CHINESE HUMAN GENOME PROJECT (HGP) MADE SIGNIFICANT PROGRESS

The first stage of HGP in China (Jan. 1994～Jun. 1997) was sponsored by the National Natural Science Foundation of China in the form of a principle project (total funding: 3,750,000 RMB) entitled "Study on gene structure of some loci in the genome of Chinese nationalities". After 3 and half years’ collaborative work by 19 groups and about 200 scientists distributed in 16 participating labs, coordinated by Prof. Bo-Qing QIANG of the Institute of Basic Medical Sciences, Chinese

Precision medicine via the integration of phenotype-genotype information in neonatal genome project

The explosion of next-generation sequencing(NGS)has enabled the widespread use of genomic data in precision medicine.Currently,several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders.These projects have made remarkable achievements,but still the genome data could be further explored with the assistance of phenotype collection.In contrast,longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period,especially the trajectory analyses for health development or disease progression.It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases.Here,we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts.Then,we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies.Finally,research collaborations,sociological issues,and future perspectives are discussed.How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort.

Carrier Rate Analysis of Single-Gene Disorders Based on 1000 Genome Project and Exome Aggregation Consortium Data

Objective:Screening variants underlying the single-gene disorder in the general population can help reduce the incidences of birth defects.To determine the most prevalent pathogenic variants causing autosomal recessive diseases,we investigated the frequencies of these variants in six major geographic ancestry groups from Exome Aggregation Consortium(ExAC)database and 26 populations from the 1,000 Genome Project,including three Chinese ethnic groups.Methods:We selected 64 autosomal recessive diseases and collected corresponding causal genes and variants from ClinVar for the analysis.The RS(reference single-nucleotide polymorphism)IDs of these variants were used to search the corresponding VCF file from the 1,000 Genomes Project and ExAC databases.We calculated the frequencies of heterozygotes of each disease variants in the 1,000 Genomes Project and ExAC samples and compared the distribution of disease alleles among different populations.Results:Our analysis revealed that 1,151/212 variants were carried by 60,706/2,504 individuals sequenced in the ExAC/1,000 Genomes Project.The average number of autosomal recessive disease alleles carried by samples from ExAC and 1,000 Genomes Project were 0.53 and 0.68,respectively.These disease alleles showed differential distribution among populations,and some disease alleles were significantly enriched in certain ethnic groups.In addition,1-2 main pathogenic variants were identified in each disease.Meanwhile,several ClinVar variants with relatively high frequency(>1%)in the samples were found to be benign instead of“conflicting evaluations of pathogenicity.”Conclusions:Our observations revealed that main pathogenic variants existed in certain autosomal recessive disease,suggesting that screening of disease hypermutations in different populations is valuable in reducing the occurrence of birth defects.

A brief overview of game-changing genetic engineering projects

In recent era,advancement of research involves computational management of large-scale genomic and post-genomic datasets in an obvious way.Rapidly emerging field of bioinformatics,fueled by high-throughput technologies and genomic scale database,is believed to reshape our approach of research to a new level.Genomics has shifted the paradigm of biological perspectives exploring many scopes.Old initiatives paved the path for the newer and more advantageous one.The present review focuses on present initiatives that are implemented till now like the famous Human Genome Project and its influence on digital biology,as well as the projects that followed in its footsteps.Additionally,the authors delve into the future potential of personalized medicine and the use of genetic engineering methods like CRISPR/Cas9 in gene editing,which are thought to have the potential to revolutionize the current treatment strategy.

Effect of Genome-Wide Genotyping and Reference Panels on Rare Variants Imputation

Common variants explain little of the variance of most common disease, prompting large-scale sequencing studies to understand the contribution of rare variants to these diseases. Imputation of rare variants from genome-wide genotypic arrays offers a cost-efficient strategy to achieve necessary sample sizes required for adequate statistical power. To estimate the performance of imputation of rare variants, we imputed 153 individuals, each of whom was genotyped on 3 different genotype arrays including 317k, 610k and 1 million single nucleotide polymorphisms （SNPs）, to two different reference panels： HapMap2 and 1000 Genomes pilot March 2010 release （1KGpilot） by using IMPUTE version 2. We found that more than 94% and 84% of all SNPs yield acceptable accuracy （info 〉 0.4） in HapMap2 and 1KGpilot-based imputation, respectively. For rare variants （minor allele frequency （MAF） 〈5%）, the proportion of well- imputed SNPs increased as the MAF increased from 0.3% to 5% across all 3 genome-wide association study （GWAS） datasets. The proportion of well-imputed SNPs was 69%, 60% and 49% for SNPs with a MAF from 0.3% to 5% for 1M, 610k and 317k, respectively. None of the very rare variants （MAF 〈 0.3%） were well imputed. We conclude that the imputation accuracy of rare variants increases with higher density of genome-wide genotyping arrays when the size of the reference panel is small. Variants with lower MAF are more difficult to impute. These findings have important implications in the design and replication of large-scale sequencing studies.