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The 8q24 region harbors multiple risk variants for distinct cancers, including >8 for prostate cancer. In this study, we conducted fine mapping of the 8q24 risk region (127.8-128.8Mb) in search of novel associations with common and rare variation in 4853 prostate cancer case patients and 4678 control subjects of African ancestry. All statistical tests were two-sided. We identified three independent associations at P values of less than 5.00×10(-8), all of which were replicated in studies from Ghana and Uganda (combined sample = 5869 case patients, 5615 control subjects; rs114798100: risk allele frequency [RAF] = 0.04, per-allele odds ratio [OR] = 2.31, 95% confidence interval [CI] = 2.04 to 2.61, P = 2.38×10(-40); rs72725879: RAF = 0.33, OR = 1.37, 95% CI = 1.30 to 1.45, P = 3.04×10(-27); and rs111906932: RAF = 0.03, OR = 1.79, 95% CI = 1.53 to 2.08, P = 1.39×10(-13)). Risk variants rs114798100 and rs111906923 are only found in men of African ancestry, with rs111906923 representing a novel association signal. The three variants are located within or near a number of prostate cancer-associated long noncoding RNAs (lncRNAs), including PRNCR1, PCAT1, and PCAT2. These findings highlight ancestry-specific risk variation and implicate prostate-specific lncRNAs at the 8q24 prostate cancer susceptibility region.

Prostate cancer is the most common non-skin cancer in males, with a ∼1.5-2-fold higher incidence in African American men when compared with whites. Epidemiologic evidence supports a large heritable contribution to prostate cancer, with over 100 susceptibility loci identified to date that can explain ∼33% of the familial risk. To explore the contribution of both rare and common variation in coding regions to prostate cancer risk, we sequenced the exomes of 2165 prostate cancer cases and 2034 controls of African ancestry at a mean coverage of 10.1×. We identified 395 220 coding variants down to 0.05% frequency [57% non-synonymous (NS), 42% synonymous and 1% gain or loss of stop codon or splice site variant] in 16 751 genes with the strongest associations observed in SPARCL1 on 4q22.1 (rs13051, Ala49Asp, OR = 0.78, P = 1.8 × 10(-6)) and PTPRR on 12q15 (rs73341069, Val239Ile, OR = 1.62, P = 2.5 × 10(-5)). In gene-level testing, the two most significant genes were C1orf100 (P = 2.2 × 10(-4)) and GORAB (P = 2.3 × 10(-4)). We did not observe exome-wide significant associations (after correcting for multiple hypothesis testing) in single variant or gene-level testing in the overall case-control or case-case analyses of disease aggressiveness. In this first whole-exome sequencing study of prostate cancer, our findings do not provide strong support for the hypothesis that NS coding variants down to 0.5-1.0% frequency have large effects on prostate cancer risk in men of African ancestry. Higher-coverage sequencing efforts in larger samples will be needed to study rarer variants with smaller effect sizes associated with prostate cancer risk.

We report targeted sequencing of 63 known prostate cancer risk regions in a multi-ancestry study of 9,237 men and use the data to explore the contribution of low-frequency variation to disease risk. We show that SNPs with minor allele frequencies (MAFs) of 0.1-1% explain a substantial fraction of prostate cancer risk in men of African ancestry. We estimate that these SNPs account for 0.12 (standard error (s.e.) = 0.05) of variance in risk (∼42% of the variance contributed by SNPs with MAF of 0.1-50%). This contribution is much larger than the fraction of neutral variation due to SNPs in this class, implying that natural selection has driven down the frequency of many prostate cancer risk alleles; we estimate the coupling between selection and allelic effects at 0.48 (95% confidence interval [0.19, 0.78]) under the Eyre-Walker model. Our results indicate that rare variants make a disproportionate contribution to genetic risk for prostate cancer and suggest the possibility that rare variants may also have an outsize effect on other common traits.

The Northern Talysh from Azerbaijan and the Southern Talysh from Iran self-identify as one ethnic group and speak a Northwestern Iranian language. However, the Northern and Southern Talysh dialects are so different that they may actually be separate languages. Does this linguistic differentiation reflect internal change due to isolation, or could contact-induced change have played a role? We analyzed mtDNA HVI sequences, 11 Y-chromosome bi-allelic markers, and 9 YSTR loci in Northern and Southern Talysh and compared them with their neighboring groups. The mtDNA data show a close relatedness of both groups with each other and with neighboring groups, whereas the Northern Talysh Y-chromosome variation differs from that of neighboring groups, probably as a result of genetic drift. This genetic drift most likely reflects a founder event in the male gene pool of Northern Talysh: either fewer males than females migrated to Azerbaijan, or there was a higher degree of relatedness among the male migrants. Since we find no evidence of substantial genetic contact between either Northern or Southern Talysh and neighboring groups, we conclude that internal change, rather than contact-induced change, most likely explains the linguistic differentiation between Northern and Southern Talysh. Am J Phys Anthropol 138:82-89, 2009. KEY WORDS Northern Talysh; Southern Talysh; Y-chromosome; mtDNA

Previous studies of Y chromosome variation have revealed that western Europe, the Volga-Ural region, and the Caucasus differ dramatically with respect to Y-SNP haplogroup composition. The European part of Russia is situated in between these three regions; to determine if these differences reflect clines or boundaries in the Y-chromosome landscape, we analyzed 12 Y-SNPs in 545 males from 12 populations from the European part of Russia. The majority of Russian Y chromosomes (from 74% to 94%) belong to three Y chromosomal lineages [I-M170, R1a1-M17, and N3-TAT] that are also frequent in the rest of east Europe, north Europe, and/or in the Volga-Ural region. We find significant but low correlations between haplogroup frequencies and the geographic location of populations, suggesting gradual change in the Y chromosome gene pool across western Eurasia. However, we also find some significant boundaries between populations, suggesting that both isolation and migration have influenced the Y chromosome landscape. KEY WORDS Y chromosome; Europe; Russia; Ural region

The Kalmyks are an ethnic group along the lower Volga River in Russia who are thought to have migrated there from Mongolia about 300 years ago. To investigate their origins, we studied mtDNA and Y-chromosome variation in 99 Kalmyks. Both mtDNA HV1 sequences and Y-chromosome SNP haplogroups indicate a close relationship of Kalmyks with Mongolians. In addition, genetic diversity for both mtDNA and the Y chromosome are comparable in Kalmyks, Mongolians, and other Central Asian groups, indicating that the Kalmyk migration was not associated with a substantial bottleneck. The so-called 'Genghis Khan' Y-chromosome short tandem repeat (STR) haplotype was found in high frequency (31.3%) among Kalmyks, further supporting a strong genetic connection between Kalmyks and Mongolians. Genetic analyses of even recent, relatively well-documented migrations such as of the Kalmyks can therefore lead to new insights concerning such migrations. KEY WORDS Kalmyks; Y chromosome; mtDNA

Background: Although the importance of the human oral microbiome for health and disease is increasingly recognized, variation in the composition of the oral microbiome across different climates and geographic regions is largely unexplored. Results: Here we analyze the saliva microbiome from native Alaskans (76 individuals from 4 populations), Germans (10 individuals from 1 population), and Africans (66 individuals from 3 populations) based on next-generation sequencing of partial 16S rRNA gene sequences. After quality filtering, a total of 67,916 analyzed sequences resulted in 5,592 OTUs (defined at =97% identity) and 123 genera. The three human groups differed significantly by the degree of diversity between and within individuals (e.g. beta diversity: Africans > Alaskans > Germans; alpha diversity: Germans > Alaskans > Africans). UniFrac, network, ANOSIM, and correlation analyses all indicated more similarities in the saliva microbiome of native Alaskans and Germans than between either group and Africans. The native Alaskans and Germans also had the highest number of shared bacterial interactions. At the level of shared OTUs, only limited support for a core microbiome shared across all three continental regions was provided, although partial correlation analysis did highlight interactions involving several pairs of genera as conserved across all human groups. Subsampling strategies for compensating for the unequal number of individuals per group or unequal sequence reads confirmed the above observations. Conclusion: Overall, this study illustrates the distinctiveness of the saliva microbiome of human groups living under very different climatic conditions. [ABSTRACT FROM AUTHOR]

Background: It is increasingly recognized that the bacteria that live in and on the human body (the microbiome) can play an important role in health and disease. The composition of the microbiome is potentially influenced by both internal factors (such as phylogeny and host physiology) and external factors (such as diet and local environment), and interspecific comparisons can aid in understanding the importance of these factors. Results: To gain insights into the relative importance of these factors on saliva microbiome diversity, we here analyze the saliva microbiomes of chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) from two sanctuaries in Africa, and from human workers at each sanctuary. The saliva microbiomes of the two Pan species are more similar to one another, and the saliva microbiomes of the two human groups are more similar to one another, than are the saliva microbiomes of human workers and apes from the same sanctuary. We also looked for the existence of a core microbiome and find no evidence for a taxon-based core saliva microbiome for Homo or Pan. In addition, we studied the saliva microbiome from apes from the Leipzig Zoo, and found an extraordinary diversity in the zoo ape saliva microbiomes that is not found in the saliva microbiomes of the sanctuary animals. Conclusions: The greater similarity of the saliva microbiomes of the two Pan species to one another, and of the two human groups to one another, are in accordance with both the phylogenetic relationships of the hosts as well as with host physiology. Moreover, the results from the zoo animals suggest that novel environments can have a large impact on the microbiome, and that microbiome analyses based on captive animals should be viewed with caution as they may not reflect the microbiome of animals in the wild. [ABSTRACT FROM AUTHOR]