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Admixture is a well known confounder in genetic association studies. If genome-wide data is not available, as would be the case for candidate gene studies, ancestry informative markers (AIMs) are required in order to adjust for admixture. The predominant population group in the Western Cape, South Africa, is the admixed group known as the South African Coloured (SAC). A small set of AIMs that is optimized to distinguish between the five source populations of this population (African San, African non-San, European, South Asian, and East Asian) will enable researchers to cost-effectively reduce false-positive findings resulting from ignoring admixture in genetic association studies of the population. Using genome-wide data to find SNPs with large allele frequency differences between the source populations of the SAC, as quantified by Rosenberg et. al's In-statistic, we developed a panel of AIMs by experimenting with various selection strategies. Subsets of different sizes were evaluated by measuring the correlation between ancestry proportions estimated by each AIM subset with ancestry proportions estimated using genome-wide data. We show that a panel of 96 AIMs can be used to assess ancestry proportions and to adjust for the confounding effect of the complex five-way admixture that occurred in the South African Coloured population.

Background Motor proteins play a key role in neuronal functions and morphology that are important for learning and memory. We have previously reported that increased expression KIF11/Kinesin‐5 overrides Aß‐mediated effects on dendritic spine density and long‐term potentiation in a mouse model of Alzheimer’s disease (AD), effectively maintaining cognitive function in the face of Aß pathology. Here, we evaluated the association of key AD phenotypes with mRNA expression levels of a select set of Dynein motor proteins Method We utilized measurements of gene expression, AD neuropathology burden, and cognition provided by the ROS/MAP study to determine whether an association exists between AD phenotypes and expression of genes for cytoplasmic and axonemal dynein heavy chains. Neuropathology burden was determined through immunohistochemistry. Z‐scores from the raw scores of 19 cognitive tests were used to determine global cognition. Neuropathology burden and global cognition measurements were provided by Rush Alzheimer’s Disease Center. Measurements of gene expression in the dorsolateral prefrontal cortex (DLPFC) (n = 634) were determined through an established RNAseq analysis pipeline (Logsdon et al., 2019, syn8456638;syn8456629) and made available to us through the Accelerating Medicines Partnership in Alzheimer’s Disease Target Discovery and Preclinical Validation knowledge portal (syn3219045). Associations of gene expression with neuropathology and cognition were determined through multiple linear regression and mixed effects models covarying for age, sex, education, and post‐mortem interval Result In participants with AD (CERAD score > 2), higher gene expression levels of DYNC1H1 and DNAH1 in the DLPFC predicted better cognitive performance longitudinally (p = 0.03 and p = 0.00197, respectively) and at the last visit prior to death (p = 8.701e‐05 and p = 3.571e‐05, respectively). Higher expression of DYNC1H1 and DNAH1 were also associated with lower amyloid pathology (p = 0.0001257 and p = 2.356e‐07, respectively) and tau tangles (p = 2.356e‐07 and p = 2.659e‐09, respectively) Conclusion Our finding that AD participants with higher expression levels of DYC1H1 and DNAH1 show reduced cognitive decline and decreased AD pathology suggest a potential beneficial effect of these motor proteins on AD progression. Based on the functions of these motor proteins, these findings warrant further studies to identify potential mechanisms underlying this association. Potential mechanisms include facilitation of higher rates of phagocytosis or neuronal lysosomal degradation of Aß

Individuals of admixed ancestries (for example, African Americans) inherit a mosaic of ancestry segments (local ancestry) originating from multiple continental ancestral populations. This offers the unique opportunity of investigating the similarity of genetic effects on traits across ancestries within the same population. Here we introduce an approach to estimate correlation of causal genetic effects ( r admix ) across local ancestries and analyze 38 complex traits in African-European admixed individuals ( N  = 53,001) to observe very high correlations (meta-analysis r admix  = 0.95, 95% credible interval 0.93–0.97), much higher than correlation of causal effects across continental ancestries. We replicate our results using regression-based methods from marginal genome-wide association study summary statistics. We also report realistic scenarios where regression-based methods yield inflated heterogeneity-by-ancestry due to ancestry-specific tagging of causal effects, and/or polygenicity. Our results motivate genetic analyses that assume minimal heterogeneity in causal effects by ancestry, with implications for the inclusion of ancestry-diverse individuals in studies. This analysis of individuals of admixed genetic ancestries suggests that complex trait causal variant effect sizes are, by and large, similar across ancestries, and discusses the implications for the study of these and other diverse populations.

Motor proteins play a key role in neuronal functions and morphology that are important for learning and memory. We have previously reported that increased expression KIF11/Kinesin-5 overrides Aß-mediated effects on dendritic spine density and long-term potentiation in a mouse model of Alzheimer's disease (AD), effectively maintaining cognitive function in the face of Aß pathology. Here, we evaluated the association of key AD phenotypes with mRNA expression levels of a select set of Dynein motor proteins METHOD: We utilized measurements of gene expression, AD neuropathology burden, and cognition provided by the ROS/MAP study to determine whether an association exists between AD phenotypes and expression of genes for cytoplasmic and axonemal dynein heavy chains. Neuropathology burden was determined through immunohistochemistry. Z-scores from the raw scores of 19 cognitive tests were used to determine global cognition. Neuropathology burden and global cognition measurements were provided by Rush Alzheimer's Disease Center. Measurements of gene expression in the dorsolateral prefrontal cortex (DLPFC) (n = 634) were determined through an established RNAseq analysis pipeline (Logsdon et al., 2019, syn8456638;syn8456629) and made available to us through the Accelerating Medicines Partnership in Alzheimer's Disease Target Discovery and Preclinical Validation knowledge portal (syn3219045). Associations of gene expression with neuropathology and cognition were determined through multiple linear regression and mixed effects models covarying for age, sex, education, and post-mortem interval RESULT: In participants with AD (CERAD score > 2), higher gene expression levels of DYNC1H1 and DNAH1 in the DLPFC predicted better cognitive performance longitudinally (p = 0.03 and p = 0.00197, respectively) and at the last visit prior to death (p = 8.701e-05 and p = 3.571e-05, respectively). Higher expression of DYNC1H1 and DNAH1 were also associated with lower amyloid pathology (p = 0.0001257 and p = 2.356e-07, respectively) and tau tangles (p = 2.356e-07 and p = 2.659e-09, respectively) CONCLUSION: Our finding that AD participants with higher expression levels of DYC1H1 and DNAH1 show reduced cognitive decline and decreased AD pathology suggest a potential beneficial effect of these motor proteins on AD progression. Based on the functions of these motor proteins, these findings warrant further studies to identify potential mechanisms underlying this association. Potential mechanisms include facilitation of higher rates of phagocytosis or neuronal lysosomal degradation of Aß.

To characterize the extent and impact of ancestry-related biases in precision genomic medicine, we use 642 whole-genome sequences from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) project to evaluate typical filters and databases. We find significant correlations between estimated African ancestry proportions and the number of variants per individual in all variant classification sets but one. The source of these correlations is highlighted in more detail by looking at the interaction between filtering criteria and the ClinVar and Human Gene Mutation databases. ClinVar’s correlation, representing African ancestry-related bias, has changed over time amidst monthly updates, with the most extreme switch happening between March and April of 2014 ( r =0.733 to r =−0.683). We identify 68 SNPs as the major drivers of this change in correlation. As long as ancestry-related bias when using these clinical databases is minimally recognized, the genetics community will face challenges with implementation, interpretation and cost-effectiveness when treating minority populations. Personalized medicine requires accurate and ethnicity-optimized reference genome panels. Here, the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) evaluates typical variant filters and existing genome databases against newly sequenced African-ancestry populations.

Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance – known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011–0.022, p  < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002–0.007, p  = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002–0.006, p  = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS’s potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.

Many patients do not experience optimal benefits from medical advances because clinical research does not adequately represent them. While the diversity of biomedical research cohorts is improving, ensuring that individual patients are adequately represented remains challenging. We propose a new approach, GenoSiS, which leverages machine learning-based similarity search to dynamically find patient-matched cohorts across different populations quickly. These cohorts could serve as reference cohorts to improve a range of clinical analyses, including disease risk score calculations and dosage decisions. While GenoSiS focuses on finding genetic similarity within a biobank, our similarity search architecture can be extended to represent other medically relevant patient characteristics and search other biobanks.

Genome-Wide Association Studies (GWAS) are widely used to investigate the role of genetics in disease traits, but the resulting file sizes from these studies are large, posing barriers to efficient storage, sharing, and querying. This issue is especially important for biobanks like the UK Biobank that publish GWAS for thousands of traits, increasing the volume of data that must be effectively managed. Current compression and query methods reduce file sizes and allow for quick genomic position-based queries but do not provide utility for quickly finding loci based on their summary statistics. For example, finding all SNVs in a particular p-value range would require decompressing and scanning the whole file. We propose a new tool, STABIX, which introduces summary-statistic-based queries and improves upon the standard bgzip compression and tabix query tool in both compression ratio and decompression speed. When applied to ten GWAS files from PanUKBB, STABIX created smaller compressed data and indices than tabix for all files, where bgzip and tbi files were an average of 1.2 times the size of STABIX compressed files and indexes. In the same ten files, STABIX per gene decompression was, on average 7x faster than tabix per gene decompression, and achieved faster per gene decompression times for over 99% of nearly 20,000 genes.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/kristen-schneider/stabix* https://github.com/kristen-schneider/stabix-analysis* https://pan.ukbb.broadinstitute.org/docs/per-phenotype-files/index.html

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals due to various confounding challenges. Here, we demonstrate that enrichment analyses which aggregate SNP-level association statistics at multiple genomic scales—from genes to genomic regions and pathways—have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan, as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African-American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits, as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.