How Genotype, Phenotype and Health data graph are related in precision health?

Personalized Medicine and Precision Health are among various categories in population health, where there is significant value to relate the phenotype and genotype. Population health focuses on the conditions and factors that influence the health of the population over their lifetime, and it measures the health outcomes of a group of individuals and the pattern of determinants (medical care, genetics, individual behavior, etc.). The groups could be nations, communities, or any other defined group.

A phenotype is a specific trait noticed in a person, such as an eye color, hair color, skin color, height, blood type, etc., and even behavior. All these traits vary from one individual to another. On the other hand, a genotype is like the base of the phenotype; it’s part of the genetic composition of a cell, and therefore of any individual, determining one of its characteristics (hair color, skin tone, etc.). The genetic information of each individual comes from each of its parents.

Many variables occur within the process of creating a living being, and this is where phenotyping comes in. There are many variations within a single genome (the whole of its hereditary information, encoded in its DNA). Our hair, eye, and skin color are polygenic traits, meaning that they are influenced by more than one gene. 

Studying subgroups of people by analyzing how phenotypes and genotypes combine in different environments and situations is a form of studying population health. There are many factors that affect visible traits in an individual, which we will explore in more detail below.

The phenotype is seen as the interaction between genetics (genotype) and the environment. One factor that has a great impact on our specific traits is the environment. For example, the amount of sunlight will and can influence some of those specific traits. Stress can also change how a living being will turn out. Sometimes bacteria can get into the cells and affect the outcome. In other words, various factors are considered relevant and have a great influence on the physical appearance.

 

Explaining Phenotype in Detail

As mentioned before, the phenotype is the overall visual trait of a living being, and it’s influenced by the genetic code and the factors in the environment. This is how your physical structure is founded, the processes that happen as a being develops, how it behaves and how that behavior impacts the creation. Physiological and biochemical properties also have an impact on how human beings will grow. Also, the environment where a living growing in greatly changes how it matures.

A). What Role does Data play in Precision Health?

Data plays a vital role in precision health but there are major trend shifts happening in this sector. Initially, the data for precision medicine was considered to be coming from one or a few genes panel tests that were performed on individuals. Notably, genetics explains one part of the story, while the other can be explained by the phenotypic data. The phenotypic data includes clinical data (disease symptoms) and demographic data (age, gender, etc.). The relationship between the phenotype and the genotype is seen as a connection between two differences (one at a genetic level and the other at the phenotypic level).

To understand the approaches behind the two data sets, the data can be divided into two distinct categories:

  • Static Health Data-This refers to anything from age, sex, gender, and genetics
  • Dynamic Health Data-refers to lifestyle, daily behavior choices, Medication, outside factors, stress, etc

It is possible to have a combination of both data sets. For personalized recommendations and targeted action plans, the patient can have a combination of static and dynamic health data.

This article seeks to have a deeper look into how the phenotype and the health data graph are structured and collected as part of the ixLayer platform.

B). What Is the ixlayer Health Data Graph?

The Health Data Graph personalizes the Precision Health test for each patient or participant. IxLayer Health Data Graph platform is designed to structure the data taxonomy and create an elegant way to collect health data from users and patients. Furthermore, this would enable researchers to conveniently classify data, and then provide unique personalized results for each user or patient. Health Data Graph structure shows the relationship of each user’s static data such as age, gender, and genetics, in addition to dynamic health data, which includes lifestyle and daily behavioral choices.

THE DIFFERENT TYPES OF INPUTS THAT WORK WITH THE SYSTEM

  • Genotype or bio-maker which can be obtained from a genetic or health test
  • Health history as part of the intake questionnaire
  • Electronic health record data

During survey and data collection, which can be made through text messages or emails, the ixLayer back-end systems are set up with functionality where each user is assigned a new attribute called tag (metadata). These tags (metadata) enable ixLayer customers to create a graphical representation for each of their end-users to his/her cohort for personalized action plans targeted to each individual.

A Typical Study

A typical example would be conducting a health study on a certain population to understand the genetic or biomarker data based on the smoking habits of that population. This can be done by engaging the population under survey questions such as: Are you a smoker? How many rolls of cigarettes do you smoke in a day? What type of cigarettes do you use? 

Based on the answer given by each individual, it will be easy to ask linked questions, (also known as nests), which will bring the different surveys on a higher-level, with additional questions asked such as: Do you smoke electronic or traditional cigarettes? How many tobacco products do you use per day? What is the frequency of anyone smoking at home?

Notably, each question identifies a user with a specific category of cigarette smokers. Attaching an attribute or tag to each of the questionnaires as part of the population enables your organization to create structural data sets for both classification and clustering into a Health Data Graph, as illustrated below.

C). Why a Health Data Graph?

1. It enables ixLayer customers to provide personalized results and action plans for their end-users. In the above example, we have a user who is a heavy smoker and uses electronic cigarettes. If we combine this information with the genetic variation, we can provide an action plan that is targeted towards offering customized preventive care to a specific individual.

2. The Health Data Graph brings rich clustered datasets for population health studies. The availability of health data graph clusters makes it possible to have ongoing research, and on a regular basis to collect phenotypic data from each user or participant. It is now possible to collect new data through the feedback loop as you can ask new phenotypic questions based on the genetics bio-marker, as well as the previous answer provided on the platform. 

3. Health Data Graph enables each organization to de-identify the entire data sets (both static and dynamic data). It enables ixLayer customers to conveniently export structured-data and use different machine learning or other scientific tools to do further research studies.

D.) How to Unlock the Value of Your Data Visualization

The IxLayer platform is unique, and it allows researchers to easily query, manipulate, and search the data in a fully aggregated and de-identified way to ensure that the privacy of each participant is protected. The privacy of end-users is of utmost importance to ixLayer and its customers. 

Through the ixLayer platform, it is now possible to integrate data with business analytics platforms such as QuickSight, which enables a researcher to perform easily comparative analysis and create comprehensive data visualizations. 

The ixLayer platform enables researchers to mix and match the different options, to investigate the effects of a genotype on multiple traits, or to investigate multiple genotypes that affect the same trait. This can be achieved by selecting genotype, phenotype combinations, and plot heat maps which assist in facilitating a comparative analysis or interaction effects of various genotypes and phenotypes.

E). Security and Data Protection

The ixLayer platform is designed and developed with the highest security and encryption standards. Data protection should be observed keenly by taking the vital steps to ensure that the data remains safe and secure and in full compliance with the industry security guidelines. There are various security standards and compliances which include:

 1. HIPAA Compliance – this deals with the designated compliance officer, training, and education of all staff and internal monitoring and auditing.

 2. GDPR Compliance – enables the user to access and move the data, delete the data, and receive breach notifications within 72 hours after an incident.

 3. SOC2 Certification – deals with encryption at rest and in transit, network firewalls, and intrusion detection and recovery.

 4. PHI Data Control – deals with the admin portal with patient health information (PHI), control, assigns clinical staff members with PHI access, and also offers limited access to marketing and support staff.

 

ixlayer

For any type of population health research, the Ixlayer platform is great in all of the features that are incorporated into the application.

It is simple to search studies performed and keeps security in mind with top of the line data encryption technology. It is the best platform to put there to calculate trends in population health.

Comparing data is also easy and allows users to use one platform instead of a few. It is a cost-saving way to conduct research. It is highly suggested to use Ixlayer as a platform for anyone who needs to utilize easy to use technology to study data trends within specific population sectors.

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