Ancestry-specific association mapping in admixed populations

Skotte, Line; Korneliussen, Thorfinn Sand; Moltke, Ida; Albrechtsen, Anders

doi:10.1101/014001

Cited by 9 publications

(6 citation statements)

References 28 publications

(31 reference statements)

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“…Only variants associated with the trait are included in the plot, assuming an additive genetic model (p < 0.01, for joint analysis of Greenlandic IHIT and Danish Inter99). The effect of each variant was assessed, using a method that provides effect size estimates for Europeans on the one hand and unmixed Greenlandic Inuit on the other [47]. These data show that most loci have comparable effects in the European ancestry component and the Greenlandic Inuit population (Figure 3).…”

Section: Genetics Of Glycemic Traits -Similarities Between the Europementioning

confidence: 99%

“…In the light of this problem, finding humans with homozygous loss-of-function variants is very valuable, because detailed physiological investigations of such individuals can shed light on gene functions in humans, and potentially pave the way for novel treatment targets in human diseases. The effects were estimated for the ancestral population using AsaMap [47]. Only variants associated with the trait in an additive model (p < 0.01, for joint analysis of Greenlandic IHIT and B99 (n = 3693 and n = 3437) and Danish Inter99 (n = 6116 and n = 5774)) for fasting and 2-h plasma glucose respectively were included in the plot.…”

Section: Characterization Of Human Loss-of-function Variants In Isolamentioning

confidence: 99%

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Diabetes in Population Isolates: Lessons from Greenland

Grarup¹,

Moltke²,

Albrechtsen³

et al. 2015

Rev Diabet Stud

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■ AbstractType 2 diabetes (T2D) is an increasing health problem worldwide with particularly high occurrence in specific subpopulations and ancestry groups. The high prevalence of T2D is caused both by changes in lifestyle and genetic predisposition. A large number of studies have sought to identify the genetic determinants of T2D in large, open populations such as Europeans and Asians. However, studies of T2D in population isolates are gaining attention as they provide several advantages over open populations in genetic disease studies, including increased linkage disequilibrium, homogeneous environmental exposure, and increased allele frequency. We recently performed a study in the small, historically isolated Greenlandic population, in which the prevalence of T2D has increased to more than 10%. In this study, we identified a common nonsense variant in TBC1D4, which has a population-wide impact on glucose-stimulated plasma glucose, serum insulin levels, and T2D. The variant defines a specific subtype of non-autoimmune diabetes characterized by decreased post-prandial glucose uptake and muscular insulin resistance. These and other recent findings in population isolates illustrate the value of performing medical genetic studies in genetically isolated populations. In this review, we describe some of the advantages of performing genetic studies of T2D and related cardio-metabolic traits in a population isolate like the Greenlandic, and we discuss potentials and perspectives for future research into T2D in this population.

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Section: Genetics Of Glycemic Traits -Similarities Between the Europementioning

confidence: 99%

Section: Characterization Of Human Loss-of-function Variants In Isolamentioning

confidence: 99%

Diabetes in Population Isolates: Lessons from Greenland

Grarup¹,

Moltke²,

Albrechtsen³

et al. 2015

Rev Diabet Stud

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“…For more information on this see the supplementary material. Furthermore standard errors on the estimated effect sizes are estimated using the observed Fisher information matrix as in Lake et al [2003] and Skotte et al [2019].…”

Section: Em Modelmentioning

confidence: 99%

Efficient approaches for large scale GWAS studies with genotype uncertainty

Jørsboe

Albrechtsen

2019

Preprint

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Introduction: Association studies using low depth NGS data provide a cost efficient design. Here we introduce an association method that works for low depth NGS data where the genotype is not directly observed. We will investigate how using different priors when calculating genotype probabilities will affect association analysis, and how this approach is affected by population structure. Doing association studies with genetic dosages is a widely used method for taking genotype uncertainty into account. We will investigate how our genotype probability based method compares to using dosages in large association studies with low depth NGS data. Methods: Our association method for low depth NGS data works by modelling the unobserved genotype as a latent variable. Our implementation is in a generalised linear model framework, using a maximum likelihood approach. We use the EM algorithm for maximising the likelihood. Results & Discussion: Our simulations using different priors in low depth NGS data in a structured population, show that using an individual allele frequency prior has better statistical power for association analysis. When there is a correlation between sequencing depth and phenotype the individual allele frequency prior also helps control the false positive rate. In the absence of population structure the sample allele frequency prior and the individual allele frequency prior perform similarly. We show through simulations that in certain scenarios the latent variable approach has better statistical power than dosages. Lastly when adding additional covariates to the model our method has more statistical power and provides less biased effect sizes than SNPTEST, while also being much faster than SNPTEST. This makes it possible to properly account

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“…The incorporation of local ancestry into variant identification for admixed populations is a concept that has been discussed previously 40–50 , particularly with regard to ‘admixture mapping,’ whereby researchers associate an elevation of a given ancestry at a locus in the genome with increased risk of a disease that is known to be stratified in prevalence across ancestries 51–55 . Admixture mapping has proven successful in diseases which are highly stratified across populations, such as asthma and cardiovascular phenotypes 56–61 .…”

Section: Introductionmentioning

confidence: 99%

Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies

Atkinson

Kanai

et al. 2020

Preprint

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38Admixed populations are routinely excluded from medical genomic studies due to concerns over 39 population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the 40 inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with 41 simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestry-42 specific effect size estimates, can boost GWAS power, and improves the resolution of association signals. 43Using a local ancestry aware regression model, we replicate known hits for blood lipids in admixed 44 populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative 45 causal variants. 46 47 48 Introduction 49Admixed groups, whose genomes contain more than one ancestral population such as African 50American and Hispanic/Latino individuals, make up more than a third of the US populace, and the population 51 is becoming increasingly mixed over time 1 . Many common, heritable, diseases including prostate cancer 2-5 , 52 asthma 6-9 , and several cardiovascular disorders such as atherosclerosis 10,11 are enriched in admixed 53 populations of the US. However, only a minute proportion of association studies address the genetic 54 architecture of complex traits in such groups 12,13 ; admixed individuals are systematically removed from many 55 studies due to the lack of methods and pipelines to effectively account for their ancestry such that population 56 substructure can infiltrate analyses and bias results [14][15][16][17][18][19][20][21] . Large-scale efforts to collect genetic data alongside 57 medically-relevant phenotypes are beginning to focus more on non-Eurasian ethnic groups that contain higher 58 amounts of admixture 22-27 , motivating the timely development of scalable methods to allow well-calibrated 59 statistical genomic work on these populations. If not addressed, this inability to analyze admixed people will 60 limit the clinical utility of large-scale data-collection efforts for minorities, exacerbating the concerning health 61 disparities that already exist 28-32 . 62In GWAS, the specific concern regarding including admixed participants is obtaining false positive hits due 63 to alleles being at different frequencies across populations. Most studies currently attempt to control for this by 64 using Principle Components (PCs) in a linear or linear mixed model framework. However, PCs capture broader 65 admixture fractions, and individuals' local ancestry makeup may differ between case and control cohorts even 66 if their global fractions are identical. Even including PCs as covariates, then, still leaves open the possibility for 67 false positive associations, as well as absorbing power. 68Studying diverse populations in gene discovery efforts not only reduces disparities but also benefits 69 genetic analysis for individuals of all ancestries. Perhaps the most notable example of this is in multi-ethnic 70 fine-mapping, which ca...

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Ancestry-specific association mapping in admixed populations

Cited by 9 publications

References 28 publications

Diabetes in Population Isolates: Lessons from Greenland

Diabetes in Population Isolates: Lessons from Greenland

Efficient approaches for large scale GWAS studies with genotype uncertainty

Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies

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