Biology subjects

Liu, C.-T.

Publications and source records attributed to Liu, C.-T..

11 recordsLinked to original sources

bioRxiv2024

Learning Gaussian Graphical Models from Correlated Data

Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a Bootstrap algorithm to infer GGM from correlated data. We use extensive simulations of correlated data from family-based studies to show that the Bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from a family-based study known as the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well in this real example.

bioinformatics↗

bioRxiv2022

Treg cells drive MYCN-mediated immunosuppression and tumor aggressiveness in high-risk neuroblastoma

Solid tumors, especially those with aberrant MYCN activation, harbor an immunosuppressive microenvironment to fuel malignant growth and trigger treatment resistance1,2, yet the underlying mechanisms are elusive and effective strategies to tackle this challenge are lacking. Here we demonstrated the crucial role of T regulatory (Treg) cells in MYCN-mediated immune repression and tumor aggression using high-risk neuroblastoma (NB) as a model system. Human MYCN-activated NB attracts CD4+ Treg cells, which are also found enriched in MYCN-high primary patient samples. Zebrafish MYCN-overexpressing neural crests recruit Cd4+ cells before tumor formation and induce an immunosuppressive microenvironment, thereby promoting tumor onset and progression. Strikingly, disruption of Treg cells through depletion of forkhead box protein 3a restores anti-tumor immunity and impairs NB development. Together, our studies establish Treg cells as the key driver of MYCN-mediated immunosuppression and tumor aggressiveness, providing mechanistic insights and therapeutic implications.

cancer biology↗

bioRxiv2020

Lifestyle Risk Score for aggregating multiple lifestyle factors: Handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly liberal. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p-values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach to handle heterogeneity in the LRS based genome-wide interaction meta-analyses.

genetics↗

bioRxiv2020

Efficient gene-environment interaction tests for large biobank-scale sequencing studies

Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

genetics↗

bioRxiv2019

Methylome-Wide Association Study Of Central Adiposity Implicates Genes Involved In Immune And Endocrine Systems

We conducted a methylome-wide association study to examine associations between DNA methylation in whole blood and central adiposity and body fat distribution, measured as waist circumference, waist- to-hip ratio, and waist-to-height ratio adjusted for body mass index, in 2684 African American adults in the Atherosclerosis Risk in Communities study. We validated significantly associated Cytosine-phosphate-Guanine methylation sites (CpGs) among adults using the Womens Health Initiative and Framingham Heart Study participants (combined N=5743) and generalized associations in adolescents from The Raine Study (N=820). We identified 11 CpGs that were robustly associated with one or more central adiposity trait in adults and 2 in adolescents, including CpG site associations near TXNIP, ADCY7, SREBF1, and RAP1GAP2 that had not previously been associated with obesity-related traits.

genomics↗

bioRxiv2019

Identification of Escherichia coli ClpAP in regulating susceptibility to type VI secretion system-mediated attack by Agrobacterium tumefaciens

Type VI secretion system (T6SS) is an effector delivery system used by gram-negative bacteria to kill other bacteria or eukaryotic host to gain fitness. In Agrobacterium tumefaciens, T6SS has been shown to kill other bacteria such as Escherichia coli. Interestingly, the A. tumefaciens T6SS killing efficiency differs when using different E. coli strains as recipient cells. Thus, we hypothesize that a successful T6SS killing not only relies on attacker T6SS activity but also depends on recipient factors. A high-throughput interbacterial competition assay was employed to test the hypothesis by screening for mutants with reduced killing outcomes caused by A. tumefaciens strain C58. From the 3909 E. coli Keio mutants screened, 16 candidate mutants were filtered out. One strain, {Delta}clpP::Kan, showed ten times more resistant to T6SS-mediating killing but restored its susceptibility when complemented with clpP in trans. ClpP is a universal and highly conserved protease that exists in both prokaryotes and eukaryotic organelles. In E. coli, ClpP uses either ClpA or ClpX as an adaptor for substrate specificity. Therefore, the susceptibility of the {Delta}clpA::Kan and {Delta}clpX::Kan was also tested. The T6SS attack susceptibility of {Delta}clpA::Kan is at the same level as {Delta}clpP::Kan, while {Delta}clpX::Kan showed no difference as compared to that of wild-type E. coli BW25113. The data also suggest that ClpA-ClpP interaction, rather than its protease activity, is responsible for enhancing susceptibility to T6SS killing. This study highlights the importance of recipient factors in determining the outcome of T6SS killing.

microbiology↗

bioRxiv2019

Impact of rare and common genetic variants on diabetes diagnosis by hemoglobin A1c in multi-ancestry cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in patients with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K/FN3KRP in Europeans and G6PD in African-Americans and Hispanics) and discovered a new African-ancestry specific low-frequency variant (rs1039215 in HBG2/HBE1, minor allele frequency (MAF)=0.03). The most associated G6PD variant (p.Val98Met, rs1050828-T, MAF=12% in African-Americans, MAF=2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693 - p.Leu353Pro, MAF=0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African-American cohorts and replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

genetics↗

bioRxiv2019

Recovery of trait heritability from whole genome sequence data

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated individuals of European ancestry. We assigned 33.7 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60 - 0.71 for height and ~0.25 - 0.35 for BMI, depending on quality control and analysis strategies. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 < MAF < 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively. Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.

genetics↗

bioRxiv2019

A unified method for rare variant analysis of gene-environment interactions

Advanced technology in whole-genome sequencing has offered the opportunity to comprehensively investigate the genetic contribution, particularly rare variants, to complex traits. Many rare variants analysis methods have been developed to jointly model the marginal effect but methods to detect gene-environment (GE) interactions are underdeveloped. Identifying the modification effects of environmental factors on genetic risk poses a considerable challenge. To tackle this challenge, we develop a unified method to detect GE interactions of a set of rare variants using generalized linear mixed effect model. The proposed method can accommodate both binary and continuous traits in related or unrelated samples. Under this model, genetic main effects, sample relatedness and GE interactions are modeled as random effects. We adopt a kernel-based method to leverage the joint information across rare variants and implement variance component score tests to reduce the computational burden. Our simulation study shows that the proposed method maintains correct type I error rates and high power under various scenarios, such as differing the direction of main genotype and GE interaction effects and the proportion of causal variants in the model for both continuous and binary traits. We illustrate our method to test gene-based interaction with smoking on body mass index or overweight status in the Framingham Heart Study and replicate the CHRNB4 gene association reported in previous large consortium meta-analysis of single nucleotide polymorphism (SNP)-smoking interaction. Our proposed set-based GE test is computationally efficient and is applicable to both binary and continuous phenotypes, while appropriately accounting for familial or cryptic relatedness.

genetics↗

bioRxiv2019

Role of Rare and Low Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

BackgroundAlcohol intake influences plasma lipid levels and such effects may be modulated by genetic variants. ObjectiveWe aimed to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. DesignIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 34,153 European Americans from five discovery studies and 32,275 individuals from six replication studies. Rare and low-frequency protein coding variants (minor allele frequency [≤] 5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction (GxE) test and a joint test of genetic main and GxE interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least two drinks per week, were considered. ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3 and CD300LG) remained significant after conditioning on the common index single nucleotide polymorphism (SNP) identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on TG was discovered at a Bonferroni corrected significance level (p-value <5x10-5) and replicated (p-value <0.013 for the interaction test) in SMC5. ConclusionsIn conclusion, this study applied new gene-based statistical approaches to uncover the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels.

epidemiology↗

bioRxiv2018

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[≤]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics↗