Differential confounding of rare and common variants in spatially structured populations
Open accessAbstract
Well-powered genome-wide association studies, now made possible through advances in technology and large-scale collaborative projects, promise to characterize the contribution of rare variants to complex traits and disease. However, while population structure is a known confounder of association studies, it remains unknown whether methods developed to control stratification are equally effective for rare variants. Here, we demonstrate that rare variants can show a stratification that is systematically different from, and typically stronger than, common variants, and this is not necessarily corrected by existing methods. We show that the same process leads to inflation for load-based tests and can obscure signals at truly associated variants. Furthermore, we show that populations can display spatial structure in rare variants, even when Wright's fixation index FST is low, but that allele frequency–dependent metrics of allele sharing can reveal localized stratification. These results underscore the importance of collecting and integrating spatial information in the genetic analysis of complex traits.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 13 cited sources checked · 0 supported · 0 flagged
- Finding
Excess allele sharing increases as frequency decreases, implying the effect will appear below some sufficiently low variant frequency even in relatively unstructured populations.
“The fact that excess allele sharing increases as frequency decreases implies that even for relatively unstructured populations, this effect will be observed below some, sufficiently low, variant frequency.”
Quote in the paperNot yet classified - Finding
When non-genetic risk varies on a small scale, rare variants have a tail of highly correlated variants that drives inflation.
“In contrast, when non-genetic risk varies on a small scale, although most variants are uncorrelated with the risk, rare variants have a tail of highly correlated variants (Fig. 2e), which drive the inflation”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 2e
Not checked in the free run.
- Finding
If misclassification is localized to a spatial area, it will produce the same effects described in the paper.
“When this misclassification is restricted to a particular spatial area, for example a single recruitment center in a large study, it will produce the effects described here.”
Quote in the paperNot yet classified - Finding
If genetic risk is driven by only a few rare-variant loci, inflation is greater for rare variants and true signals are more likely to be obscured.
“However, when there are only a few loci driving risk, inflation is greater for rare variants (Supplementary Fig. 4). Consequently, if genetic risk is driven by small numbers of rare variants, then true signals are more likely to be obscured by rare variants that show association even though they are not physically linked to the causal variants.”
Quote in the paperNot yet classified0 of 1 sources checked- cites Supplementary Fig. 4
Not checked in the free run.
- Finding
Populations can show spatial structure in rare variants even when FST is low, and allele-frequency-dependent allele-sharing metrics can reveal localized stratification.
“We show that populations can display spatial structure in rare variants even when FST is low, but that allele-frequency dependent metrics of allele sharing can reveal localized stratification.”
Quote in the paperNot yet classified
- Finding
These correction methods are effective when non-genetic risk is broadly and smoothly distributed.
“we find they are all effective when non-genetic risk has a large and smooth distribution (Fig. 3a).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 3a
Not checked in the free run.
- Finding
When causal rare variants are geographically localized, test-statistic inflation can occur even without spatial structure in non-genetic risk.
“Thus even when there is no spatial structure to non-genetic risk, test-statistic inflation will be observed.”
Quote in the paperNot yet classified - Finding
Common variants can show effectively no excess allele sharing at short ranges while rare variants still show excess clustering, even with M=10.
“Analysis of allele sharing by distance as a function of distance shows that while common variants show effectively no excess allele sharing at short ranges, even with M=10 rare variants still show excess clustering (Fig. 4)”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 4
Not checked in the free run.
- Finding
For sharply defined risk, aggregation reduces inflation but low P-values remain strongly inflated.
“For sharply-defined risk, test-statistic inflation is reduced as more variants are considered, but still increases sharply for low P-values (Fig. 3d).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 3d
Not checked in the free run.
- Finding
Rare variants can show a systematically different and typically stronger stratification than common variants, and existing methods do not necessarily correct it.
“Here we demonstrate that rare variants can show a systematically different and typically stronger stratification than common variants, and that this is not necessarily corrected by existing methods.”
Quote in the paperNot yet classified - Finding
For smoothly varying Gaussian risk, load-based test-statistic inflation is largely independent of the number of rare variants aggregated.
“For smoothly-varying Gaussian risk, test-statistic inflation is largely independent of the number of variants considered (Fig. 3c).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 3c
Not checked in the free run.
- Finding
The same process can inflate load-based tests and obscure signals at truly associated variants.
“We show that the same process leads to inflation for load-based tests and can obscure signals at truly associated variants.”
Quote in the paperNot yet classified - Finding
The same correction methods are not effective when non-genetic risk is concentrated in a small, sharp region.
“However, none of them are effective for the small, sharp distribution of risk (Fig. 3b).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 3b
Not checked in the free run.
- Method
Several standard stratification-correction methods were evaluated, including genomic control, principal component analysis, and linear mixed models.
“The most popular are genomic control (GC)21,22, principal component analysis (PCA)23 and linear mixed models24.”
Quote in the paperNot yet classified0 of 3 sources checked- cites [21] Bacanu, S.A., Devlin, B. & Roeder, K. The power of genomic control. Am. J. Hum. Genet. 66, 1933–1944 (2000). source
Not checked in the free run.
- cites [22] Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999). source
Not checked in the free run.
- cites [23] Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006). source
Not checked in the free run.
- Background
Rare variants tend to be more geographically clustered than common variants because they are typically recent.
“Rare variants, through being recent, tend to show greater geographic clustering than common ones (Fig. 2a-c).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Fig. 2a-c
Not checked in the free run.
- Method
The paper concludes that future studies must collect fine-grained geographic origins and recruitment-path information.
“Whichever approach is taken, it is likely to require fine-grained information about the geographic origins and recruitment path of each sample.”
Quote in the paperNot yet classified - Method
The authors used a simple lattice model to approximate population structure across a geographical region and studied how non-genetic risk distribution interacts with null association-test inflation.
“We used a simple lattice model to approximate population structure across a geographical region and investigated the interaction between the spatial distribution of non-genetic risk and inflation of standard association tests under the null model of no genetic risk (Online methods).”
Quote in the paperNot yet classified - Background
The collection of such information must be an important consideration in the design of future studies.
“The collection of such information must be an important consideration in the design of future studies.”
Quote in the paperNot yet classified - Background
There are three potential sources of sharply defined non-genetic risk boundaries: localized environmental exposure, systematic measurement bias at a recruitment centre, and local variation in recruitment policy or misclassification.
“There are three ways in which non-genetic risk might show sharply-defined boundaries of the type for which we have shown differential inflation. First, localized environmental exposure may be highly patchy, for example associated with urban areas. Second, there may be systematic measurement bias at a single recruitment centre. Third, and more subtly, there may be local variation in recruitment policy or rates of misclassification”
Quote in the paperNot yet classified - Method
The authors highlight the need for explicit spatial-structure measures such as allele-sharing plots or Moran's I.
“These results highlight the need for methods for explicitly showing spatial structure, such as the allele-sharing plot (Fig. 4) or other spatial correlation measures such as Moran’s I statistic27, which provides a much richer and more informative representation than any single statistic.”
Quote in the paperNot yet classified0 of 1 sources checked- cites [27] Moran, P.A.P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950). source
Not checked in the free run.
- Background
FST is driven by common variants and depends on the relative sizes and number of sub-populations.
“However, FST estimates are driven by common variants, and also depend on the relative sizes and number of the sub-populations (Supplementary Fig. 5).”
Quote in the paperNot yet classified0 of 1 sources checked- cites Supplementary Fig. 5
Not checked in the free run.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Typical approaches to account for population stratification based on principal components may not be adequate because PCs computed from common SNPs may not account for stratification at NIMs that tend to be rare on average.
“Typical approaches to account for population stratification based on the inclusion of principal components (PCs) may not be adequate as these PCs are computed from common SNPs on the UKBB genotyping array and may not account for stratification at the NIMs that tend to be rare on average (Mathieson and McVean, 2012).”
From The lingering effects of Neanderthal introgression on human complex traits (2023) · cites this paper as Mathieson and McVean, 2012
The Results (page unavailable) show that PCA can fail to correct rare-variant stratification when non-genetic risk is spatially localized. The paper does not examine UKBB array SNPs or NIMs, and it attributes the simulated PCA failure to the limits of correction using the top components, rather than specifically to computing PCs from common SNPs. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Cites
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248 · see citing works
NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
MORAN, P. A. P. (1950) · Biometrika · doi:10.1093/biomet/37.1-2.17 · Cited by 8,622 · see citing works
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336 · see citing works
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877 · see citing works
Genetic Dissection of Complex Traits
Lander, E. S., & Schork, N. J. (1994) · Science · doi:10.1126/science.8091226 · Cited by 3,425 · see citing works
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162 · see citing works
Efficient Control of Population Structure in Model Organism Association Mapping
Kang, H. M., Zaitlen, N., Wade, C. M., Kirby, A. W., Heckerman, D. E., Daly, M. J., & Eskin, E. (2008) · Genetics · doi:10.1534/genetics.107.080101 · Cited by 1,848 · see citing works
Li, B., & Leal, S. M. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.024 · Cited by 1,527 · see citing works
Population stratification and spurious allelic association
Cardon, L. R., & Palmer, L. J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12520-2 · Cited by 1,189 · see citing works
Common and rare variants in multifactorial susceptibility to common diseases
Bodmer, W., & Bonilla, C. (2008) · Nature Genetics · doi:10.1038/ng.f.136 · Cited by 1,131 · see citing works
A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic
Madsen, B. E., & Browning, S. R. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000384 · Cited by 1,113 · see citing works
Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol
Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., & Hobbs, H. H. (2004) · Science · doi:10.1126/science.1099870 · Cited by 1,108 · see citing works
BIAS DUE TO MISCLASSIFICATION IN THE ESTIMATION OF RELATIVE RISK
COPELAND, K. T., Checkoway, H., McMichael, A. J., & HOLBROOK, R. H. (1977) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112408 · Cited by 1,006 · see citing works
Nejentsev, S., Walker, N., Riches, D., Egholm, M., & Todd, J. A. (2009) · Science · doi:10.1126/science.1167728 · Cited by 958 · see citing works
The effects of human population structure on large genetic association studies
Marchini, J., Cardon, L. R., Phillips, M. S., & Donnelly, P. (2004) · Nature Genetics · doi:10.1038/ng1337 · Cited by 869 · see citing works
Spencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000477 · Cited by 606 · see citing works
Bustamante, C. D., Vega, F. M. D. L., & Burchard, E. G. (2011) · Nature · doi:10.1038/475163a · Cited by 601 · see citing works
Testing for an Unusual Distribution of Rare Variants
Neale, B. M., Rivas, M. A., Voight, B. F., Altshuler, D., Devlin, B., Orho‐Melander, M., Kathiresan, S., Purcell, S., Roeder, K., & Daly, M. J. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001322 · Cited by 583 · see citing works
Clayton, D. G., Walker, N. M., Smyth, D. J., Pask, R., Cooper, J. D., Maier, L. M., Smink, L. J., Lam, A. C., Ovington, N. R., Stevens, H. E., et al. (2005) · Nature Genetics · doi:10.1038/ng1653 · Cited by 526 · see citing works
An evaluation of statistical approaches to rare variant analysis in genetic association studies
Morris, A. P., & Zeggini, E. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20450 · Cited by 481 · see citing works
Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455 · see citing works
Genetic Structure of Europeans: A View from the North–East
Nelis, M., Esko, T., Mägi, R., Zimprich, F., Zimprich, A. A., Toncheva, D. I., Karachanak-Yankova, S., Piskackova, T., Balaščák, I., Peltonen, L., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005472 · Cited by 425 · see citing works
Case–Control Studies of Association in Structured or Admixed Populations
Pritchard, J. K., & Donnelly, P. (2001) · Theoretical Population Biology · doi:10.1006/tpbi.2001.1543 · Cited by 397 · see citing works
Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture.
Knowler, W. C., Williams, R. C., Pettitt, D. J., & Steinberg, A. G. (1988) · Europe PMC (PubMed Central) · Cited by 378 · see citing works
Bacanu, S.-A., Devlin, B., & Roeder, K. (2000) · The American Journal of Human Genetics · doi:10.1086/302929 · Cited by 340 · see citing works
A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease
Ionita‐Laza, I., Buxbaum, J. D., Laird, N. M., & Lange, C. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1001289 · Cited by 146 · see citing works
Common and Rare ABCA1 Variants Affecting Plasma HDL Cholesterol
Wang, J., Burnett, J. R., Near, S., Young, K., Zinman, B., Hanley, A. J., Connelly, P. W., Harris, S. B., & Hegele, R. A. (2000) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.20.8.1983 · Cited by 121 · see citing works
Association tests using kernel‐based measures of multi‐locus genotype similarity between individuals
Mukhopadhyay, I., Feingold, E., Weeks, D. E., & Thalamuthu, A. (2009) · Genetic Epidemiology · doi:10.1002/gepi.20451 · Cited by 79 · see citing works
Flagged first, then by citations
1 of the 29 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711 · see citing works
Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations
Martin, A. R., Gignoux, C. R., Walters, R. K., Wojcik, G. L., Neale, B. M., Gravel, S., Daly, M. J., Bustamante, C. D., & Kenny, E. E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.03.004 · Cited by 1,391 · see citing works
Guidelines for investigating causality of sequence variants in human disease
MacArthur, D. G., Manolio, T. A., Dimmock, D. P., Rehm, H. L., Shendure, J., Abecasis, G. R., Adams, D. R., Altman, R. B., Antonarakis, S. E., Ashley, E. A., et al. (2014) · Nature · doi:10.1038/nature13127 · Cited by 1,224 · see citing works
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · see citing works
Rare-Variant Association Analysis: Study Designs and Statistical Tests
Lee, S., Abecasis, G. R., Boehnke, M., & Lin, X. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.06.009 · Cited by 1,042 · see citing works
“102 105 PCA performance heavily depends on the underlying risk distribution and population structure, 100 and alternative strategies to using PCA as covariates, such as using PCA to guide the matching of affected and control individuals, might be useful.”
BackgroundMethodExome sequencing and analysis of 454,787 UK Biobank participants
Backman, J., Li, A. H., Marcketta, A., Sun, D., Mbatchou, J. S., Kessler, M. D., Benner, C., Liu, D., Locke, A. E., Balasubramanian, S., et al. (2021) · Nature · doi:10.1038/s41586-021-04103-z · Cited by 915 · see citing works
Advantages and pitfalls in the application of mixed-model association methods
Yang, J., Zaitlen, N. A., Goddard, M. E., Visscher, P. M., & Price, A. L. (2014) · Nature Genetics · doi:10.1038/ng.2876 · Cited by 981 · see citing works
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes
Kelleher, J., Etheridge, A. M., & McVean, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004842 · Cited by 660 · see citing works
“Aside from its intrinsic interest, correctly accounting for population stratification is critical for the interpretation of association studies [108, 109], particularly for rare variants [110, 111].”
BackgroundSelf-citationFrancioli, L. C., Menelaou, A., Pulit, S. L., Elbers, C. C., Kloosterman, W. P., van Setten, J., Nijman, I. J., Renkens, I., de Bakker, P. I. W., van Dijk, F., et al. (2014) · Nature Genetics · doi:10.1038/ng.3021 · Cited by 642 · see citing works
Visualizing spatial population structure with estimated effective migration surfaces
Petkova, D., Novembre, J., & Stephens, M. (2015) · Nature Genetics · doi:10.1038/ng.3464 · Cited by 624 · see citing works
Estimating and interpreting <i>F</i> <sub>ST</sub> : The impact of rare variants
Bhatia, G., Patterson, N., Sankararaman, S., & Price, A. L. (2013) · Genome Research · doi:10.1101/gr.154831.113 · Cited by 612 · see citing works
“These estimates have subsequently been used to simulate properties of recent rare variants (Mathieson and McVean 2012), making it imperative to know whether this reduction in FST is a meaningful result of the inclusion of rare variants or merely an artifact of estimation.”
MethodResultInfluentialAn overview of STRUCTURE: applications, parameter settings, and supporting software
Porras-Hurtado, L., Ruíz, Y., Santos, C., Phillips, C., Carracedo, Á., & Lareu, M. V. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00098 · Cited by 610 · see citing works
“%) minor alleles can inflate the false positive rate significantly (Babron et al., 2012; Mathieson and McVean, 2012) while showing different patterns of stratification from the same population comparisons.”
BackgroundStatistical power and significance testing in large-scale genetic studies
Sham, P. C., & Purcell, S. M. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3706 · Cited by 573 · see citing works
15 years of GWAS discovery: Realizing the promise
Abdellaoui, A., Yengo, L., Verweij, K. J. H., & Visscher, P. M. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.011 · Cited by 542 · see citing works
Loss-of-function mutations in SLC30A8 protect against type 2 diabetes
Go-T2D Consortium, Flannick, J., T2D-GENES Consortium, Thorleifsson, G., Beer, N. L., Jacobs, S. B. R., Grarup, N., Burtt, N. P., Mahajan, A., Fuchsberger, C., et al. (2014) · Nature Genetics · doi:10.1038/ng.2915 · Cited by 492 · see citing works
Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer
Wang, Y., McKay, J., Rafnar, Þ., Wang, Z., Timofeeva, M. N., Broderick, P., Zong, X., Laplana, M., Wei, Y., Han, Y., et al. (2014) · Nature Genetics · doi:10.1038/ng.3002 · Cited by 453 · see citing works
Chen, H., Wang, C., Conomos, M. P., Stilp, A. M., Li, Z., Sofer, T., Szpiro, A. A., Chen, W., Brehm, J. M., Celedon, J. C., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.012 · Cited by 415 · see citing works
Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia
Galinsky, K. J., Bhatia, G., Loh, P., Georgiev, S., Mukherjee, S., Patterson, N. J., & Price, A. L. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.12.022 · Cited by 357 · see citing works
Brumpton, B., Sanderson, E., Heilbron, K., Hartwig, F. P., Harrison, S., Vie, G. Å., Cho, Y., Howe, L. D., Hughes, A., Boomsma, D. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17117-4 · Cited by 402 · see citing works
Class II HLA interactions modulate genetic risk for multiple sclerosis
the International Multiple Sclerosis Genetics Consortium (2015) · Nature Genetics · doi:10.1038/ng.3395 · Cited by 418 · see citing works
Self-citationLinck, E. B., & Battey, C. J. (2019) · Molecular Ecology Resources · doi:10.1111/1755-0998.12995 · Cited by 395 · see citing works
Barton, A. R., Sherman, M. A., Mukamel, R. E., & Loh, P. (2021) · Nature Genetics · doi:10.1038/s41588-021-00892-1 · Cited by 331 · see citing works
The impact of rare and low-frequency genetic variants in common disease
Bomba, L., Walter, K., & Soranzo, N. (2017) · Genome biology · doi:10.1186/s13059-017-1212-4 · Cited by 342 · see citing works
“patterns of population stratification are predicted to arise in the presence of sharp spatial distributions for nongenetic risk of disease [106].”
BackgroundApparent latent structure within the UK Biobank sample has implications for epidemiological analysis
Haworth, S., Mitchell, R., Corbin, L., Wade, K. H., Dudding, T., Budu-Aggrey, A., Carslake, D., Hemani, G., Paternoster, L., Smith, G. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08219-1 · Cited by 314 · see citing works
Wainschtein, P., Jain, D., Zheng, Z., TOPMed Anthropometry Working Group, Aslibekyan, S. W., Becker, D. M., Bi, W., Brody, J. A., Carlson, J. C., Correa, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00997-7 · Cited by 504 · see citing works
Genomics of disease risk in globally diverse populations
Gurdasani, D., Barroso, I., Zeggini, E., & Sandhu, M. S. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0144-0 · Cited by 299 · see citing works
Length Distributions of Identity by Descent Reveal Fine-Scale Demographic History
Palamara, P. F., Lencz, T., Darvasi, A., & Pe’er, I. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.030 · Cited by 247 · see citing works
Population structure in genetic studies: Confounding factors and mixed models
Sul, J. H., Martin, L. S., & Eskin, E. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007309 · Cited by 264 · see citing works
“In general, whether or not mixed model approaches developed for common variants will be effective for rare variants is an active area of current research [42].”
BackgroundAtkinson, E. G., Maihofer, A. X., Kanai, M., Martin, A. R., Karczewski, K. J., Santoro, M. L., Ulirsch, J. C., Kamatani, Y., Okada, Y., Finucane, H. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00766-y · Cited by 229 · see citing works
Solving the missing heritability problem
Young, A. I. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008222 · Cited by 235 · see citing works
Rare variant association studies: considerations, challenges and opportunities
Auer, P. L., & Lettre, G. (2015) · Genome Medicine · doi:10.1186/s13073-015-0138-2 · Cited by 246 · see citing works
“Considering family-based designs or including spatial/geographical information in the tests might be strategies to limit confounding due to population structure [57].”
MethodThe search for loci under selection: trends, biases and progress
Ahrens, C. W., Rymer, P. D., Stow, A., Bragg, J. G., Dillon, S. K., Umbers, K. D. L., & Dudaniec, R. Y. (2018) · Molecular Ecology · doi:10.1111/mec.14549 · Cited by 243 · see citing works
Modeling non-syndromic autism and the impact of TRPC6 disruption in human neurons
Griesi-Oliveira, K., Acab, A., Gupta, A. R., Sunaga, D. Y., Chailangkarn, T., Nicol, X., Nunez, Y., Walker, M. F., Murdoch, J. D., Sanders, S. J., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.141 · Cited by 236 · see citing works
Whole-genome sequencing of a sporadic primary immunodeficiency cohort
Thaventhiran, J. E. D., Allen, H. L., Burren, O. S., Rae, W. M., Greene, D. J., Staples, E., Zhang, Z., Farmery, J. H. R., Simeoni, I., Rivers, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2265-1 · Cited by 240 · see citing works
Vithana, E. N., Khor, C. C., Qiao, C., Nongpiur, M. E., George, R. J., Chen, L., Do, T., Abu‐Amero, K. K., Huang, C. K., Low, S., et al. (2012) · Nature Genetics · doi:10.1038/ng.2390 · Cited by 241 · see citing works
High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants
Marigorta, U. M., & Navarro, A. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003566 · Cited by 207 · see citing works
“In addition, most meta-analysis of GWAS data, that could shed light on these issues, either have ignored population heterogeneity [2,20] or have focused on a limited set of traits [21] and GWAS [22].”
BackgroundGompert, Z., Lucas, L. K., Buerkle, C. A., Forister, M. L., Fordyce, J. A., & Nice, C. C. (2014) · Molecular Ecology · doi:10.1111/mec.12811 · Cited by 206 · see citing works
“For example, if rare variants exhibit different population structure than common variants, attempts to control for spatial confounding of genotype and phenotype based on one class of variants will not work for the other class of variants (Mathieson & McVean 2012).”
BackgroundA Unified Characterization of Population Structure and Relatedness
Weir, B. S., & Goudet, J. (2017) · Genetics · doi:10.1534/genetics.116.198424 · Cited by 191 · see citing works
“This amplifies the comment “populations can display spatial structure in rare variants, even when Wright’s fixation index FST is low” of Mathieson and McVean (2012).”
BackgroundAmeur, A., Dahlberg, J., Olason, P. I., Vezzi, F., Karlsson, R., Martin, M., Viklund, J., Kähäri, A., Lundin, P., Che, H., et al. (2017) · European Journal of Human Genetics · doi:10.1038/ejhg.2017.130 · Cited by 203 · see citing works
Recovery of trait heritability from whole genome sequence data
Wainschtein, P., Jain, D., Zheng, Z., TOPMed Anthropometry Working Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Cupples, L. A., Shadyab, A. H., McKnight, B., Shoemaker, B. M., Mitchell, B. D., et al. (2019) · doi:10.1101/588020 · Cited by 515 · see citing works
Cayuela, H., Rougemont, Q., Prunier, J. G., Moore, J., Clobert, J., Besnard, A. G., & Bernatchez, L. (2018) · Molecular Ecology · doi:10.1111/mec.14848 · Cited by 197 · see citing works
van Rheenen, W., Pulit, S. L., Dekker, A. M., Khleifat, A. A., Brands, W. J., Iacoangeli, A., Kenna, K. P., Kooyman, M., McLaughlin, R. L., Middelkoop, B. M., et al. (2018) · European Journal of Human Genetics · doi:10.1038/s41431-018-0177-4 · Cited by 178 · see citing works
Wang, C., Zhan, X., Liang, L., Abecasis, G. R., & Lin, X. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.04.018 · Cited by 160 · see citing works
Zhan, X., Larson, D. E., Wang, C., Koboldt, D. C., Sergeev, Y. V., Fulton, R. S., Fulton, L., Fronick, C. C., Branham, K. E. H., Bragg‐Gresham, J. L., et al. (2013) · Nature Genetics · doi:10.1038/ng.2758 · Cited by 177 · see citing works
“Since careful matching of genetic ancestry is critical for rare variant association studies 24,25 , we selected an ancestry-matched subset of our samples and of samples from the NHLBI Exome Sequencing Project.”
Within family Mendelian randomization studies
Davies, N. M., Howe, L. J., Brumpton, B., Havdahl, A., Evans, D. M., & Davey Smith, G. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz204 · Cited by 158 · see citing works
Demography and the Age of Rare Variants
Mathieson, I., & McVean, G. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004528 · Cited by 146 · see citing works
“These are highly differentiated between populations [1,2], may make an important contribution to genetic susceptibility to disease [3–7], and provide information about both demographic history, and fine-scale population structure [8,9].”
Self-citationDeletion of TOP3β, a component of FMRP-containing mRNPs, contributes to neurodevelopmental disorders
Stoll, G., Pietiläinen, O. P. H., Linder, B., Suvisaari, J., Brosi, C., Hennah, W., Leppä, V., Torniainen‐Holm, M., Ripatti, S., Ala‐Mello, S., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3484 · Cited by 172 · see citing works
Genomic analysis of 6,000-year-old cultivated grain illuminates the domestication history of barley
Mascher, M., Schuenemann, V. J., Davidovich, U., Marom, N., Himmelbach, A., Hübner, S., Korol, A. B., David, M., Reiter, E., Riehl, S., et al. (2016) · Nature Genetics · doi:10.1038/ng.3611 · Cited by 162 · see citing works
Fine-Scale Genetic Structure in Finland
Kerminen, S., Havulinna, A. S., Hellenthal, G., Martin, A. R., Sarin, A., Perola, M., Palotie, A. V., Salomaa, V. V., Daly, M. J., Ripatti, S., et al. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300217 · Cited by 147 · see citing works
Ancestry estimation and control of population stratification for sequence-based association studies
Study, T. F., Wang, C., Zhan, X., Bragg‐Gresham, J. L., Kang, H. M., Stambolian, D. E., Chew, E. Y., Branham, K. E. H., Heckenlively, J. R., Fulton, R. S., et al. (2014) · Nature Genetics · doi:10.1038/ng.2924 · Cited by 133 · see citing works
“A key challenge in genetic association studies is to avoid spurious association signals caused by differences in ancestral background 13–16 .”
BackgroundMethodGompert, Z., & Buerkle, C. A. (2016) · Evolutionary Applications · doi:10.1111/eva.12380 · Cited by 146 · see citing works
“…variants with minor allele frequencies <1%, have become more accessible with current sequencing methods and could further help discriminate among different histories and provide information about recent evolutionary dynamics (Gravel et al. 2011; Mathieson and McVean 2012; Nelson et al. 2012).”
BackgroundComplex Patterns of Local Adaptation in Teosinte
Pyhäjärvi, T., Hufford, M. B., Mezmouk, S., & Ross-Ibarra, J. (2013) · Genome Biology and Evolution · doi:10.1093/gbe/evt109 · Cited by 134 · see citing works
“…these findings may reflect true local adaptation, they should be interpreted with caution since such geographically restricted environmental variation has been shown to cause false positives in association studies even when population structure is taken into account (Mathieson and McVean 2012).”
BackgroundSaada, J. N., Kalantzis, G., Shyr, D., Cooper, F., Robinson, M. J., Gusev, A., & Palamara, P. F. (2020) · Nature Communications · doi:10.1038/s41467-020-19588-x · Cited by 125 · see citing works
Auer, P. L., Johnsen, J. M., Johnson, A. D., Logsdon, B. A., Lange, L. A., Nalls, M. A., Zhang, G., Franceschini, N., Fox, K., Lange, E. M., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.08.031 · Cited by 147 · see citing works
“Finally, we found no evidence suggesting that the association was due to confounding of a small and undetected The American population stratum, as stipulated by a model in Mathieson and McVean.54 The mechanisms behind the association of LCT variants with higher WBC are not known.”
BackgroundExome sequencing and complex disease: practical aspects of rare variant association studies
Do, R., Kathiresan, S., & Abecasis, G. R. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds387 · Cited by 138 · see citing works
“It is expected that many rare variants will have a very restricted geographic distribution (51,52) so that careful matching of case and control ancestries is likely to be extremely important.”
BackgroundDemographic history mediates the effect of stratification on polygenic scores
Zaidi, A. A., & Mathieson, I. (2020) · eLife · doi:10.7554/elife.61548 · Cited by 122 · see citing works
“Because they aggregate across rare variants, such tests have the potential to be affected by rare variant stratification (Mathieson and McVean, 2012).”
BackgroundSelf-citationFarlow, J. L., Lin, H., Sauerbeck, L. R., Lai, D., Koller, D. L., Pugh, E., Hetrick, K. N., Ling, H., Kleinloog, R., van der Vlies, P., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121104 · Cited by 143 · see citing works
“Given that rare variants can be population-specific [48], the selection of appropriate allele frequency databases is critical.”
Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003443 · Cited by 135 · see citing works
“This result supports findings of [44], but is contrary to other predictions [43].”
BackgroundResultUltrarare variants drive substantial cis heritability of human gene expression
Hernandez, R. D., Uricchio, L. H., Hartman, K., Ye, C., Dahl, A., & Zaitlen, N. (2019) · Nature Genetics · doi:10.1038/s41588-019-0487-7 · Cited by 116 · see citing works
Sinclair‐Waters, M., Ødegård, J., Korsvoll, S. A., Moen, T., Lien, S., Primmer, C. R., & Barson, N. J. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-0529-8 · Cited by 118 · see citing works
The omnigenic model and polygenic prediction of complex traits.
Mathieson, I. (2021) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.07.003 · Cited by 100 · see citing works
“Rare variants have different properties than common variants, for example in terms of their effect size distribution,(16) population specificity,(17) and environmental stratification.(18,19) Differences in the proportion of heritability explained by rare variants would therefore contribute to differences in transferab…”
BackgroundSelf-citationSpace is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data
Battey, C. J., Ralph, P. L., & Kern, A. D. (2020) · Genetics · doi:10.1534/genetics.120.303143 · Cited by 98 · see citing works
“This pattern has been demonstrated in a variety of simulation and empirical contexts (Price et al. 2006; Yu et al. 2005; Young et al. 2018; Mathieson and McVean 2012; Kang et al. 2008, 2010; Bulik-Sullivan et al. 2015; Berg et al. 2018; Sohail et al. 2018).”
BackgroundMethodResultInfluentialComplex patterns of local adaptation in teosinte.
Pyhäjärvi, T., Hufford, M. B., Mezmouk, S., & Ross‐Ibarra, J. (2013) · eScholarship (California Digital Library) · Cited by 108 · see citing works
Whole-genome and whole-exome sequencing in neurological diseases
Foo, J. N., Liu, J., & Tan, E. (2012) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.148 · Cited by 121 · see citing works
Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans
Mez, J. B., Chung, J., Jun, G., Kriegel, J., Bourlas, A. P., Sherva, R., Logue, M. W., Barnes, L. L., Bennett, D. A., Buxbaum, J. D., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.09.002 · Cited by 96 · see citing works
“1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 low-frequency variants in GWAS can increase genomic inflation [56].”
267 Spanish Exomes Reveal Population-Specific Differences in Disease-Related Genetic Variation
Dopazo, J., Amadoz, A., Bleda, M., García‐Alonso, L., Alemán, A., Garcia‐García, F., Rodríguez, J. A., Daub, J. T., Muntané, G., Rueda, A., et al. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw005 · Cited by 96 · see citing works
“2012) and a high stratification level (Mathieson and McVean 2012; Moreno-Estrada et al. 2013) in many rare variants with uncertain functional consequences.”
BackgroundMangold, E., Böhmer, A. C., Ishorst, N., Hoebel, A., Gültepe, P., Schünke, H., Klamt, J., Hofmann, A., Gölz, L., Raff, R., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.013 · Cited by 105 · see citing works
Rare Variant Analysis for Family-Based Design
De, G., Yip, W., Ionita‐Laza, I., & Laird, N. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0048495 · Cited by 103 · see citing works
“Moreover a recent study [29] suggests that for rare variants these existing method show suboptimal performance in correcting for population stratification.”
BackgroundBycroft, C., Fernández–Rozadilla, C., Ruíz-Ponte, C., Quintela, I., Carracedo, Á., Donnelly, P., & Myers, S. (2019) · Nature Communications · doi:10.1038/s41467-018-08272-w · Cited by 97 · see citing works
Diogo, D., Kurreeman, F. A. S., Stahl, E. A., Liao, K. P., Gupta, N., Greenberg, J. D., Rivas, M. A., Hickey, B., Flannick, J. A., Thomson, B. P., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.11.012 · Cited by 93 · see citing works
Genetic investigation of 100 heart genes in sudden unexplained death victims in a forensic setting
Christiansen, S. L., Hertz, C. L., Ferrero‐Miliani, L., et al. (2016) · European Journal of Human Genetics · doi:10.1038/ejhg.2016.118 · Cited by 90 · see citing works
Casals, F., Hodgkinson, A., Hussin, J., Idaghdour, Y., Bruat, V., de Maillard, T., Grenier, J., Gbeha, E., Hamdan, F. F., Girard, S., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003815 · Cited by 79 · see citing works
“Furthermore, these findings predict a lack of replication in association studies using rare functional variants across populations, since rare variants can show higher levels of stratification [16], thus emphasizing the need of population-specific catalogues of genetic variation [12].”
BackgroundNeed, A. C., McEvoy, J. P., Gennarelli, M., Heinzen, E. L., Ge, D., Maia, J. M., Shianna, K. V., He, M., Cirulli, E. T., Gumbs, C. E., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.06.018 · Cited by 91 · see citing works
“Additionally, existing methods are not effective for the type of structure caused by rare variants.(53) We therefore employed the approach that was maximally inclusive in this exploratory study and intended that any variants that were supported in the second stage would be further investigated in cohorts that are robu…”
MethodIonita‐Laza, I., Lee, S., Makarov, V., Buxbaum, J. D., & Lin, X. (2013) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.308 · Cited by 87 · see citing works
“We found the PCA adjustment to work well in our scenarios with a small number of discrete populations (Figure 1c and Supplementary Figure S1c), although such an adjustment may not be sufficient in more subtle scenarios, when the substructure is less discrete and the risk has a sharp spatial distribution ([32]).”
BackgroundVisualizing population structure with variational autoencoders
Battey, C. J., Coffing, G. C., & Kern, A. D. (2021) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkaa036 · Cited by 78 · see citing works
“Downstream studies that use GWAS information can themselves be compromised by inadequate controls for structure, for instance in recent work trying to identify the effects of natural selection on complex traits (Mathieson and McVean, 2012; Berg et al., 2019; Sohail et al., 2019).”
BackgroundIn search of low-frequency and rare variants affecting complex traits
Panoutsopoulou, K., Tachmazidou, I., & Zeggini, E. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt376 · Cited by 87 · see citing works
“Based on the theoretical examination, rare variants can show a stronger pattern of population stratification than common variants, particularly in the presence of sharp spatial distributions for non-genetic risk of disease (62).”
BackgroundXu, H. Y., Zhang, H., Yang, W., Yadav, R., Morrison, A. C., Qian, M., Devidas, M., Liu, Y., Pérez-Andreu, V., Zhao, X., et al. (2015) · Nature Communications · doi:10.1038/ncomms8553 · Cited by 82 · see citing works
“We elected to focus on individuals of European descent to minimize population stratification 36 .”
MethodInferences from Genomic Models in Stratified Populations
Janss, L., Campos, G. D. L., Sheehan, N. A., & Sørensen, D. (2012) · Genetics · doi:10.1534/genetics.112.141143 · Cited by 81 · see citing works
“This may not be appropriate for the analysis of traits affected by genes with sizable effects, traits affected by rare variants (Mathieson and McVean 2012), and data from populations with short span linkage disequilibrium.”
BackgroundFaST-LMM-Select for addressing confounding from spatial structure and rare variants
Listgarten, J., Lippert, C., & Heckerman, D. E. (2013) · Nature Genetics · doi:10.1038/ng.2620 · Cited by 66 · see citing works
The Critical Needs and Challenges for Genetic Architecture Studies in Africa
Martin, A. R., Teferra, S., Möller, M., Hoal, E. G., & Daly, M. J. (2018) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2018.08.005 · Cited by 76 · see citing works
“to unaccounted population stratification, especially for rare variants, which principal 5 components analysis is insufficient to correct [67,68].”
Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland
Martin, A. R., Karczewski, K. J., Kerminen, S., Kurki, M., Sarin, A., Artomov, M., Eriksson, J. G., Esko, T., Genovese, G., Havulinna, A. S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.003 · Cited by 69 · see citing works
He, Z., O’Roak, B. J., Smith, J. D., Wang, G. T., Hooker, S. E., Santos‐Cortez, R. L. P., Li, B., Kan, M., Krumm, N., Nickerson, D. A., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.11.021 · Cited by 73 · see citing works
“These differences can be even more extreme when studying admixed populations such as African-Americans and Hispanics.”
BackgroundMethodInfluentialWhole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage
Belkadi, A., Pedergnana, V., Cobat, A., Itan, Y., Vincent, Q. B., Abhyankar, A., Shang, L., Baghdadi, J. E., Bousfiha, A. A., Alcaïs, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606460113 · Cited by 71 · see citing works
“It will be particularly important to assess the influence of these stratification patterns on the association studies focusing on the role of rare variants (23, 46).”
BackgroundBrumpton, B., Sanderson, E., Hartwig, F., Harrison, S., Vie, G. Å., Cho, Y., Howe, L., Hughes, A. M., Boomsma, D., Havdahl, A., et al. (2019) · bioRxiv · doi:10.1101/602516 · Cited by 75 · see citing works
Ju, D., Hui, D., Hammond, D. A., Wonkam, A., & Tishkoff, S. A. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122220-112550 · Cited by 79 · see citing works
Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes
Wang, M., Roux, F., Bartoli, C., Huard‐Chauveau, C., Meyer, C. G., Lee, H., Roby, D., McPeek, M. S., & Bergelson, J. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710980115 · Cited by 70 · see citing works
“Other forms of partition may also be beneficial, such as using separate GRMs for common and rare variants (36).”
BackgroundProkopenko, D., Hecker, J., Silverman, E. K., Pagano, M., Nöthen, M. M., Christian Dina, C., Lange, C., & Fier, H. L. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv752 · Cited by 65 · see citing works
“…analysis holds great promise, the effects of population substructure/ admixture on the association analysis of rare variants are unclear and suitable adjustment methods have not been discussed widely in the literature (Babron et al. , 2012; Epstein et al. , 2012; Mathieson and McVean, 2012).”
BackgroundMethodAn Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines
Dutil, J., Chen, Z., Monteiro, Á. N. A., Teer, J. K., & Eschrich, S. A. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2747 · Cited by 70 · see citing works
Dai, C. L., Vazifeh, M. M., Yeang, C., Combes, R. T. D., Wells, R. S., Vilar, M. G., Daly, M. J., Ratti, C., & Martin, A. R. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.02.002 · Cited by 59 · see citing works
275 years of forestry meets genomics inPinus sylvestris
Pyhäjärvi, T., Kujala, S. T., & Savolainen, O. (2019) · Evolutionary Applications · doi:10.1111/eva.12809 · Cited by 57 · see citing works
“Therefore, more rigorous methods to deal with the fine spatial structure are needed when working with rare variants (Mathieson & McVean, 2012).”
MethodButler‐Laporte, G., Povysil, G., Kosmicki, J. A., Cirulli, E. T., Drivas, T. G., Furini, S., Saad, C., Schmidt, A., Olszewski, P., Korotko, U., et al. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010367 · Cited by 63 · see citing works
“Additionally, given that population genetic structure and its confounding effect on phenotypes is different at the rare variant level [26], we also used the first 20 genetic principal components from rare variants (MAF<1%) as covariates in all our analyses.”
MethodHERTZ, C. L., Christiansen, S. L., Larsen, M. K., et al. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2015.198 · Cited by 66 · see citing works
Negative selection on complex traits limits phenotype prediction accuracy between populations
Durvasula, A., & Lohmueller, K. E. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.02.013 · Cited by 57 · see citing works
“In addition to this analysis, we were also concerned with the effect of differential population structure from rare variants.(35,44) Therefore, we checked the robustness of our results to allele frequency filters.”
ResultLuCamp, HERTZ, C. L., Christiansen, S. L., et al. (2015) · International Journal of Legal Medicine · doi:10.1007/s00414-015-1261-8 · Cited by 65 · see citing works
“Given that rare variants are also subject to population stratification [27], we identified a local Danish reference population (n= 2000) that was whole-exome sequenced.”
BackgroundEmpirical Distributions of FST from Large-Scale Human Polymorphism Data
Elhaik, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049837 · Cited by 57 · see citing works
“Processes such as selection and genetic drift increased the frequency of rare alleles and the genetic diversity among populations [1].”
BackgroundFast and robust ancestry prediction using principal component analysis
Zhang, D., Dey, R., & Lee, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa152 · Cited by 54 · see citing works
Rediscovering the value of families for psychiatric genetics research
Glahn, D. C., Nimgaonkar, V. L., Raventós, H., Rojas, J. C., McIntosh, A. M., Thomson, P. A., Jablensky, A. V., McCarthy, N. S., Charlesworth, J., Blackburn, N. B., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0073-x · Cited by 58 · see citing works
Villanueva, P., Nudel, R., Hoischen, A., Fernández, M. A., Simpson, N. H., Gilissen, C., Reader, R. H., Jara, L., Echeverry, M. M., Francks, C., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004925 · Cited by 59 · see citing works
“Where given variants were observed in alternative populations, these data are reported but were not included in the statistical analyses since population admixture and stratification can lead to false positives, especially when investigating rare variants [83].”
Park, S., Supek, F., & Lehner, B. (2018) · Nature Communications · doi:10.1038/s41467-018-04900-7 · Cited by 50 · see citing works
Reduced signal for polygenic adaptation of height in UK BiobankPreprint
Berg, J. J., Harpak, A., Sinnott-Armstrong, N. A., Joergensen, A., Mostafavi, H., Field, Y., Boyle, E., Zhang, X.-J., Racimo, F., Pritchard, J., et al. (2018) · bioRxiv · doi:10.1101/354951 · Cited by 341 · see citing works
- Authors
- Mathieson, I., & McVean, G.
- Affiliations
- Centre for Human Genetics (GB)
- Corresponding author
- Mathieson, Iain
- Venue
- Nature Genetics
- Published
- 2012-02-05
- Volume
- 44
- Issue
- 3
- Pages
- 243-246
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3303124
- OpenAlex
- W2093636770
- Semantic Scholar
- 1fb05f44fa9013902b269a1b68611b19f70c5173
- Cited by
- 385
- References
- 29
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 13 · 2023: 20 · 2022: 28 · 2021: 31 · 2020: 32 · 2019: 25 · 2018: 26 · 2017: 19 · 2016: 25 · 2015: 39 · 2014: 45 · 2013: 36 · 2012: 21
- FWCI
- 27.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (086084/Z/08/Z, 089250, 086084, 089250/Z/09/Z, 090532, 090532/Z/09/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- population stratification, rare variants, common genetic variants, spatial population structure, genome-wide association study, confounding, allele sharing, fixation index, complex traits
- MeSH terms
- Data Interpretation, Statistical, Gene Frequency, Genetics, Population, Geography, Humans, Phenotype, Genetic Variation, Risk Assessment, Quantitative Trait, Heritable, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read (extract-v1) on 2026-09-25.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
