The recent development of genotyping and sequencing technologies has enabled geneticists to produce a flood of data at different levels of biochemical activity and organization. In contrast to this increasingly growing amount of genetic information, our understanding of the mechanisms of human diseases and drug response, essential for personalized medicine and translational medicine, still remain elusive. We propose to address this fundamental question by developing a handful of statistical models and computational algorithms for analyzing and interpreting genetic data of various complexities. Contributions from leaders in the field provide unparalleled insight into current frontier technologies and applications in analyzing and modeling genetic problems in clinical medicine from a mechanistic perspective. The topics covered include, but are not limited to, the following areas:
1. Organization, variation and expression of the human genome associated with complex diseases
We hope to publish statistical models for detecting genetic variants and their interactions that control complex traits or diseases through candidate gene approaches or genome-wide association studies. In particular, new concepts of genetic variability are needed to translate genetic discoveries from genome to personalized medicine.
2. Population and comparative genomics
Critical to the utilization of population and comparative genomics to discover and exploit natural variation in drug development is the detection of specific genes that contribute to population diversity, as well as thoughtful applications of the theories of evolutionary biology. Models are needed to study the dynamic change of human genome organization and variation through evolutionary processes and human intervention.
3. Interaction between the genome and environment
Subtle differences in genetic components may cause people to differ dramatically in response to the same environmental exposure. We need models that can predict how genes and environmental factors work together to affect human diseases, from which new strategies are further framed for the prevention and treatment of these illnesses.
3. Genomic imprinting, epigenomics, transgenerational epigenetics and their implications for medicine
Genomic imprinting results from allele-specific epigenetic modifications such as CpG dinucleotide methylation, histone methylation, or histone acetylation. One view suggests that some environmental factors have an ability to reprogram the germline epigenome, whereas the other argues that epigenetic marks are transmitted from one generation to next, promoting a transgenerational inheritance of phenotypes and disease states. Various designs and models are solicited to test and validate these views and study their implications for clinical medicine.
4. Network biology
DNA affects final phenotypes through perturbations in transcriptomic and proteomic activity. A study of this process is called network biology. It studies life as a network of biological objects with DNA, RNA, proteins and metabolites. Mathematical and statistical models are needed to model network biology, providing tools for helping understand disease mechanisms and drug responses.
5. Case studies with statistical and computational components
We welcome some particular case studies in this thematic issue if they involve a statistical and computational component. This includes an innovative use of existing statistical models to solve real-world problems, leading to significant biological advances.
The recent development of genotyping and sequencing technologies has enabled geneticists to produce a flood of data at different levels of biochemical activity and organization. In contrast to this increasingly growing amount of genetic information, our understanding of the mechanisms of human diseases and drug response, essential for personalized medicine and translational medicine, still remain elusive. We propose to address this fundamental question by developing a handful of statistical models and computational algorithms for analyzing and interpreting genetic data of various complexities. Contributions from leaders in the field provide unparalleled insight into current frontier technologies and applications in analyzing and modeling genetic problems in clinical medicine from a mechanistic perspective. The topics covered include, but are not limited to, the following areas:
1. Organization, variation and expression of the human genome associated with complex diseases
We hope to publish statistical models for detecting genetic variants and their interactions that control complex traits or diseases through candidate gene approaches or genome-wide association studies. In particular, new concepts of genetic variability are needed to translate genetic discoveries from genome to personalized medicine.
2. Population and comparative genomics
Critical to the utilization of population and comparative genomics to discover and exploit natural variation in drug development is the detection of specific genes that contribute to population diversity, as well as thoughtful applications of the theories of evolutionary biology. Models are needed to study the dynamic change of human genome organization and variation through evolutionary processes and human intervention.
3. Interaction between the genome and environment
Subtle differences in genetic components may cause people to differ dramatically in response to the same environmental exposure. We need models that can predict how genes and environmental factors work together to affect human diseases, from which new strategies are further framed for the prevention and treatment of these illnesses.
3. Genomic imprinting, epigenomics, transgenerational epigenetics and their implications for medicine
Genomic imprinting results from allele-specific epigenetic modifications such as CpG dinucleotide methylation, histone methylation, or histone acetylation. One view suggests that some environmental factors have an ability to reprogram the germline epigenome, whereas the other argues that epigenetic marks are transmitted from one generation to next, promoting a transgenerational inheritance of phenotypes and disease states. Various designs and models are solicited to test and validate these views and study their implications for clinical medicine.
4. Network biology
DNA affects final phenotypes through perturbations in transcriptomic and proteomic activity. A study of this process is called network biology. It studies life as a network of biological objects with DNA, RNA, proteins and metabolites. Mathematical and statistical models are needed to model network biology, providing tools for helping understand disease mechanisms and drug responses.
5. Case studies with statistical and computational components
We welcome some particular case studies in this thematic issue if they involve a statistical and computational component. This includes an innovative use of existing statistical models to solve real-world problems, leading to significant biological advances.
