Genetic structure in the Sherpa and neighboring Nepalese populations

Cole, Amy M.; Cox, Sean P.; Jeong, Choongwon; Petousi, Nayia; Aryal, Dhana R.; Droma, Yunden; Hanaoka, Masayuki; Ôta, Masao; Kobayashi, Nozomu; Gasparini, Paolo; Montgomery, Hugh; Robbins, Peter A.; Rienzo, Anna Di; Cavalleri, Gianpiero L.

doi:10.1186/s12864-016-3469-5

Cited by 23 publications

(25 citation statements)

References 39 publications

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“…The power of this approach was first illustrated in human data, whereby adaptive regions of the genome may be revealed in as few as 30 individuals (Pickrell et al, 2007). The extent of distinct and shared genetic adaptations and how these factors relate to the distinct suite of physiological traits exhibited within and across populations is an active area of research (Wuren et al, 2014;Jha et al, 2016;Cole et al, 2017).…”

Section: The Power Of Detecting Selection In Human Genomesmentioning

confidence: 99%

Cross-Species Insights Into Genomic Adaptations to Hypoxia

et al. 2020

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Over millions of years, vertebrate species populated vast environments spanning the globe. Among the most challenging habitats encountered were those with limited availability of oxygen, yet many animal and human populations inhabit and perform life cycle functions and/or daily activities in varying degrees of hypoxia today. Of particular interest are species that inhabit high-altitude niches, which experience chronic hypobaric hypoxia throughout their lives. Physiological and molecular aspects of adaptation to hypoxia have long been the focus of high-altitude populations and, within the past decade, genomic information has become increasingly accessible. These data provide an opportunity to search for common genetic signatures of selection across uniquely informative populations and thereby augment our understanding of the mechanisms underlying adaptations to hypoxia. In this review, we synthesize the available genomic findings across hypoxia-tolerant species to provide a comprehensive view of putatively hypoxia-adaptive genes and pathways. In many cases, adaptive signatures across species converge on the same genetic pathways or on genes themselves [i.e., the hypoxia inducible factor (HIF) pathway). However, specific variants thought to underlie function are distinct between species and populations, and, in most cases, the precise functional role of these genomic differences remains unknown. Efforts to standardize these findings and explore relationships between genotype and phenotype will provide important clues into the evolutionary and mechanistic bases of physiological adaptations to environmental hypoxia.

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Section: The Power Of Detecting Selection In Human Genomesmentioning

confidence: 99%

Cross-Species Insights Into Genomic Adaptations to Hypoxia

et al. 2020

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“…Population-genetic studies in worldwide human populations over the past decade have found ROA ranging in size from tens of kb to multiple Mb to be ubiquitous and frequent even in ostensibly outbred populations [ 1 – 28 ] and to have a non-uniform distribution across the genome [ 7 , 10 , 13 , 18 ] that is correlated with spatially variable genomic properties [ 2 – 4 , 18 ] creating autozygosity hotspots and coldspots [ 18 ]. ROA of different sizes have different continental patterns both with regards to their total lengths in individual genomes [ 12 , 18 , 22 , 24 , 26 – 28 ] and in their distribution across the genome [ 18 ] reflecting the distinct forces generating ROA of different lengths. Studies of ROA in the genomes of ancient hominins [ 29 – 31 ] and early Europeans [ 32 ] have provided unique insights into the mating patterns and effective population sizes of our early forbearers.…”

Section: Introductionmentioning

confidence: 99%

Weighted likelihood inference of genomic autozygosity patterns in dense genotype data

et al. 2017

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BackgroundGenomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.MethodsWe report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.ResultsForward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.ConclusionsThis weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users.

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“…The Sherpa share more genetic affinity with these Tibeto-Burman speaking populations than with other Indo-Aryan populations of Nepal. However, the Sherpa are distinct from other Nepalese populations in that the Sherpa have elevated levels of runs of homozygosity (Cole et al, 2017), and illustrate very little or no admixture with Nepalese or South Asian populations (Cole et al, 2017). Thus, the Khumbu Valley Sherpa can be considered from the perspective of population genetics as a “bottlenecked” population recently derived from Tibetans.…”

Section: The Sherpa a Recently Derived Tibetan Populationmentioning

confidence: 96%

“…Analysis of both autosomal data (Lu et al, 2016; Gnecchi-Ruscone et al, 2017) and uniparental mtDNA and Y-chromosome markers (Bhandari et al, 2015) have shown the Sherpa and Tibetans to share relatively recent common ancestry. Tibetans also share recent common ancestry with other Nepalese populations including the Rai, Magar, Tamang, and Gurung (Cole et al, 2017). The Sherpa share more genetic affinity with these Tibeto-Burman speaking populations than with other Indo-Aryan populations of Nepal.…”

Section: The Sherpa a Recently Derived Tibetan Populationmentioning

confidence: 99%

Population History and Altitude-Related Adaptation in the Sherpa

Bhandari

Cavalleri

2019

Front. Physiol.

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The first ascent of Mount Everest by Tenzing Norgay and Sir Edmund Hillary in 1953 brought global attention to the Sherpa people and human performance at altitude. The Sherpa inhabit the Khumbu Valley of Nepal, and are descendants of a population that has resided continuously on the Tibetan plateau for the past ∼25,000 to 40,000 years. The long exposure of the Sherpa to an inhospitable environment has driven genetic selection and produced distinct adaptive phenotypes. This review summarizes the population history of the Sherpa and their physiological and genetic adaptation to hypoxia. Genomic studies have identified robust signals of positive selection across EPAS1, EGLN1, and PPARA, that are associated with hemoglobin levels, which likely protect the Sherpa from altitude sickness. However, the biological underpinnings of other adaptive phenotypes such as birth weight and the increased reproductive success of Sherpa women are unknown. Further studies are required to identify additional signatures of selection and refine existing Sherpa-specific adaptive phenotypes to understand how genetic factors have underpinned adaptation in this population. By correlating known and emerging signals of genetic selection with adaptive phenotypes, we can further reveal hypoxia-related biological mechanisms of adaptation. Ultimately this work could provide valuable information regarding treatments of hypoxia-related illnesses including stroke, heart failure, lung disease and cancer.

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Genetic structure in the Sherpa and neighboring Nepalese populations

Cited by 23 publications

References 39 publications

Cross-Species Insights Into Genomic Adaptations to Hypoxia

Cross-Species Insights Into Genomic Adaptations to Hypoxia

Weighted likelihood inference of genomic autozygosity patterns in dense genotype data

Population History and Altitude-Related Adaptation in the Sherpa

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