arXiv subjects

Bingxin Zhao

Publications and source records attributed to Bingxin Zhao.

21 records · Page 2Linked to original sources

arXiv2022

On block-wise and reference panel-based estimators for genetic data prediction in high dimensions

Genetic prediction of complex traits and diseases has attracted enormous attention in precision medicine, mainly because it has the potential to translate discoveries from genome-wide association studies (GWAS) into medical advances. As the high dimensional covariance matrix (or the linkage disequilibrium (LD) pattern) of genetic variants has a block-diagonal structure, many existing methods attempt to account for the dependence among variants in predetermined local LD blocks/regions. Moreover, due to privacy restrictions and data protection concerns, genetic variant dependence in each LD block is typically estimated from external reference panels rather than the original training dataset. This paper presents a unified analysis of block-wise and reference panel-based estimators in a high-dimensional prediction framework without sparsity restrictions. We find that, surprisingly, even when the covariance matrix has a block-diagonal structure with well-defined boundaries, block-wise estimation methods adjusting for local dependence can be substantially less accurate than methods controlling for the whole covariance matrix. Further, estimation methods built on the original training dataset and external reference panels are likely to have varying performance in high dimensions, which may reflect the cost of having only access to summary level data from the training dataset. This analysis is based on our novel results in random matrix theory for block-diagonal covariance matrix. We numerically evaluate our results using extensive simulations and the large-scale UK Biobank real data analysis of 36 complex traits.

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arXiv2019

Cross-trait prediction accuracy of high-dimensional ridge-type estimators in genome-wide association studies

Marginal association summary statistics have attracted great attention in statistical genetics, mainly because the primary results of most genome-wide association studies (GWAS) are produced by marginal screening. In this paper, we study the prediction accuracy of marginal estimator in dense (or sparsity free) high-dimensional settings with $(n,p,m) \to \infty$, $m/n \to γ\in (0,\infty)$, and $p/n \to ω\in (0,\infty)$. We consider a general correlation structure among the $p$ features and allow an unknown subset $m$ of them to be signals. As the marginal estimator can be viewed as a ridge estimator with regularization parameter $λ\to \infty$, we further investigate a class of ridge-type estimators in a unifying framework, including the popular best linear unbiased prediction (BLUP) in genetics. We find that the influence of $λ$ on out-of-sample prediction accuracy heavily depends on $ω$. Though selecting an optimal $λ$ can be important when $p$ and $n$ are comparable, it turns out that the out-of-sample $R^2$ of ridge-type estimators becomes near-optimal for any $λ\in (0,\infty)$ as $ω$ increases. For example, when features are independent, the out-of-sample $R^2$ is always bounded by $1/ω$ from above and is largely invariant to $λ$ given large $ω$ (say, $ω>5$). We also find that in-sample $R^2$ has completely different patterns and depends much more on $λ$ than out-of-sample $R^2$. In practice, our analysis delivers useful messages for genome-wide polygenic risk prediction and computation-accuracy trade-off in dense high-dimensions. We numerically illustrate our results in simulation studies and a real data example.

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arXiv2019

On genetic correlation estimation with summary statistics from genome-wide association studies

Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently, cross-trait polygenic risk score (PRS) method has gained extremely popular for assessing genetic correlation of complex traits based on GWAS summary statistics (e.g., SNP effect size). However, empirical evidence has shown a common bias phenomenon that even highly significant cross-trait PRS can only account for a very small amount of genetic variance (R^2 often <1%). The aim of this paper is to develop a novel and powerful method to address the bias phenomenon of cross-trait PRS. We theoretically show that the estimated genetic correlation is asymptotically biased towards zero when complex traits are highly polygenic/omnigenic. When all p SNPs are used to construct PRS, we show that the asymptotic bias of PRS estimator is independent of the unknown number of causal SNPs m. We propose a consistent PRS estimator to correct such asymptotic bias. We also develop a novel estimator of genetic correlation which is solely based on two sets of GWAS summary statistics. In addition, we investigate whether or not SNP screening by GWAS p-values can lead to improved estimation and show the effect of overlapping samples among GWAS. Our results may help demystify and tackle the puzzling "missing genetic overlap" phenomenon of cross-trait PRS for dissecting the genetic similarity of closely related heritable traits. We illustrate the finite sample performance of our bias-corrected PRS estimator by using both numerical experiments and the UK Biobank data, in which we assess the genetic correlation between brain white matter tracts and neuropsychiatric disorders.

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