Long-Range LD Can Confound Genome Scans in Admixed Populations

Price, Alkes L.; Weale, Michael E.; Myers, Simon; Need, Anna C.; Shianna, Kevin V.; Ge, Dongliang; Rotter, Jerome I.; Torres, Esther A.; Taylor, Kent D.; Goldstein, David B.; Reich, David

doi:10.1016/j.ajhg.2008.06.005

Cited by 402 publications

(377 citation statements)

References 11 publications

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“…Indeed, it has been pointed out that some regions showing extended LD may be mistakenly interpreted as signals of selection. 36 Of the four regions we highlight, the LCT and HLA regions have long range LD. In general, though, we expect long range pairwise LD will not introduce substantial bias in our estimates of IBD.…”

Section: Discussionmentioning

confidence: 92%

Using identity by descent estimation with dense genotype data to detect positive selection

Han

Abney

2012

Eur J Hum Genet

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Identification of genomic loci and segments that are identical by descent (IBD) allows inference on problems such as relatedness detection, IBD disease mapping, heritability estimation and detection of recent or ongoing positive selection. Here, employing a novel statistical method, we use IBD to find signals of selection in the Maasai from Kinyawa, Kenya (MKK). In doing so, we demonstrate the advantage of statistical tools that can probabilistically estimate IBD sharing without having to thin genotype data because of linkage disequilibrium (LD), and that allow for both inbreeding and more than one allele to be shared IBD. We use our novel method, GIBDLD, to estimate IBD sharing between all pairs of individuals at all genotyped SNPs in the MKK, and, by looking for genomic regions showing excess IBD sharing in unrelated pairs, find loci that are known to have undergone recent selection (eg, the LCT gene and the HLA region) as well as many novel loci. Intriguingly, those loci that show the highest amount of excess IBD, with the exception of HLA, also show a substantial number of unrelated pairs sharing all four of their alleles IBD. In contrast to other IBD detection methods, GIBDLD provides accurate probabilistic estimates at each locus for all nine possible IBD sharing states between a pair of individuals, thus allowing for consanguinity, while also modeling LD, thus removing the need to thin SNPs. These characteristics will prove valuable for those doing genetic studies, and estimating IBD, in the wide variety of human populations. European Journal of Human Genetics (2013) 21, 205-211; doi:10.1038/ejhg.2012.148; published online 11 July 2012Keywords: identity by descent; natural selection; consanguinity; cryptic relatedness; relationship inference; SNPs INTRODUCTIONThe ability to discover recent positive selection in the human genome is one compelling reason to estimate identity by descent (IBD) in a cohort of largely unrelated individuals. In particular, IBD can be used to find selection on standing variation, a situation where many methods used for detecting selection may not perform well. 1 Additionally, other genetic questions can be well addressed by estimating IBD within a set of individuals. These include, detection of unknown or mistaken relationships, 2-6 estimation of heritability and genomic partitioning of genetic variance, 7,8 and mapping by the identification of shared segments. 9-13 IBD, however, is not directly observed but must be inferred from the available data. Traditionally, the combination of a pedigree with genotype data enabled the efficient computation of IBD using either 'peeling' 14 or hidden Markov models (HMMs). [15][16][17] More recently, however, the large amounts of information made available from high density SNP genotyping arrays has enabled estimation of IBD even for very distantly related pairs of individuals (ie, 410 generations) in the absence of pedigree information. This additional data, however, also presents the difficulty of accommodating the linkage disequilibrium (LD...

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Section: Discussionmentioning

confidence: 92%

Using identity by descent estimation with dense genotype data to detect positive selection

Han

Abney

2012

Eur J Hum Genet

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“…To identify population outliers, we carried out principal components analysis (PCA). We computed PCs for each of the three variant types (SNVs, short indels, large deletions) using EPACTS on an LD-pruned (r 2 <0.20) set of autosomal variants obtained by removing large high-LD regions 59,60 , variants with MAF<0.01, and variants with Hardy-Weinberg equilibrium p<10 −6 . Inspecting the first ten PCs for each variant type, we identified 43 population outliers and 136 additional outliers for large deletions only; we excluded these 179 individuals.…”

Section: Integration Of Genotype and Sequence Datamentioning

confidence: 99%

Genetic architecture of type 2 diabetes

Hara

Shojima

Hosoe

et al. 2014

Biochemical and Biophysical Research Communications

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The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lowerfrequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes.

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“…Very strong and/or long-range LD at a particular locus can even result in PCs that only reflect genetic variation at that specific locus. 6,7 Price et al 7 therefore recommended the exclusion of long-range LD regions for PCAs, but advised against pruning for LD, as it did not significantly affect PCs in HapMap populations. 6 We hypothesize that these LD artifacts may have larger confounding effects when carrying out a PCA in a single relatively small population, where ancestry differences are relatively small, than in a PCA that is run on a pooled data set of multiple populations with greater between-population differences.…”

Section: Introductionmentioning

confidence: 99%

Population structure, migration, and diversifying selection in the Netherlands

et al. 2013

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Genetic variation in a population can be summarized through principal component analysis (PCA) on genome-wide data. PCs derived from such analyses are valuable for genetic association studies, where they can correct for population stratification. We investigated how to capture the genetic population structure in a well-characterized sample from the Netherlands and in a worldwide data set and examined whether (1) removing long-range linkage disequilibrium (LD) regions and LD-based SNP pruning significantly improves correlations between PCs and geography and (2) whether genetic differentiation may have been influenced by migration and/or selection. In the Netherlands, three PCs showed significant correlations with geography, distinguishing between: (1) North and South; (2) East and West; and (3) the middle-band and the rest of the country. The third PC only emerged with minimized LD, which also significantly increased correlations with geography for the other two PCs. In addition to geography, the Dutch North-South PC showed correlations with genome-wide homozygosity (r ¼ 0.245), which may reflect a serial-founder effect due to northwards migration, and also with height (#: r ¼ 0.142,~: r ¼ 0.153). The divergence between subpopulations identified by PCs is partly driven by selection pressures. The first three PCs showed significant signals for diversifying selection (545 SNPs -the majority within 184 genes). The strongest signal was observed between North and South for the functional SNP in HERC2 that determines human blue/brown eye color. Thus, this study demonstrates how to increase ancestry signals in a relatively homogeneous population and how those signals can reveal evolutionary history.

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Long-Range LD Can Confound Genome Scans in Admixed Populations

Cited by 402 publications

References 11 publications

Using identity by descent estimation with dense genotype data to detect positive selection

Using identity by descent estimation with dense genotype data to detect positive selection

Genetic architecture of type 2 diabetes

Population structure, migration, and diversifying selection in the Netherlands

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