM in lipid droplet (LD) accumulation. Allele-specific open-chromatin mapping revealed functional risk variants for 26 LOAD-risk loci, mostly specific to microglia. At the microglial-specific PICALM locus, the LOAD-risk allele of the single-nucleotide polymorphism rs10792832 reduced transcription factor (PU.1) binding and PICALM expression, impairing the uptake of amyloid beta (Aβ) and myelin debris. Notably, microglia carrying the PICALM risk allele showed transcriptional enrichment of pathways for cholesterol synthesis and LD formation. Genetic and pharmacological perturbations of microglia further established a causal link between reduced PICALM expression, LD accumulation and phagocytosis deficits. Our work elucidates the selective LOAD vulnerability in microglia at the PICALM locus through detrimental LD accumulation, providing a neurobiological basis that can be exploited for developing clinical interventions.

Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses remain unclear. We present a multidimensional framework for functional analysis of rare copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ), autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965). Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL) burden across 2,645 functional gene sets defined by the intersections of pathways, cell types, and cortical regions. While diagnoses converge on shared pathways, mixed-effects modeling revealed divergence of pathway effects by cell type, brain region, and gene dosage. Factor analysis identified latent dimensions aligned with clinical axes. A primary factor (F1) captured reciprocal dose-dependent effects of DUP and DEL in SCZ reflecting positive and negative effects in excitatory versus inhibitory neurons and association versus sensory cortex. SCZ and ASD were both strongly aligned with F1 but with opposing directionalities. Orthogonal factors highlighted neuronal versus non-neuronal effects in mood disorders (F2) and differential spatial distributions of DEL effects in ADHD and MDD (F3). High-impact CNVs at 16p11.2 and 22q11.2 were enriched for combinations of cell-type-specific genes involved in pathways consistent with our broader findings. These results reveal molecular and cellular mechanisms that are broadly shared across psychiatric traits but differ between diagnostic categories in context and directionality.

PURPOSE: Family history (FH) and pathogenic variants (PVs) in Lynch syndrome (LS) genes are established risk factors of colorectal cancer (CRC). This study evaluates whether newly published polygenic scores (PGSs) improve CRC prediction of known risk factors. METHODS: The associations of CRC risk with FH, PVs in LS genes ( MSH2 , MLH1 , MSH6 , PMS2 ), and PGS were investigated in the UK Biobank (UKB) using population attributable risk percentage (PAR%) and Cox regression. Model performance was assessed using Concordance Index (C-index) in UKB and validated in the Genomic Health Initiative (GHI), a US health care biobank with more diverse ancestry populations. RESULTS: In UKB, 18.99%, 11.43%, and 0.42% of participants were positive for PGS, FH, and PVs, respectively, with corresponding PAR% of 29.97%, 6.27%, and 1.25%. In multivariable analysis adjusting for age, sex, and genetic background, each genetic factor independently predicted CRC risk ( P < .001). Significant interactions between PVs and FH ( P < .001) and PVs and PGS ( P = .04) were found; hazard ratio (95% CI) of PVs was significantly higher in those with FH (7.90 [6.27 to 9.94]) than without FH (2.89 [2.32 to 3.60]) and that of PGS was significantly lower in PV carriers (1.69 [1.18 to 2.42]) than noncarriers (2.85 [2.73 to 2.98]). The combined genetic model had a significantly higher C-index (0.677) than models with PVs (0.573), FH (0.578), and PGS (0.669) alone ( P < .001). This enhanced performance was validated in GHI across European and non-European ancestries. CONCLUSION: PGS complements FH and PVs of LS genes in predicting CRC risk and improves prediction performance beyond traditional genetic factors across diverse populations.