Nicole L. Washington

BACKGROUND: The Helix Research Network program is a large population genomics initiative that screens an all-comers population of patients for Centers for Disease Control and Prevention Tier 1 genetic conditions, including familial hypercholesterolemia (FH). We evaluated changes in clinical management and low-density lipoprotein cholesterol (LDL-C) levels among patients we identified to have FH. METHODS: Participants across 9 US health systems provided samples that underwent clinical-grade exome sequencing. Individuals with a positive screening result for a Tier 1 condition were offered no-cost genetic counseling through their health system. Using medication and laboratory testing records, we evaluated changes in patients’ lipid-lowering therapies and LDL-C levels. RESULTS: Among 228 602 adults enrolled between 2017 to 2025, 1155 (≈1/198) had a pathogenic FH variant in LDLR (74%), APOB (25%), or PCSK9 (1%). Of the 622 with retrospective and prospective electronic health record data available (mean of 11.8 and 2.1 years, respectively), 84% lacked a prior clinical FH diagnosis. Overall, 33% received new/modified lipid-lowering therapy within the first year, but this proportion was higher in those with a newly documented FH diagnosis code (57% versus 17% for those without documentation; P P <0.001). CONCLUSIONS: Following genetic screening, many patients with a pathogenic FH variant experienced improvements in clinical management and LDL-C levels. Electronic health record documentation of the diagnosis code was associated with a greater likelihood of therapeutic modifications, which, in turn, were associated with larger LDL-C reductions. Findings underscore the powerful potential of population genomic screening for supporting optimal lipid management in individuals with FH.

The clinical utility of genomic testing is constrained by variants of uncertain significance (VUS), which complicate diagnostic interpretation and patient management. The ACMG/AMP PS4 criterion, “prevalence in affected individuals statistically increased compared to controls,” offers strong evidence for pathogenicity but is often challenging to apply due to the limited availability of robust, matched case-control genomic and phenotypic data. Further, there are currently no options available to score evidence from case-control studies towards benignity. We propose and validate a new code, RWE (real world evidence), by integrating de-identified, longitudinal clinical data with variant carriers and non-carriers identified from exome or genome sequence data across three large-scale clinicogenomic datasets: the Helix Research Network (HRN) dataset, UK Biobank (UKB) and All of US (AoU). Phenotypes for established gene-level disease associations were compiled from the longitudinal medical records of the individuals, enabling rigorous variant-specific case-control analyses from population data. This RWE approach was systematically applied to all variants, including previously identified VUS in clinically relevant genes, powering our VUS Early Surveillance Platform. Across 20 hereditary cancer and cardiovascular genes, the application of RWE provided sufficient evidence to reclassify a VUS in 32% of VUS carriers–99.7% to B/LB and 0.3% to P/LP–directly resolving their ambiguous status. This reclassification rate varied by gene, ranging from 0.7% for BRCA2 up to 50% for LDLR. The systematic integration of Real-World Evidence from large-scale clinicogenomic datasets into the ACMG/AMP scoring rubric through our newly developed and statistically robust RWE category is a significant improvement to variant interpretation that is projected to resolve over 50% of VUS carriers once longitudinal clinico-genomic databases are available for ~3M individuals. This approach markedly reduces the burden of Variants of Uncertain Significance, provides more definitive diagnoses for a substantial proportion of previously unresolved cases, and ultimately increases the clinical utility and adoption of genomic testing, representing a critical advancement for precision medicine.

Background: The Helix Research Network ^TM program is a large population genomics initiative that screens an all-comers population of patients for CDC Tier 1 genetic conditions, including familial hypercholesterolemia (FH). We evaluated changes in clinical management and LDL cholesterol (LDL-C) levels among patients we identified to have FH. Methods: Participants across eight U.S. health systems provided samples that underwent clinical-grade exome sequencing. Individuals with a positive screening result for a Tier 1 condition were offered no-cost genetic counseling through their health system. Using medication and laboratory testing records, we evaluated changes in patients’ lipid-lowering therapies and LDL-C levels. Results: Among 211,263 adults enrolled between 2017-2024, 1,020 (∼1/207) had a pathogenic FH variant in LDLR (72%), APOB (27%), or PCSK9 (1%). Of the 453 with retrospective and prospective electronic health record (EHR) data available (mean of 13.6 and 2.3 years, respectively), 85% lacked a prior clinical FH diagnosis. Overall, 33% received new/modified lipid-lowering therapy within the first year, but this proportion was higher in those with a newly documented FH diagnosis code (55% vs 18% for those without documentation, p<0.001). Patients with new/modified therapies had a mean LDL-C reduction of 59 mg/dL, compared to 18 mg/dL in patients with no therapeutic change (difference=41 mg/dL, p<0.001). Conclusions: Identification of patients with FH likely led to improvements in clinical management and LDL-C levels. EHR documentation of the diagnosis code was associated with greater likelihood of therapeutic modifications which, in turn, were associated with larger LDL-C reductions. Findings underscore the powerful potential of population genomic screening for promoting optimal lipid management in individuals with FH.

Background Natural HbA1c levels in GCK Maturity-onset diabetes of the young (GCK-MODY) patients often sit above the diagnostic threshold for type 2 diabetes (T2D). Treatments to lower HbA1c levels show reduced effectiveness in these individuals, yet in case studies to date, GCK-MODY patients often evade secondary T2D complications. Given these deviations, genetic screening of GCK may be clinically useful, but population studies are needed to more broadly understand T2D-related complications in GCK variant carriers. Methods To identify GCK variant carriers at the population level, we used both ACMG/AMP variant interpretation for GCK-MODY pathogenicity and a state-of-the-art variant interpretation strategy based on functional and statistical evidence to predict glucose elevations. Presence of pathogenic and glucose-elevating GCK variants was assessed in two cohorts (n~535,000). We identified 442 individuals with GCK variants predicted to increase glucose (~1/1200), with 150 (34%) of these individuals harboring variants reaching a pathogenic interpretation. Results In a retrospective analysis, we show that in addition to elevated HbA1c, pathogenic variant carriers are 10x as likely, and all other glucose-elevating GCK variant carriers are 3x as likely, to receive a T2D diagnosis compared to non-GCK carriers. Surprisingly, carriers of pathogenic and glucose-elevating GCK variants with T2D develop T2D-related complications at rates more than double that of individuals without T2D, comparable to non-GCK individuals with T2D. Conclusions This population-level assessment shows secondary complications in individuals with pathogenic and glucose-elevating GCK variants and T2D and suggests that genotyping for these variants should be considered in a precision medicine approach for T2D treatment and prevention.

Background: Individual weight loss response to the GLP-1 receptor agonist semaglutide varies considerably, with many possible contributing factors. Leveraging multiple clinico-genomic cohorts, we analyzed differences in weight loss trajectories according to patient characteristics, including a polygenic score (PGS) and metabolic risk factors, in semaglutide initiators with BMI ≥27 kg/m ² . Methods: This longitudinal study utilized clinical-grade exome sequencing and electronic health record data from six U.S. cohorts within the Helix Research Network (n=134,806). A BMI PGS was calculated using 26,941 variants. Twelve-month weight loss trajectories were modeled using mixed effects models, and associations with demographics, PGS, comorbidities, medications, and laboratory results were evaluated. Findings: Among 1,923 semaglutide users, the mean pretreatment BMI was 38.4 kg/m ² . For those on doses ≥1.7 mg, the mean body weight reduction was 7.3% at 6 months and 9.9% at 12 months. Over 12 months, low PGS was associated with an adjusted 1.5% and 1.8% additional weight loss compared to intermediate and high PGS, respectively (both p<0.01). Male sex, type 2 diabetes, hypertension, obstructive sleep apnea, and non-alcoholic fatty liver disease were each associated with 1.2%-1.9% less weight loss (all p<0.05). In type 2 diabetes, each 1%-increase in pretreatment hemoglobin A1c was associated with 0.6% less weight loss (p=0.0019). Interpretation: Among adults with overweight or obesity, a lower genetic predisposition to obesity is linked to greater weight loss on semaglutide. Additionally, metabolic health significantly impacts the drug’s effectiveness. These findings underscore the importance of precision medicine in obesity management. Funding: Renown Health Foundation. Nevada Governor’s Office of Economic Development. HealthPartners.

Natural HbA1c levels in GCK -MODY patients often sit above the diagnostic threshold for type 2 diabetes (T2D). Standard treatments to lower HbA1c levels are ineffective in these individuals, yet in case studies to date, GCK -MODY patients often evade secondary T2D complications. Given these deviations from a more typical T2D disease course, genetic screening of GCK may be clinically useful, but population studies are needed to more precisely quantify T2D-related outcomes in GCK variant carriers. Using a state-of-the-art variant interpretation strategy based on glucose elevations, we genotyped all individuals in two real-world cohorts (n~535,000) for GCK risk variants and examined rates of T2D and T2D-complications from seven disease categories. We identified 439 individuals with GCK variants predicted to increase glucose (~1/1200). Aligning with their glucose elevations, GCK -MODY variant carriers were 12x as likely, and all other GCK risk carriers 4x as likely, to receive a T2D diagnosis, compared to non- GCK carriers. Surprisingly, GCK risk carriers with T2D develop a range of T2D-related complications at rates comparable to non- GCK T2D patients. Although the penetrance for secondary complications is lower than that for glucose elevations, GCK risk carriers remain at elevated risk of T2D and secondary complications.

Withdrawal Statement: This version of the manuscript and associated files have been removed owing to concerns of data privacy.

Purpose: In the United States, breast cancer clinical risk assessments are inconsistent and inequitable. The previous success of BRCA1 and BRCA2 screening has demonstrated that genetics could be used to reduce these inequities, if they are available and accessible across the population. The aim of this study is to evaluate the performance of different genetic screening approaches to identify women at high-risk of breast cancer in the general population. Methods: We did a retrospective study on 25,591 women participating in the Healthy Nevada Project. Electronic Health Records (EHR) data were used to identify women with a family history of, and women who were diagnosed with breast cancer. The genetic analysis assessed the role of rare predicted loss-of-function (pLOF) variants in BRCA1 , BRCA2 , PALB2 , ATM and CHEK2 , as well as the combined role of common variants via a polygenic risk score. Women were considered at high-risk of breast cancer if they had greater than 20% probability of being diagnosed with breast cancer by age 70. Results: Family history of breast cancer (FHx-BrCa) was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this method for risk assessment is not being properly utilized for early screening. Genetics offered an alternative method for risk assessment. 11.4% of women in HNP were at high-risk of breast cancer based on their genetic risk: having a pLOF variant in BRCA1 , BRCA2 or PALB2 (hazard ratio = 10.4, 95% confidence interval: 8.1-13.5), or a pLOF variant in ATM or CHEK2 (HR = 3.4, CI: 2.4-4.8), or being in the top 10% of the polygenic risk score distribution (HR = 2.4, CI: 2.0-2.8). We also showed that combining PRS with pLOF in ATM and CHEK2 allowed a better identification of participants with high risk while minimizing false positives. Women with a pLOF in ATM or CHEK2 and in the top 50% of the PRS are at high risk (39.2% probability of breast cancer at age 70), while those with a pLOF in ATM or CHEK2 and in the bottom 50% of the PRS are not at high risk (14.4% probability of breast cancer at age 70). Conclusion: These results suggest that a combined monogenic and polygenic approach may best capture the inherited risk for breast cancer across the population.