Genetic continuity of Indo-Iranian speakers since the Iron Age in southern Central Asia

Since prehistoric times, southern Central Asia has been at the crossroads of the movement of people, culture, and goods. Today, the Central Asian populations are divided into two cultural and linguistic groups: the Indo-Iranian and the Turko-Mongolian groups. Previous genetic studies unveiled that migrations from East Asia contributed to the spread of Turko-Mongolian populations in Central Asia and the partial replacement of the Indo-Iranian populations. However, little is known about the origin of the latters. To shed light on this, we compare the genetic data on two current-day Indo-Iranian populations — Yaghnobis and Tajiks — with genome-wide data from published ancient individuals. The present Indo-Iranian populations from Central Asia display a strong genetic continuity with Iron Age samples from Turkmenistan and Tajikistan. We model Yaghnobis as a mixture of 93% Iron Age individual from Turkmenistan and 7% from Baikal. For the Tajiks, we observe a higher Baikal ancestry and an additional admixture event with a South Asian population. Our results, therefore, suggest that in addition to a complex history, Central Asia shows a remarkable genetic continuity since the Iron Age, with only limited gene flow.

The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.

Recent selection is a major force driving cancer evolution

Cancer is one of the most threatening diseases to humans. Understanding the evolution of cancer genes is helpful for therapy management. However, systematic investigation of the evolution of cancer driver genes is sparse. Using comparative genomic analysis, population genetics analysis and computational molecular evolutionary analysis, we detected the evolution of 568 cancer driver genes of 66 cancer types across the primate phylogeny (long timescale selection), and in modern human populations from the 1000 human genomics project (recent selection). We found that recent selection pressures, rather than long timescale selection, significantly affect the evolution of cancer driver genes in humans. Cancer driver genes related to morphological traits and local adaptation are under positive selection in different human populations. The African population showed the largest extent of divergence compared to other populations. It is worth noting that the corresponding cancer types of positively selected genes exhibited population-specific patterns, with the South Asian population possessing the least numbers of cancer types. This helps explain why the South Asian population usually has low cancer incidence rates. Population-specific patterns of cancer types whose driver genes are under positive selection also give clues to explain discrepancies of cancer incidence rates in different geographical populations, such as the high incidence rate of Wilms tumour in the African population and of Ewing's sarcomas in the European population. Our findings are thus helpful for understanding cancer evolution and providing guidance for further precision medicine.

Performance of Fetal Medicine Foundation algorithm for first trimester preeclampsia screening in an indigenous south Asian population

Background To evaluate the performance of the Fetal Medicine Foundation (FMF) preterm preeclampsia (PE) screening algorithm in an indigenous South Asian population. Methods This was a prospective observational cohort study conducted in a tertiary maternal fetal unit in Delhi, India over 2 years. The study population comprised of 1863 women carrying a singleton pregnancy and of South Asian ethnicity who were screened for preterm pre-eclampsia (PE) between 11 and 14 weeks of gestation using Mean Arterial Pressure (MAP), transvaginal Mean Uterine Artery Pulsatility Index (UtAPI) and biochemical markers - Pregnancy Associated Plasma Protein-A (PAPP-A) and Placental Growth Factor.. Absolutemeasurements of noted biomarkers were converted to multiples of the expected gestational median (MoMS) which were then used to estimate risk for preterm PE < 37 weeks using Astraia software. Women with preterm PE risk of ≥1:100 was classified as as high risk. Detection rates (DR) at 10% false positive rate were calculated after adjusting for prophylactic aspirin use (either 75 or 150 mg). Results The incidence of PE and preterm PE were 3.17% (59/1863) and 1.34% (25/1863) respectively. PAPP-A and PlGF MoM distribution medians were 0.86 and 0.87 MoM and significantly deviated from 1 MoM. 431 (23.1%) women had a risk of ≥1:100, 75 (17.8%) of who received aspirin. Unadjusted DR using ≥1:100 threshold was 76%.Estimated DRs for a fixed 10% FPR ranged from 52.5 to 80% depending on biomarker combination after recentering MoMs and adjusting for aspirin use. Conclusion The FMF algorithm whilst performing satisfactorily could still be further improved to ensure that biophysical and biochemical markers are correctly adjusted for indigenous South Asian women.

Genetic epidemiology of autoinflammatory disease variants in Indian population from 1029 whole genomes

Background Autoinflammatory disorders are the group of inherited inflammatory disorders caused due to the genetic defect in the genes that regulates innate immune systems. These have been clinically characterized based on the duration and occurrence of unprovoked fever, skin rash, and patient’s ancestry. There are several autoinflammatory disorders that are found to be prevalent in a specific population and whose disease genetic epidemiology within the population has been well understood. However, India has a limited number of genetic studies reported for autoinflammatory disorders till date. The whole genome sequencing and analysis of 1029 Indian individuals performed under the IndiGen project persuaded us to perform the genetic epidemiology of the autoinflammatory disorders in India. Results We have systematically annotated the genetic variants of 56 genes implicated in autoinflammatory disorder. These genetic variants were reclassified into five categories (i.e., pathogenic, likely pathogenic, benign, likely benign, and variant of uncertain significance (VUS)) according to the American College of Medical Genetics and Association of Molecular pathology (ACMG-AMP) guidelines. Our analysis revealed 20 pathogenic and likely pathogenic variants with significant differences in the allele frequency compared with the global population. We also found six causal founder variants in the IndiGen dataset belonging to different ancestry. We have performed haplotype prediction analysis for founder mutations haplotype that reveals the admixture of the South Asian population with other populations. The cumulative carrier frequency of the autoinflammatory disorder in India was found to be 3.5% which is much higher than reported. Conclusion With such frequency in the Indian population, there is a great need for awareness among clinicians as well as the general public regarding the autoinflammatory disorder. To the best of our knowledge, this is the first and most comprehensive population scale genetic epidemiological study being reported from India.

Genetic continuity of Indo-Iranian speakers since the Iron Age in southern Central Asia.

Since prehistoric times, South Central Asia has been at the crossroads of the movement of people, culture, and goods. Today, the Central Asia's populations are divided into two cultural and linguistic groups: the Indo-Iranian and the Turko-Mongolian groups. Previous genetic studies unveiled that migrations from East Asia contributed to the spread of Turko-Mongolian populations in Central Asia and the partial replacement of the Indo-Iranian population. However, little is known about the origin of the latter. To shed light on this, we compare the genetic data on two current-day populations - Yaghnobis and Tajiks - with genome-wide data from published ancient individuals. The present Indo-Iranian populations from Central Asia display a strong genetic continuity with Iron Age samples from Turkmenistan and Tajikistan. We model Yaghnobis as a mixture of 93% Iron Age individual from Turkmenistan and 7% from Baikal. For the Tajiks, we observe a higher Baikal ancestry and an additional admixture event with a South Asian population. Our results, therefore, suggest that in addition to a complex history, Central Asia shows a remarkable genetic continuity since the Iron Age, with only limited gene flow.

Dealing with Cultural Identities: A Study of Nepalese Families in Tokyo

In recent years, the Nepalese migrant population in Japan has increased exponentially. The number reached 88,951 in 2018, becoming the largest south Asian population in Japan. This number includes people in various visa categories: skilled labor, engineer, business, dependent, student and so on. The number of school children lies somewhere around 10,000. A child born and raised in a culture different to their parents’ culture goes through a complex cultural identity formation process. In this context, this paper explores children’s cultural identity development and promotion by migrant Nepalese families in Tokyo. In particular, it examines which cultural identities they are prioritizing and how they are developing host cultural identities while maintaining their native culture. The experiences of these migrant Nepalese parents were collected through in-depth interviews with 45 parents. The responses suggest that these parents are prioritizing the promotion of a Nepalese cultural identity for their child(ren). Parents focus on promoting and participating in Nepalese festivals, cooking Nepalese food at home, and meeting other Nepalese families in Japan. However, almost every parent expressed their desire for the development of a multicultural sense in their child(ren).