Skip to main content

Volume 5 Supplement 9

Genetic Analysis Workshop 17: Unraveling Human Exome Data

Proceedings

Edited by S Ghosh, H Bickeböller, J Bailey, JE Bailey-Wilson, R Cantor, W Daw, AL DeStefano, CD Engelman, A Hinrichs, J Houwing-Duistermaat, IR König, J Kent Jr., N Pankratz, A Paterson, E Pugh, Y Sun, A Thomas, N Tintle, X Zhu, JW MacCluer and L Almasy

Genetic Analysis Workshop 17. Go to conference site.

Boston, MA, USA13-16 October 2010

Page 3 of 3

  1. Existing methods for analyzing rare variant data focus on collapsing a group of rare variants into a single common variant; collapsing is based on an intuitive function of the rare variant genotype information...

    Authors: Yuan Jiang, Jennifer S Brennan, Rose Calixte, Yunxiao He, Epiphanie Nyirabahizi and Heping Zhang
    Citation: BMC Proceedings 2011 5(Suppl 9):S102
  2. Identifying rare variants that are responsible for complex disease has been promoted by advances in sequencing technologies. However, statistical methods that can handle the vast amount of data generated and t...

    Authors: Jeesun Jung, Jessica Dantzer and Yunlong Liu
    Citation: BMC Proceedings 2011 5(Suppl 9):S103
  3. Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional as...

    Authors: Yoonhee Kim, Qing Li, Cheryl D Cropp, Heejong Sung, Juanliang Cai, Claire L Simpson, Brian Perry, Abhijit Dasgupta, James D Malley, Alexander F Wilson and Joan E Bailey-Wilson
    Citation: BMC Proceedings 2011 5(Suppl 9):S104
  4. Both common variants and rare variants are involved in the etiology of most complex diseases in humans. Developments in sequencing technology have led to the identification of a high density of rare variant si...

    Authors: Ying Liu, Chien Hsun Huang, Inchi Hu, Shaw-Hwa Lo and Tian Zheng
    Citation: BMC Proceedings 2011 5(Suppl 9):S106
  5. Genome-wide association studies have been firmly established in investigations of the associations between common genetic variants and complex traits or diseases. However, a large portion of complex traits and...

    Authors: Yue S Niu, Ning Hao and Lingling An
    Citation: BMC Proceedings 2011 5(Suppl 9):S108
  6. Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identi...

    Authors: Christopher E Schlosberg, Tae-Hwi Schwantes-An, Weimin Duan and Nancy L Saccone
    Citation: BMC Proceedings 2011 5(Suppl 9):S109
  7. Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risks or quantitative traits of interest. Despite these successes, the variants identified ...

    Authors: Libo Wang, Vitara Pungpapong, Yanzhu Lin, Min Zhang and Dabao Zhang
    Citation: BMC Proceedings 2011 5(Suppl 9):S110
  8. We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and ...

    Authors: Zhaogong Zhang, Qiuying Sha, Xinli Wang and Shuanglin Zhang
    Citation: BMC Proceedings 2011 5(Suppl 9):S112
  9. With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants n...

    Authors: Han Chen, Audrey E Hendricks, Yansong Cheng, Adrienne L Cupples, Josée Dupuis and Ching-Ti Liu
    Citation: BMC Proceedings 2011 5(Suppl 9):S113
  10. Genome-wide association studies have been used successfully to detect associations between common genetic variants and complex diseases, but common single-nucleotide polymorphisms (SNPs) detected by these stud...

    Authors: Yilin Dai, Ling Guo, Jianping Dong and Renfang Jiang
    Citation: BMC Proceedings 2011 5(Suppl 9):S114
  11. Principal components analysis (PCA) has been successfully used to correct for population stratification in genome-wide association studies of common variants. However, rare variants also have a role in common ...

    Authors: Hua He, Xue Zhang, Lili Ding, Tesfaye M Baye, Brad G Kurowski and Lisa J Martin
    Citation: BMC Proceedings 2011 5(Suppl 9):S116
  12. Recently there has been great interest in identifying rare variants associated with common diseases. We apply several collapsing-based and kernel-based single-gene association tests to Genetic Analysis Worksho...

    Authors: Lun Li, Wei Zheng, Joon Sang Lee, Xianghua Zhang, John Ferguson, Xiting Yan and Hongyu Zhao
    Citation: BMC Proceedings 2011 5(Suppl 9):S117
  13. Genome-wide association studies have successfully identified many common variants associated with complex human diseases. However, a large portion of the remaining heritability cannot be explained by these com...

    Authors: Wan-Yu Lin, Boshao Zhang, Nengjun Yi, Guimin Gao and Nianjun Liu
    Citation: BMC Proceedings 2011 5(Suppl 9):S118
  14. A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequenc...

    Authors: Alexander Luedtke, Scott Powers, Ashley Petersen, Alexandra Sitarik, Airat Bekmetjev and Nathan L Tintle
    Citation: BMC Proceedings 2011 5(Suppl 9):S119
  15. Using the exome sequencing data from 697 unrelated individuals and their simulated disease phenotypes from Genetic Analysis Workshop 17, we develop and apply a gene-based method to identify the relationship be...

    Authors: Yan V Sun, Wei Zhao, Kerby A Shedden and Sharon LR Kardia
    Citation: BMC Proceedings 2011 5(Suppl 9):S120
  16. Genetic Analysis Workshop 17 used real sequence data from the 1000 Genomes Project and simulated phenotypes influenced by a large number of rare variants. Our aim is to evaluate the performance of various coll...

    Authors: Yun Ju Sung, Treva K Rice and Dabeeru C Rao
    Citation: BMC Proceedings 2011 5(Suppl 9):S121

Annual Journal Metrics

  • 2022 Citation Impact
    0.914 - SNIP (Source Normalized Impact per Paper)
    0.506 - SJR (SCImago Journal Rank)

    2022 Usage 
    575,965 downloads
    477 Altmetric mentions