The Origins of Ashkenaz, Ashkenazic Jews, and Yiddish

Carrier screening in the Mexican Jewish community using a pan-ethnic expanded carrier screening NGS panel

PURPOSE

The Mexican Jewish community (MJC) is a previously uncharacterized, genetically isolated group composed of Ashkenazi and Sephardi-Mizrahi Jews who migrated in the early 1900s. We aimed to determine the heterozygote frequency of disease-causing variants in 302 genes in this population.

METHODS

We conducted a cross-sectional study of the MJC involving individuals representing Ashkenazi Jews, Sephardi-Mizrahi Jews, or mixed-ancestry Jews. We offered saliva-based preconception pan-ethnic expanded carrier screening, which examined 302 genes. We analyzed heterozygote frequencies of pathogenic/likely pathogenic variants and compared them with those in the Genome Aggregation Database (gnomAD).

RESULTS

We recruited 208 participants. The carrier screening results showed that 72.1% were heterozygous for at least 1 severe disease-causing variant in 1 of the genes analyzed. The most common genes with severe disease-causing variants were CFTR (16.8% of participants), MEFV (11.5%), WNT10A (6.7%), and GBA (6.7%). The allele frequencies were compared with those in the gnomAD; 85% of variant frequencies were statistically different from those found in gnomAD (P <.05). Finally, 6% of couples were at risk of having a child with a severe disorder.

CONCLUSION

The heterozygote frequency of at least 1 severe disease-causing variant in the MJC was 72.1%. The use of carrier screening in the MJC and other understudied populations could help parents make more informed decisions.

The impact of cross-kingdom molecular forensics on genetic privacy

Recent advances in metagenomic technology and computational prediction may inadvertently weaken an individual's reasonable expectation of privacy. Through cross-kingdom genetic and metagenomic forensics, we can already predict at least a dozen human phenotypes with varying degrees of accuracy. There is also growing potential to detect a "molecular echo" of an individual's microbiome from cells deposited on public surfaces. At present, host genetic data from somatic or germ cells provide more reliable information than microbiome samples. However, the emerging ability to infer personal details from different microscopic biological materials left behind on surfaces requires in-depth ethical and legal scrutiny. There is potential to identify and track individuals, along with new, surreptitious means of genetic discrimination. This commentary underscores the need to update legal and policy frameworks for genetic privacy with additional considerations for the information that could be acquired from microbiome-derived data. The article also aims to stimulate ubiquitous discourse to ensure the protection of genetic rights and liberties in the post-genomic era. Video abstract.

Population genetic considerations for using biobanks as international resources in the pandemic era and beyond

The past years have seen the rise of genomic biobanks and mega-scale meta-analysis of genomic data, which promises to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limits the global understanding of disease risk but also inhibits viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable and computable and to operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of the most common genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks (locally and internationally), increase the accuracy of association analyses and inform developmental efforts.

The story of the lost twins: decoding the genetic identities of the Kumhar and Kurcha populations from the Indian subcontinent

Background

The population structure of the Indian subcontinent is a tapestry of extraordinary diversity characterized by the amalgamation of autochthonous and immigrant ancestries and rigid enforcement of sociocultural stratification. Here we investigated the genetic origin and population history of the Kumhars, a group of people who inhabit large parts of northern India. We compared 27 previously published Kumhar SNP genotype data sampled from Uttar Pradesh in north India to various modern day and ancient populations.

Results

Various approaches such as Principal Component Analysis (PCA), Admixture, TreeMix concurred that Kumhars have high ASI ancestry, minimal Steppe component and high genomic proximity to the Kurchas, a small and relatively little-known population found ~ 2500 km away in Kerala, south India. Given the same, biogeographical mapping using Geographic Population Structure (GPS) assigned most Kumhar samples in areas neighboring to those where Kurchas are found in south India.

Conclusions

We hypothesize that the significant genomic similarity between two apparently distinct modern-day Indian populations that inhabit well separated geographical areas with no known overlapping history or links, likely alludes to their common origin during or post the decline of the Indus Valley Civilization (estimated by ALDER). Thereafter, while they dispersed towards opposite ends of the Indian subcontinent, their genomic integrity and likeness remained preserved due to endogamous social practices. Our findings illuminate the genomic history of two Indian populations, allowing a glimpse into one or few of numerous of human migrations that likely occurred across the Indian subcontinent and contributed to shape its varied and vibrant evolutionary past.

Pair Matcher (PaM): fast model-based optimization of treatment/case-control matches

Motivation

In clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drug’s efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups.

Results

Pair Matcher (PaM) addresses stratification bias by optimizing pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenizing the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM’s ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment.

Availability and implementation

PaM is freely available via Rhttps://github.com/eelhaik/PAM and a web-interface at http://elhaik-matcher.sheffield.ac.uk/ElhaikLab/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Application of the geographic population structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas

Background

The utilization of high resolution genome data has important implications for the phylogeographical evaluation of non-human species. Biogeographical analyses can yield detailed understanding of their population biology and facilitate the geo-localization of individuals to promote their efficacious management, particularly when bred in captivity. The Geographic Population Structure (GPS) algorithm is an admixture based tool for inference of biogeographical affinities and has been employed for the geo-localization of various human populations worldwide. Here, we applied the GPS tool for biogeographical analyses and localization of the ancestral origins of wild and captive gorilla genomes, of unknown geographic source, available in the Great Ape Genome Project (GAGP), employing Gorillas with known ancestral origin as the reference data.

Results

Our findings suggest that GPS was successful in recapitulating the population history and estimating the geographic origins of all gorilla genomes queried and localized the wild gorillas with unknown geographical origin < 150 km of National Parks/Wildlife Reserves within the political boundaries of countries, considered as prominent modern-day abode for gorillas in the wild. Further, the GPS localization of most captive-born gorillas was congruent with their previously presumed ancestral homes.

Conclusions

Currently there is limited knowledge of the ancestral origins of most North American captive gorillas, and our study highlights the usefulness of GPS for inferring ancestry of captive gorillas. Determination of the native geographical source of captive gorillas can provide valuable information to guide breeding programs and ensure their appropriate management at the population level. Finally, our findings shine light on the broader applicability of GPS for protecting the genetic integrity of other endangered non-human species, where controlled breeding is a vital component of their conservation.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2568-5) contains supplementary material, which is available to authorized users.

Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians

The rapid accumulation of ancient human genomes from various areas and time periods potentially enables the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations, which are known to misrepresent the population structure. Past studies addressed these problems by using ancestry informative markers (AIMs). It is, thereby, unclear whether AIMs derived from contemporary human genomes can capture ancient population structures, and whether AIM-finding methods are applicable to aDNA, provided that the high missingness rates in ancient-and oftentimes haploid-DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperform all of the competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of single nucleotide polymorphism (SNP) microarrays and the interpretation of aDNA results, which enables a population-wide testing of primordialist theories.

The Diversity of REcent and Ancient huMan (DREAM): A New Microarray for Genetic Anthropology and Genealogy, Forensics, and Personalized Medicine

The human population displays wide variety in demographic history, ancestry, content of DNA derived from hominins or ancient populations, adaptation, traits, copy number variation, drug response, and more. These polymorphisms are of broad interest to population geneticists, forensics investigators, and medical professionals. Historically, much of that knowledge was gained from population survey projects. Although many commercial arrays exist for genome-wide single-nucleotide polymorphism genotyping, their design specifications are limited and they do not allow a full exploration of biodiversity. We thereby aimed to design the Diversity of REcent and Ancient huMan (DREAM)—an all-inclusive microarray that would allow both identification of known associations and exploration of standing questions in genetic anthropology, forensics, and personalized medicine. DREAM includes probes to interrogate ancestry informative markers obtained from over 450 human populations, over 200 ancient genomes, and 10 archaic hominins. DREAM can identify 94% and 61% of all known Y and mitochondrial haplogroups, respectively, and was vetted to avoid interrogation of clinically relevant markers. To demonstrate its capabilities, we compared its F_ST distributions with those of the 1000 Genomes Project and commercial arrays. Although all arrays yielded similarly shaped (inverse J) F_ST distributions, DREAM’s autosomal and X-chromosomal distributions had the highest mean F_ST, attesting to its ability to discern subpopulations. DREAM performances are further illustrated in biogeographical, identical by descent, and copy number variation analyses. In summary, with approximately 800,000 markers spanning nearly 2,000 genes, DREAM is a useful tool for genetic anthropology, forensic, and personalized medicine studies.

Application of geographic population structure (GPS) algorithm for biogeographical analyses of populations with complex ancestries: a case study of South Asians from 1000 genomes project

Background

The utilization of biological data to infer the geographic origins of human populations has been a long standing quest for biologists and anthropologists. Several biogeographical analysis tools have been developed to infer the geographical origins of human populations utilizing genetic data. However due to the inherent complexity of genetic information these approaches are prone to misinterpretations. The Geographic Population Structure (GPS) algorithm is an admixture based tool for biogeographical analyses and has been employed for the geo-localization of various populations worldwide. Here we sought to dissect its sensitivity and accuracy for localizing highly admixed groups. Given the complex history of population dispersal and gene flow in the Indian subcontinent, we have employed the GPS tool to localize five South Asian populations, Punjabi, Gujarati, Tamil, Telugu and Bengali from the 1000 Genomes project, some of whom were recent migrants to USA and UK, using populations from the Indian subcontinent available in Human Genome Diversity Panel (HGDP) and those previously described as reference.

Results

Our findings demonstrate reasonably high accuracy with regards to GPS assignment even for recent migrant populations sampled elsewhere, namely the Tamil, Telugu and Gujarati individuals, where 96%, 87% and 79% of the individuals, respectively, were positioned within 600 km of their native locations. While the absence of appropriate reference populations resulted in moderate-to-low levels of precision in positioning of Punjabi and Bengali genomes.

Conclusions

Our findings reflect that the GPS approach is useful but likely overtly dependent on the relative proportions of admixture in the reference populations for determination of the biogeographical origins of test individuals. We conclude that further modifications are desired to make this approach more suitable for highly admixed individuals.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12863-017-0579-2) contains supplementary material, which is available to authorized users.