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A computational method for genotype calling in family-based sequencing data

Overview of attention for article published in BMC Bioinformatics, January 2016
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8 tweeters

Citations

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6 Dimensions

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29 Mendeley
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Title
A computational method for genotype calling in family-based sequencing data
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0880-5
Pubmed ID
Authors

Lun-Ching Chang, Bingshan Li, Zhou Fang, Scott Vrieze, Matt McGue, William G. Iacono, George C. Tseng, Wei Chen

Abstract

As sequencing technologies can help researchers detect common and rare variants across the human genome in many individuals, it is known that jointly calling genotypes across multiple individuals based on linkage disequilibrium (LD) can facilitate the analysis of low to modest coverage sequence data. However, genotype-calling methods for family-based sequence data, particularly for complex families beyond parent-offspring trios, are still lacking. In this study, first, we proposed an algorithm that considers both linkage disequilibrium (LD) patterns and familial transmission in nuclear and multi-generational families while retaining the computational efficiency. Second, we extended our method to incorporate external reference panels to analyze family-based sequence data with a small sample size. In simulation studies, we show that modeling multiple offspring can dramatically increase genotype calling accuracy and reduce phasing and Mendelian errors, especially at low to modest coverage. In addition, we show that using external panels can greatly facilitate genotype calling of sequencing data with a small number of individuals. We applied our method to a whole genome sequencing study of 1339 individuals at ~10X coverage from the Minnesota Center for Twin and Family Research. The aggregated results show that our methods significantly outperform existing ones that ignore family constraints or LD information. We anticipate that our method will be useful for many ongoing family-based sequencing projects. We have implemented our methods efficiently in a C++ program FamLDCaller, which is available from http://www.pitt.edu/~wec47/famldcaller.html .

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 3%
Netherlands 1 3%
France 1 3%
Unknown 26 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 31%
Student > Master 5 17%
Student > Ph. D. Student 4 14%
Other 3 10%
Student > Bachelor 2 7%
Other 3 10%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 34%
Computer Science 4 14%
Biochemistry, Genetics and Molecular Biology 3 10%
Mathematics 2 7%
Medicine and Dentistry 2 7%
Other 5 17%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 August 2016.
All research outputs
#4,933,348
of 16,557,200 outputs
Outputs from BMC Bioinformatics
#2,099
of 5,964 outputs
Outputs of similar age
#97,388
of 343,152 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 7 outputs
Altmetric has tracked 16,557,200 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,964 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 63% 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 343,152 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.