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Surrogate variables and the risk of overfitting in neuropsychiatric gene expression analyses
Hoang DT, Thompson RC, Lund AN, Kritzer E, Buxbaum-Grice AS, John D, Nadkarni GN, Golestani N et al.. bioRxiv, 2026-10-12
Topic: Genetics and APOE
The reproducibility crisis in biomedical research calls for re-evaluation of how common analytic approaches affect results. One such approach, surrogate variable analysis (SVA), is widely used to adjust for unmeasured drivers of variation in transcriptomic differential expression analyses. We systematically quantified the impact of SVA using synthetic data with known ground truth and fourteen brain RNA-seq and microarray datasets spanning Alzheimer’s disease, autism spectrum disorder, bipolar disorder, schizophrenia, and Parkinson’s disease. In synthetic data, SVA usage frequently led to high false positive rates. This issue was not revealed by standard diagnostics nor resolved by strategies such as maximizing cross-dataset signal correlation. In real data, using SVA reduced cross-cohort reproducibility of differential expression signatures regardless of the number of surrogate variables included. These findings show that SVA risks inducing overfitting at realistic signal levels, advocating for a cautious, case-by-case application, with a preference for modeling known covariates.
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APA 7th
Hoang, D. T., Thompson, R. C., Lund, A. N., Kritzer, E., Buxbaum-Grice, A. S., John, D., et al. (2026). Surrogate variables and the risk of overfitting in neuropsychiatric gene expression analyses. bioRxiv. https://doi.org/10.64898/2026.10.05.756826
Vancouver
Hoang DT, Thompson RC, Lund AN, Kritzer E, Buxbaum-Grice AS, John D, et al. Surrogate variables and the risk of overfitting in neuropsychiatric gene expression analyses. bioRxiv. 2026. doi:10.64898/2026.10.05.756826.
BibTeX
@article{hoang2026-cc41296c,
author = {Hoang, D. T. and Thompson, R. C. and Lund, A. N. and Kritzer, E. and Buxbaum-Grice, A. S. and John, D. and Nadkarni, G. N. and Golestani, N. and others},
title = {{Surrogate variables and the risk of overfitting in neuropsychiatric gene expression analyses}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.10.05.756826},
url = {https://doi.org/10.64898/2026.10.05.756826}
}
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Key findings
- The reproducibility crisis in biomedical research calls for re-evaluation of how common analytic approaches affect results.
- This issue was not revealed by standard diagnostics nor resolved by strategies such as maximizing cross-dataset signal correlation.
- These findings show that SVA risks inducing overfitting at realistic signal levels, advocating for a cautious, case-by-case application, with a preference for modeling known covariates.
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Study details
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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