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Longda Jiang
University of Queensland
Genome-wide association studyGeneticsMedicineBiologyGenetic architecture
11Publications
5H-index
397Citations
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Publications 13
Newest
#1Jun Xu (UQ: University of Queensland)H-Index: 2
#2Caitlin Falconer (UQ: University of Queensland)H-Index: 2
Last. Lachlan J. M. CoinH-Index: 47
view all 18 authors...
A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant w...
5 CitationsSource
#1Jing Guo (UQ: University of Queensland)H-Index: 3
#1Jian-ping Guo (UQ: University of Queensland)H-Index: 3
Last. Jian Yang (WMU: Wenzhou Medical College)H-Index: 96
view all 8 authors...
Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide significant SNPs...
4 CitationsSource
#1Jian Zeng (UQ: University of Queensland)H-Index: 13
#2Angli Xue (UQ: University of Queensland)H-Index: 8
Last. Jian Yang (WMU: Wenzhou Medical College)H-Index: 97
view all 13 authors...
Understanding how natural selection has shaped the genetic architecture of complex traits and diseases is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level data to estimate multiple features of genetic architecture, including signatures of natural selection. Here, we present an enhanced method (SBayesS) that only requires GWAS summary statistics and incorporates functional genomic annotations. We analysed GWAS data with large sample s...
4 CitationsSource
#1Martina Rosticci (UNIBO: University of Bologna)H-Index: 15
#2Natalia Pervjakova (Imperial College London)H-Index: 1
Last. Inga Prokopenko (Imperial College London)H-Index: 78
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: Aim: The aim of the study was to explore the effects of variants at HMGCR-KIF6loci on a range of cardio-metabolic phenotypes. Methods: We analyzed the range of variants within Genetics in Brisighella Health Study and KIF6 genes using an additive genetic model on 18 cardiometabolic phenotypes in a sample of 1645 individuals from the Genetics in Brisighella Health Study and replicated in 10,662 individuals from the Estonian Genome Center University of Tartu. Results: We defined directly the effe...
Source
#1Longda Jiang (UQ: University of Queensland)H-Index: 5
#2Zhili Zheng (WMU: Wenzhou Medical College)H-Index: 14
Last. Jian Yang (WMU: Wenzhou Medical College)H-Index: 96
view all 7 authors...
The genome-wide association study (GWAS) has been widely used as an experimental design to detect associations between genetic variants and a phenotype. Two major confounding factors, population stratification and relatedness, could potentially lead to inflated GWAS test-statistics and thereby spurious associations. Mixed linear model (MLM)-based approaches can be used to account for sample structure. However, genome-wide association (GWA) analyses in biobank samples such as the UK Biobank (UKB)...
8 CitationsSource
#1Jian YangH-Index: 97
#2Zhili ZhengH-Index: 14
Last. Longda JiangH-Index: 5
view all 3 authors...
3 CitationsSource
#1Ting Qi (UQ: University of Queensland)H-Index: 7
#2Yang Wu (UQ: University of Queensland)H-Index: 15
Last. Jian Yang (WMU: Wenzhou Medical College)H-Index: 97
view all 17 authors...
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent b...
55 CitationsSource
#1Ting Qi (UQ: University of Queensland)H-Index: 7
#2Yang Wu (UQ: University of Queensland)H-Index: 15
Last. Jian Yang (UQ: University of Queensland)H-Index: 97
view all 17 authors...
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (r_b). Using publicly available data (n = 72 to 1,366), we fin...
5 CitationsSource
#1Rob ScottH-Index: 97
#2Laura J. ScottH-Index: 63
Last. John D. EicherH-Index: 16
view all 175 authors...
#1Rob ScottH-Index: 97
#2Laura J. ScottH-Index: 63
Last. John D. EicherH-Index: 16
view all 175 authors...
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