Biology subjects

Wang, G. T.

Publications and source records attributed to Wang, G. T..

3 recordsLinked to original sources

bioRxiv2026

LDSC regression-based heritability estimates can be biased when summary statistics are obtained from meta-analysis or imputed variants

MotivationLinkage disequilibrium score (LDSC) regression is a popular method to estimate heritability for complex traits using summary statistics and linkage disequilibrium (LD) reference panels, offering a practical alternative to methods requiring individual-level data. Despite its widespread use, LDSC regression can produce biased heritability estimates. The properties of LDSC regression were investigated using summary statistics from several large-scale Alzheimers disease (AD) studies and a variety of LD reference panels. These heritability estimates were compared with those obtained from individual-level data. ResultsWhen LDSC regression was applied to summary statistics obtained from meta-analysis, it led to an underestimation of heritability. This can occur if meta-analysis is used to combine studies of different ancestries leading to the caveat of the lack of an appropriate LD reference panel. Additionally meta-analyses often include studies with different phenotype definitions, that not only impacts heritability estimates but also makes them uninterpretable. Summary statistics generated from imputed variants, even those with high imputation accuracy, can lead to underestimation of heritability. For example, the heritability estimates for AD were reduced from 0.265 (se 0.148) to 0.160 (se 0.041) when imputed variants (INFO>0.9) were included compared to analyzing only genotype array variants. A decrease in heritability estimates was also observed when individual-level imputed variant data were analyzed using GCTA-GREML. Our findings highlight the caveats of estimating heritability using meta-analysis summary statistics or imputed data instead of genotyped or sequence data.

genetics↗

bioRxiv2025

Rare-variant aggregate association analysis using imputed data is a powerful approach.

Imputation can cost-effectively generate genotypes for millions of variants. The power of performing rare variant aggregate association tests using imputed genotypes was evaluated. White Europeans from the UK Biobank with exome sequence and genotype array data were analyzed. Using the genotype type array data from the UK Biobank, imputation was performed using the HRC r1.1 (N=64,976 Haplotypes) and TOPMed r3 (N=267,194 haplotypes) reference panels. Simulations were used to compare the power of performing rare variant aggregate association analysis using sequence and imputed data. Additionally, the number of genes with >2 rare variants (missense, nonsense, splice site) was approximately the same for exome sequence and TOPMed imputed data, but HRC imputed data had [~]10% fewer genes. For the imputed data, using a less stringent R2 threshold (i.e., >0.3 vs. >0.8) led to greater power to detect aggregate associations due to additional rare variants included in the test. Exome sequence data provided the highest power for rare variant aggregate association testing, with TOPMed imputed variants usually having less than a 20% reduction in power. HRC imputed variants provided substantially less power. We also performed rare variant aggregate association analyses using UK Biobank phenotype, exome sequence data, and imputed variants for PCSK9 and low-density lipoprotein (N=159,904 study subjects) and APOC3 and triglyceride levels (N=160,036 study subjects). For these analyses, even when ultra-rare variants (minor allele frequency<0.001) were analyzed, significant aggregate associations could be detected for the exome sequence data and TOPMed and HRC imputed variants. Although there is a decrease in power for rare variant aggregate association tests when analyzing imputed variants compared to sequence data due to missing variants and uncertainty in genotypes, analysis of imputed data is a viable approach to detect rare variant aggregate associations when sequence data is unavailable.

bioinformatics↗

bioRxiv2018

PhyloSuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies

Multi-gene and genomic datasets have become commonplace in the field of phylogenetics, but many of the existing tools are not designed for such datasets, which makes the analysis time-consuming and tedious. We therefore present PhyloSuite, a user-friendly workflow desktop platform dedicated to streamlining molecular sequence data management and evolutionary phylogenetics studies. It employs a plugin-based system that integrates a number of useful phylogenetic and bioinformatic tools, thereby streamlining the entire procedure, from data acquisition to phylogenetic tree annotation, with the following features: (i) point-and-click and drag-and-drop graphical user interface, (ii) a workspace to manage and organize molecular sequence data and results of analyses, (iii) GenBank entries extraction and comparative statistics, (iv) a phylogenetic workflow with batch processing capability, (v) elaborate bioinformatic analysis for mitochondrial genomes. The aim of PhyloSuite is to enable researchers to spend more time playing with scientific questions, instead of wasting it on conducting standard analyses. The compiled binary of PhyloSuite is available under the GPL license at https://github.com/dongzhang0725/PhyloSuite/releases, implemented in Python and runs on Windows, Mac OSX and Linux.

bioinformatics↗