Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits

Zhao, Bingxin; Shan, Yue; Yang, Yue; Li, Tengfei; Luo, Tianyou; Zhu, Ziliang; Li, Yun; Zhu, Hongtu

doi:10.1101/842872

Cited by 3 publications

(3 citation statements)

References 103 publications

(99 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, TWAS have emerging applications on identifying novel candidate genes of complex human diseases such as cancers [47,[59][60][61][62], neuropsychiatric diseases [41,[63][64][65][66] (see Supplementary Table S1). In addition to the macro-phenotypes, micro-phenotypes such as chromatin status has also been considered.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Transcriptome wide association studies: general framework and methods

et al. 2021

View full text Add to dashboard Cite

Background: Genome-wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and difficulties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome-wide association studies (TWAS) provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. Results: In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will briefly introduce methods that are closely related to TWAS, including MR-based methods and colocalization approaches. Connection and difference between these approaches will be discussed. Conclusion: Finally, we will summarize strengths, limitations, and potential directions for TWAS.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In recent years, TWAS have emerging applications on identifying novel candidate genes of complex human diseases such as cancers [47,59–62], neuropsychiatric diseases [41,63], etc . [64–66]. (see Table S1).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome wide association studies: general framework and methods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…TWAS was originally designed to study the effect of cis-regulated gene expression on disease outcomes, but has since been generalized for the study of other intermediary endophenotypes, including neuroimaging endophenotypes [15]. In particular, TWAS has been applied to GWAS of neuroimaging phenotypes [16,17].…”

Section: Introductionmentioning

confidence: 99%

Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease

Knutson

Pan

2021

Quant. Biol.

View full text Add to dashboard Cite

Background: Genome wide association studies (GWAS) have identified many genetic variants associated with increased risk of Alzheimer's disease (AD). These susceptibility loci may effect AD indirectly through a combination of physiological brain changes. Many of these neuropathologic features are detectable via magnetic resonance imaging (MRI). Methods: In this study, we examine the effects of such brain imaging derived phenotypes (IDPs) with genetic etiology on AD, using and comparing the following methods: two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome wide association studies (TWAS) and the adaptive sum of powered score (aSPU) test. These methods do not require individual-level genotypic and phenotypic data but instead can rely only on an external reference panel and GWAS summary statistics. Results: Using publicly available GWAS datasets from the International Genomics of Alzheimer's Project (IGAP) and UK Biobank's (UKBB) brain imaging initiatives, we identify 35 IDPs possibly associated with AD, many of which have well established or biologically plausible links to the characteristic cognitive impairments of this neurodegenerative disease. Conclusions: Our results highlight the increased power for detecting genetic associations achieved by multiple correlated SNP-based methods, i.e., aSPU, GSMR and TWAS, over MR methods based on independent SNPs (as instrumental variables).

show abstract

Genetic signatures of human brain structure: A comparison between GWAS and relatedness-based regression

Lyu

Tsvetanov

Tyler

et al. 2020

Preprint

View full text Add to dashboard Cite

Identifying the genetic variations impacting human brain structure and further affecting cognitive functions, will enhance our understanding of the fundamental bases of cognition. In this study, we take two different approaches to this issue: classical genome-wide association analysis (GWAS) and a relatedness-based regression approach (REL) to search for associations between genotype and brain structural measures of gray matter and white matter. Instead of searching genetic variants by testing the association between a phenotype trait and the genotype of each single-nucleotide polymorphism (SNP) as in GWAS, REL takes advantage of multiple SNPs within a genomic window as a single measure, which potentially find associations wherever the functional SNP is in linkage disequilibrium (LD) with SNPs that have been sampled. We also conducted a simulation analysis to systemically compare GWAS and REL with respect to different levels of LD. Both methods succeed in identifying genetic variations associated with regional and global brain structural measures, though they tend to give complementary results due to the very different aspects of genetic properties used. Simulation results suggest that GWAS outperforms REL when the signal is relatively weak. However, the collective effects due to local LD boost the performance of REL with increasing signal strength, resulting in better performance than GWAS. Our study suggests that the optimal approach may vary across the genome and that pre-testing for LD could allow GWAS to be preferred where LD is high and REL to be used where LD is low, or the local pattern of LD is complex.

show abstract

Transcriptome-wide association analysis of 211 neuroimaging traits identifies new genes for brain structures and yields insights into the gene-level pleiotropy with other complex traits

Cited by 3 publications

References 103 publications

Transcriptome wide association studies: general framework and methods

Transcriptome wide association studies: general framework and methods

Integrating brain imaging endophenotypes with GWAS for Alzheimer’s disease

Genetic signatures of human brain structure: A comparison between GWAS and relatedness-based regression

Contact Info

Product

Resources

About