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. 2022 Oct 14;13(1):6071.
doi: 10.1038/s41467-022-33829-1.

Genetic map of regional sulcal morphology in the human brain from UK biobank data

Benjamin B Sun  1   2 , Stephanie J Loomis #  3 , Fabrizio Pizzagalli #  4   5 , Natalia Shatokhina  5 , Jodie N Painter  6 , Christopher N Foley  7   8 ; Biogen Biobank Team; Megan E Jensen  9 , Donald G McLaren  9 , Sai Spandana Chintapalli  10 , Alyssa H Zhu  5 , Daniel Dixon  5 , Tasfiya Islam  5 , Iyad Ba Gari  5 , Heiko Runz  3 , Sarah E Medland  6 , Paul M Thompson  11 , Neda Jahanshad  12 , Christopher D Whelan  13
Collaborators, Affiliations

Genetic map of regional sulcal morphology in the human brain from UK biobank data

Benjamin B Sun et al. Nat Commun. .

Abstract

Genetic associations with macroscopic brain structure can provide insights into brain function and disease. However, specific associations with measures of local brain folding are largely under-explored. Here, we conducted large-scale genome- and exome-wide associations of regional cortical sulcal measures derived from magnetic resonance imaging scans of 40,169 individuals in UK Biobank. We discovered 388 regional brain folding associations across 77 genetic loci, with genes in associated loci enriched for expression in the cerebral cortex, neuronal development processes, and differential regulation during early brain development. We integrated brain eQTLs to refine genes for various loci, implicated several genes involved in neurodevelopmental disorders, and highlighted global genetic correlations with neuropsychiatric phenotypes. We provide an interactive 3D visualisation of our summary associations, emphasising added resolution of regional analyses. Our results offer new insights into the genetic architecture of brain folding and provide a resource for future studies of sulcal morphology in health and disease.

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Conflict of interest statement

The authors declare the following competing interests: B.B.S., S.J.L., Biogen Biobank Team, M.J., D.G.M., H.R., C.D.W. are employees of Biogen. P.M.T and N.J received grant support from Biogen for this work. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Summary of brain sulcal association results.
a Schematic of brain sulcal folds and shape parameters. Brain region legend corresponds to colours in figures a–c. b t-SNE of regional brain sulcal measures for each shape parameter. c Manhattan plots by brain region, shape parameter and side. Diamonds indicate lead associations that replicated (p < 0.05). N = 26,530 (discovery) and n = 13,639 (replication) biologically independent sample measures. Points above 0 in the y-axis in each plot refers to associations with left sided sulcal measures, below 0 with right sided measures. Diamonds along 0 in the y-axis indicate lead associations for bilateral sulcal measures. Dashed horizonal line indicate GWAS significance thresholds (grey: p = 5 × 10−8, dark red: p = 2 × 10−10). High resolution of the figure is available in the Supplementary Files. d Top: boxplot of genetic and phenotypic correlations between left and right sides. Number of sulcal traits listed beneath the boxplot. Each box plot presents the median, first and third quartiles, with upper and lower whiskers representing 1.5× inter-quartile range above and below the third and first quartiles respectively. Middle: Genetic correlation between shape parameters. Bottom: Phenotypic correlation between shape parameters. Middle and bottom: left hemisphere correlations in upper triangle, right hemisphere correlations in lower triangle. Phenotypic and genetic correlations were averaged across each hemisphere separately per sulcal parameter. Extended phenotypic and genetic correlation heatmap is shown in Supplementary Fig. 6.
Fig. 2
Fig. 2. Enrichment of genes in significant loci for tissue expression, pathways and brain developmental stages.
a Gene expression across various tissues (inset shows sensitivity analysis at other GWAS thresholds), blue points indicate multiple adjusted p < 0.05. Hypergeometric test used to derive p-values (uncorrected). b GO and KEGG pathways (FDR < 0.05). c Differentially expressed genes across brain development stages. Hypergeometric test used to derive p-values. Blue points indicate FDR < 0.05.
Fig. 3
Fig. 3. KCNK2 locus associations.
a Association of the lead rs1452628:T variant with reduced sulcal widths across the brain. (Grey colours indicate associations with prep > 0.05). b Left: regional association plot of MetaBrain KCNK2 eQTLs for spinal cord, basal ganglia, hippocampus and cerebellum. Right: regional association plots and colocalization of cortex KCNK2 eQTL and different lead variants in the KCNK2 locus. A subset of associations shown for each different lead variant shown due to space constraints. P derived from regression-based tests.
Fig. 4
Fig. 4. Genetic correlations with neuropsychiatric conditions.
a Genetic correlations between shape parameters and neuropsychiatric conditions. Genetic correlations (GCs) were averaged across all brain regions for each sulcal parameter separately – the mean GCs are displayed in each entry. * indicate p < 0.001 (adjusted for number of tests) derived from two-sided t-tests. b Examples of genetic correlations across brain sulcal folds with cognitive performance, Parkinson’s disease, attention deficit hyperactive disorder (ADHD) and major depressive disorder.
Fig. 5
Fig. 5. Three-dimensional visualisation of brain sulcal associations (Z-scores) for four exemplar pleiotropic loci.
An interactive online tool, available at https://enigma-brain.org/sulci-browser, was used to visualise genetic influences on sulcal morphology in three dimensions, revealing (a) widespread, positive associations between rs12146713 (NUAK1) and sulcal width measures, (b) widespread positive and negative associations between rs4843553 (near C16orf95) and sulcal width, depth and surface area measures, (c) widespread positive associations between rs11759026, containing CENPW, and all four sulcal measures, and (d) bilateral associations between an intergenic variant, rs2033939, and length, width, depth and surface area of the central sulci, precentral sulci, and posterior lateral fissures.
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References

    1. Hibar DP, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520:224–229. doi: 10.1038/nature14101. - DOI - PMC - PubMed
    1. Elliott LT, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562:210–216. doi: 10.1038/s41586-018-0571-7. - DOI - PMC - PubMed
    1. Shen L, Thompson PM. Brain imaging genomics: integrated analysis and machine learning. Proc. IEEE Inst. Electr. Electron Eng. 2020;108:125–162. doi: 10.1109/JPROC.2019.2947272. - DOI - PMC - PubMed
    1. Lee PH, et al. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol. Psychiatry. 2016;21:1680–1689. doi: 10.1038/mp.2016.164. - DOI - PMC - PubMed
    1. Hofer E, et al. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat. Commun. 2020;11:4796. doi: 10.1038/s41467-020-18367-y. - DOI - PMC - PubMed

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