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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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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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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 .

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 4%
Netherlands 1 4%
France 1 4%
Unknown 25 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Master 5 18%
Student > Ph. D. Student 4 14%
Other 3 11%
Student > Bachelor 2 7%
Other 3 11%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 36%
Computer Science 4 14%
Mathematics 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Medicine and Dentistry 2 7%
Other 5 18%
Unknown 3 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#7,741,279
of 23,541,818 outputs
Outputs from BMC Bioinformatics
#3,085
of 7,411 outputs
Outputs of similar age
#125,560
of 395,812 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 140 outputs
Altmetric has tracked 23,541,818 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,411 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 395,812 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 55% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.