A whole-genome simulator capable of modeling high-order epistasis for complex disease

Cancer-inspired genomics mapper model for the generation of synthetic DNA sequences with desired genomics signatures

Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased genomes have already been sequenced; however, obtaining the most suitable data for a specific study, and specifically for validation studies, remains challenging with respect to scale and access. Therefore, in silico genomics sequence generators have been proposed as a possible solution. However, the current generators produce inferior data using mostly shallow (stochastic) connections, detected with limited computational complexity in the training data. This means they do not take the appropriate biological relations and constraints, that originally caused the observed connections, into consideration. To address this issue, we propose cancer-inspired genomics mapper model (CGMM), that combines genetic algorithm (GA) and deep learning (DL) methods to tackle this challenge. CGMM mimics processes that generate genetic variations and mutations to transform readily available control genomes into genomes with the desired phenotypes. We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer that are indistinguishable from real genomes of such phenotypes, based on unsupervised clustering. Our results show that CGMM outperforms four current state-of-the-art genomics generators on two different tasks, suggesting that CGMM will be suitable for a wide range of purposes in genomic medicine, especially for much-needed validation studies.

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10-7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

EpiGEN: an epistasis simulation pipeline

Summary

Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes.

Availability and implementation

EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen.

Supplementary information

Supplementary data are available at Bioinformatics online.

PGsim: A Comprehensive and Highly Customizable Personal Genome Simulator

Although genome sequencing has become increasingly popular, the simulation of individual genomes is still important. This is because sequencing a large number of individual genomes is costly and genome data with extreme and boundary conditions, such as fatal genetic defects, are difficult to obtain. Privacy and legal barriers also prevent many applications of real data. Large sequencing projects in recent years have provided a deeper understanding of the human genome. However, there is a lack of tools to leverage known data to simulate personal genomes as real as possible. Here, we designed and developed PGsim, a comprehensive and highly customizable individual genome simulator, that fully uses existing knowledge, such as variant allele frequencies in global or world main populations, mutation probability differences between protein-coding regions and non-coding regions, transition/transversion (Ti/Tv) ratios, Indel incidence, Indel length distribution, structural variation sites, and pathogenic mutation sites. Users can flexibly control the proportion and quantity of known variants, common variants, novel variants in both coding and non-coding regions, and special variants through detailed parameter settings. To ensure that the simulated personal genome has sufficient randomness, PGsim makes the generated variants more real and reliable in terms of variant distribution, proportion, and population characteristics. PGsim is able to employ a huge volume database as background data to simulate personal genomes and does not require SQL database support. Users can easily change the variant databases used as needed. As a Perl script, there is no obstacle to running PGsim on any version of the MAC OS or Linux systems, and no libraries, packages, interpreters, compilers, or other dependencies need to be installed in advance. The PGsim tool is publicly available at https://github.com/lrjuan/PGsim.

Systematic review of next-generation sequencing simulators: computational tools, features and perspectives

High-throughput next-generation sequencing (NGS) technologies have rapidly generated a large volume of genomic data. To aid the development and evaluation of new statistical models and computational methods, NGS-based simulators have been proposed to construct better experimental workflows. However, the comparative performance of these NGS simulators remains unclear. In this review, we conducted a comprehensive investigation of NGS simulators for various sequencing techniques, including DNA sequencing, metagenomic sequencing, RNA-seq, ChIP-seq and bisulfite sequencing for methylation.

A Hierarchical Clustering Method of Selecting Kernel SNP to Unify Informative SNP and Tag SNP

Various strategies can be used to select representative single nucleotide polymorphisms (SNPs) from a large number of SNPs, such as tag SNP for haplotype coverage and informative SNP for haplotype reconstruction, respectively. Representative SNPs are not only instrumental in reducing the cost of genotyping, but also serve an important function in narrowing the combinatorial space in epistasis analysis. The capacity of kernel SNPs to unify informative SNP and tag SNP is explored, and inconsistencies are minimized in further studies. The correlation between multiple SNPs is formalized using multi-information measures. In extending the correlation, a distance formula for measuring the similarity between clusters is first designed to conduct hierarchical clustering. Hierarchical clustering consists of both information gain and haplotype diversity, so that the proposed approach can achieve unification. The kernel SNPs are then selected from every cluster through the top rank or backward elimination scheme. Using these kernel SNPs, extensive experimental comparisons are conducted between informative SNPs on haplotype reconstruction accuracy and tag SNPs on haplotype coverage. Results indicate that the kernel SNP can practically unify informative SNP and tag SNP and is therefore adaptable to various applications.

Genetic data simulators and their applications: an overview

Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators.