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[Preprint]. 2025 Feb 19:2024.12.27.24319111.
doi: 10.1101/2024.12.27.24319111.

Whole genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia

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Whole genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia

Runjia Li et al. medRxiv. .

Update in

  • Whole-genome sequence-based association analysis of African American individuals with bipolar disorder and schizophrenia.
    Li R, Gagliano Taliun SA, Liao K, Flickinger M, Sobell JL, Genovese G, Locke AE, Chiu RR, LeFaive J, Wang J, Martins T, Chapman S, Neumann A, Handsaker RE, Arnett DK, Barnes KC, Boerwinkle E, Braff D, Cade BE, Fornage M, Gibbs RA, Hoth KF, Hou L, Kooperberg C, Loos RJF, Metcalf GA, Montgomery CG, Morrison AC, Qin ZS, Redline S, Reiner AP, Rich SS, Rotter JI, Taylor KD, Viaud-Martinez KA; National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium; Genomic Psychiatry Cohort Investigators; Bigdeli TB, Gabriel S, Zollner S, Smith AV, Abecasis G, McCarroll SA, Pato MT, Pato CN, Boehnke M, Knowles J, Kang HM, Ophoff RA, Ernst J, Scott LJ. Li R, et al. HGG Adv. 2025 Aug 30;7(1):100499. doi: 10.1016/j.xhgg.2025.100499. Online ahead of print. HGG Adv. 2025. PMID: 40886051 Free PMC article.

Abstract

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole genome sequencing of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls. To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of BD association with single-variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD GWAS loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

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Conflict of interest statement

Declaration of Interests A.E.L. is a shareholder of Regeneron Pharmaceuticals. K.C.B. is an employee of Oxford Nanopor Technologies Ltd. G.R.A. is a shareholder of Regeneron Pharmaceuticals.

Figures

Figure 1.
Figure 1.. Study overview
(A) Number of total and of unrelated study participants for each case or control group; (B) Seven analysis groups: control/control and case/control; (C) Four analysis types (Total or Unrelated samples used in analysis); PTV: protein truncating variant
Figure 2.
Figure 2.
Regional view of chromosome 18 locus showing evidence of association in the InPSYght BD versus InPSYght+TOPMed controls. Horizontal line shows genome-wide significance threshold of 5 x 10−9.
Figure 3.
Figure 3.
Chromatin and conservation state burden test results for InPSYght cases versus InPSYght controls (A, B) and InPSYght cases versus InPSYght and TOPMed controls (C, D), with each point representing a ChromHMM or a ConsHMM state. Dashed lines show Bonferroni-based p-value thresholds (p=0.05/200). Diagonal lines show the unit slope. (A, C) QQ-plots with genomic inflation factors (λ) before and after inclusion of sequencing metadata PCs covariates. (B,D) Signed (by direction of enrichment coefficient) −log10 p-values before and after inclusion of sequencing metadata PCs covariates. Sign direction: Case enrichment values are positive, control enrichment values are negative.
Figure 4.
Figure 4.
Test of repeat categories for enrichment of rare and low-frequency variants in TOPMed controls versus InPSYght controls, without (A, B) or with (C, D) sequencing metadata PCs covariates. “DNA?” represents elements with uncertain category classification to the DNA repeat element category. Horizontal dashed lines show Bonferroni-based p-value thresholds (p = 0.05/22). (A), (C): volcano plots with log odds ratio on the x-axis and −log10 p-values on the y-axis. (B), (D): Mean % of variants overlapping each repeat category on the x-axis (weighted by the minor allele frequencies, Methods), and −log10 p-values on the y-axis.
Figure 5.
Figure 5.
WGScan genome-wide sliding window burden test for rare and low-frequency variants for InPSYght controls versus TOPMed controls. Horizontal lines show genome-wide p-value significance thresholds (p=2.18 x 10−8). Diagonal line shows the unit slope. (A, B) Manhattan plots of sliding window p-values without sequencing metadata PCs (A) and with sequencing metadata PCs (B). (C) QQ-plot for the window p-values, before and after inclusion of sequencing metadata PCs covariates. (D) Comparison between sliding window p-values before and after inclusion of sequencing metadata PCs covariates, showing windows in the chr5 region 58,500,010-58,550,007 (red).

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