@article {2188, title = {Discovery of genomic loci of the human cerebral cortex using genetically informed brain atlases.}, journal = {Science}, volume = {375}, year = {2022}, month = {2022 02 04}, pages = {522-528}, abstract = {

To determine the impact of genetic variants on the brain, we used genetically informed brain atlases in genome-wide association studies of regional cortical surface area and thickness in 39,898 adults and 9136 children. We uncovered 440 genome-wide significant loci in the discovery cohort and 800 from a post hoc combined meta-analysis. Loci in adulthood were largely captured in childhood, showing signatures of negative selection, and were linked to early neurodevelopment and pathways associated with neuropsychiatric risk. Opposing gradations of decreased surface area and increased thickness were associated with common inversion polymorphisms. Inferior frontal regions, encompassing Broca{\textquoteright}s area, which is important for speech, were enriched for human-specific genomic elements. Thus, a mixed genetic landscape of conserved and human-specific features is concordant with brain hierarchy and morphogenetic gradients.

}, keywords = {Adult, Aged, Aged, 80 and over, Cerebral Cortex, Child, Chromatin, Cohort Studies, Female, Gene Ontology, Genetic Association Studies, Genetic Loci, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Male, Mental Disorders, Middle Aged, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Regulatory Sequences, Nucleic Acid}, issn = {1095-9203}, doi = {10.1126/science.abe8457}, author = {Makowski, Carolina and van der Meer, Dennis and Dong, Weixiu and Wang, Hao and Wu, Yan and Zou, Jingjing and Liu, Cin and Rosenthal, Sara B and Hagler, Donald J and Fan, Chun Chieh and Kremen, William S and Andreassen, Ole A and Jernigan, Terry L and Dale, Anders M and Zhang, Kun and Visscher, Peter M and Yang, Jian and Chen, Chi-Hua} } @article {214, title = {Covariate-modulated local false discovery rate for genome-wide association studies.}, journal = {Bioinformatics}, volume = {30}, year = {2014}, month = {2014 Aug 1}, pages = {2098-104}, abstract = {

MOTIVATION: Genome-wide association studies (GWAS) have largely failed to identify most of the genetic basis of highly heritable diseases and complex traits. Recent work has suggested this could be because many genetic variants, each with individually small effects, compose their genetic architecture, limiting the power of GWAS, given currently obtainable sample sizes. In this scenario, Bonferroni-derived thresholds are severely underpowered to detect the vast majority of associations. Local false discovery rate (fdr) methods provide more power to detect non-null associations, but implicit assumptions about the exchangeability of single nucleotide polymorphisms (SNPs) limit their ability to discover non-null loci.

METHODS: We propose a novel covariate-modulated local false discovery rate (cmfdr) that incorporates prior information about gene element-based functional annotations of SNPs, so that SNPs from categories enriched for non-null associations have a lower fdr for a given value of a test statistic than SNPs in unenriched categories. This readjustment of fdr based on functional annotations is achieved empirically by fitting a covariate-modulated parametric two-group mixture model. The proposed cmfdr methodology is applied to a large Crohn{\textquoteright}s disease GWAS.

RESULTS: Use of cmfdr dramatically improves power, e.g. increasing the number of loci declared significant at the 0.05 fdr level by a factor of 5.4. We also demonstrate that SNPs were declared significant using cmfdr compared with usual fdr replicate in much higher numbers, while maintaining similar replication rates for a given fdr cutoff in de novo samples, using the eight Crohn{\textquoteright}s disease substudies as independent training and test datasets. Availability an implementation: https://sites.google.com/site/covmodfdr/

CONTACT: : wes.stat@gmail.com

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Analysis of Variance, Bayes Theorem, Computational Biology, False Positive Reactions, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu145}, author = {Zablocki, Rong W and Schork, Andrew J and Levine, Richard A and Andreassen, Ole A and Dale, Anders M and Thompson, Wesley K} }