Human genomics

Human genomics

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Further reading and resources

Disease genomics and GWAS

MassGenomics blog

Jia & Zhao (2014) Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum Genet 133:125-38. (pmid: 24122152)

[ PubMed ] [ DOI ] Abstract 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)

[ PubMed ] [ DOI ] Abstract 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)

[ PubMed ] [ DOI ] Abstract 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)

[ PubMed ] [ DOI ] Abstract 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)

[ PubMed ] [ DOI ] Abstract 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.

Human ancestry

Gibbons (2013) Human Evolution. Elusive Denisovans sighted in oldest human DNA. Science 342:1156. (pmid: 24311652)

[ PubMed ] [ DOI ]

Lowery et al. (2013) Neanderthal and Denisova genetic affinities with contemporary humans: introgression versus common ancestral polymorphisms. Gene 530:83-94. (pmid: 23872234)

[ PubMed ] [ DOI ] Abstract Analyses of the genetic relationships among modern humans, Neanderthals and Denisovans have suggested that 1-4% of the non-Sub-Saharan African gene pool may be Neanderthal derived, while 6-8% of the Melanesian gene pool may be the product of admixture between the Denisovans and the direct ancestors of Melanesians. In the present study, we analyzed single nucleotide polymorphism (SNP) diversity among a worldwide collection of contemporary human populations with respect to the genetic constitution of these two archaic hominins and Pan troglodytes (chimpanzee). We partitioned SNPs into subsets, including those that are derived in both archaic lineages, those that are ancestral in both archaic lineages and those that are only derived in one archaic lineage. By doing this, we have conducted separate examinations of subsets of mutations with higher probabilities of divergent phylogenetic origins. While previous investigations have excluded SNPs from common ancestors in principal component analyses, we included common ancestral SNPs in our analyses to visualize the relative placement of the Neanderthal and Denisova among human populations. To assess the genetic similarities among the various hominin lineages, we performed genetic structure analyses to provide a comparison of genetic patterns found within contemporary human genomes that may have archaic or common ancestral roots. Our results indicate that 3.6% of the Neanderthal genome is shared with roughly 65.4% of the average European gene pool, which clinally diminishes with distance from Europe. Our results suggest that Neanderthal genetic associations with contemporary non-Sub-Saharan African populations, as well as the genetic affinities observed between Denisovans and Melanesians most likely result from the retention of ancient mutations in these populations.

Disotell (2012) Archaic human genomics. Am J Phys Anthropol 149 Suppl 55:24-39. (pmid: 23124308)

[ PubMed ] [ DOI ] Abstract For much of the 20th century, the predominant view of human evolutionary history was derived from the fossil record. Homo erectus was seen arising in Africa from an earlier member of the genus and then spreading throughout the Old World and into the Oceania. A regional continuity model of anagenetic change from H. erectus via various intermediate archaic species into the modern humans in each of the regions inhabited by H. erectus was labeled the multiregional model of human evolution (MRE). A contrasting model positing a single origin, in Africa, of anatomically modern H. sapiens with some populations later migrating out of Africa and replacing the local archaic populations throughout the world with complete replacement became known as the recent African origin (RAO) model. Proponents of both models used different interpretations of the fossil record to bolster their views for decades. In the 1980s, molecular genetic techniques began providing evidence from modern human variation that allowed not only the different models of modern human origins to be tested but also the exploration demographic history and the types of selection that different regions of the genome and even specific traits had undergone. The majority of researchers interpreted these data as strongly supporting the RAO model, especially analyses of mitochondrial DNA (mtDNA). Extrapolating backward from modern patterns of variation and using various calibration points and substitution rates, a consensus arose that saw modern humans evolving from an African population around 200,000 years ago. Much later, around 50,000 years ago, a subset of this population migrated out of Africa replacing Neanderthals in Europe and western Asia as well as archaics in eastern Asia and Oceania. mtDNA sequences from more than two-dozen Neanderthals and early modern humans re-enforced this consensus. In 2010, however, the complete draft genomes of Neanderthals and of heretofore unknown hominins from Siberia, called Denisovans, demonstrated gene flow between these archaic human species and modern Eurasians but not sub-Saharan Africans. Although the levels of gene flow may be very limited, this unexpected finding does not fit well with either the RAO model or MRE model. More thorough sampling of modern human diversity, additional fossil discoveries, and the sequencing of additional hominin fossils are necessary to throw light onto our origins and our history.