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Risse-Adams, O. S.

Publications and source records attributed to Risse-Adams, O. S..

2 recordsLinked to original sources

Whole-genome benchmarking reveals context-specific error rates in the Ultima UG100 and Illumina NovaSeqX Platforms.

Whole genome sequencing (WGS) is becoming more common in research and clinical applications, but there is a paucity of comparative data between high-throughput WGS platforms. We benchmarked the Ultima Genomics UG100 system against Illumina NovaSeqX using the Genome in a Bottle (GIAB) WGS reference set sample HG002. Across NIST v4.2.1 benchmark regions, UG100 exhibited higher total variant-calling errors (27x), primarily driven by indel false negatives. Restricting to Ultima Genomics high confidence regions (UG HCR v3.1, 90.3% of GRCh38) reduced the error burden by 89.6%, indicating most errors lie outside these regions. Strong degradation of variant calling performances occurred in homopolymer tracts > 10 bp, and base calling error rates increased sharply after position 200 bp within each read, whereas Illuminas error rates were higher earlier in the read. Coverage dropouts in UG100 data were pronounced in GC-rich regions. Of note, 2.24% of ClinVar Pathogenic and Likely Pathogenic variants and 22.6% of a common catalogue of polymorphic STRs were found to be excluded from UG HCR v3.1, indicating potential impact to clinical applications. These results highlight context-specific genotyping errors of the UG100 and NovaSeqX platforms, and underscore the importance of whole genome benchmarking for adjudicating accuracy of NGS platforms beyond the current HG002 reference set.

genomics↗

Examining the Effect of Social Determinants of Health on Human Trait Heritability

Social factors of health (SFOH) are critical determinants of human traits but are rarely incorporated into genetic models. Here, we assess how participant-completed SFOH survey data influence heritability estimates and trait variance in 85,963 diverse All of Us participants spanning multiple ancestries and racial identities. We summarized SFOH survey data using multiple correspondence analysis (MCA) into axes representing distinct social dimensions (e.g., social support, perceived stress) and developed a Social Similarity Index (SSI) to capture social environment similarity among individuals. To analyze trait heritability, we used Haseman-Elston (HE) regression for its sensitivity to residual structure. Including SFOH measures as covariates in HE regression models significantly reduced heritability estimates for four of 18 traits, three of which are anthropometric: body mass index, hip circumference, waist circumference, and HDL cholesterol. This suggests that unmodeled social structure can be misattributed to genetic effects. Though several SFOH measures have non-zero heritability when adjusting for three genetic principal components (h2{approx} 0.01-0.09), these are reduced to similar estimates when adjusting for seven genetic principal components (h2{approx} 0.01-0.02). This convergence across SFOH measures, which capture different social dimensions, indicates that SFOH measures capture population stratification, emphasizing their utility for reducing confounding in heritability estimates. SFOH measures were also associated with trait variance, with anthropometric traits--those with reduced heritability--exhibiting the most extensive dispersion effects. This implies that social environment influences trait variability. Our findings highlight the necessity of including social and environmental factors in genetic studies to reduce potential confounding.

genetics↗