Review Synthetic Article: Sardinian Population (Italy): a Genetic Review

Calò, CM; Melis, Alessandra; Vona, Giuseppe; Piras, IS

doi:10.4314/ijma.v1i1.60356

Cited by 18 publications

(21 citation statements)

References 45 publications

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“…Interestingly, Sardinians have been described both as a genetic isolate with endogamy peaking in the central-southern and mountain areas with little internal mobility 31 and a heterogeneous population when microareas or close single village are considered 32-34 .…”

Section: Resultsmentioning

confidence: 99%

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

et al. 2014

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The search for a method that utilizes biological information to predict humans’ place of origin has occupied scientists for millennia. Over the past four decades, scientists have employed genetic data in an effort to achieve this goal but with limited success. While biogeographical algorithms using next-generation sequencing data have achieved an accuracy of 700 km in Europe, they were inaccurate elsewhere. Here we describe the Geographic Population Structure (GPS) algorithm and demonstrate its accuracy with three data sets using 40,000–130,000 SNPs. GPS placed 83% of worldwide individuals in their country of origin. Applied to over 200 Sardinians villagers, GPS placed a quarter of them in their villages and most of the rest within 50 km of their villages. GPS’s accuracy and power to infer the biogeography of worldwide individuals down to their country or, in some cases, village, of origin, underscores the promise of admixture-based methods for biogeography and has ramifications for genetic ancestry testing.

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Section: Resultsmentioning

confidence: 99%

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

et al. 2014

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“…The population genetics of Sardinia has long been studied (e.g. see C alò et al , 2008) in part because of its importance as a population for medical genetics (L ettre and H irschhorn , 2015). Pioneering studies, using classical genetic loci such as G6PD, HBB, and HLA and later maps of linkage disequilibrium, revealed that Sardinia is a genetic isolate with heterogeneous population sub-structure (e.g.…”

Section: Introductionmentioning

confidence: 99%

Population history from the Neolithic to present on the Mediterranean island of Sardinia: An ancient DNA perspective

Marcus

Posth

Ringbauer

et al. 2019

Preprint

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34Recent ancient DNA studies of western Eurasia have revealed a dynamic history of admixture, 35 with evidence for major migrations during the Neolithic and Bronze Age. The population of the 36 Mediterranean island of Sardinia has been notable in these studies -Neolithic individuals from 37 mainland Europe cluster more closely with Sardinian individuals than with all other present-day 38 Europeans. The current model to explain this result is that Sardinia received an initial influx 39 of Neolithic ancestry and then remained relatively isolated from expansions in the later Ne-40 olithic and Bronze Age that took place in continental Europe. To test this model, we generated 41 genome-wide capture data (approximately 1.2 million variants) for 43 ancient Sardinian individu-42 als spanning the Neolithic through the Bronze Age, including individuals from Sardinia's Nuragic 43 culture, which is known for the construction of numerous large stone towers throughout the is-44 land. We analyze these new samples in the context of previously generated genome-wide ancient 45 DNA data from 972 ancient individuals across western Eurasia and whole-genome sequence data 46 from approximately 1,500 modern individuals from Sardinia. The ancient Sardinian individuals 47 show a strong affinity to western Mediterranean Neolithic populations and we infer a high degree 48of genetic continuity on the island from the Neolithic (around fifth millennium BCE) through 49 the Nuragic period (second millennium BCE). In particular, during the Bronze Age in Sardinia, 50 we do not find significant levels of the "Steppe" ancestry that was spreading in many other parts 51 of Europe at that time. We also characterize subsequent genetic influx between the Nuragic 52 period and the present. We detect novel, modest signals of admixture between 1,000 BCE and 53 present-day, from ancestry sources in the eastern and northern Mediterranean. Within Sardinia, 54 we confirm that populations from the more geographically isolated mountainous provinces have 55 experienced elevated levels of genetic drift and that northern and southwestern regions of the 56 island received more gene flow from outside Sardinia. Overall, our genetic analysis sheds new 57 light on the origin of Neolithic settlement on Sardinia, reinforces models of genetic continuity on 58 the island, and provides enhanced power to detect post-Bronze-Age gene flow. Together, these 59 findings offer a refined demographic model for future medical genetic studies in Sardinia.60

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“…In contrast, the SardiNIA cohort (4,303 samples) showed heterogeneity with nearly all other cohorts ( Fig. 7b ), perhaps due to unknown artifacts or a slightly different genetic architecture for height as result of demographic history 23 .…”

Section: Resultsmentioning

confidence: 88%

Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

Robinson

et al. 2015

Preprint

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Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.

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Review Synthetic Article: Sardinian Population (Italy): a Genetic Review

Cited by 18 publications

References 45 publications

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

Geographic population structure analysis of worldwide human populations infers their biogeographical origins

Population history from the Neolithic to present on the Mediterranean island of Sardinia: An ancient DNA perspective

Across-cohort QC analyses of genome-wide association study summary statistics from complex traits

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