The genetic history of Cochin Jews from India.

Cochin Jews form a small and unique community on the Malabar coast in southwest India. While the arrival time of any putative Jewish ancestors of the community has been speculated to have taken place as far back as biblical times (King Solomon's era), a Jewish community in the Malabar coast has been documented only since the 9th century CE. Here, we explore the genetic history of Cochin Jews by collecting and genotyping 21 community members and combining the data with that of 707 individuals from 72 other Indian, Jewish, and Pakistani populations, together with additional individuals from worldwide populations. We applied comprehensive genome-wide analyses based on principal component analysis, F ST, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing, allele sharing autocorrelation decay and contrasting the X chromosome with the autosomes. We find that, as reported by several previous studies, the genetics of Cochin Jews resembles that of local Indian populations. However, we also identify considerable Jewish genetic ancestry that is not present in any other Indian or Pakistani populations (with the exception of the Jewish Bene Israel, which we characterized previously). Combined, Cochin Jews have both Jewish and Indian ancestry. Specifically, we detect a significant recent Jewish gene flow into this community 13-22 generations (~470-730 years) ago, with contributions from Yemenite, Sephardi, and Middle-Eastern Jews, in accordance with historical records. Genetic analyses also point to high endogamy and a recent population bottleneck in this population, which might explain the increased prevalence of some recessive diseases in Cochin Jews.

The Genetics of Bene Israel from India Reveals Both Substantial Jewish and Indian Ancestry.

The Bene Israel Jewish community from West India is a unique population whose history before the 18th century remains largely unknown. Bene Israel members consider themselves as descendants of Jews, yet the identity of Jewish ancestors and their arrival time to India are unknown, with speculations on arrival time varying between the 8th century BCE and the 6th century CE. Here, we characterize the genetic history of Bene Israel by collecting and genotyping 18 Bene Israel individuals. Combining with 486 individuals from 41 other Jewish, Indian and Pakistani populations, and additional individuals from worldwide populations, we conducted comprehensive genome-wide analyses based on FST, principal component analysis, ADMIXTURE, identity-by-descent sharing, admixture linkage disequilibrium decay, haplotype sharing and allele sharing autocorrelation decay, as well as contrasted patterns between the X chromosome and the autosomes. The genetics of Bene Israel individuals resemble local Indian populations, while at the same time constituting a clearly separated and unique population in India. They are unique among Indian and Pakistani populations we analyzed in sharing considerable genetic ancestry with other Jewish populations. Putting together the results from all analyses point to Bene Israel being an admixed population with both Jewish and Indian ancestry, with the genetic contribution of each of these ancestral populations being substantial. The admixture took place in the last millennium, about 19-33 generations ago. It involved Middle-Eastern Jews and was sex-biased, with more male Jewish and local female contribution. It was followed by a population bottleneck and high endogamy, which can lead to increased prevalence of recessive diseases in this population. This study provides an example of how genetic analysis advances our knowledge of human history in cases where other disciplines lack the relevant data to do so.

North African Jewish and non-Jewish populations form distinctive, orthogonal clusters.

North African Jews constitute the second largest Jewish Diaspora group. However, their relatedness to each other; to European, Middle Eastern, and other Jewish Diaspora groups; and to their former North African non-Jewish neighbors has not been well defined. Here, genome-wide analysis of five North African Jewish groups (Moroccan, Algerian, Tunisian, Djerban, and Libyan) and comparison with other Jewish and non-Jewish groups demonstrated distinctive North African Jewish population clusters with proximity to other Jewish populations and variable degrees of Middle Eastern, European, and North African admixture. Two major subgroups were identified by principal component, neighbor joining tree, and identity-by-descent analysis-Moroccan/Algerian and Djerban/Libyan-that varied in their degree of European admixture. These populations showed a high degree of endogamy and were part of a larger Ashkenazi and Sephardic Jewish group. By principal component analysis, these North African groups were orthogonal to contemporary populations from North and South Morocco, Western Sahara, Tunisia, Libya, and Egypt. Thus, this study is compatible with the history of North African Jews-founding during Classical Antiquity with proselytism of local populations, followed by genetic isolation with the rise of Christianity and then Islam, and admixture following the emigration of Sephardic Jews during the Inquisition.

Interplay between BRCA1 and RHAMM regulates epithelial apicobasal polarization and may influence risk of breast cancer.

Differentiated mammary epithelium shows apicobasal polarity, and loss of tissue organization is an early hallmark of breast carcinogenesis. In BRCA1 mutation carriers, accumulation of stem and progenitor cells in normal breast tissue and increased risk of developing tumors of basal-like type suggest that BRCA1 regulates stem/progenitor cell proliferation and differentiation. However, the function of BRCA1 in this process and its link to carcinogenesis remain unknown. Here we depict a molecular mechanism involving BRCA1 and RHAMM that regulates apicobasal polarity and, when perturbed, may increase risk of breast cancer. Starting from complementary genetic analyses across families and populations, we identified common genetic variation at the low-penetrance susceptibility HMMR locus (encoding for RHAMM) that modifies breast cancer risk among BRCA1, but probably not BRCA2, mutation carriers: n = 7,584, weighted hazard ratio ((w)HR) = 1.09 (95% CI 1.02-1.16), p(trend) = 0.017; and n = 3,965, (w)HR = 1.04 (95% CI 0.94-1.16), p(trend) = 0.43; respectively. Subsequently, studies of MCF10A apicobasal polarization revealed a central role for BRCA1 and RHAMM, together with AURKA and TPX2, in essential reorganization of microtubules. Mechanistically, reorganization is facilitated by BRCA1 and impaired by AURKA, which is regulated by negative feedback involving RHAMM and TPX2. Taken together, our data provide fundamental insight into apicobasal polarization through BRCA1 function, which may explain the expanded cell subsets and characteristic tumor type accompanying BRCA1 mutation, while also linking this process to sporadic breast cancer through perturbation of HMMR/RHAMM.

Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction.

The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10(-11) - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.