Genome-wide association studies (GWAS) for complex disorders with large case-control populations have been performed on hundreds of traits in more than 1200 published studies (http://www.genome.gov/gwastudies/) but the variants detected by GWAS account for little of the heritability of these traits, leading to an increasing interest in using family based designs. While GWAS studies are designed to find common variants with low to moderate attributable risks, family based studies are expected to find rare variants with high attributable risk. Because family-based designs can better control both genetic and environmental background, this study design is robust to heterogeneity and population stratification. Moreover, in family-based analysis, the background genetic variation can be modeled to control the residual variance which could increase the power to identify disease associated rare variants. Analysis of families can also help us gain knowledge about disease transmission and inheritance patterns. New study designs involves the combination of family based and unrelated case-control studies and can provide an alternative robust and powerful approach for detecting genetic variants.
Although a family-based design has the advantage of being robust to false positives, novel and powerful methods to analyze families in genetic epidemiology continue to be needed, especially for the interaction between genetic and environmental factors associated with disease. Moreover, with the rapid development of sequencing technology, advances in approaches to the design and analysis of sequencing data in families are also greatly needed.
Some specific topics could be, but are not limited to the following:
1. Apply a novel design or approach to analyze complex traits with family data or both family and population data to identify causal genetic variants, environmental risk factors, as well their interactions
2. Predict disease risk using family data by integrating genetic, environmental and clinical information
3. New design and analysis approaches for using sequencing technology in family data to identify disease-associated variants for complex disorders
4. Insight review on the studies and methods to analyze family data in genetic epidemiology
Genome-wide association studies (GWAS) for complex disorders with large case-control populations have been performed on hundreds of traits in more than 1200 published studies (http://www.genome.gov/gwastudies/) but the variants detected by GWAS account for little of the heritability of these traits, leading to an increasing interest in using family based designs. While GWAS studies are designed to find common variants with low to moderate attributable risks, family based studies are expected to find rare variants with high attributable risk. Because family-based designs can better control both genetic and environmental background, this study design is robust to heterogeneity and population stratification. Moreover, in family-based analysis, the background genetic variation can be modeled to control the residual variance which could increase the power to identify disease associated rare variants. Analysis of families can also help us gain knowledge about disease transmission and inheritance patterns. New study designs involves the combination of family based and unrelated case-control studies and can provide an alternative robust and powerful approach for detecting genetic variants.
Although a family-based design has the advantage of being robust to false positives, novel and powerful methods to analyze families in genetic epidemiology continue to be needed, especially for the interaction between genetic and environmental factors associated with disease. Moreover, with the rapid development of sequencing technology, advances in approaches to the design and analysis of sequencing data in families are also greatly needed.
Some specific topics could be, but are not limited to the following:
1. Apply a novel design or approach to analyze complex traits with family data or both family and population data to identify causal genetic variants, environmental risk factors, as well their interactions
2. Predict disease risk using family data by integrating genetic, environmental and clinical information
3. New design and analysis approaches for using sequencing technology in family data to identify disease-associated variants for complex disorders
4. Insight review on the studies and methods to analyze family data in genetic epidemiology
