SummaryBiobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries - with most biobanks and preceding GWAS studies composed of individuals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1–3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural–immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4–6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions.

Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1–2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

Background: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry-or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. Results: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. Conclusions: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString’s commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

No abstract

1 Background: The relationship between germline genetic variation and breast cancer 2 survival is largely unknown, especially in understudied minority populations who often 3 have poorer survival. Genome-wide association studies (GWAS) have interrogated 4 breast cancer survival but often are underpowered due to subtype heterogeneity and 5 many clinical covariates and detect loci in non-coding regions that are difficult to interpret. 6 Transcriptome-wide association studies (TWAS) show increased power in detecting 7 functionally-relevant loci by leveraging expression quantitative trait loci (eQTLs) from 8 external reference panels in relevant tissues. However, ancestry-or race-specific 9 reference panels may be needed to draw correct inference in ancestrally-diverse cohorts. 10 Such panels for breast cancer are lacking. 12Results: We provide a framework for TWAS for breast cancer in diverse populations, 13 using data from the Carolina Breast Cancer Study (CBCS), a North Carolina population-14 based cohort that oversampled black women. We perform eQTL analysis for 406 breast 15 cancer-related genes to train race-stratified predictive models of tumor expression from 16 germline genotypes. Using these models, we impute expression in independent data from 17 CBCS and TCGA, accounting for sampling variability in assessing performance. These 18 models are not applicable across race, and their predictive performance varies across 19 tumor subtype. Within CBCS (ܰ = 3,828), at a false discovery-adjusted significance of 20 0.10 and stratifying for race, we identify associations in black women near AURKA, 21 CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. 22Conclusions: We show that carefully implemented and thoroughly validated TWAS is an 24 efficient approach for understanding the genetics underpinning breast cancer outcomes 25 in diverse populations. 26 27 Keywords: transcriptome-wide analysis (TWAS); breast cancer; expression quantitative 28 trait loci (eQTL); survival; polygenic traits 29 4 Background 30 Breast cancer remains the most common cancer among women in the world [1]. Breast 31 cancer tends to be more aggressive in young women and African American women, 32 though underlying germline determinants of poor outcomes are not well-studied. Cohorts 33 that represent understudied minority populations, like the Carolina Breast Cancer Study 34 (CBCS), have identified differences in healthcare access, socioeconomics, and 35 environmental exposures associated with disparities in outcome [2-4], but more targeted 36 genomic studies are necessary to interrogate these disparities from a biologic and genetic 37 perspective. 38 39 Few genome-wide association studies (GWAS) have studied the relationship between 40 germline variation and survival outcomes in breast cancer, with most focusing instead on 41 genetic predictors of risk [5,6]. Recently, GWAS have shown evidence of association 42 between candidate common germline variants and breast cancer survival, but these 43 studies are often underpow...

This study evaluated the hypothesis that prenatal maternal socioeconomic status (SES) adversity is associated with DNA methylation in the placenta. SES adversity was defined by the presence of, as well as a summative count of, four factors: less than college education, single marital status, food and nutritional service assistance, and public health insurance. Epigenome-wide DNA methylation was assessed using the Illumina EPIC array in 426 placentas from a sample of infants born < 28 weeks of gestation from the Extremely Low Gestational Age Newborn cohort. Associations between SES adversity and DNA methylation were assessed with robust linear regressions adjusted for covariates and controlled the false discovery rate at < 10%. We also examined whether such associations were sex specific. Indicators of SES adversity were associated with differential methylation at 33 CpG sites. Of the 33 identified CpG sites, 19 (57.6%) displayed increased methylation, and 14 (42.4%) displayed decreased methylation in association with at least one of the SES adversity factors. Sex differences were observed in DNA methylation associated with summative SES score; in which placentas derived from female pregnancies showed more robust differential CpG methylation than placentas from male pregnancies. Maternal SES adversity was associated with differential methylation of genes with key role in gene transcription and placental function, potentially altering immunity and stress response. Further investigation is needed to evaluate the role of epigenetic differences in mediating the association between maternal socioeconomic status during pregnancy and later life health outcomes in children.