Next-generation genotype imputation service and methods

S, Das; Forer, Lukas; Schönherr, Sebastian; Sidore, Carlo; Locke, Adam E.; Kwong, Alan; Vrieze, Scott; Chew, Emily Y.; Levy, Shawn; McGue, Matt; Schlessinger, David; Stambolian, Dwight; Loh, Po Ru; Iacono, William G.; Swaroop, Anand; Scott, Laura J.; Cucca, Francesco; Kronenberg, Florian; Boehnke, Michael; Abecasis, Gonçalo R.; Fuchsberger, Christian

doi:10.1038/ng.3656

Cited by 3,293 publications

(2,754 citation statements)

References 37 publications

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“…The reference panel came from two datasets: filtered variants from the 135 African genomes and TGP (10). After phasing, imputation was performed using Minimac3 (75). Imputation performed very well at most loci (R 2 > 0.91 with MAF ≥ 0.05) (table S3).…”

Section: Methodsmentioning

confidence: 99%

Loci associated with skin pigmentation identified in African populations

Crawford

Kelly

Hansen

et al. 2017

Science

296

321

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Despite the wide range of skin pigmentation in humans, little is known about its genetic basis in global populations. Examining ethnically diverse African genomes, we identify variants in or near SLC24A5, MFSD12, DDB1, TMEM138, OCA2 and HERC2 that are significantly associated with skin pigmentation. Genetic evidence indicates that the light pigmentation variant at SLC24A5 was introduced into East Africa by gene flow from non-Africans. At all other loci, variants associated with dark pigmentation in Africans are identical by descent in southern Asian and Australo-Melanesian populations. Functional analyses indicate that MFSD12 encodes a lysosomal protein that affects melanogenesis in zebrafish and mice, and that mutations in melanocyte-specific regulatory regions near DDB1/TMEM138 correlate with expression of UV response genes under selection in Eurasians.

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Section: Methodsmentioning

confidence: 99%

Loci associated with skin pigmentation identified in African populations

Crawford

Kelly

Hansen

et al. 2017

Science

296

321

View full text Add to dashboard Cite

show abstract

“…For pathway-level analysis, we pruned SNPs with a criterion of linkage disequilibrium r 2 > 0.8 using a sliding window of size 50 SNPs and a moving step of 5. We imputed the missing SNPs via a Michigan Imputation Server (Das et al, 2016) In contrast, The HDGLM (Guo and Chen, 2016) yielded significant p-values for only two chromosomes. As expected, the p-values of HDGLM were close to that of SPU (2) since the two test statistics are similar.…”

Section: Real Data Analysismentioning

confidence: 99%

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

Pan³

2019

STAT SINICA

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Significance testing for high-dimensional generalized linear models (GLMs) has been increasingly needed in various applications, however, existing methods are mainly based on a sum of squares of the score vector and only powerful under certain alternative hypotheses. In practice, depending on whether the true association pattern under an alternative hypothesis is sparse or dense or between, the existing tests may or may not be powerful. In this paper, we propose an adaptive test on a high-dimensional parameter of a GLM (in the presence of a low-dimensional nuisance parameter), which can maintain high power across a wide range of scenarios. To evaluate its p-value, its asymptotic null distribution is derived. We conduct simulations to demonstrate the superior performance of the proposed test. In addition, we apply it and other existing tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) * Correspondence: wuxx0845@umn.edu (C.W.), weip@biostat.umn.edu (W.P.) † Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

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“…The latter dataset was imputed using SHAPEIT (Delaneau et al 2013) on the Michigan Imputation Server (Das et al 2016) with the 1000 Genomes data as the reference panel (Bhérer et al in prep. ).…”

Section: Building Admixture Graphsmentioning

confidence: 99%

Detecting Polygenic Adaptation in Admixture Graphs

2018

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An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method -which we call PolyGraph -has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

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Next-generation genotype imputation service and methods

Cited by 3,293 publications

References 37 publications

Loci associated with skin pigmentation identified in African populations

Loci associated with skin pigmentation identified in African populations

An Adaptive Test on High-dimensional Parameters in Generalized Linear Models

Detecting Polygenic Adaptation in Admixture Graphs

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