Biology subjects

Zheng, H.-F.

Publications and source records attributed to Zheng, H.-F..

4 recordsLinked to original sources

bioRxiv2024

An Atlas of Linkage Disequilibrium Across Species

Linkage disequilibrium (LD) is a key metric that characterizes populations in flux. To reach a genomic scale LD illustration, which has a substantial computational cost of[O] (nm2), we introduce a framework with two novel algorithms for LD estimation: X-LD, with a time complexity of[O] (n2m) suitable for small sample sizes (n < 104); X-LDR, a stochastic algorithm with a time complexity of[O] (nmB) for biobank-scale data (B iterations); n the sample size, and m the number of SNPs. These methods can refine the entire genome into high-resolution LD grids, such as more than 9 million grids for UK Biobank samples ([~]4.2 million SNPs). The efficient resolution for genome-wide LD leads to intriguing biological discoveries. I) High-resolution LD illustrations revealed how the pericentromeric regions and the HLA region lead to intense and extended LD patterns. II) Two universal LD patterns, identified as Norm I and Norm II patterns, provide insights on the evolutionary history of populations and can also highlight genomic regions of deviation, such as chromosomes 6 and 11 or ncRNA regions. III) The results of our innovative LD decay method aligned with the LD decay scores of 59.5 for Europeans, 60.2 for East Asians, and 33.2 for Africans; correspondingly, the length of the LD was approximately 2.85 Mb, 2.18 Mb, and 1.58 Mb for these three ethnicities. Rare or imputed variants universally increased LD. IV) An unprecedented LD atlas for 25 reference populations contoured interspecies diversity in terms of their Norm I and Norm II LD patterns, highlighting the impact of refined population structure, quality of reference genomes, and uncovered a profound status quo of these populations. The algorithms have been implemented in C++ and are freely available (https://github.com/gc5k/gear2).

genetics↗

bioRxiv2024

UK BioCoin: Swift Trait-Specific Summary Statistics Regression for UK Biobank

Summary statistics derived from large-scale biobanks facilitate the sharing of genetic discoveries while minimizing the risk of compromising individual-level data privacy. However, these summary statistics, such as those from the UK Biobank (UKB) provided by Neales lab, are often adjusted by a fixed set of covariates to all traits (12 covariates including 10 PCs, sex and age), preventing the exploration of trait-specific summary statistics. In this study, we present a novel computational device UK BioCoin (UKC), which is designed to provide an efficient framework for trait-specific adjustment for covariates. Without requiring access to individual-level data from UKB, UKC leverages summary statistics regression technique and resources from UKB (289 GB of 199 phenotypes and 10 million SNPs), to enable the generation of GWAS summary statistics adjusted by user-specified covariates. Through a comprehensive analysis of height under trait-specific adjustments, we demonstrate that the GWAS summary statistics generated by UKC closely mirror those generated from individual-level UKB GWAS ({rho} [≥] 0.99 for effect sizes and{rho} [≥] 0.99 for p-values). Furthermore, we demonstrate the results for GWAS, SNP-heritability estimation, polygenic score, and Mendelian randomization, after various trait-specific covariate adjustments as allowed by UKC, indicating UKC a platform that harnesses in-depth exploration for researchers lacking access to UKB. The whole framework of UKC is portable for other biobank, as demonstrated in Westlake Biobank, which can equivalently be converted to a UKC-like" platform and promote data sharing. UKC has its computational engine fully optimized, and the computational efficiency of UKC is about 70 times faster than that of UKB. We package UKC as a Docker image of 20 GB (https://github.com/Ttttt47/UKBioCoin), which can be easily deployed on an average computer (e.g. laptop). One sentence summaryWe develop UK BioCoin (UKC), which allows fine-tuning of covariates for each UK Biobank trait but does not relay on UK Biobank individual-level data. It will change the current landscape of GWAS and reshape its downstream analyses.

genetics↗

bioRxiv2022

Searching across-cohort relatives via encrypted genotype regression

Explicitly sharing individual level data in genomics studies has many merits comparing to sharing summary statistics, including more strict QCs, common statistical analyses, relative identification and improved statistical power in GWAS, but it is hampered by privacy or ethical constraints. In this study, we developed encG-reg, a regression approach that can detect relatives of various degrees based on encrypted genomic data, which is immune of ethical constraints. The encryption properties of encG-reg are based on the random matrix theory by masking the original genotypic matrix without sacrificing precision of individual-level genotype data. We established a connection between the dimension of a random matrix, which masked genotype matrices, and the required precision of a study for encrypted genotype data. encG-reg has false positive and false negative rates equivalent to sharing original individual level data, and is computationally efficient when searching relatives. We split the UK Biobank into their respective centers, and then encrypted the genotype data. We observed that the relatives estimated using encG-reg was equivalently accurate with the estimation by KING, which is a widely used software but requires original genotype data. In a more complex application, we launched a finely devised multi-center collaboration across 5 research institutes in China, covering 9 cohorts of 54,092 GWAS samples. encG-reg again identified true relatives existing across the cohorts with even different ethnic backgrounds and genotypic qualities. Our study clearly demonstrates that encrypted genomic data can be used for data sharing without loss of information or data sharing barrier. Author SummaryEstimating pairwise genetic relatedness within a single cohort is straightforward. However, in practice, related samples are often distributed across different cohorts, making it challenging to estimate inter-cohort relatedness. In this study, we propose a method called encrypted genotype regression (encG-reg), which provides an unbiased estimation of inter-cohort relatedness using encrypted genotypes. The genotype matrix of each cohort is masked by a random matrix, which acts similarly to a private key in a cryptographic scheme. This masking process produces encrypted genotypes, which are a projection of the original genotype matrix. We derive the expectation and particularly the sampling variance for encG-reg, the latter involves eighth-order moments calculation. encG-reg allows us to accurately identify relatedness across cohorts, even for large-scale biobank data. To demonstrate the efficacy of encG-reg, we verified it in a multi-ethnicity UK Biobank dataset comprising 485,158 samples. For this case, we successfully tracked down to the 1st-degree relatedness (such as full sibs and parent-offspring). Furthermore, we used encG-reg in a collaboration involving 9 Chinese cohorts, encompassing a total of 54,092 samples from 5 genomic centers. It is worth noting that if the number of effective markers is sufficient encG-reg has the potential to detect even more distant degrees of relatedness beyond what we demonstrated.

genetics↗

bioRxiv2019

Genotype Imputation and Reference Panel: A Systematic Evaluation

Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that the imputation accuracy could always benefit from the increased panel size when the reference panel was population-specific, the results brought two new thoughts as follows. First, when the haplotype size of reference panel was fixed, the imputation accuracy of common and low-frequency variants (MAF>0.5%) decreased while the population-diversity of reference panel increased, but for rare variants (MAF<0.5%), a fraction of diversity (<20%) of panel could improve the imputation accuracy. Second, when the haplotype size of reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF>5%) for European population could always benefit from the expanding sample size. But for Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of extra diverse sample (15%[~]21%). In addition, we evaluated the existing reference panels such as the HRC and 1000G Phase3 and CONVERGE. For European population, HRC was the best reference panel. For Han Chinese population, we proposed an optimum constituent ratio for the Han Chinese imputation if researchers would like to customize their own sequenced reference panel, but a high quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome, a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).\n\nHighlights (Key points)O_LIA total of 394 reference panels were designed and customized by three strategies, and large-scale genotype imputations were performed with these panels for systematic evaluation in Han Chinese and European populations.\nC_LIO_LIThe accuracy of imputed variants reached the highest when reference panel contains a fraction of extra diverse sample (15%[~]21%) for Han Chinese population, if the haplotype size of the reference panel was increased with extra samples, which is the most common cases.\nC_LIO_LIThe imputation accuracy showed the different trends between Han Chinese and European populations. In a sense, the European genome may more diverse than Han Chinese genome by itself.\nC_LIO_LIExisting reference panels were not the best choice for Chinese imputation, a high quality and large-scale Chinese reference panel was still needed.\nC_LI

bioinformatics↗