Archaeology Augments Tibet's Genetic History—Response

Yi, Xin; Liu, Yu; Huerta‐Sánchez, Emilia; Jin, Xin; Cuo, Zha Xi Ping; Pool, John E.; Xu, Xun; Jiang, Hui; Vinckenbosch, Nicolas; Korneliussen, Thorfinn; Zheng, Hancheng; Liu, Tao; He, Wei‐Ming; Li, Kui; Luo, Ruibang; Nie, Xifang; Wu, Honglong; Zhao, Meiru; Zou, Jing; Shi, Ying; Li, Shuzheng; Yang, Qi; Asan,; Ni, Peixiang; Tian, Geng; Xu, Junming; Liu, Xiao; Jiang, Tao; Wu, Renhua; Zhou, Guangyu; Tang, Meifang; Qin, Junjie; Wang, Tong; Feng, Shuijian; Li, Guohong; Huasang,; Luosang, Jiangbai; Wang, Wei; Chen, Fang; Wang, Yading; Zheng, Xiaoguang; Li, Zhuo; Bianba, Zhuoma; Ge, Yang; Wang, Xinping; Tang, Shuhui; Gao, Guoyi; Chen, Yong; Zhang, Luo; Gusang, Lamu; Cao, Zheng; Ouyang, Weihan; Ren, Xiaoyong; Liang, Huiqing; Zheng, Huisong; Huang, Yi; Li, Jingxiang; Bolund, Lars; Kristiansen, Karsten; Li, Yingrui; Zhang, Yong; Xiu-qing, Zhang; Li, Ruiqiang; Li, Songgang; Yang, Huanming; Nielsen, Rasmus; Wang, Jun; Jian, Wang

doi:10.1126/science.329.5998.1467-b

Cited by 11 publications

(11 citation statements)

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“…Most current methods for conducting population genetic analysis are based on allele frequencies (reviewed in Nielsen, 2005) or a summary of allele frequencies (e.g., Gutenkunst et al, 2009; but see Yi et al, 2010), and, broadly speaking, two statistical approaches have been proposed to estimate this information from NGS data. The first approach entails calling genotypes of each individual using either a Bayesian or Likelihood framework (Hoberman et al, 2009; Li et al, 2009b; Bansal et al, 2010; DePristo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The other approach attempts to estimate allele frequencies directly from the data without first inferring individual genotypes (Lynch, 2009; Kim et al, 2010, 2011; Martin et al, 2010). In some cases, a posterior probability is generated that provides a quantification of the uncertainty of each genotype call (e.g., Martin et al, 2010; DePristo et al, 2011) that could be directly incorporated into population genetic analyses (e.g., Yi et al, 2010). However, until population genetic analyses are further adapted to incorporate posterior probabilities, standard population genetic analyses must be applied directly to genotype calls.…”

Section: Introductionmentioning

confidence: 99%

“…However, until population genetic analyses are further adapted to incorporate posterior probabilities, standard population genetic analyses must be applied directly to genotype calls. The number of applications of such statistical approaches to empirical data is thus far relatively small with a bias toward human-based studies (e.g., Hellmann et al, 2008; Durbin et al, 2010; Yi et al, 2010), but some examples in non-human systems exist as well (e.g., Williams et al, 2010; Ahmad et al, 2011). More importantly, the biases introduced when population genetic analyses are applied to genotypes inferred from NGS data have not been well characterized, particularly in systems other than humans.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

Crawford¹,

Lazzaro²

2012

Front. Gene.

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Next-generation sequencing (NGS) technologies have made it possible to address population genetic questions in almost any system, but high error rates associated with such data can introduce significant biases into downstream analyses, necessitating careful experimental design and interpretation in studies based on short-read sequencing. Exploration of population genetic analyses based on NGS has revealed some of the potential biases, but previous work has emphasized parameters relevant to human population genetics and further examination of parameters relevant to other systems is necessary, including situations where sample sizes are small and genetic variation is high. To assess experimental power to address several principal objectives of population genetic studies under these conditions, we simulated population samples under selective sweep, population growth, and population subdivision models and tested the power to accurately infer population genetic parameters from sequence polymorphism data obtained through simulated 4×, 8×, and 15× read depth sequence data. We found that estimates of population genetic differentiation and population growth parameters were systematically biased when inference was based on 4× sequencing, but biases were markedly reduced at even 8× read depth. We also found that the power to identify footprints of positive selection depends on an interaction between read depth and the strength of selection, with strong selection being recovered consistently at all read depths, but weak selection requiring deeper read depths for reliable detection. Although we have explored only a small subset of the many possible experimental designs and population genetic models, using only one SNP-calling approach, our results reveal some general patterns and provide some assessment of what biases could be expected under similar experimental structures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

Crawford¹,

Lazzaro²

2012

Front. Gene.

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“…To minimize the effect of low-coverage sequencing, we extracted the sites that were covered by high-quality reads (at least six of nine individuals covered by >2X reads). To prepare for ∂a∂i program, we called 1,420,399 SNPs using a published method [69]. The little egret reference genome sequence was used to infer ancestral alleles.…”

Section: Resequencing Data Analysismentioning

confidence: 99%

Genomic signatures of near-extinction and rebirth of the crested ibis and other endangered bird species

Cheng

et al. 2014

Genome Biol

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Background: Nearly one-quarter of all avian species is either threatened or nearly threatened. Of these, 73 species are currently being rescued from going extinct in wildlife sanctuaries. One of the previously most critically-endangered is the crested ibis, Nipponia nippon. Once widespread across North-East Asia, by 1981 only seven individuals from two breeding pairs remained in the wild. The recovering crested ibis populations thus provide an excellent example for conservation genomics since every individual bird has been recruited for genomic and demographic studies. Results: Using high-quality genome sequences of multiple crested ibis individuals, its thriving co-habitant, the little egret, Egretta garzetta, and the recently sequenced genomes of 41 other avian species that are under various degrees of survival threats, including the bald eagle, we carry out comparative analyses for genomic signatures of near extinction events in association with environmental and behavioral attributes of species. We confirm that both loss of genetic diversity and enrichment of deleterious mutations of protein-coding genes contribute to the major genetic defects of the endangered species. We further identify that genetic inbreeding and loss-of-function genes in the crested ibis may all constitute genetic susceptibility to other factors including long-term climate change, over-hunting, and agrochemical overuse. We also establish a genome-wide DNA identification platform for molecular breeding and conservation practices, to facilitate sustainable recovery of endangered species.Conclusions: These findings demonstrate common genomic signatures of population decline across avian species and pave a way for further effort in saving endangered species and enhancing conservation genomic efforts.

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“…Li et al, 2013;Qu et al, 2013;Li et al, 2014). EGLN1 (1egl 9 homolog 1), PPARA (peroxisome proliferator-activated receptor-a genes) and EPAS1, also known as HIF2α (endothelial PAS domain 1) were significantly associated with hypoxia adaptation (Beall et al, 2010;Bigham et al, 2010;Simonson et al, 2010;Storz, 2010;Yi et al, 2010). However, few studies have been performed on poikilothermic species.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome analysis of the plateau fish (Triplophysa dalaica): Implications for adaptation to hypoxia in fishes

Wang

Yang

et al. 2015

Gene

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Archaeology Augments Tibet's Genetic History—Response

Cited by 11 publications

References 1 publication

Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

Assessing the Accuracy and Power of Population Genetic Inference from Low-Pass Next-Generation Sequencing Data

Genomic signatures of near-extinction and rebirth of the crested ibis and other endangered bird species

Transcriptome analysis of the plateau fish (Triplophysa dalaica): Implications for adaptation to hypoxia in fishes

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