| Application: | 788, Heritability of disease frequency |
| Title: | Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach |
| Size: | 7.9 MB |
| Archived: | 29 Jun 2020 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
Return 2245
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Notes
The ability to translate genetic findings from genome wide association studies into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. In this project we showed how the use of a more powerful statistical approach, the use of which has traditionally been limited due to computational requirements and lack of sufficiently large individual level genotyped cohorts, improves the prediction of multiple human anthropometric traits when applied to large datasets such as UK Biobank.Application 788
Heritability of disease frequency
Genes and environmental exposures determine susceptibility to common diseases such as diabetes or cancer. The relative contribution of genes to disease risk is known as the heritability. Heritability is often estimated using twin pairs. However, heritability estimates obtained from twins have limitations that could be overcome by using sibling and parental information on disease. That is, a person?s family history. We will calculate heritability by comparing the disease frequency among relatives to the frequency in the general population. We will use the full UKbiobank cohort data on family history to estimate the heritability of a broad range of medical conditions. We will use information on smoking, anthropometric and reproductive factors to understand to what degree these risk factors are determined by genetics; and to what degree genes influencing risky behaviours (e.g. smoking) and diseases are shared. In addition, we will estimate the heritability of reproductive fitness (a measure of natural selection).Estimates of heritability are important because they set the potential utility of genetics to predict disease risk. Stratification of the population by their level of risk would allow tailoring the level of medical intervention to the level of risk and facilitate early diagnosis.
| Lead investigator: | Professor Albert Tenesa |
| Lead institution: | University of Edinburgh |
1 related Return
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 3616 | 788 | Evidence of horizontal indirect genetic effects in humans | 30 Jun 2021 |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 2246 | Improved Genetic Profiling of Anthropometric Traits Using a Big Data Approach | Oriol Canela-Xandri (+3) | 2016 | PLOS ONE |
Enabling scientific discoveries that improve human health