Retrieving Y chromosomal haplogroup trees using GWAS data

Peng, Min‐Sheng; He, Jun-Dong; Fan, Lu; Liu, Jie; Adeola, Adeniyi C.; Wu, Shi-Fang; Murphy, Robert W.; Yao, Yong‐Gang; Zhang, Yaping

doi:10.1038/ejhg.2013.272

Cited by 9 publications

(5 citation statements)

References 28 publications

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“…Haplogroups O2 (35.4%) and O1b (21.9%) were dominant in the Myanmar population. Although the samples analyzed in this study do not represent the entire Myanmar population, it is meaningful in that the distribution of Y-haplogroups in Myanmar is similar to that of a previous study [23].…”

Section: Forensic Statistical Analysismentioning

confidence: 60%

Investigation of SNPs in the Precision ID Identity Panel using next-generation sequencing in a Myanmar population

Joo

Kwon

Moon

et al. 2022

Preprint

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Background—Single nucleotide polymorphisms (SNPs) have become popular in forensic genetics as an alternative to short tandem repeats (STRs) due to low mutation rates and small amplicon sizes. The Precision ID Identity Panel (Thermo Fisher Scientific), consisting of 90 autosomal SNPs and 34 Y-SNPs, was introduced for human identification by next-generation sequencing (NGS), enabling many studies on the global population; however, few reports are available on the Southeast Asian population.Methods and Results—A total of 96 unrelated male samples from Myanmar (Yangon) were analyzed with the Precision ID Identity Panel on a MiSeq (Illumina) using an in-house TruSeq compatible universal adapter. The sequencing performance was evaluated by locus balance and heterozygote balance, and the results were comparable to those of the Ion Torrent platform. For 90 autosomal SNPs, minor allele frequencies ranged between 0.068 and 0.500, and combined match probability (6.994×10−34) was lower than that of 22 PowerPlex Fusion autosomal STRs (3.130×10−26). Moreover, we identified 51 cryptic variations around the target SNPs using a custom variant caller, Visual SNP. For 34 Y-SNPs, 14 Y-haplogroups were observed—mostly O2 and O1b groups. Interpopulation analysis revealed that the Myanmar population is genetically closer to the East and Southeast Asian populations than the South Asian population.Conclusions—We demonstrate that the Precision ID Identity Panel can be successfully analyzed on a MiSeq using a custom data analysis pipeline and provide high discrimination power for human identification in the Myanmar population, while extending the accessibility of NGS analysis for SNPs in forensics.

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Section: Forensic Statistical Analysismentioning

confidence: 60%

Investigation of SNPs in the Precision ID Identity Panel using next-generation sequencing in a Myanmar population

Joo

Kwon

Moon

et al. 2022

Preprint

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“…We performed quality control ( Peng et al 2014 ) for the Y-chromosomal SNPs of 77 males extracted from the WGS data. The Y-chromosomal haplogrouping was conducted by using yHaplo ( Poznik 2016 ), which was also checked with HaploGrouper ( Jagadeesan et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

The Genetic Echo of the Tarim Mummies in Modern Central Asians

Dai

Sulaiman

Isakova³

et al. 2022

Molecular Biology and Evolution

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The diversity of Central Asians has been shaped by multiple migrations and cultural diffusion. Although ancient DNA studies have revealed the demographic changes of the Central Asian since the Bronze Age, the contribution of the ancient populations to the modern Central Asian remains opaque. Herein, we performed high-coverage sequencing of 131 whole genomes of Indo-European-speaking Tajik and Turkic-speaking Kyrgyz populations to explore their genomic diversity and admixture history. By integrating the ancient DNA data, we revealed more details of the origins and admixture history of Central Asians. We found that the major ancestry of present-day Tajik populations can be traced back to the admixture of the Bronze Age Bactria–Margiana Archaeological Complex and Andronovo related populations. Highland Tajik populations further received additional gene flow from the Tarim mummies, an isolated ancient North Eurasian related population. The West Eurasian ancestry of Kyrgyz is mainly derived from Historical Era populations in Xinjiang of China. Furthermore, the recent admixture signals detected in both Tajik and Kyrgyz are ascribed to the expansions of Eastern Steppe nomadic pastoralists during the Historical Era.

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“…This section describes a novel DNNs model and the hyperparameters used in this study. To ensure that both the network and hyperparameters are independent of the datasets used in the same problem domain since overfitting is a serious problem in neural networks, we used the preprocessed SNPs of 39 individuals of the CEU population [61] of the International HapMap Project [62] for the initial exploration of various deep learning models.…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Furthermore, recent research has shown than ANNs can handle small datasets [63], and this characteristic of the neural network fits our problem domain as it is challenging to obtain large datasets when compared to other big data domains. Upon selecting the DNNs as the model, we used the Myanmar dataset [61] (see Section IV for details) to search for the optimal hyperparameters.…”

Section: Deep Neural Networkmentioning

confidence: 99%

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A Novel Deep Neural Networks Model Based on Prime Numbers for Y DNA Haplogroup Prediction

2020

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Most of the Y chromosome (Ychr) region (approximately 95%) passes unchanged from father to son, except by the gradual accumulation of single-nucleotide polymorphism (SNP) mutations. This results in mutations being inherited together, where all males in the direct family will have an identical pattern of variations. These mutation patterns serve as markers and can be mapped into clusters known as Y DNA haplogroups. Besides lineage tracing, haplogroups have been associated with male infertility, semen parameters, and, more recently, disease progression in several populations. Thus, haplogroup prediction research is gaining importance because of the increasing interest in personalized medicine. Of note, there are two approaches to predicting haplogroups, where the difference lies in the genetic markers: short tandem repeats (STRs) or SNPs are inputs to the haplogroup prediction tools. STRs are not without limitations, as similar STR haplotypes exist between haplogroups, and this reduces the effectiveness of STR-based haplogroup prediction tools. By contrast, current SNP-based haplogroup prediction tools are computationally expensive. There have been no studies to date that leverage traditional machine learning and deep learning algorithms to identify mutation patterns using SNPs only, and this paper proposes a novel SNP-based deep neural networks (DNNs) model. However, DNNs suffer the curse of dimensionality and become computationally expensive with large datasets. Thus, this paper overcomes the limitation of the network by proposing a novel feature extraction method based on prime numbers that computes features in either the forward or reverse direction of the SNPs data. Our experimental results show that the model achieves a categorical cross-entropy loss value as low as 0.001 on the training dataset and as low as 0.039 on the test dataset.

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Retrieving Y chromosomal haplogroup trees using GWAS data

Cited by 9 publications

References 28 publications

Investigation of SNPs in the Precision ID Identity Panel using next-generation sequencing in a Myanmar population

Investigation of SNPs in the Precision ID Identity Panel using next-generation sequencing in a Myanmar population

The Genetic Echo of the Tarim Mummies in Modern Central Asians

A Novel Deep Neural Networks Model Based on Prime Numbers for Y DNA Haplogroup Prediction

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