1) Mennonite migrations: Demographic and genetic consequences: This is a summary of some work I did some time ago and will be published in a forthcoming article in the journal Human Biology.
Throughout history, religious persecution has often led to human migration. Prime examples of these demographic events are those that impacted populations belonging to the Anabaptist movement (Amish, Hutterites, and Men-nonites) after the Reformation in 16th century Europe. Each of these groups went through persecutions and demographic events that led to population movement in Europe and eventual emigration to the Americas. This study focuses on maternal molecular genetic diversity in six Mennonite communities from Kansas (Goessel, Lone Tree, Garden View, Meridian, and Garden City) and Ne-braska (Henderson) in order to determine their genetic structure and its relationship to understanding the biologi-cal impact of these demographic events. The Mennonite samples exhibited eight western European mtDNA haplogroups – H, HV0, I, J, K, T, U, and X. Comparable to other populations of European descent, haplogroup H was the most frequent in all six communities and ranged from 35% in Lone Tree to 75% in Old Order Mennonites from Garden City, Kansas. Molecular genetic variation was ex-amined and compared to classical genetic markers. The Goessel and Henderson communities demonstrate a shared biological relationship with a comparative population from the Netherlands. Additional analyses of molecular data indicate these Anabaptist communities formed new con-gregations by fissioning along familial lines. This popula-tion subdivision of into congregations support previously documented patterns of fission-fusion from classical genetic data but demonstrates a more accurate reflection of these communities into their modern day congregations.
3) New type of genetic variation discoveredThis study investigated genetic and cultural data in order to understand the population history and migration of the Rama Indians in relation to neighboring Central American indigenous groups. The Rama belong to the Chibchan linguistic family (Votic sub-family) and inhabit the coast and rivers of southern Nicaragua. Due to limited ethno-historical and ethnographical information, the origins and population dynamics of the Rama remain unclear, both prior to and after the colonization of the Caribbean coast of Nicaragua. We analyzed maternal (mtDNA) and paternal (Y-chromosome) molecular genetics from 75 Rama partici-pants in order to assess the provenience of the Rama to neighboring Native American populations. These data were analyzed using median-joining networks, multi-dimensional scaling plots, and analysis of molecular vari-ance (AMOVA). Ethnographic and genealogical data were used to better understand both cultural ecology and inter-nal migration of the Rama within their ancestral territory. The results of this study indicate that the Rama are diver-gent from comparative Chibchan populations and are char-acterized by a high frequency of mtDNA haplogroup B (92%) and low frequency of haplogroup A (8%). This re-duced haplogroup diversity characterize the Rama and in-dicate maternal genetic drift. In addition, Y chromosome data indicate paternal gene flow between the Rama and neighboring Mesoamerican groups. Together, these data challenge previous assumptions regarding the relation-ships of the Rama with neighboring Central American groups, and suggest an early divergence from other Votic populations as well as a pattern of seasonal movements within their territory.
4)Potential for Revealing Individual-Level Information in Genome-wide Association Studies. An interesting article from JAMA regarding the high level sequencing data and privacy protection.
5) Identification of functional modules that correlate with phenotypic difference: the influence of network topology: a interesting article that looks at comparing pathways between phenotypes.
One of the important challenges to post-genomic biology is relating phenotypic alterations to the collective alterations in genes underlying observed state changes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis.
