Information Wellness Blog

Wave genome theory suggests that DNA waves teleport information and alter DNA’s topology by way of electric charges and moments which produce electromagnetic waves.

Wave-2 samples exhibited higher frequencies of amino acid altering mutation events compared to waves 1 and 3. C to T transition mutations were the predominant type among them both waves, while TCTGGTTTT11288 deletion events were most frequently seen among samples from Wave 2.

Background

In April 2003, scientists involved with the Human Genome Project (HGP) proudly announced that essential sequence of the genome had been successfully decoded – similar to Apollo moon landing or splitting an atom. They anticipated that this discovery would have profound ramifications for medicine, forensics, agriculture and other fields – along with political capital invested in projects for pharmaceutical and biomedical applications that might follow from such research. It created widespread enthusiasm among policymakers who invested large sums of money and political capital for future commercial applications projected from biomedical applications for commercial pharmaceutical and biomedical applications and commercially lucrative pharmaceutical applications for such research projects.

However, genomic research has yet to deliver its anticipated revolution; instead it has failed to address some of the most pressing health concerns such as rising obesity, opiate addiction and mental illness in most countries. Instead, genomic research agendas remain the main recipient of funding in many nations and are therefore unlikely to address such pressing matters as rising obesity levels, addiction issues or mental illness effectively.

One major cause is that genetic findings have yet to translate into medically useful diagnostic and therapeutic tools, with copy number variants (CNVs) not easily detected by lab tests; incorrect interpretation of CNV data a significant contributor to poor outcomes from genetic-based treatments, leading to inaccurate diagnoses and unnecessary interventions.

Multiple factors contribute to an inability to accurately detect CNVs. Chief among them is an interference from genome-wide spatial autocorrelation or “wave” pattern in signal intensity that prevents accurate CNV detection. This genomic wave results from two phenomena interacting: genetic hitchhiking between advantageous and neutral genes and nonlinear interaction between mating systems and gene flow.

Recenty, we developed an algorithm for normalizing genomic waves and accurately detecting CNVs. The method relies on the assumption that genomic waves reflect sample properties such as length and GC content of DNA segments containing SNPs; we tested our algorithm on four distinct genotyped DNA samples using Affymetrix Mapping 250K NSP and Genome-Wide 6.0 arrays and saw identical or opposite wavy patterns with identical or opposite peaks and troughs in all samples regardless of technical platform used to genotype them.

Our experiments support the theory that information biomacromolecules such as DNA, RNA and proteins act like lasers to emit radio waves containing semantic information – transmitting genetic and general regulative information of organisms through electromagnetic radiations with variable spin and frequency of their polarization modes to biocomputers that process this data.

Methods

As part of the genome sequencing process, DNA experiences high-amplitude vibrations and shaking. Due to these vibrations, nucleobases on each chromosome strand become displaces from their initial positions by an amount equal to two centimeters – due to quantum mechanical interactions which lead to nonlocality causing genomic wave effects.

These genomic wave effects appear as wavy patterns of signal intensities. This result is caused by different frequencies resonating at various parts of DNA sequence, creating different peaks and troughs in signal intensity distribution that result in inaccurate CNV calling on genotyping arrays. While various methods have been proposed to mitigate genomic waves’ effect on signal intensities (Lowess regression or including probe/target sequence characteristics in Genomic Imbalance Map (GIM), none address the mechanisms causing such effects on intensities.

We have developed an innovative computational method to effectively reduce genomic wave effects and demonstrate that it significantly increases CNV detection accuracy. Our approach utilizes genomic wave to identify and filter out high-frequency signal components from genomic-wide PCR intensity data. Furthermore, genetic resonance theory can be applied across all genotyping array platforms since genomic resonance is inherent to all DNA samples regardless of technical platform used for analysis.

Under our new model of DNA biocomputers, chromosomes are seen as gene-sign continuums for any biosystem, providing pre-images of its structures before an organism acts as an organism – in other words a registry of dynamical “wave copies” or matrixes that function like reference points for physical and chemical reconstruction of any living system’s biosynthesis process – such as protein synthesis apparatus or even gene regulation and evolution processes – providing a basis for biosynthesis itself. Our study demonstrated this theory’s capability of explaining all these phenomena effectively.

Results

Many experimental research studies have demonstrated that DNA can be altered by various kinds of waves – including acoustic, electromagnetic and scalar waves – including soundwaves. Some scientists have even claimed it’s possible to change our genetic code entirely. Furthermore, emotions, words, music and electromagnetic fields can all have an effect on DNA as can emotions themselves – giving rise to what has come to be known as wave genome theory, which asserts our genes are like biological networks connecting us all together.

The frequency and intensity of these waves is determined by the GC content in DNA; that is, as more GC is present in DNA, more intense these genomic waves become. Affymetrix arrays show evidence of this effect by producing wavy patterns of signal intensities; hence why Komura et al’s wave adjustment procedure helps reduce such irregularities while improving CNV detection accuracy.

At our lab, we studied GCWF data collected with Affymetrix 250K NSP, genome-wide 6.0 and Illumina HumanHap550 arrays (Supplemental Figure 2). We observed that its magnitude correlates with total amount of DNA used for genotyping but not its quality; while its directionality relates to sample properties such as GC content, segmental duplication rate, exon/intron content etc.

GCWF measures were then employed to correct genome-wide 6.0 data sets and generate CNV calls with PennCNV and cnvPartition, respectively. Results show that GCWF adjustment significantly decreases spurious CNV calls and improves concordance rates on duplicated regions across all five tested samples. The improved results are consistent with the hypothesis that genomic wave is a sample property which influences SNP genotyping sensitivity. More stringent thresholds should be implemented when testing DNA samples through repeated genotyping platforms to reduce false-positive CNV detections, leading to more accurate diagnosis.

Conclusions

Gene expression experiments typically involve injecting DNA samples into living cells that produce proteins of interest and then analyzing their proteins for any changes to their DNA sequence, whether this involves one nucleotide or several hundred. The goal is ultimately to find specific genetic variations within human samples which correspond with known diseases.

Genome sequencing has revolutionized medicine by enabling scientists to study an individual’s genetic composition and ascertain what exactly caused their illness. Yet despite HGP’s success at identifying genetic mutations, many fundamental questions still remain unanswered, including how genes encode proteins and the roles non-coding genes may have (Salzberg 2018).

Experimental studies are showing that human DNA plays a more expansive role than has traditionally been assumed. Researchers have discovered that it can be affected by electromagnetic, acoustic and scalar waves; such studies have given birth to wave genome theory.

One of the key findings from this research is that genomic waves are an inherently human trait. This becomes evident when we compare results obtained from various genotyping platforms and study four DNA samples where visually discernible genomic waves with either identical or contrasting peaks and troughs were present within their wavy patterns; furthermore, alteration of genomic waves led to improved CNV detection in these DNA samples.

These findings support the hypothesis that DNA serves as a quantum holographic biocomputer which stores information about an organism as a whole and stores this knowledge within multidimensional physical space-time continuums. Polarizations from genetic-sign laser radiation emitted by chromosomes or DNA itself connect nonlocally with radio waves of similar frequency polarization in similar fashion as photons and electromagnetic waves are interlinked through quantum nonlocality.

These findings indicate the need for a new theory of life that transcends today’s genetic-code paradigm, in order to address urgent health problems such as obesity, the opioid epidemic and mental illness.