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1.  Extended Y chromosome haplotypes resolve multiple and unique lineages of the Jewish priesthood 
Human Genetics  2009;126(5):707-717.
It has been known for over a decade that a majority of men who self report as members of the Jewish priesthood (Cohanim) carry a characteristic Y chromosome haplotype termed the Cohen Modal Haplotype (CMH). The CMH has since been used to trace putative Jewish ancestral origins of various populations. However, the limited number of binary and STR Y chromosome markers used previously did not provide the phylogenetic resolution needed to infer the number of independent paternal lineages that are encompassed within the Cohanim or their coalescence times. Accordingly, we have genotyped 75 binary markers and 12 Y-STRs in a sample of 215 Cohanim from diverse Jewish communities, 1,575 Jewish men from across the range of the Jewish Diaspora, and 2,099 non-Jewish men from the Near East, Europe, Central Asia, and India. While Cohanim from diverse backgrounds carry a total of 21 Y chromosome haplogroups, 5 haplogroups account for 79.5% of Cohanim Y chromosomes. The most frequent Cohanim lineage (46.1%) is marked by the recently reported P58 T->C mutation, which is prevalent in the Near East. Based on genotypes at 12 Y-STRs, we identify an extended CMH on the J-P58* background that predominates in both Ashkenazi and non-Ashkenazi Cohanim and is remarkably absent in non-Jews. The estimated divergence time of this lineage based on 17 STRs is 3,190 ± 1,090 years. Notably, the second most frequent Cohanim lineage (J-M410*, 14.4%) contains an extended modal haplotype that is also limited to Ashkenazi and non-Ashkenazi Cohanim and is estimated to be 4.2 ± 1.3 ky old. These results support the hypothesis of a common origin of the CMH in the Near East well before the dispersion of the Jewish people into separate communities, and indicate that the majority of contemporary Jewish priests descend from a limited number of paternal lineages.
Electronic supplementary material
The online version of this article (doi:10.1007/s00439-009-0727-5) contains supplementary material, which is available to authorized users.
doi:10.1007/s00439-009-0727-5
PMCID: PMC2771134  PMID: 19669163
2.  Decreased Rate of Evolution in Y Chromosome STR Loci of Increased Size of the Repeat Unit 
PLoS ONE  2009;4(9):e7276.
Background
Polymorphic Y chromosome short tandem repeats (STRs) have been widely used in population genetic and evolutionary studies. Compared to di-, tri-, and tetranucleotide repeats, STRs with longer repeat units occur more rarely and are far less commonly used.
Principal Findings
In order to study the evolutionary dynamics of STRs according to repeat unit size, we analysed variation at 24 Y chromosome repeat loci: 1 tri-, 14 tetra-, 7 penta-, and 2 hexanucleotide loci. According to our results, penta- and hexanucleotide repeats have approximately two times lower repeat variance and diversity than tri- and tetranucleotide repeats, indicating that their mutation rate is about half of that of tri- and tetranucleotide repeats. Thus, STR markers with longer repeat units are more robust in distinguishing Y chromosome haplogroups and, in some cases, phylogenetic splits within established haplogroups.
Conclusions
Our findings suggest that Y chromosome STRs of increased repeat unit size have a lower rate of evolution, which has significant relevance in population genetic and evolutionary studies.
doi:10.1371/journal.pone.0007276
PMCID: PMC2748704  PMID: 19789645
3.  Combining p-values in large scale genomics experiments 
Pharmaceutical statistics  2007;6(3):217-226.
Summary
In large-scale genomics experiments involving thousands of statistical tests, such as association scans and microarray expression experiments, a key question is: Which of the L tests represent true associations (TAs)? The traditional way to control false findings is via individual adjustments. In the presence of multiple TAs, p-value combination methods offer certain advantages. Both Fisher’s and Lancaster’s combination methods use an inverse gamma transformation. We identify the relation of the shape parameter of that distribution to the implicit threshold value; p-values below that threshold are favored by the inverse gamma method (GM). We explore this feature to improve power over Fisher’s method when L is large and the number of TAs is moderate. However, the improvement in power provided by combination methods is at the expense of a weaker claim made upon rejection of the null hypothesis – that there are some TAs among the L tests. Thus, GM remains a global test. To allow a stronger claim about a subset of p-values that is smaller than L, we investigate two methods with an explicit truncation: the rank truncated product method (RTP) that combines the first K ordered p-values, and the truncated product method (TPM) that combines p-values that are smaller than a specified threshold. We conclude that TPM allows claims to be made about subsets of p-values, while the claim of the RTP is, like GM, more appropriately about all L tests. GM gives somewhat higher power than TPM, RTP, Fisher, and Simes methods across a range of simulations.
doi:10.1002/pst.304
PMCID: PMC2569904  PMID: 17879330
multiple testing; p-value ranking; p-value combination; truncated product method; genetic association testing; microarray statistical testing
4.  Robustness of the inference of human population structure: A comparison of X-chromosomal and autosomal microsatellites 
Human Genomics  2004;1(2):87-97.
In this paper, data on 20 X-chromosomal microsatellite polymorphisms from the HGDP-CEPH cell line panel are used to infer human population structure. Inferences from these data are compared to those obtained from autosomal microsatellites. Some of the major features of the structure seen with 377 autosomal markers are generally visible with the X-linked markers, although the latter provide less resolution. Differences between the X-chromosomal and autosomal results can be explained without requiring major differences in demographic parameters between males and females. The dependence of the partitioning on the number of individuals sampled from each region and on the number of markers used is discussed.
doi:10.1186/1479-7364-1-2-87
PMCID: PMC3525066  PMID: 15601537
AMOVA; Bayesian inference; clustering; human evolution; population divergence; X chromosome

Results 1-4 (4)