Human genomics
Human genomics
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Further reading and resources
| Jia & Zhao (2014) Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum Genet 133:125-38. (pmid: 24122152) |
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[ PubMed ] [ DOI ] Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area. |
| Lehrach (2013) DNA sequencing methods in human genetics and disease research. F1000Prime Rep 5:34. (pmid: 24049638) |
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[ PubMed ] [ DOI ] DNA sequencing has revolutionized biological and medical research, and is poised to have a similar impact in medicine. This tool is just one of a number of developments in our capability to identify, quantitate and functionally characterize the components of the biological networks keeping us healthy or making us sick, but in many respects it has played the leading role in this process. The new technologies do, however, also provide a bridge between genotype and phenotype, both in man and model (as well as all other) organisms, revolutionize the identification of elements involved in a multitude of human diseases or other phenotypes, and generate a wealth of medically relevant information on every single person, as the basis of a truly personalized medicine of the future. |
| Wang et al. (2013) The role and challenges of exome sequencing in studies of human diseases. Front Genet 4:160. (pmid: 24032039) |
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[ PubMed ] [ DOI ] Recent advances in next-generation sequencing technologies have transformed the genetics study of human diseases; this is an era of unprecedented productivity. Exome sequencing, the targeted sequencing of the protein-coding portion of the human genome, has been shown to be a powerful and cost-effective method for detection of disease variants underlying Mendelian disorders. Increasing effort has been made in the interest of the identification of rare variants associated with complex traits in sequencing studies. Here we provided an overview of the application fields for exome sequencing in human diseases. We describe a general framework of computation and bioinformatics for handling sequencing data. We then demonstrate data quality and agreement between exome sequencing and exome microarray (chip) genotypes using data collected on the same set of subjects in a genetic study of panic disorder. Our results show that, in sequencing data, the data quality was generally higher for variants within the exonic target regions, compared to that outside the target regions, due to the target enrichment. We also compared genotype concordance for variant calls obtained by exome sequencing vs. exome genotyping microarrays. The overall consistency rate was >99.83% and the heterozygous consistency rate was >97.55%. The two platforms share a large amount of agreement over low frequency variants in the exonic regions, while exome sequencing provides much more information on variants not included on exome genotyping microarrays. The results demonstrate that exome sequencing data are of high quality and can be used to investigate the role of rare coding variants in human diseases. |
| Visscher et al. (2012) Five years of GWAS discovery. Am J Hum Genet 90:7-24. (pmid: 22243964) |
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[ PubMed ] [ DOI ] The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section. |
| Juran & Lazaridis (2011) Genomics in the post-GWAS era. Semin Liver Dis 31:215-22. (pmid: 21538286) |
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[ PubMed ] [ DOI ] The field of genomics has entered a new era in which the ability to identify genetic variants that impact complex human traits and disease in an unbiased fashion using genome-wide approaches is widely accessible. To date, the workhorse of these efforts has been the genome-wide association study (GWAS), which has quickly moved from novel to routine, and has provided key insights into aspects of the underlying allelic architecture of complex traits. The main lesson learned from the early GWAS efforts is that though many disease-associated variants are often discovered, most have only a minor effect on disease, and in total explain only a small amount of the apparent heritability. Here we provide a brief overview of the genetic variation classes that may harbor the heritability missing from GWAS, and touch on approaches that will be leveraged in the coming years as genomics-and by extension medicine-becomes increasingly personalized. |
