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Detection of identity by descent using next-generation whole genome sequencing data.

Su SY, Kasberger J, Baranzini S, Byerley W, Liao W, Oksenberg J, Sherr E, Jorgenson E - BMC Bioinformatics (2012)

Bottom Line: We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate.We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments.These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton St., Suite 200, Emeryville, CA 94608, USA. shuyisu@gallo.ucsf.edu

ABSTRACT

Background: Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and evidence of ancient IBD can be used to detect population structure in genetic association studies. Various methods for detecting IBD, including those implemented in the soft- ware programs fastIBD and GERMLINE, have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, in- cluding identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data.

Results: Here, we investigate how different levels of variant coverage in sequencing and microarray genotype data influences the resolution at which IBD can be detected. This includes microarray genotype data from the WTCCC study, denser genotype data from the HapMap Project, low coverage sequencing data from the 1000 Genomes Project, and deep coverage complete genome data from our own projects. With high power (78%), we can detect segments of length 0.4 cM or larger using fastIBD and GERMLINE in sequencing data. This compares to similar power to detect segments of length 1.0 cM or higher with microarray genotype data. We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate.

Conclusion: We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments. These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.

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Empirical power of fastIBD. Empirical power of fastIBD to detect an IBD segment as a function of the number of SNPs within a segment in the simulation study. Each plot presents different lengths of IBD segments examined. The power of each dataset is represented by different colored circles and plotted against the number of SNPs contained within a given region.
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Figure 1: Empirical power of fastIBD. Empirical power of fastIBD to detect an IBD segment as a function of the number of SNPs within a segment in the simulation study. Each plot presents different lengths of IBD segments examined. The power of each dataset is represented by different colored circles and plotted against the number of SNPs contained within a given region.

Mentions: In Figure 1, we present the power to detect a segment of IBD as a function of the number of SNPs within that segment for the five IBD segment lengths examined in this study using the program fastIBD. The average power across 100 simulated datasets is shown in Table 1. Using sequence data with high density SNP coverage improves the power to resolve IBD segments significantly over microarray-based genotype data, particularly for small IBD segments. The fastIBD method is able to detect IBD segments 0.2 cM in length with a power of 62.9% for high coverage sequence data (Complete Genomics), while it only has power of 12.6% for low density microarray genotype data (WTCCC). Power increases both as a function of the length of the IBD segment examined and the number of SNPs in the segment. Sequence data provides greater numbers of SNP genotypes for the same segment length than microarray-based genotype data. For all four datasets, fastIBD provides good power 76.7% to detect IBD for segments of size 1 cM and larger.


Detection of identity by descent using next-generation whole genome sequencing data.

Su SY, Kasberger J, Baranzini S, Byerley W, Liao W, Oksenberg J, Sherr E, Jorgenson E - BMC Bioinformatics (2012)

Empirical power of fastIBD. Empirical power of fastIBD to detect an IBD segment as a function of the number of SNPs within a segment in the simulation study. Each plot presents different lengths of IBD segments examined. The power of each dataset is represented by different colored circles and plotted against the number of SNPs contained within a given region.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3403908&req=5

Figure 1: Empirical power of fastIBD. Empirical power of fastIBD to detect an IBD segment as a function of the number of SNPs within a segment in the simulation study. Each plot presents different lengths of IBD segments examined. The power of each dataset is represented by different colored circles and plotted against the number of SNPs contained within a given region.
Mentions: In Figure 1, we present the power to detect a segment of IBD as a function of the number of SNPs within that segment for the five IBD segment lengths examined in this study using the program fastIBD. The average power across 100 simulated datasets is shown in Table 1. Using sequence data with high density SNP coverage improves the power to resolve IBD segments significantly over microarray-based genotype data, particularly for small IBD segments. The fastIBD method is able to detect IBD segments 0.2 cM in length with a power of 62.9% for high coverage sequence data (Complete Genomics), while it only has power of 12.6% for low density microarray genotype data (WTCCC). Power increases both as a function of the length of the IBD segment examined and the number of SNPs in the segment. Sequence data provides greater numbers of SNP genotypes for the same segment length than microarray-based genotype data. For all four datasets, fastIBD provides good power 76.7% to detect IBD for segments of size 1 cM and larger.

Bottom Line: We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate.We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments.These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton St., Suite 200, Emeryville, CA 94608, USA. shuyisu@gallo.ucsf.edu

ABSTRACT

Background: Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and evidence of ancient IBD can be used to detect population structure in genetic association studies. Various methods for detecting IBD, including those implemented in the soft- ware programs fastIBD and GERMLINE, have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, in- cluding identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data.

Results: Here, we investigate how different levels of variant coverage in sequencing and microarray genotype data influences the resolution at which IBD can be detected. This includes microarray genotype data from the WTCCC study, denser genotype data from the HapMap Project, low coverage sequencing data from the 1000 Genomes Project, and deep coverage complete genome data from our own projects. With high power (78%), we can detect segments of length 0.4 cM or larger using fastIBD and GERMLINE in sequencing data. This compares to similar power to detect segments of length 1.0 cM or higher with microarray genotype data. We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate.

Conclusion: We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments. These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.

Show MeSH