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Our WGS DNA testing Accreditation
Whole Genome Sequencing (WGS) for Personalized Wellness Program 

We apply the latest Whole Genome Sequencing (WGS) technology coupled with Clinical Genetics, Clinical Genomics, Advanced Bioinformatics, and Biostatistics, to map against our large and routinely updated disease and heritage genomic variations database (DHGVDB) containing over 670 million of disease-associated genetic markers for over 10,000 curated diseases and heritage patterns.

WGS has been extensively applied in research for years for determining the complete DNA sequence of organisms’ genomes.  It is currently introduced by us for clinical use and through researches into the future of personalized medicine to guide therapeutic intervention and drug response.

We are not agent nor trader to intermediate or resell services.  Among us are professions of Harvard genetic researchers, medical doctors, clinical and wellness professions who are dedicated to provide genomic health services for people and medical professions.  

Personalized Wellness Program - Genetic Cancer and Health Screen Risk Report

Personalized Wellness Program of Genetic Mutation and Risk: Monitoring people's risk of cancer and health development associated with genomic variations allows people and their healthcare providers to establish personalized body-tests, daily nutrition regimes and healthcare plans that can maintain an energetic lifestyle and healthiness throughout ones’ life and particularly between the age of 40-83.

“Cancer is a Preventable Disease” [1].  All cancers are a result of multiple mutations caused by gene-environment interaction [2][3].  75%-80% of cancer-associated death worldwide are potentially avoidable by reducing exposure to known factors [4][5]. Common environmental factors that contribute to cancer death include exposure to different environmental and air pollutants, cancer-causing diet, problematic lifestyle and behavioral exposures.  These daily harmful factors may induce mutation leading to cancer development via alternation of nucleotide sequences across the genome [6].  Among others, one of the critical factors that raise the risk of cancer development dramatically is aging.  When you are getting older, many more body cells will mutate abnormally and eventually become cancer [7].  

In view of that, we are promoting people’s Quality Long Living with Personalized Wellness Program.  With the genetic message, we are to tailor our living style, regular body check with targeted 

The follow we have shared some cancers that have been linked to genetic defects are shown in Table 1:

Colorectal Cancer: MLH1, MSH2, MSH6, PMS2, APC, DPC4, Bmpr1, PTEN, MYH

Prostate cancer: HPC1, TLR1, TLR4, TLR6, TLR9; Breast & Ovarian Cancer: BRCA1 and BRCA2    

Gastric cancer:  TLR4; Lung Cancer: SCLC1; Pancreatic cancer: DPC4; Malignant melanoma:  CDKN2  

Retinoblastoma: RB1; Chronic myeloid leukemia: ABL, BCR; Hemangioblastoma: VHL;

Nasopharyngeal Cancer: TLR9; Neurofibromatosis: NF2; Multiple endocrine neoplasia: MEN1, RET

Li-Fraumeni syndrome: P53, CHEK2

Some types of cancers are in higher risk than others.  Aging raised the risk of cancers development.   It is already known that mutation frequency can vary between cancer types: in germline cells, mutation rates occur at approximately 0.023 mutations per megabase, but this number is much higher in breast cancer (1.18-1.66 somatic mutations per Mb), in lung cancer (17.7) or in melanomas (≈33) [8].  To ensure a quality aging process, people are suggested to actively monitor their health after age 45.  Targeted Wellness Program can help us to tailor our own living style and regular medical check up program to maximize the return of our health investment.  

You need only one test to maximize your investment on your health and quality life.


According to the National Human Genome Research Institute, NHGRI, USA, there are many different types of genetic tests.  Genetic tests can help to: 

  • Diagnose disease

  • Identify gene changes that are responsible for an already diagnosed disease

  • Determine the severity of the disease

  • Guide doctors in deciding on the best medicine or treatment to use for certain individuals

  • Identify gene changes that may increase the risk to develop the disease

  • Identify gene changes that could be passed on to children

  • Screen newborn babies for certain treatable conditions

How Whole Genome Sequencing (WGS) works?
Qualified genomic DNA samples were randomly sheared by an ultrasonic High-Performance Sample Processing System (Covaris), and the fragments of 150bp-250bp were isolated. The DNA fragment is then repaired at the end, with the "A" base at the 3 'end and the platform-specific adapters at both ends. The hybrid library was prepared by linear amplification (LM- PCR) of the ligated library. A proper amount of amplification products was taken for single-chain separation and cyclization treatment. After quality control, the products could be sequenced on the machine. Base Calling was converted into raw reads, i.e. paired-end reads, and the data was stored in FASTQ file format, called raw data.


Schematic diagram 1 indicates the workflow of WGS bioinformatical analysis


Data analysis begins with raw data of sequencing. Raw data contains adapter sequences, bases of low sequencing quality and undetected bases (expressed as N), which will cause a great interference to downstream data analysis. Therefore, it is necessary to filter raw data to obtain cleandata or cleanreads.

Data filtering

To remove the noise from the sequencing data, we first filtered the data. The original data filtering method is as follows:

  1. Filter out the adaptor sequence of reads;

  2. When the low-quality base number in the single-end sequencing reads exceeds 50% of the reads, this pair of reads will be removed.

  3. When the content of N base in the single-end sequencing reads exceeds 10% of the reads, this pair of reads will be removed.

After filtration, "cleandata" was obtained and the sequencing data were statistically analyzed, including the number of sequencing reads, data yield, and distribution of quality value, etc.

Data aligning
All cleanreads were aligned to the human reference genome (GRCh37/HG19) using the Burrows-Wheeler Aligner alignment software (BWA V0.7.12). The sequencing data of each lane were compared, and the read group ID was added in the comparison result.

Base Quality Score Recalibration (BQSR)
Variation detection methods rely on the quality values of base sequencing. Systematic errors caused by various sequencing instruments may cause the base quality score to be too high or too low. Therefore, it is necessary to correct the base quality score to obtain a more accurate base quality score, so as to improve the accuracy of mutation detection.


Filtration variation result
When obtaining original variation sets including SNP and InDel, it is necessary to obtain high-quality and highly reliable variation sets for downstream analysis by filtering. Here, the VQSR method based on a machine learning algorithm is adopted to filter the original variation set. GATK VQSR uses the known and high-quality variation set as the training set and the real set and establishes a prediction model to filter out the false variation. The output VCF result is marked as"PASS" of the SNPs and InDel, a reliable set of mutations that PASS the filter.

Copy number variation detection
CNVnator\[8\] v0.2.7 based on the depth signal algorithm was used to detect copy number variation. In this method, the genome was segmented into non-overlapping Windows of equal length, and the number of reads aligned on each window was standardized as a depth signal to detect copy number variation.

Structural variation detection
The procedure detected 5 types of structural variation (SV), (1) interchromosomal translocation (CTX), (2) interchromosomal translocation (ITX), (3) INV, (4) deletion (DEL), and (5) insertion (INS).


Annotation and prediction of variation results
After obtaining a highly reliable, high-quality variation set, use SnpEff to perform annotation:

gene-based annotation:\identify SNPs/InDels resulting in an alternation of protein-coding and amino acid sequences.

database-based annotations:Identify mutations that appear in the dbSNP library (v141), or whose minor allele frequency (MAF) in the 1000 genome project is less than 1%, or whose coding region is not synonymous with the mutation 

Whether the SIFT value of SNPs is less than 0.05, or whether the conservative predictive value of variation GERP++ value is greater than 2, or other annotation information.

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