If no other source is provided by me, all articles are from the meeting of the American Society of Human Genetics which took place this October. I found it here:

I just posted the abstracts which were of greater importance and interest to me, for more just follow the link, because in fact all abstracts are worth reading me thinks.

The genetic variation and population history in the Baltic Sea region

Sharp genetic borders within a geographically restricted region are known to exist among the populations around the northern Baltic Sea on the northern edge of Europe. We studied the population history of this area in greater detail from paternal and maternal perspectives with Y chromosomal and mitochondrial DNA markers. Over 1700 DNA samples from Finland, Karelia, Estonia, Latvia, Lithuania and Sweden were genotyped for 18 Y-chromosomal biallelic polymorphisms and 8 microsatellite loci, together with 18 polymorphisms from the coding area of mtDNA and sequencing of the HVR1. Y chromosomal haplogroups from the biallelic data indicate both various phases of gene flow and existence of genetic barriers within the Baltic region. Haplogroup N3, being abundant on the eastern side of the Baltic, differentiates between eastern and western sides of the Baltic Sea, just like R1b that has a reverse frequency pattern to N3. The typically Scandinavian haplogroup Ia1 has a high frequency of up to 40%, separating not only Sweden but also Western Finland from the other populations. The frequency of haplogroup R1a1, most characteristic to Slavic peoples, varied substantially across the populations. In addition to biallelic markers, Y-chromosomal microsatellite loci were analyzed for a more detailed approach to the history of the paternal lineages in the region. We also analyzed mtDNA markers with special interest for sub-haplogroups of H and U, that among other haplogroups, show substantial variation between the populations (e.g. haplogroups H1, H2, T and J1). In conclusion, our current Y-chromosomal and mtDNA data suggest various incidents of gene flow from different sources, each reaching partly different areas of the Baltic region, which can be thus seen as a meeting point of a not only culturally but also genetically diverse set of populations.
Asian Nomads traces in the mitochondrial gene pool of Slavs.

Mitochondrial DNA (mtDNA) variability was studied in a sample of 179 individuals representing Czech population from west Bohemia. MtDNA analysis revealed that the majority of Czech mtDNAs belongs to the common West Eurasian mitochondrial haplogroups. However, about 3 per cent of Czech mtDNAs encompass East Eurasian lineages (A, N9a, D4, M*). Comparative analysis of published data has shown that different Slavonic populations contain small but marked amount of East Eurasian mtDNAs (e.g. 1.3 per cent in Eastern Slavs, 1.8 per cent in Western Slavs, and 1.2 per cent in Southern Slavs). It is noteworthy that Baltic populations (Latvians, Lithuanians and Estonians) have avoided a marked influence of maternal lineages of East Eurasian origin (0.3-0.6 per cent). The two East Eurasian mtDNA haplogroups, Z1 and D5, are present in gene pools of North European Finnic populations (Saami, Finns, and Karelians). Unlike them, Slavonic populations in general are characterized by heterogeneous mtDNA structure, defined, in addition to Z1 and D5, by haplogroups A, C, D4, G2a, M*, N9a, F and Y. Therefore, different scenarios of female-mediated East Eurasian genetic influence on Northern and Eastern Europeans should be highlighted: (1) the most ancient, probably originated in the early Holocene, influx of Asian tribes, which brought a few selected East Asian mtDNA haplotypes (like Z and D5) to Fennoscandia (Tambets et al. 2004), and (2) gradual gene flows of historic times occurred mostly in the Middle Ages due to migrations of nomadic peoples (such as the Huns, Avars, Bulgars, Mongols) to Eastern and Central European territories inhabited mainly by Slavonic tribes. We suggest that the presence of East Eurasian mtDNA haplotypes is not original feature of gene pool of the proto-Slavs, but mostly is a consequence of admixture with Central Asian nomadic tribes, who migrated into Central and Eastern Europe in the early middle Ages.
Quote Originally Posted by Ilmatar
Dienekes published the following abstract of a study on Samish mtDNA hg Z1a just a couple of weeks ago:


According to this Sámi mtDNA haplogroup Z lineage is closely related to the one found among the Volga Finnic populations. The Sámi (and Eastern Finnish) Z lineage has a timedepth of 2700 YBP. We already knew populations living in the Northern and Eastern Finland during the late Bronze age were influenced by the Kama-Oka bronze cultures, while Western Finland belonged to the Scandinavian sphere of influence. Therefore, previous archeological findings seem to back the latest genetic knowledge beautifully here.

Compare with this threads about Osteuropids and Baltisation too:


Estimating the split time of Human and Neanderthal populations
Previous genetic studies of Neanderthal ancestry have used mtDNA and thus have been limited in their conclusions on the relationship of humans and Neanderthals. We present here the first use of Neanderthal genomic DNA to assess the joint history of human and Neanderthal populations. Our data consist of 37kb of short fragments of genomic DNA sequenced in Neanderthal. By studying the degree to which modern human diversity is shared with Neanderthal we can assess the time at which the human and Neanderthal populations split. We use a flexible simulation based approach that demonstrates the power of using human variation data in such analyses. We find that the two populations split ~400,000 years, predating the emergence of modern humans. Our best fitting model predicts that the Neanderthal lineage will be outgroup to the human population ~52% of the time.
The Genetic Structure of Human Populations in Africa.
Africa contains the greatest levels of human genetic variation and is the source of the worldwide range expansion of all modern humans. Knowledge of the genetic population boundaries within Africa has important implications for the design and implementation of genetic epidemiologic studies of Africans and African Americans, and for reconstructing modern human origins. A dataset consisting of ~3.7 million genotypes has been generated from the Marshfield panel of 773 microsatellites and 392 in-del polymorphic genetic markers. These markers were genotyped in ~3,200 individuals from >100 diverse ethnic populations across Africa as well as in 118 African Americans and in the CEPH Human Genome Diversity Panel, consisting of 1048 individuals from 51 globally diverse populations. Preliminary analysis of population structure using the program STRUCTURE1 indicates considerably more substructure amongst global populations (estimate for the number of genetic clusters, K, is 12) and amongst African populations (K = 9) than had previously been recognized2. Population clusters are correlated with self-described ethnicity and shared cultural and/or linguistic properties (e.g. Pygmies, Khoisan-speakers, Bantu-speakers, etc). African Americans have predominantly West African Bantu (~80%) and European (~17%) ancestry, although individual admixture levels vary considerably. These results justify the need to include a broad range of geographically and ethnically diverse African populations in studies of human genetic variation. 1Pritchard JK, et al. Genetics 155:945-59 (2000) 2Rosenberg NA, et al. Science 298:2381- 5 (2002).
The rare nonsynonymous SCN5A-S1103Y variant in Caucasians is due to recent African Admixture as revealed by 100k SNP genotyping.
The SCN5A-S1103Y variant is an established and confirmed risk factor conferring an odds ratio up to 8.5 for cardiac ventricular arrhythmias and sudden cardiac death (Splawski et al, Science, 2002, Burke et al., Circulation, 2005, Plant et al., J. Clin. Invest. 2006). In Africans it is a common nonsynonymous SNP (MAF=8%), but it is rarely observed in Caucasians (Chen et al, J. Med. Genet. 2002). In a Bavarian family appearing of entirely Caucasian descent and affected with long QT Syndrome we have detected this variant in heterozygote state as the only causal nonsynonymous variation upon diagnostic ion channel resequencing. To resolve the question, whether in the family the variant was (a) of ancient African descent, (b) due to recent African admixture or (c) a de novo mutation, we analyzed the genetic segment it resided on. Dense SNP genotyping in admixed individuals allows to infer the ethnicity of chromosomal regions if allele frequencies are known in the original populations. Ethnicity inference for any given locus can be carried out by applying the product rule to a sliding window of neighboring SNPs or via modeling ancestry by hidden Markov Chain Monte Carlo Methods (Tang et al. Am. J. Hum. Genet, 2006). By 100k SNP genotyping of the Bavarian family, we demonstate that the S1103 variant is due to recent African admixture (b) and could rule out possibilities (a) and (c). This application demonstrates that inferring ethnicity of chromosomal regions by high density SNP genotyping is a powerful approach with prospects also to admixture mapping of disease loci and population stratification correction of genomewide association mapping of complex disease loci.