GenomeLaser: fast and accurate haplotyping from pedigree genotypes

scHaplotyper: haplotype construction and visualization for genetic diagnosis using single cell DNA sequencing data

BACKGROUND

Haplotyping reveals chromosome blocks inherited from parents to in vitro fertilized (IVF) embryos in preimplantation genetic diagnosis (PGD), enabling the observation of the transmission of disease alleles between generations. However, the methods of haplotyping that are suitable for single cells are limited because a whole genome amplification (WGA) process is performed before sequencing or genotyping in PGD, and true haplotype profiles of embryos need to be constructed based on genotypes that can contain many WGA artifacts.

RESULTS

Here, we offer scHaplotyper as a genetic diagnosis tool that reconstructs and visualizes the haplotype profiles of single cells based on the Hidden Markov Model (HMM). scHaplotyper can trace the origin of each haplotype block in the embryo, enabling the detection of carrier status of disease alleles in each embryo. We applied this method in PGD in two families affected with genetic disorders, and the result was the healthy live births of two children in the two families, demonstrating the clinical application of this method.

CONCLUSION

Next generation sequencing (NGS) of preimplantation embryos enable genetic screening for families with genetic disorders, avoiding the birth of affected babies. With the validation and successful clinical application, we showed that scHaplotyper is a convenient and accurate method to screen out embryos. More patients with genetic disorder will benefit from the genetic diagnosis of embryos. The source code of scHaplotyper is available at GitHub repository: https://github.com/yzqheart/scHaplotyper.

Chromosome-Range Whole-Genome High-Throughput Experimental Haplotyping by Single-Chromosome Microdissection

Haplotype is fundamental genetic information; it provides essential information for deciphering the functional and etiological roles of genetic variants. As haplotype information is closely related to the functional and etiological impact of genetic variants, it is widely anticipated that haplotype information will be extremely valuable in a wide spectra of applications, including academic research, clinical diagnosis of genetic disease and in the pharmaceutical industry. Haplotyping is essential for LD (linkage disequilibrium) mapping, functional studies on cis-interactions, big data imputation, association studies, population studies, and evolutionary studies. Unfortunately, current sequencing technologies and genotyping arrays do not routinely deliver this information for each individual, but yield only unphased genotypes. Here, we describe a high-throughput and cost-effective experimental protocol to obtain high-resolution chromosomal haplotypes of each individual diploid (including human) genome by the single-chromosome microdissection and sequencing approach.

Data supporting the high-accuracy haplotype imputation using unphased genotype data as the references

The data presented in this article is related to the research article entitled "High-accuracy haplotype imputation using unphased genotype data as the references" which reports the unphased genotype data can be used as reference for haplotyping imputation [1]. This article reports different implementation generation pipeline, the results of performance comparison between different implementations (A, B, and C) and between HiFi and three major imputation software tools. Our data showed that the performances of these three implementations are similar on accuracy, in which the accuracy of implementation-B is slightly but consistently higher than A and C. HiFi performed better on haplotype imputation accuracy and three other software performed slightly better on genotype imputation accuracy. These data may provide a strategy for choosing optimal phasing pipeline and software for different studies.

References for Haplotype Imputation in the Big Data Era

Imputation is a powerful in silico approach to fill in those missing values in the big datasets. This process requires a reference panel, which is a collection of big data from which the missing information can be extracted and imputed. Haplotype imputation requires ethnicity-matched references; a mismatched reference panel will significantly reduce the quality of imputation. However, currently existing big datasets cover only a small number of ethnicities, there is a lack of ethnicity-matched references for many ethnic populations in the world, which has hampered the data imputation of haplotypes and its downstream applications. To solve this issue, several approaches have been proposed and explored, including the mixed reference panel, the internal reference panel and genotype-converted reference panel. This review article provides the information and comparison between these approaches. Increasing evidence showed that not just one or two genetic elements dictate the gene activity and functions; instead, cis-interactions of multiple elements dictate gene activity. Cis-interactions require the interacting elements to be on the same chromosome molecule, therefore, haplotype analysis is essential for the investigation of cis-interactions among multiple genetic variants at different loci, and appears to be especially important for studying the common diseases. It will be valuable in a wide spectrum of applications from academic research, to clinical diagnosis, prevention, treatment, and pharmaceutical industry.

The mirror RNA expression pattern in human tissues

It has been realized in recent years that non-coding RNAs are playing important roles in genome functions and human diseases. Here we developed a new technology and observed a new pattern of gene expression. We observed that over 72% of RNAs in human genome are expressed in forward-reverse pairs, just like mirror images of each other between forward expression and reverse expression; the overview showed that it cannot be simply described as transcript overlapping, so we designated it as mirror expression. Furthermore, we found that the mirror expression is gene-specific and tissue-specific, and less common in the proximal promoter regions. The size of the shadows varies between different genes, different tissues and different classes. The shadow expression is most significant in the Alu element, it was also observed among L1, Simple Repeats and LTR elements, but rare in other repeats such as low-complexity, LINE/L2, DNA and MIRs. Although there is no evidence yet about the relationship of this mirror pattern and double-strand RNA (dsRNA), this new striking pattern provides a new clue and a new direction to unveil the role of RNAs in the genome functions and diseases.