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Two-phase and family-based designs for next-generation sequencing studies

Overview of attention for article published in Frontiers in Genetics, January 2013
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  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

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Citations

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45 Mendeley
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Title
Two-phase and family-based designs for next-generation sequencing studies
Published in
Frontiers in Genetics, January 2013
DOI 10.3389/fgene.2013.00276
Pubmed ID
Authors

Duncan C. Thomas, Zhao Yang, Fan Yang

Abstract

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 7%
Sweden 2 4%
Sri Lanka 1 2%
Brazil 1 2%
Unknown 38 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 29%
Student > Ph. D. Student 11 24%
Professor > Associate Professor 4 9%
Student > Doctoral Student 3 7%
Student > Master 3 7%
Other 5 11%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 36%
Biochemistry, Genetics and Molecular Biology 10 22%
Medicine and Dentistry 8 18%
Mathematics 2 4%
Economics, Econometrics and Finance 1 2%
Other 2 4%
Unknown 6 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2016.
All research outputs
#5,867,076
of 22,736,112 outputs
Outputs from Frontiers in Genetics
#1,659
of 11,757 outputs
Outputs of similar age
#62,023
of 280,808 outputs
Outputs of similar age from Frontiers in Genetics
#71
of 319 outputs
Altmetric has tracked 22,736,112 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 11,757 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,808 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 319 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.