Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?

Lawson, Daniel J.; Davies, Neil M; Haworth, Simon; Ashraf, Bilal; Howe, Laurence J; Crawford, Andrew; Hemani, Gibran; Smith, George Davey; Timpson, Nicholas J.

doi:10.1007/s00439-019-02014-8

Cited by 105 publications

(108 citation statements)

References 114 publications

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“…This resolution limit also confines the extent to which the confounding effects of population structure can be controlled in genomic studies of health and disease such as genome-wide association studies (GWAS). As these studies continue to seek ever-rarer genetic variation with ever-increasing cohort sizes, intricate understanding and fine control of population structure is becoming increasingly relevant, but increasingly challenging 3 .…”

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confidence: 99%

Dutch population structure across space, time and GWAS design

Byrne

Rheenen

Berg

et al. 2020

Preprint

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We studied fine-grained population genetic structure and demographic change across the Netherlands using 1 genome-wide single nucleotide polymorphism data (1,626 individuals) with associated geography (1,422 2 individuals). We applied ChromoPainter/fineSTRUCTURE, identifying 40 haplotypic clusters exhibiting 3 strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide 4 ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. 5 Despite superexponential population growth, north-south structure is temporally stable, with west-east 6 differentiation more transient, potentially influenced by migrations during the middle ages. Within Dutch 7 and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than 8 standard methods. 9 The Netherlands is a densely populated country on the northwestern edge of the European continent, bounded by 10 Germany, Belgium and the North Sea. The country is divided into twelve provinces and has a complex demographic 11 history, with occupation by several Germanic peoples since the collapse of the Roman Empire, including the 12 Frisians, the Low Saxons and the Franks. Over 17 million individuals now inhabit this relatively small region 13 (41,500km 2 ), making it one of the most densely populated countries in Europe. Despite its small geographical size, 14 previous genetic studies of the people of the Netherlands have demonstrated coarse population structure that 15 correlates with its geography, as well as apparent heterogeneity in effective population sizes across provinces 1,2 . 16 These observations suggest that the demographic past of the Dutch population has left residual signatures in its 17 present regional genetic structure; however this has not been fully explained in the context of neighbouring 18 populations and thus far the use of unlinked genetic markers have limited the resolution at which this structure can 19 be described. This resolution limit also confines the extent to which the confounding effects of population structure 20 can be controlled in genomic studies of health and disease such as genome-wide association studies (GWAS). As 21 these studies continue to seek ever-rarer genetic variation with ever-increasing cohort sizes, intricate understanding 22 and fine control of population structure is becoming increasingly relevant, but increasingly challenging 3 . 23 Recent studies have showcased the power of leveraging shared haplotypes to uncover and characterise previously 24 unrecognised fine-grained genetic structure within populations, yielding novel insights into the demographic 25 composition and history of Britain and Ireland 4-7 , Finland 8 , Japan 9 , Italy 10 and Spain 11 . Haplotype sharing has also 26 revealed genetic affinities between populations, enabling inference of historical admixture events using modern 27 populations as proxies for ancestral admixing sources 12 . Furthermore, geographic informat...

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confidence: 99%

Dutch population structure across space, time and GWAS design

Byrne

Rheenen

Berg

et al. 2020

Preprint

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“…These effects were observed with all spatially correlated environmental patterns, but were particularly pronounced if environmental effects are concentrated in one region, as was also found by Mathieson and McVean (2012). Though increasing the number of PC axes used in the analysis may reduce the false-positive rate, this may also decrease the power of the test to detect truly causal alleles (Lawson et al . 2019).…”

Section: Discussionmentioning

confidence: 71%

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

Battey

Ralph

Kern

2019

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Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies. We find that most common summary statistics have distributions that differ substantially from that seen in well-mixed populations, especially when Wright's neighborhood size is less than 100 and sampling is spatially clustered. Stepping-stone models reproduce some of these effects, but discretizing the landscape introduces artifacts which in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations were surprisingly robust to isolation by distance. We also show that the combination of spatially autocorrelated environments and limited dispersal causes genome-wide association studies to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms. 7 8

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“…Alternatively, the findings may also reflect complexity introduced by the scale and sampling frame of UK Biobank. Although the LCV model is more robust than MR to biasing effects from sources such as horizontal pleiotropy and sample overlap, the LCV model can become biased by correlation between genetic variation and environmental factors which affect disease 17,35 . This aggregation might be due to factors such as ancient ancestry 36 , genetic nurture effects 37 or sampling phenomena 38 and is a concern in the UK Biobank sample 39 where much of the data used in this experiment were obtained.…”

Section: Discussionmentioning

confidence: 99%

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

Haworth

Kho

Holgerson

et al. 2019

Preprint

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BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messages1) The latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.2) Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.3) Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.4) The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. 5)We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

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Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?

Cited by 105 publications

References 114 publications

Dutch population structure across space, time and GWAS design

Dutch population structure across space, time and GWAS design

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

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