Whole population, genome-wide mapping of hidden relatedness

Gusev, Alexander; Lowe, Jennifer K.; Stoffel, Markus; Daly, Mark J.; Altshuler, David; Breslow, Jan L.; Friedman, Jeffrey M.; Pe’er, Itsik

doi:10.1101/gr.081398.108

Cited by 428 publications

(502 citation statements)

References 39 publications

Supporting

Mentioning

498

Contrasting

Order By: Relevance

“…One approach is to infer IBD based on the presence of matching haplotypes in a pair of individuals. 5,10 Although very fast, these methods do not, by themselves, result in a posterior probability of IBD at each locus. Another approach is to create a model, typically based on a HMM, that allows for LD.…”

Section: Introductionmentioning

confidence: 99%

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

Han

Abney

2012

Eur J Hum Genet

View full text Add to dashboard Cite

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...

show abstract

Section: Introductionmentioning

confidence: 99%

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

Han

Abney

2012

Eur J Hum Genet

View full text Add to dashboard Cite

show abstract

“…Compared with identical by state segments, IBD segments are at the same time more informative about remote relatedness (Gusev et al, 2012) but also more difficult to detect so that dedicated algorithms have been devised for this purpose (Gusev et al, 2009). Between pairs of individuals, the length of the IBD segments decay exponentially with the size of the segments (in centimorgans) and the rate of decay depends on the number of meioses d on the path relating the two individuals (Huff et al, 2011).…”

Section: Identical By Descent (Ibd) Segments and Runs Of Homozygositymentioning

confidence: 99%

Inferring population size changes with sequence and SNP data: lessons from human bottlenecks

2013

View full text Add to dashboard Cite

Reconstructing historical variation of population size from sequence and single-nucleotide polymorphism (SNP) data is valuable for understanding the evolutionary history of species. Changes in the population size of humans have been thoroughly investigated, and we review different methodologies of demographic reconstruction, specifically focusing on human bottlenecks. In addition to the classical approaches based on the site-frequency spectrum (SFS) or based on linkage disequilibrium, we also review more recent approaches that utilize atypical shared genomic fragments, such as identical by descent or homozygous segments between or within individuals. Compared with methods based on the SFS, these methods are well suited for detecting recent bottlenecks. In general, all these various methods suffer from bias and dependencies on confounding factors such as population structure or poor specification of the mutational and recombination processes, which can affect the demographic reconstruction. With the exception of SFS-based methods, the effects of confounding factors on the inference methods remain poorly investigated. We conclude that an important step when investigating population size changes rests on validating the demographic model by investigating to what extent the fitted demographic model can reproduce the main features of the polymorphism data.

show abstract

“…Assuming a random, uniform, and independent error rate of e per SNP, the number of falsely genotyped SNPs in an interval of length d SNPs is a Poisson random variable with parameter l¼2de for each individual's genome. 16 In this study we use e¼0.01, allowing an average of one potentially erroneous non-sharing SNP per 100 SNPs in a SGS run involving n individuals. In other words, we will incorporate SNPs that are not shared by all n individuals (that is, S i on) into the shared SGS run if this results in a non-sharing error rate of at most 1 SNP per 100.…”

Section: Sgs Error Modelmentioning

confidence: 99%

Identification of regions of positive selection using Shared Genomic Segment analysis

et al. 2011

View full text Add to dashboard Cite

We applied a shared genomic segment (SGS) analysis, incorporating an error model, to identify complete, or near complete, selective sweeps in the HapMap phase II data sets. This method is based on detecting heterozygous sharing across all individuals within a population, to identify regions of sharing with at least one allele in common. We identified multiple interesting regions, many of which are concordant with positive selection regions detected by previous population genetic tests. Others are suggested to be novel regions. Our finding illustrates the utility of SGS as a method for identifying regions of selection, and some of these regions have been proposed to be candidate regions for harboring disease genes.

show abstract

Whole population, genome-wide mapping of hidden relatedness

Cited by 428 publications

References 39 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

Inferring population size changes with sequence and SNP data: lessons from human bottlenecks

Identification of regions of positive selection using Shared Genomic Segment analysis

Contact Info

Product

Resources

About