Seyedmohammadarian Akbari

It has been shown that genes can contribute and partly predict one's likelihood of success in life. The contributions of genetics to success are widely studied in Multi-traits analysis. The most extensive study was on a large cohort of individuals that analyzed their educational attainments and their genetics. They found that in addition to genetics, environmental factors play a role in shaping success in a particular area. Many previous studies have found correlations between indicators of 'success' such as education level, wealth and income, and particular genetic markers. However, it is difficult to separate the influence of genes from environmental factors. In this study, we aimed to estimate the role of genetics and non-genetic factors such as the socioeconomic level and implemented efforts of individuals. Genome-wide association study (GWAS) has been a practical approach to investigate genetic components underlying different skills such as mathematic or music learning abilities. Previous studies reported several candidate genetic variants, but none exceeded the significance threshold in general populations. It was proposed that polygenic scores can be applied to predict genetic predisposition variation in individuals. This means it is possible to calculate the number of genetic markers involved in the personal success rate. We formulized the genetics of success by considering several genetic susceptibility variants as potential candidates with a significant threshold in the general population. We defined six variables for our formula as Coefficient of Effort (COE), Amount of Predisposing Genes (APG), Current Status/Situation (CS), Reminding to Fullest Extend (REF), Effort Unit (EU), and heritability (h2). For example, the total number of existing genes and SNPs associated with a particular trait, also known as APG, is essential. For instance, AVPR1A or SLC6A4 has been associated with music perception, listening, memory, and choir participation. Finally, we assigned a range of one to 10 for the EU, ten means the highest individual effort level, and RFE has a range of one to 100; that indicates the total effort. Our evaluation showed a meaningful correlation between the six variables of our formula and yield of success. This formula can use one's genetics to help an individual to choose a career path that will ultimately help them achieve success.

Stage 4: Transition to Scale

Focus Areas:

Agriculture

AgricultureSEE LESS

Implemented In:

Canada, United States and Iran

Canada, United States and IranSEE LESS

3
Countries Implemented In
Verified Funding
?

Problem

It has been shown that genes can contribute and partly predict one's likelihood of success in life. The contributions of genetics to success are widely studied in Multi-traits analysis. The most extensive study was on a large cohort of individuals that analyzed their educational attainments and their genetics. They found that in addition to genetics, environmental factors play a role in shaping success in a particular area. Many previous studies have found correlations between indicators of 'success' such as education level, wealth and income, and particular genetic markers. However, it is difficult to separate the influence of genes from environmental factors. In this study, we aimed to estimate the role of genetics and non-genetic factors such as the socioeconomic level and implemented efforts of individuals. Genome-wide association study (GWAS) has been a practical approach to investigate genetic components underlying different skills such as mathematic or music learning abilities. Previous studies reported several candidate genetic variants, but none exceeded the significance threshold in general populations. It was proposed that polygenic scores can be applied to predict genetic predisposition variation in individuals. This means it is possible to calculate the number of genetic markers involved in the personal success rate. We formulized the genetics of success by considering several genetic susceptibility variants as potential candidates with a significant threshold in the general population. We defined six variables for our formula as Coefficient of Effort (COE), Amount of Predisposing Genes (APG), Current Status/Situation (CS), Reminding to Fullest Extend (REF), Effort Unit (EU), and heritability (h2). For example, the total number of existing genes and SNPs associated with a particular trait, also known as APG, is essential. For instance, AVPR1A or SLC6A4 has been associated with music perception, listening, memory, and choir participation. Finally, we assigned a range of one to 10 for the EU, ten means the highest individual effort level, and RFE has a range of one to 100; that indicates the total effort. Our evaluation showed a meaningful correlation between the six variables of our formula and yield of success. This formula can use one's genetics to help an individual to choose a career path that will ultimately help them achieve success

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Solution

It has been shown that genes can contribute and partly predict one's likelihood of success in life. The contributions of genetics to success are widely studied in Multi-traits analysis. The most extensive study was on a large cohort of individuals that analyzed their educational attainments and their genetics. They found that in addition to genetics, environmental factors play a role in shaping success in a particular area. Many previous studies have found correlations between indicators of 'success' such as education level, wealth and income, and particular genetic markers. However, it is difficult to separate the influence of genes from environmental factors. In this study, we aimed to estimate the role of genetics and non-genetic factors such as the socioeconomic level and implemented efforts of individuals. Genome-wide association study (GWAS) has been a practical approach to investigate genetic components underlying different skills such as mathematic or music learning abilities. Previous studies reported several candidate genetic variants, but none exceeded the significance threshold in general populations. It was proposed that polygenic scores can be applied to predict genetic predisposition variation in individuals. This means it is possible to calculate the number of genetic markers involved in the personal success rate. We formulized the genetics of success by considering several genetic susceptibility variants as potential candidates with a significant threshold in the general population. We defined six variables for our formula as Coefficient of Effort (COE), Amount of Predisposing Genes (APG), Current Status/Situation (CS), Reminding to Fullest Extend (REF), Effort Unit (EU), and heritability (h2). For example, the total number of existing genes and SNPs associated with a particular trait, also known as APG, is essential. For instance, AVPR1A or SLC6A4 has been associated with music perception, listening, memory, and choir participation. Finally, we assigned a range of one to 10 for the EU, ten means the highest individual effort level, and RFE has a range of one to 100; that indicates the total effort. Our evaluation showed a meaningful correlation between the six variables of our formula and yield of success. This formula can use one's genetics to help an individual to choose a career path that will ultimately help them achieve success

The Team Behind the Innovation

EXECUTIVE TEAM INCLUDES WOMEN AND YOUTH

Milestone

Jun 2021
New Country Implemented In
Canada, United States and Iran
Date Unknown
Funds RaisedVERIFIED
$150,000
TITLEBreeding and enhancement of tolerance to water deficit, resistance to leaf spot and improved oil composition in peanut
TYPEGrant
FOCUS AREAS
Agriculture
Implemented InGhana