gtag('config', 'UA-114241270-1');
Your browser is out-of-date!

Update your browser to view this website correctly. Update my browser now

×

Discipline
Biological
Keywords
GWAS
Finding Missing Heritability
Genotype Imputation
Observation Type
Standalone
Nature
Standard Data
Submitted
Jan 21st, 2016
Published
May 4th, 2016
  • Abstract

    Genome-wide association studies (GWAS) have identified thousands of genetic risk variants. However, these variants have explained relatively little of estimated heritability for most complex diseases. The 1000 Genomes Project is a good source to impute missing genotypes for previous GWAS data. Imputation-based GWAS can identify more associated signals on a genome-wide scale. These new markers can be potential sources of missing heritability. In this study, we did the genotype imputation on the Wellcome Trust Case Control Consortium Phase I genotype data using 1000 genomes as reference. Then we estimated the phenotypic variance explained by all significant association signals. The results suggested that the proportions of phenotypic variance explained by genetic variants increased significantly when the new association variants identified through 1000 Genomes-based imputation were included. These results were consistent with the hypothesis that larger number of variants that are yet to be identified as potential sources of missing heritability.

  • Figure
  • Introduction

    Although genome-wide association studies (GWAS) have identified thousands of genetic variants that associated with different complex diseases, a wide gap exists between the estimates of heritability and the heritability that are explained by the genetic variants via GWAS. The potential reasons for the missing heritability include myriads of common variants with small effects yet to be found, rare variants and structure variants (insertions, deletions, duplications, inversions, translocations, and copy number variants) that are poorly detected by available genotyping arrays, and insufficient capability to detect epistasis effects, parental age effects, epigenetic effects, and gene-environment (G×E) interactions.

    Yang et al. reported a joint estimate of all SNPs and found that their method (GCTA) can explain a large proportion of the heritability for human height. Park et al. re-examined existing GWAS to estimate the number of susceptible loci and the distribution of their effect sizes. They used such estimates to ascertain power and sample size requirements for future new GWAS or meta-analyses. Heritability on the liability scale estimated by GCTA ranged from 0.05 to 0.38 across 13 cancer types. These studies argued that a large proportion of the missing heritability can be explained by common variants.

    Previous study have demonstrated that 1000 Genomes-based imputation could identify both novel and refined association loci due to the increased density of marks. We hypothesize that the increased density of GWAS marks will also facilitate the investigation of missing heritability without the need for additional genotyping or sequencing. We use IMPUTE2 for genotype imputation and then apply GCTA to the association results before and after imputation to estimate the heritability of each disease.

  • Objective

    We hypothesize that the 1000 Genomes-based imputation will increase the density of GWAS marks and will facilitate the investigation of missing heritability without the need for additional genotyping or sequencing.

  • Results & Discussion

    After quality control, a total of 444,167 SNPs for 16,179 individuals were retained for the initial association analysis. These SNPs were used as the input genotype data for imputation. Approximately 2.7 million SNPs for each trait were used for association analysis after imputation. The estimation of the phenotypic variance explained by all SNPs with p-value less than 1×10-8 was performed using the restricted maximum likelihood (REML) analysis, which was implemented in GCTA.

    Figure 1 shows the number of SNPs and the estimate of phenotypic variance was explained by these SNPs for the 6 traits. The numbers of SNPs that passed the significant threshold increased more than 10 times after imputation compared with the number before imputation. Before imputation, only several to 12.65 percent of the phenotypic variance was explained by the significant SNPs. After imputation, 25.52% to 56.28% of the phenotypic variance was explained by the significant SNPs. SNPs with p-value less than 1×10-8 after imputation can explain 33.91% to 40.40% of BD phenotypic variance when different prevalence was used. The proportion of phenotypic variance explained by genetic variants in T1D was almost tripled in the 1000 Genomes imputation based association analysis than in the association analysis without imputation. The explained proportion of phenotypic variance were increased approximately 14 and 17 times in RA and CD, respectively. The proportion were increased even higher in CAD and T2D, about 62 and 95 times, respectively.

    We then grouped SNPs with association p-value reached the genome wide significant level (1×10-8) after imputation but were not in LD (r2 >0.8) with any SNP with association p-value less than 1×10-5 before imputation as "novel" SNPs. The number of "novel" SNPs and the estimate of phenotypic variance explained by them for the 6 traits were listed in supplementary figure 2. The results suggested that the novel SNPs are the main reasons behind the increasing of heritability estimate.

    Since, most variants have relatively small effect size, sample size of most studies were not big enough, and the limitation of current genotyping technology, more common variants with intermediate effect and rare variants may be with large effect are yet to be identified. These variants should be tractable through large meta-analysis and imputation based association analysis. This is the first study that comprehensively examined the utility of 1000 Genomes based imputation for finding missing heritability. The proportion of phenotypic variance that was explained by genetic variants increased when the contribution of these new variants was included. These findings support that a larger number of variants are yet to be found. These variants are potential sources of missing heritability.

  • Conclusions

    The new additional identified trait-associated variants identified through 1000 Genomes-based imputation can explain part of missing heritability.

  • Limitations

    One potential problem is that the heritability estimates produced by GCTA is sensitive to the chosen sample and may be biased. Although the 1000 Genomes based imputation increased the proportion of phenotypic variance explained by genetic variants, a substantial proportion of heritability remains unexplained for these diseases. The next-generation sequencing data will accelerate the process of exploring missing heritability. With the rapid increase of the implementation of next-generation sequencing technology, large-scale next-generation sequence data from well phenotyped individuals will be available. It will be a great opportunity to unveil the missing heritability unexplained by common variants that were not covered by current genome-wide association studies.

  • Methods

    Genotype Data

    Previously published GWAS genotype data from the WTCCC were used. The 500K Affymetrix chip genotype data included 1,500 individuals from the 1958 British Birth Cohort, 1,500 individuals from the UK Blood Services controls, and about 2,000 cases for each of 6 common diseases, namely, bipolar disorder (BD), coronary artery disease (CAD), Crohn’s disease (CD), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The genotype calls that were generated by the CHIAMO algorithm were downloaded.

    Quality Control

    For each dataset, the same recommended quality control (QC) thresholds removed the samples listed in the “exclusion-list” files. SNPs with a value of 0 for “good clustering” variable and SNPs listed in the “exclusion-list-snps” files in the data repository were excluded. The samples and SNPs lists were downloaded from European Genotype Archive (http://www.ebi.ac.uk/ega). Individuals with discordant sex information were excluded. Only one individual with higher call rate in the cryptically related individuals was kept. We also excluded SNPs with Hardy-Weinberg exact p-value less than 10-3 in the combined control groups. Only SNPs on autosome were used in this study. The EIGENSTRAT was used to correct the population stratification.

    Genotype imputation

    The genotypes were imputed by IMPUTE2 (version 2.2.2) using the 1000 Genomes phased haplotype as reference panel (download from IMPUTE2 webpage). The recommended standard approach with default parameters was used. Firstly, pre-phasing step produces best-guess haplotypes from the genotypes, then impute into the estimated GWAS haplotypes in the second step. Each chromosome was split into smaller chunks of 5Mb. Only SNPs passed info score filter of 0.9 were used to keep the high-quality genotypes using IPGWAS. SNPs have minor allele frequency (MAF) less than 0.05 or missing rate larger than 0.05 were excluded from association analysis.

    Statistical analysis

    PLINK was used to run the association test. GCTA was used to estimate the phenotypic variance that was explained by SNPs with allelic p-value less than 1.0×10-8. We reviewed published papers to find the prevalence for the 6 common diseases.

  • Funding statement

    This work was funded by grants from NSFC (No. 81271226), the Research Grant Council of Hong Kong (HKU775208M, HKU 777212M), the Research Fund for the Control of Infectious Diseases of Hong Kong (No.11101032), and the Health and Medical Research Fund of Hong Kong Government (HMRF) (No: 01121726).

  • Acknowledgements

    We acknowledge the WTCCC for making the data available.

  • Ethics statement

    Not applicable.

  • References
  • 1
    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum
    2
    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum
    3
    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum
    4
    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum
    5
    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ipsum

    Lorem ipsum Lorem ipsum Lorem ipsum
    Matters9/20

    Finding the missing heritability of genome-wide association study using genotype imputation

    Affiliation listing not available.
    Abstractlink

    Genome-wide association studies (GWAS) have identified thousands of genetic risk variants. However, these variants have explained relatively little of estimated heritability for most complex diseases. The 1000 Genomes Project is a good source to impute missing genotypes for previous GWAS data. Imputation-based GWAS can identify more associated signals on a genome-wide scale. These new markers can be potential sources of missing heritability. In this study, we did the genotype imputation on the Wellcome Trust Case Control Consortium Phase I genotype data using 1000 genomes as reference. Then we estimated the phenotypic variance explained by all significant association signals. The results suggested that the proportions of phenotypic variance explained by genetic variants increased significantly when the new association variants identified through 1000 Genomes-based imputation were included. These results were consistent with the hypothesis that larger number of variants that are yet to be identified as potential sources of missing heritability.

    Figurelink

    Figure 1.

    BD: bipolar disorder; CAD: coronary artery disease; CD: Crohn’s disease; RA: rheumatoid arthritis; T1D: type 1 diabetes; T2D: type 2 diabetes; K: prevalence; VE: explained variance; SE: standard error.

    a: Genome-wide association analysis without imputation. b: Genome-wide association analysis with imputation using 1000 Genomes data as reference panel. c: The number of SNPs with p-value less than 1×10-8. d: The estimate of phenotypic variance explained by SNPs with p-value less than 1×10-8. The values in the parentheses are the standard error of the explained phenotypic variance.

    Supplementary Figure 1. Estimate of the phenotypic variance explained by SNPs.

    Supplementary Figure 2. Estimation of the phenotypic variance explained by novel SNPs.

    Introductionlink

    Although genome-wide association studies (GWAS) have identified thousands of genetic variants that associated with different complex diseases, a wide gap exists between the estimates of heritability and the heritability that are explained by the genetic variants via GWAS[1]. The potential reasons for the missing heritability include myriads of common variants with small effects yet to be found, rare variants and structure variants (insertions, deletions, duplications, inversions, translocations, and copy number variants) that are poorly detected by available genotyping arrays, and insufficient capability to detect epistasis effects, parental age effects, epigenetic effects, and gene-environment (G×E) interactions[2][3][4][5][6][7][8][9][10].

    Yang et al. reported a joint estimate of all SNPs and found that their method (GCTA) can explain a large proportion of the heritability for human height[11]. Park et al. re-examined existing GWAS to estimate the number of susceptible loci and the distribution of their effect sizes. They used such estimates to ascertain power and sample size requirements for future new GWAS or meta-analyses[12]. Heritability on the liability scale estimated by GCTA ranged from 0.05 to 0.38 across 13 cancer types[13]. These studies argued that a large proportion of the missing heritability can be explained by common variants.

    Previous study have demonstrated that 1000 Genomes-based imputation could identify both novel and refined association loci due to the increased density of marks[14][15]. We hypothesize that the increased density of GWAS marks will also facilitate the investigation of missing heritability without the need for additional genotyping or sequencing. We use IMPUTE2[16] for genotype imputation and then apply GCTA[17] to the association results before and after imputation to estimate the heritability of each disease.

    Objectivelink

    We hypothesize that the 1000 Genomes-based imputation will increase the density of GWAS marks and will facilitate the investigation of missing heritability without the need for additional genotyping or sequencing.

    Results & Discussionlink

    After quality control, a total of 444,167 SNPs for 16,179 individuals were retained for the initial association analysis. These SNPs were used as the input genotype data for imputation. Approximately 2.7 million SNPs for each trait were used for association analysis after imputation. The estimation of the phenotypic variance explained by all SNPs with p-value less than 1×10-8 was performed using the restricted maximum likelihood (REML) analysis, which was implemented in GCTA[17].

    Figure 1 shows the number of SNPs and the estimate of phenotypic variance was explained by these SNPs for the 6 traits. The numbers of SNPs that passed the significant threshold increased more than 10 times after imputation compared with the number before imputation. Before imputation, only several to 12.65 percent of the phenotypic variance was explained by the significant SNPs. After imputation, 25.52% to 56.28% of the phenotypic variance was explained by the significant SNPs. SNPs with p-value less than 1×10-8 after imputation can explain 33.91% to 40.40% of BD phenotypic variance when different prevalence was used. The proportion of phenotypic variance explained by genetic variants in T1D was almost tripled in the 1000 Genomes imputation based association analysis than in the association analysis without imputation. The explained proportion of phenotypic variance were increased approximately 14 and 17 times in RA and CD, respectively. The proportion were increased even higher in CAD and T2D, about 62 and 95 times, respectively.

    We then grouped SNPs with association p-value reached the genome wide significant level (1×10-8) after imputation but were not in LD (r2 >0.8) with any SNP with association p-value less than 1×10-5 before imputation as "novel" SNPs. The number of "novel" SNPs and the estimate of phenotypic variance explained by them for the 6 traits were listed in supplementary figure 2. The results suggested that the novel SNPs are the main reasons behind the increasing of heritability estimate.

    Since, most variants have relatively small effect size, sample size of most studies were not big enough, and the limitation of current genotyping technology, more common variants with intermediate effect and rare variants may be with large effect are yet to be identified. These variants should be tractable through large meta-analysis and imputation based association analysis. This is the first study that comprehensively examined the utility of 1000 Genomes based imputation for finding missing heritability. The proportion of phenotypic variance that was explained by genetic variants increased when the contribution of these new variants was included. These findings support that a larger number of variants are yet to be found. These variants are potential sources of missing heritability.

    Conclusionslink

    The new additional identified trait-associated variants identified through 1000 Genomes-based imputation can explain part of missing heritability.

    Limitationslink

    One potential problem is that the heritability estimates produced by GCTA is sensitive to the chosen sample and may be biased[18]. Although the 1000 Genomes based imputation increased the proportion of phenotypic variance explained by genetic variants, a substantial proportion of heritability remains unexplained for these diseases. The next-generation sequencing data will accelerate the process of exploring missing heritability. With the rapid increase of the implementation of next-generation sequencing technology, large-scale next-generation sequence data from well phenotyped individuals will be available. It will be a great opportunity to unveil the missing heritability unexplained by common variants that were not covered by current genome-wide association studies.

    Methodslink

    Genotype Data

    Previously published GWAS genotype data from the WTCCC[19] were used. The 500K Affymetrix chip genotype data included 1,500 individuals from the 1958 British Birth Cohort, 1,500 individuals from the UK Blood Services controls, and about 2,000 cases for each of 6 common diseases, namely, bipolar disorder (BD), coronary artery disease (CAD), Crohn’s disease (CD), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The genotype calls that were generated by the CHIAMO algorithm were downloaded.

    Quality Control

    For each dataset, the same recommended quality control (QC) thresholds removed the samples listed in the “exclusion-list” files. SNPs with a value of 0 for “good clustering” variable and SNPs listed in the “exclusion-list-snps” files in the data repository were excluded. The samples and SNPs lists were downloaded from European Genotype Archive (http://www.ebi.ac.uk/ega). Individuals with discordant sex information were excluded. Only one individual with higher call rate in the cryptically related individuals was kept. We also excluded SNPs with Hardy-Weinberg exact p-value less than 10-3 in the combined control groups. Only SNPs on autosome were used in this study. The EIGENSTRAT[20] was used to correct the population stratification.

    Genotype imputation

    The genotypes were imputed by IMPUTE2 (version 2.2.2)[16] using the 1000 Genomes[21] phased haplotype as reference panel (download from IMPUTE2 webpage). The recommended standard approach with default parameters was used. Firstly, pre-phasing step produces best-guess haplotypes from the genotypes, then impute into the estimated GWAS haplotypes in the second step. Each chromosome was split into smaller chunks of 5Mb. Only SNPs passed info score filter of 0.9 were used to keep the high-quality genotypes using IPGWAS[22]. SNPs have minor allele frequency (MAF) less than 0.05 or missing rate larger than 0.05 were excluded from association analysis.

    Statistical analysis

    PLINK[23] was used to run the association test. GCTA[17] was used to estimate the phenotypic variance that was explained by SNPs with allelic p-value less than 1.0×10-8. We reviewed published papers[24][25][26][27][28][29][30][31][32] to find the prevalence for the 6 common diseases.

    Funding Statementlink

    This work was funded by grants from NSFC (No. 81271226), the Research Grant Council of Hong Kong (HKU775208M, HKU 777212M), the Research Fund for the Control of Infectious Diseases of Hong Kong (No.11101032), and the Health and Medical Research Fund of Hong Kong Government (HMRF) (No: 01121726).

    Acknowledgementslink

    We acknowledge the WTCCC for making the data available.

    Conflict of interestlink

    The authors declare no conflicts of interest.

    Ethics Statementlink

    Not applicable.

    No fraudulence is committed in performing these experiments or during processing of the data. We understand that in the case of fraudulence, the study can be retracted by ScienceMatters.

    Referenceslink
    1. Paolo Vineis, Neil Pearce,
      Missing heritability in genome-wide association study research
      Nature Reviews Genetics, 11/2010, pages 589-589 DOI: 10.1038/nrg2809-c2chrome_reader_mode
    2. Anne Goriely, Andrew O. M. Wilkie
      Missing heritability: paternal age effect mutations and selfish spermatogonia
      Nature Reviews Genetics, 11/2010, pages 589-589 DOI: 10.1038/nrg2809-c1chrome_reader_mode
    3. Evan E. Eichler, Jonathan Flint, Greg Gibson, Augustine Kong, Suzanne M. Leal, Jason H. Moore, Joseph H. Nadeau,
      Missing heritability and strategies for finding the underlying causes of complex disease
      Nature Reviews Genetics, 11/2010, pages 446-450 DOI: 10.1038/nrg2809chrome_reader_mode
    4. Angus J Clarke, David N Cooper,
      GWAS: heritability missing in action?
      European Journal of Human Genetics, 18/2010, pages 859-861 DOI: 10.1038/ejhg.2010.35chrome_reader_mode
    5. Teri A. Manolio, Francis S. Collins, Nancy J. Cox, David B. Goldstein, Lucia A. Hindorff, David J. Hunter, Mark I. McCarthy, Erin M. Ramos, Lon R. Cardon, Aravinda Chakravarti, Judy H. Cho, Alan E. Guttmacher, Augustine Kong, Leonid Kruglyak, Elaine Mardis, Charles N. Rotimi, Montgomery Slatkin, David Valle, Alice S. Whittemore, Michael Boehnke, Andrew G. Clark, Evan E. Eichler, Greg Gibson, Jonathan L. Haines, Trudy F. C. Mackay, Steven A. McCarroll, Peter M. Visscher,
      Finding the missing heritability of complex diseases
      Nature, 461/2009, pages 747-753 DOI: 10.1038/nature08494chrome_reader_mode
    6. Ali J. Marian,
      Elements of ‘missing heritability’
      Current Opinion in Cardiology, 27/2012, pages 197-201 DOI: 10.1097/hco.0b013e328352707dchrome_reader_mode
    7. Eamonn Mm Quigley,
      Epigenetics: filling in the 'heritability gap' and identifying gene-environment interactions in ulcerative colitis
      Genome Medicine, 4/2012, page 72 DOI: 10.1186/gm373chrome_reader_mode
    8. Noah Zaitlen, Peter Kraft,
      Heritability in the genome-wide association era
      Human Genetics, 131/2012, pages 1655-1664 DOI: 10.1007/s00439-012-1199-6chrome_reader_mode
    9. Greg Gibson,
      Hints of hidden heritability in GWAS
      Nature Genetics, 42/2010, pages 558-560 DOI: 10.1038/ng0710-558chrome_reader_mode
    10. O. Zuk, E. Hechter, S. R. Sunyaev, E. S. Lander,
      The mystery of missing heritability: Genetic interactions create phantom heritability
      Proceedings of the National Academy of Sciences, 109/2012, pages 1193-1198 DOI: 10.1073/pnas.1119675109chrome_reader_mode
    11. Jian Yang, Beben Benyamin, Brian P McEvoy, Scott Gordon, Anjali K Henders, Dale R Nyholt, Pamela A Madden, Andrew C Heath, Nicholas G Martin, Grant W Montgomery, Michael E Goddard, Peter M Visscher,
      Common SNPs explain a large proportion of the heritability for human height
      Nature Genetics, 42/2010, pages 565-569 DOI: 10.1038/ng.608chrome_reader_mode
    12. Ju-Hyun Park, Sholom Wacholder, Mitchell H Gail, Ulrike Peters, Kevin B Jacobs, Stephen J Chanock, Nilanjan Chatterjee,
      Estimation of effect size distribution from genome-wide association studies and implications for future discoveries
      Nature Genetics, 42/2010, pages 570-575 DOI: 10.1038/ng.610chrome_reader_mode
    13. Joshua N. Sampson, William A. Wheeler, Meredith Yeager, Orestis Panagiotou, Zhaoming Wang, Sonja I. Berndt, Qing Lan, Christian C. Abnet, Laufey T. Amundadottir, Jonine D. Figueroa, Maria Teresa Landi, Lisa Mirabello, Sharon A. Savage, Philip R. Taylor, Immaculata de Vivo, Katherine A. McGlynn, Mark P. Purdue, Preetha Rajaraman, Hans-Olov Adami, Anders Ahlbom, Demetrius Albanes, Maria Fernanda Amary, She-Juan An, Ulrika Andersson, Gerald Andriole, Irene L. Andrulis, Emanuele Angelucci, Stephen M. Ansell, Cecilia Arici, Bruce K. Armstrong, Alan A. Arslan, Melissa A. Austin, Dalsu Baris, Donald A. Barkauskas, Bryan A. Bassig, Nikolaus Becker, Yolanda Benavente, Simone Benhamou, Christine Berg, David van Den Berg, Leslie Bernstein, Kimberly A. Bertrand, Brenda M. Birmann, Amanda Black, Heiner Boeing, Paolo Boffetta, Marie-Christine Boutron-Ruault, Paige M. Bracci, Louise Brinton, Angela R. Brooks-Wilson, H. Bas Bueno-de-Mesquita, Laurie Burdett, Julie Buring, Mary Ann Butler, Qiuyin Cai, Geraldine Cancel-Tassin, Federico Canzian, Alfredo Carrato, Tania Carreon, Angela Carta, John K. C. Chan, Ellen T. Chang, Gee-Chen Chang, I-Shou Chang, Jiang Chang, Jenny Chang-Claude, Chien-Jen Chen, Chih-Yi Chen, Chu Chen, Chung-Hsing Chen, Constance Chen, Hongyan Chen, Kexin Chen, Kuan-Yu Chen, Kun-Chieh Chen, Ying Chen, Ying-Hsiang Chen, Yi-Song Chen, Yuh-Min Chen, Li-Hsin Chien, María-Dolores Chirlaque, Jin Eun Choi, Yi Young Choi, Wong-Ho Chow, Charles C. Chung, Jacqueline Clavel, Françoise Clavel-Chapelon, Pierluigi Cocco, Joanne S. Colt, Eva Comperat, Lucia Conde, Joseph M. Connors, David Conti, Victoria K. Cortessis, Michelle Cotterchio, Wendy Cozen, Simon Crouch, Marta Crous-Bou, Olivier Cussenot, Faith G. Davis, Ti Ding, W. Ryan Diver, Miren Dorronsoro, Laure Dossus, Eric J. Duell, Maria Grazia Ennas, Ralph L. Erickson, Maria Feychting, Adrienne M. Flanagan, Lenka Foretova, Joseph F. Fraumeni, Neal D. Freedman, Laura E. Beane Freeman, Charles Fuchs, Manuela Gago-Dominguez, Steven Gallinger, Yu-Tang Gao, Susan M. Gapstur, Montserrat Garcia-Closas, Reina García-Closas, Randy D. Gascoyne, Julie Gastier-Foster, Mia M. Gaudet, J. Michael Gaziano, Carol Giffen, Graham G. Giles, Edward Giovannucci, Bengt Glimelius, Michael Goggins, Nalan Gokgoz, Alisa M. Goldstein, Richard Gorlick, Myron Gross, Robert Grubb, Jian Gu, Peng Guan, Marc Gunter, Huan Guo, Thomas M. Habermann, Christopher A. Haiman, Dina Halai, Goran Hallmans, Manal Hassan, Claudia Hattinger, Qincheng He, Xingzhou He, Kathy Helzlsouer, Brian Henderson, Roger Henriksson, Henrik Hjalgrim, Judith Hoffman-Bolton, Chancellor Hohensee, Theodore R. Holford, Elizabeth A. Holly, Yun-Chul Hong, Robert N. Hoover, Pamela L. Horn-Ross, G. M. Monawar Hosain, H. Dean Hosgood, Chin-Fu Hsiao, Nan Hu, Wei Hu, Zhibin Hu, Ming-Shyan Huang, Jose-Maria Huerta, Jen-Yu Hung, Amy Hutchinson, Peter D. Inskip, Rebecca D. Jackson, Eric J. Jacobs, Mazda Jenab, Hyo-Sung Jeon, Bu-Tian Ji, Guangfu Jin, Li Jin, Christoffer Johansen, Alison Johnson, Yoo Jin Jung, Rudolph Kaaks, Aruna Kamineni, Eleanor Kane, Chang Hyun Kang, Margaret R. Karagas, Rachel S. Kelly, Kay-Tee Khaw, Christopher Kim, Hee Nam Kim, Jin Hee Kim, Jun Suk Kim, Yeul Hong Kim, Young Tae Kim, Young-Chul Kim, Cari M. Kitahara, Alison P. Klein, Robert J. Klein, Manolis Kogevinas, Takashi Kohno, Laurence N. Kolonel, Charles Kooperberg, Anne Kricker, Vittorio Krogh, Hideo Kunitoh, Robert C. Kurtz, Sun-Seog Kweon, Andrea Lacroix, Charles Lawrence, Fernando Lecanda, Victor Ho Fun Lee, Donghui Li, Haixin Li, Jihua Li, Yao-Jen Li, Yuqing Li, Linda M. Liao, Mark Liebow, Tracy Lightfoot, Wei-Yen Lim, Chien-Chung Lin, Dongxin Lin, Sara Lindstrom, Martha S. Linet, Brian K. Link, Chenwei Liu, Jianjun Liu, Li Liu, Börje Ljungberg, Josep Lloreta, Simonetta Di Lollo, Daru Lu, Eiluv Lund, Nuria Malats, Satu Mannisto, Loic Le Marchand, Neyssa Marina, Giovanna Masala, Giuseppe Mastrangelo, Keitaro Matsuo, Marc Maynadie, James McKay, Roberta McKean-Cowdin, Mads Melbye, Beatrice S. Melin, Dominique S. Michaud, Tetsuya Mitsudomi, Alain Monnereau, Rebecca Montalvan, Lee E. Moore, Lotte Maxild Mortensen, Alexandra Nieters, Kari E. North, Anne J. Novak, Ann L. Oberg, Kenneth Offit, In-Jae Oh, Sara H. Olson, Domenico Palli, William Pao, In Kyu Park, Jae Yong Park, Kyong Hwa Park, Ana Patiño-Garcia, Sofia Pavanello, Petra H. M. Peeters, Reury-Perng Perng, Ulrike Peters, Gloria M. Petersen, Piero Picci, Malcolm C. Pike, Stefano Porru, Jennifer Prescott, Ludmila Prokunina-Olsson, Biyun Qian, You-Lin Qiao, Marco Rais, Elio Riboli, Jacques Riby, Harvey A. Risch, Cosmeri Rizzato, Rebecca Rodabough, Eve Roman, Morgan Roupret, Avima M. Ruder, Silvia de Sanjose, Ghislaine Scelo, Alan Schned, Fredrick Schumacher, Kendra Schwartz, Molly Schwenn, Katia Scotlandi, Adeline Seow, Consol Serra, Massimo Serra, Howard D. Sesso, Veronica Wendy Setiawan, Gianluca Severi, Richard K. Severson, Tait D. Shanafelt, Hongbing Shen, Wei Shen, Min-Ho Shin, Kouya Shiraishi, Xiao-Ou Shu, Afshan Siddiq, Luis Sierrasesúmaga, Alan Dart Loon Sihoe, Christine F. Skibola, Alex Smith, Martyn T. Smith, Melissa C. Southey, John J. Spinelli, Anthony Staines, Meir Stampfer, Marianna C. Stern, Victoria L. Stevens, Rachael S. Stolzenberg-Solomon, Jian Su, Wu-Chou Su, Malin Sund, Jae Sook Sung, Sook Whan Sung, Wen Tan, Wei Tang, Adonina Tardón, David Thomas, Carrie A. Thompson, Lesley F. Tinker, Roberto Tirabosco, Anne Tjønneland, Ruth C. Travis, Dimitrios Trichopoulos, Fang-Yu Tsai, Ying-Huang Tsai, Margaret Tucker, Jenny Turner, Claire M. Vajdic, Roel C. H. Vermeulen, Danylo J. Villano, Paolo Vineis, Jarmo Virtamo, Kala Visvanathan, Jean Wactawski-Wende, Chaoyu Wang, Chih-Liang Wang, Jiu-Cun Wang, Junwen Wang, Fusheng Wei, Elisabete Weiderpass, George J. Weiner, Stephanie Weinstein, Nicolas Wentzensen, Emily White, Thomas E. Witzig, Brian M. Wolpin, Maria Pik Wong, Chen Wu, Guoping Wu, Junjie Wu, Tangchun Wu, Wei Wu, Xifeng Wu, Yi-Long Wu, Jay S. Wunder, Yong-Bing Xiang, Jun Xu, Ping Xu, Pan-Chyr Yang, Tsung-Ying Yang, Yuanqing Ye, Zhihua Yin, Jun Yokota, Ho-Il Yoon, Chong-Jen Yu, Herbert Yu, Kai Yu, Jian-Min Yuan, Andrew Zelenetz, Anne Zeleniuch-Jacquotte, Xu-Chao Zhang, Yawei Zhang, Xueying Zhao, Zhenhong Zhao, Hong Zheng, Tongzhang Zheng, Wei Zheng, Baosen Zhou, Meng Zhu, Mariagrazia Zucca, Simina M. Boca, James R. Cerhan, Giovanni M. Ferri, Patricia Hartge, Chao Agnes Hsiung, Corrado Magnani, Lucia Miligi, Lindsay M. Morton, Karin E. Smedby, Lauren R. Teras, Joseph Vijai, Sophia S. Wang, Paul Brennan, Neil E. Caporaso, David J. Hunter, Peter Kraft, Nathaniel Rothman, Debra T. Silverman, Susan L. Slager, Stephen J. Chanock, Nilanjan Chatterjee,
      Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for Thirteen Cancer Types
      Journal of the National Cancer Institute, 107/2015, page djv279 DOI: 10.1093/jnci/djv279chrome_reader_mode
    14. Jie Huang, David Ellinghaus, Andre Franke, Bryan Howie, Yun Li,
      1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data
      European Journal of Human Genetics, 20/2012, pages 801-805 DOI: 10.1038/ejhg.2012.3chrome_reader_mode
    15. C Herold, B V Hooli, K Mullin, T Liu, J T Roehr, M Mattheisen, A R Parrado, L Bertram, C Lange, R E Tanzi,
      Family-based association analyses of imputed genotypes reveal genome-wide significant association of Alzheimer’s disease with OSBPL6, PTPRG, and PDCL3
    16. Bryan N. Howie, Peter Donnelly, Jonathan Marchini,
      A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies
      PLOS Genetics, 5/2009, page e1000529 DOI: 10.1371/journal.pgen.1000529chrome_reader_mode
    17. Jian Yang, S. Hong Lee, Michael E. Goddard, Peter M. Visscher,
      GCTA: A Tool for Genome-wide Complex Trait Analysis
      The American Journal of Human Genetics, 88/2011, pages 76-82 DOI: 10.1016/j.ajhg.2010.11.011chrome_reader_mode
    18. Siddharth Krishna Kumar, Marcus W. Feldman, David H. Rehkopf, Shripad Tuljapurkar,
      Limitations of GCTA as a solution to the missing heritability problem
      Proceedings of the National Academy of Sciences, 113/2015, pages E61-E70 DOI: 10.1073/pnas.1520109113chrome_reader_mode
    19. Paul R. Burton, David G. Clayton, Lon R. Cardon, Nick Craddock, Panos Deloukas, Audrey Duncanson, Dominic P. Kwiatkowski, Mark I. McCarthy, Willem H. Ouwehand, Nilesh J. Samani, John A. Todd, Peter Donnelly, Jeffrey C. Barrett, Paul R. Burton, Dan Davison, Peter Donnelly, Doug Easton, David Evans, Hin-Tak Leung, Jonathan L. Marchini, Andrew P. Morris, Chris C. A. Spencer, Martin D. Tobin, Lon R. Cardon, David G. Clayton, Antony P. Attwood, James P. Boorman, Barbara Cant, Ursula Everson, Judith M. Hussey, Jennifer D. Jolley, Alexandra S. Knight, Kerstin Koch, Elizabeth Meech, Sarah Nutland, Christopher V. Prowse, Helen E. Stevens, Niall C. Taylor, Graham R. Walters, Neil M. Walker, Nicholas A. Watkins, Thilo Winzer, John A. Todd, Willem H. Ouwehand, Richard W. Jones, Wendy L. McArdle, Susan M. Ring, David P. Strachan, Marcus Pembrey, Gerome Breen, David St Clair, Sian Caesar, Katherine Gordon-Smith, Lisa Jones, Christine Fraser, Elaine K. Green, Detelina Grozeva, Marian L. Hamshere, Peter A. Holmans, Ian R. Jones, George Kirov, Valentina Moskvina, Ivan Nikolov, Michael C. O'Donovan, Michael J. Owen, Nick Craddock, David A. Collier, Amanda Elkin, Anne Farmer, Richard Williamson, Peter McGuffin, Allan H. Young, I. Nicol Ferrier, Stephen G. Ball, Anthony J. Balmforth, Jennifer H. Barrett, D. Timothy Bishop, Mark M. Iles, Azhar Maqbool, Nadira Yuldasheva, Alistair S. Hall, Peter S. Braund, Paul R. Burton, Richard J. Dixon, Massimo Mangino, Suzanne Stevens, Martin D. Tobin, John R. Thompson, Nilesh J. Samani, Francesca Bredin, Mark Tremelling, Miles Parkes, Hazel Drummond, Charles W. Lees, Elaine R. Nimmo, Jack Satsangi, Sheila A. Fisher, Alastair Forbes, Cathryn M. Lewis, Clive M. Onnie, Natalie J. Prescott, Jeremy Sanderson, Christopher G. Mathew, Jamie Barbour, M. Khalid Mohiuddin, Catherine E. Todhunter, John C. Mansfield, Tariq Ahmad, Fraser R. Cummings, Derek P. Jewell, John Webster, Morris J. Brown, David G. Clayton, G. Mark Lathrop, John Connell, Anna Dominiczak, Nilesh J. Samani, Carolina A. Braga Marcano, Beverley Burke, Richard Dobson, Johannie Gungadoo, Kate L. Lee, Patricia B. Munroe, Stephen J. Newhouse, Abiodun Onipinla, Chris Wallace, Mingzhan Xue, Mark Caulfield, Martin Farrall, Anne Barton, The Biologics In Ra Genetics, Genomics (braggs), Ian N. Bruce, Hannah Donovan, Steve Eyre, Paul D. Gilbert, Samantha L. Hider, Anne M. Hinks, Sally L. John, Catherine Potter, Alan J. Silman, Deborah P. M. Symmons, Wendy Thomson, Jane Worthington, David G. Clayton, David B. Dunger, Sarah Nutland, Helen E. Stevens, Neil M. Walker, Barry Widmer, John A. Todd, Timothy M. Frayling, Rachel M. Freathy, Hana Lango, John R. B. Perry, Beverley M. Shields, Michael N. Weedon, Andrew T. Hattersley, Graham A. Hitman, Mark Walker, Kate S. Elliott, Christopher J. Groves, Cecilia M. Lindgren, Nigel W. Rayner, Nicholas J. Timpson, Eleftheria Zeggini, Mark I. McCarthy, Melanie Newport, Giorgio Sirugo, Emily Lyons, Fredrik Vannberg, Adrian V. S. Hill, Linda A. Bradbury, Claire Farrar, Jennifer J. Pointon, Paul Wordsworth, Matthew A. Brown, Jayne A. Franklyn, Joanne M. Heward, Matthew J. Simmonds, Stephen C. L. Gough, Sheila Seal, Breast Cancer Susceptibility Collaboration (uk), Michael R. Stratton, Nazneen Rahman, Maria Ban, An Goris, Stephen J. Sawcer, Alastair Compston, David Conway, Muminatou Jallow, Melanie Newport, Giorgio Sirugo, Kirk A. Rockett, Dominic P. Kwiatkowski, Suzannah J. Bumpstead, Amy Chaney, Kate Downes, Mohammed J. R. Ghori, Rhian Gwilliam, Sarah E. Hunt, Michael Inouye, Andrew Keniry, Emma King, Ralph McGinnis, Simon Potter, Rathi Ravindrarajah, Pamela Whittaker, Claire Widden, David Withers, Panos Deloukas, Hin-Tak Leung, Sarah Nutland, Helen E. Stevens, Neil M. Walker, John A. Todd, Doug Easton, David G. Clayton, Paul R. Burton, Martin D. Tobin, Jeffrey C. Barrett, David Evans, Andrew P. Morris, Lon R. Cardon, Niall J. Cardin, Dan Davison, Teresa Ferreira, Joanne Pereira-Gale, Ingileif B. Hallgrimsdóttir, Bryan N. Howie, Jonathan L. Marchini, Chris C. A. Spencer, Zhan Su, Yik Ying Teo, Damjan Vukcevic, Peter Donnelly, David Bentley, Matthew A. Brown, Lon R. Cardon, Mark Caulfield, David G. Clayton, Alistair Compston, Nick Craddock, Panos Deloukas, Peter Donnelly, Martin Farrall, Stephen C. L. Gough, Alistair S. Hall, Andrew T. Hattersley, Adrian V. S. Hill, Dominic P. Kwiatkowski, Christopher G. Mathew, Mark I. McCarthy, Willem H. Ouwehand, Miles Parkes, Marcus Pembrey, Nazneen Rahman, Nilesh J. Samani, Michael R. Stratton, John A. Todd, Jane Worthington,
      Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
      Nature, 447/2007, pages 661-678 DOI: 10.1038/nature05911chrome_reader_mode
    20. Alkes L Price, Nick J Patterson, Robert M Plenge, Michael E Weinblatt, Nancy A Shadick, David Reich,
      Principal components analysis corrects for stratification in genome-wide association studies
      Nature Genetics, 38/2006, pages 904-909 DOI: 10.1038/ng1847chrome_reader_mode
    21. Richard M. Durbin, David L. Altshuler, Richard M. Durbin, Gonçalo R. Abecasis, David R. Bentley, Aravinda Chakravarti, Andrew G. Clark, Francis S. Collins, Francisco M. de La Vega, Peter Donnelly, Michael Egholm, Paul Flicek, Stacey B. Gabriel, Richard A. Gibbs, Bartha M. Knoppers, Eric S. Lander, Hans Lehrach, Elaine R. Mardis, Gil A. McVean, Debbie A. Nickerson, Leena Peltonen, Alan J. Schafer, Stephen T. Sherry, Jun Wang, Richard K. Wilson, Richard A. Gibbs, David Deiros, Mike Metzker, Donna Muzny, Jeff Reid, David Wheeler, Jun Wang, Jingxiang Li, Min Jian, Guoqing Li, Ruiqiang Li, Huiqing Liang, Geng Tian, Bo Wang, Jian Wang, Wei Wang, Huanming Yang, Xiuqing Zhang, Huisong Zheng, Eric S. Lander, David L. Altshuler, Lauren Ambrogio, Toby Bloom, Kristian Cibulskis, Tim J. Fennell, Stacey B. Gabriel, David B. Jaffe, Erica Shefler, Carrie L. Sougnez, David R. Bentley, Niall Gormley, Sean Humphray, Zoya Kingsbury, Paula Koko-Gonzales, Jennifer Stone, Kevin J. McKernan, Gina L. Costa, Jeffry K. Ichikawa, Clarence C. Lee, Ralf Sudbrak, Hans Lehrach, Tatiana A. Borodina, Andreas Dahl, Alexey N. Davydov, Peter Marquardt, Florian Mertes, Wilfiried Nietfeld, Philip Rosenstiel, Stefan Schreiber, Aleksey V. Soldatov, Bernd Timmermann, Marius Tolzmann, Michael Egholm, Jason Affourtit, Dana Ashworth, Said Attiya, Melissa Bachorski, Eli Buglione, Adam Burke, Amanda Caprio, Christopher Celone, Shauna Clark, David Conners, Brian Desany, Lisa Gu, Lorri Guccione, Kalvin Kao, Andrew Kebbel, Jennifer Knowlton, Matthew Labrecque, Louise McDade, Craig Mealmaker, Melissa Minderman, Anne Nawrocki, Faheem Niazi, Kristen Pareja, Ravi Ramenani, David Riches, Wanmin Song, Cynthia Turcotte, Shally Wang, Elaine R. Mardis, Richard K. Wilson, David Dooling, Lucinda Fulton, Robert Fulton, George Weinstock, Richard M. Durbin, John Burton, David M. Carter, Carol Churcher, Alison Coffey, Anthony Cox, Aarno Palotie, Michael Quail, Tom Skelly, James Stalker, Harold P. Swerdlow, Daniel Turner, Anniek de Witte, Shane Giles, Richard A. Gibbs, David Wheeler, Matthew Bainbridge, Danny Challis, Aniko Sabo, Fuli Yu, Jin Yu, Jun Wang, Xiaodong Fang, Xiaosen Guo, Ruiqiang Li, Yingrui Li, Ruibang Luo, Shuaishuai Tai, Honglong Wu, Hancheng Zheng, Xiaole Zheng, Yan Zhou, Guoqing Li, Jian Wang, Huanming Yang, Gabor T. Marth, Erik P. Garrison, Weichun Huang, Amit Indap, Deniz Kural, Wan-Ping Lee, Wen Fung Leong, Aaron R. Quinlan, Chip Stewart, Michael P. Stromberg, Alistair N. Ward, Jiantao Wu, Charles Lee, Ryan E. Mills, Xinghua Shi, Mark J. Daly, Mark A. Depristo, David L. Altshuler, Aaron D. Ball, Eric Banks, Toby Bloom, Brian L. Browning, Kristian Cibulskis, Tim J. Fennell, Kiran V. Garimella, Sharon R. Grossman, Robert E. Handsaker, Matt Hanna, Chris Hartl, David B. Jaffe, Andrew M. Kernytsky, Joshua M. Korn, Heng Li, Jared R. Maguire, Steven A. McCarroll, Aaron McKenna, James C. Nemesh, Anthony A. Philippakis, Ryan E. Poplin, Alkes Price, Manuel A. Rivas, Pardis C. Sabeti, Stephen F. Schaffner, Erica Shefler, Ilya A. Shlyakhter, David N. Cooper, Edward V. Ball, Matthew Mort, Andrew D. Phillips, Peter D. Stenson, Jonathan Sebat, Vladimir Makarov, Kenny Ye, Seungtai C. Yoon, Carlos D. Bustamante, Andrew G. Clark, Adam Boyko, Jeremiah Degenhardt, Simon Gravel, Ryan N. Gutenkunst, Mark Kaganovich, Alon Keinan, Phil Lacroute, Xin Ma, Andy Reynolds, Laura Clarke, Paul Flicek, Fiona Cunningham, Javier Herrero, Stephen Keenen, Eugene Kulesha, Rasko Leinonen, William M. McLaren, Rajesh Radhakrishnan, Richard E. Smith, Vadim Zalunin, Xiangqun Zheng-Bradley, Jan O. Korbel, Adrian M. Stütz, Sean Humphray, Markus Bauer, R. Keira Cheetham, Tony Cox, Michael Eberle, Terena James, Scott Kahn, Lisa Murray, Aravinda Chakravarti, Kai Ye, Francisco M. de La Vega, Yutao Fu, Fiona C. L. Hyland, Jonathan M. Manning, Stephen F. McLaughlin, Heather E. Peckham, Onur Sakarya, Yongming A. Sun, Eric F. Tsung, Mark A. Batzer, Miriam K. Konkel, Jerilyn A. Walker, Ralf Sudbrak, Marcus W. Albrecht, Vyacheslav S. Amstislavskiy, Ralf Herwig, Dimitri V. Parkhomchuk, Stephen T. Sherry, Richa Agarwala, Hoda M. Khouri, Aleksandr O. Morgulis, Justin E. Paschall, Lon D. Phan, Kirill E. Rotmistrovsky, Robert D. Sanders, Martin F. Shumway, Chunlin Xiao, Gil A. McVean, Adam Auton, Zamin Iqbal, Gerton Lunter, Jonathan L. Marchini, Loukas Moutsianas, Simon Myers, Afidalina Tumian, Brian Desany, James Knight, Roger Winer, David W. Craig, Steve M. Beckstrom-Sternberg, Alexis Christoforides, Ahmet A. Kurdoglu, John V. Pearson, Shripad A. Sinari, Waibhav D. Tembe, David Haussler, Angie S. Hinrichs, Sol J. Katzman, Andrew Kern, Robert M. Kuhn, Molly Przeworski, Ryan D. Hernandez, Bryan Howie, Joanna L. Kelley, S. Cord Melton, Gonçalo R. Abecasis, Yun Li, Paul Anderson, Tom Blackwell, Wei Chen, William O. Cookson, Jun Ding, Hyun Min Kang, Mark Lathrop, Liming Liang, Miriam F. Moffatt, Paul Scheet, Carlo Sidore, Matthew Snyder, Xiaowei Zhan, Sebastian Zöllner, Philip Awadalla, Ferran Casals, Youssef Idaghdour, John Keebler, Eric A. Stone, Martine Zilversmit, Lynn Jorde, Jinchuan Xing, Evan E. Eichler, Gozde Aksay, Can Alkan, Iman Hajirasouliha, Fereydoun Hormozdiari, Jeffrey M. Kidd, S. Cenk Sahinalp, Peter H. Sudmant, Elaine R. Mardis, Ken Chen, Asif Chinwalla, Li Ding, Daniel C. Koboldt, Mike D. McLellan, David Dooling, George Weinstock, John W. Wallis, Michael C. Wendl, Qunyuan Zhang, Richard M. Durbin, Cornelis A. Albers, Qasim Ayub, Senduran Balasubramaniam, Jeffrey C. Barrett, David M. Carter, Yuan Chen, Donald F. Conrad, Petr Danecek, Emmanouil T. Dermitzakis, Min Hu, Ni Huang, Matt E. Hurles, Hanjun Jin, Luke Jostins, Thomas M. Keane, Si Quang Le, Sarah Lindsay, Quan Long, Daniel G. Macarthur, Stephen B. Montgomery, Leopold Parts, James Stalker, Chris Tyler-Smith, Klaudia Walter, Yujun Zhang, Mark B. Gerstein, Michael Snyder, Alexej Abyzov, Suganthi Balasubramanian, Robert Bjornson, Jiang Du, Fabian Grubert, Lukas Habegger, Rajini Haraksingh, Justin Jee, Ekta Khurana, Hugo Y. K. Lam, Jing Leng, Xinmeng Jasmine Mu, Alexander E. Urban, Zhengdong Zhang, Yingrui Li, Ruibang Luo, Gabor T. Marth, Erik P. Garrison, Deniz Kural, Aaron R. Quinlan, Chip Stewart, Michael P. Stromberg, Alistair N. Ward, Jiantao Wu, Charles Lee, Ryan E. Mills, Xinghua Shi, Steven A. McCarroll, Eric Banks, Mark A. Depristo, Robert E. Handsaker, Chris Hartl, Joshua M. Korn, Heng Li, James C. Nemesh, Jonathan Sebat, Vladimir Makarov, Kenny Ye, Seungtai C. Yoon, Jeremiah Degenhardt, Mark Kaganovich, Laura Clarke, Richard E. Smith, Xiangqun Zheng-Bradley, Jan O. Korbel, Sean Humphray, R. Keira Cheetham, Michael Eberle, Scott Kahn, Lisa Murray, Kai Ye, Francisco M. de La Vega, Yutao Fu, Heather E. Peckham, Yongming A. Sun, Mark A. Batzer, Miriam K. Konkel, Jerilyn A. Walker, Chunlin Xiao, Zamin Iqbal, Brian Desany, Tom Blackwell, Matthew Snyder, Jinchuan Xing, Evan E. Eichler, Gozde Aksay, Can Alkan, Iman Hajirasouliha, Fereydoun Hormozdiari, Jeffrey M. Kidd, Ken Chen, Asif Chinwalla, Li Ding, Mike D. McLellan, John W. Wallis, Matt E. Hurles, Donald F. Conrad, Klaudia Walter, Yujun Zhang, Mark B. Gerstein, Michael Snyder, Alexej Abyzov, Jiang Du, Fabian Grubert, Rajini Haraksingh, Justin Jee, Ekta Khurana, Hugo Y. K. Lam, Jing Leng, Xinmeng Jasmine Mu, Alexander E. Urban, Zhengdong Zhang, Richard A. Gibbs, Matthew Bainbridge, Danny Challis, Cristian Coafra, Huyen Dinh, Christie Kovar, Sandy Lee, Donna Muzny, Lynne Nazareth, Jeff Reid, Aniko Sabo, Fuli Yu, Jin Yu, Gabor T. Marth, Erik P. Garrison, Amit Indap, Wen Fung Leong, Aaron R. Quinlan, Chip Stewart, Alistair N. Ward, Jiantao Wu, Kristian Cibulskis, Tim J. Fennell, Stacey B. Gabriel, Kiran V. Garimella, Chris Hartl, Erica Shefler, Carrie L. Sougnez, Jane Wilkinson, Andrew G. Clark, Simon Gravel, Fabian Grubert, Laura Clarke, Paul Flicek, Richard E. Smith, Xiangqun Zheng-Bradley, Stephen T. Sherry, Hoda M. Khouri, Justin E. Paschall, Martin F. Shumway, Chunlin Xiao, Gil A. McVean, Sol J. Katzman, Gonçalo R. Abecasis, Tom Blackwell, Elaine R. Mardis, David Dooling, Lucinda Fulton, Robert Fulton, Daniel C. Koboldt, Richard M. Durbin, Senduran Balasubramaniam, Allison Coffey, Thomas M. Keane, Daniel G. Macarthur, Aarno Palotie, Carol Scott, James Stalker, Chris Tyler-Smith, Mark B. Gerstein, Suganthi Balasubramanian, Aravinda Chakravarti, Bartha M. Knoppers, Gonçalo R. Abecasis, Carlos D. Bustamante, Neda Gharani, Richard A. Gibbs, Lynn Jorde, Jane S. Kaye, Alastair Kent, Taosha Li, Amy L. McGuire, Gil A. McVean, Pilar N. Ossorio, Charles N. Rotimi, Yeyang Su, Lorraine H. Toji, Chris Tyler-Smith, Lisa D. Brooks, Adam L. Felsenfeld, Jean E. McEwen, Assya Abdallah, Christopher R. Juenger, Nicholas C. Clemm, Francis S. Collins, Audrey Duncanson, Eric D. Green, Mark S. Guyer, Jane L. Peterson, Alan J. Schafer, Gonçalo R. Abecasis, David L. Altshuler, Adam Auton, Lisa D. Brooks, Richard M. Durbin, Richard A. Gibbs, Matt E. Hurles, Gil A. McVean,
      A map of human genome variation from population-scale sequencing
      Nature, 467/2010, pages 1061-1073 DOI: 10.1038/nature09534chrome_reader_mode
    22. Yan-Hui Fan, You-Qiang Song,
      IPGWAS: An integrated pipeline for rational quality control and association analysis of genome-wide genetic studies
      Biochemical and Biophysical Research Communications, 422/2012, pages 363-368 DOI: 10.1016/j.bbrc.2012.04.117chrome_reader_mode
    23. Shaun Purcell, Benjamin Neale, Kathe Todd-Brown, Lori Thomas, Manuel A.R. Ferreira, David Bender, Julian Maller, Pamela Sklar, Paul I.W. de Bakker, Mark J. Daly, Pak C. Sham,
      PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses
      The American Journal of Human Genetics, 81/2007, pages 559-575 DOI: 10.1086/519795chrome_reader_mode
    24. Jonas Halfvarson, Lennart Bodin, Curt Tysk, Eva Lindberg, Gunnar Järnerot,
      Inflammatory bowel disease in a Swedish twin cohort: a long-term follow-up of concordance and clinical characteristics
      Gastroenterology, 124/2003, pages 1767-1773 DOI: 10.1016/s0016-5085(03)00385-8chrome_reader_mode
    25. Alexander J. Macgregor, Harold Snieder, Alan S. Rigby, Markku Koskenvuo, Jaakko Kaprio, Kimmo Aho, Alan J. Silman,
      Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins
    26. Paul Lichtenstein, Benjamin H Yip, Camilla Björk, Yudi Pawitan, Tyrone D Cannon, Patrick F Sullivan, Christina M Hultman,
      Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study
      The Lancet, 373/2009, pages 234-239 DOI: 10.1016/s0140-6736(09)60072-6chrome_reader_mode
    27. V. Hyttinen, J. Kaprio, L. Kinnunen, M. Koskenvuo, J. Tuomilehto,
      Genetic Liability of Type 1 Diabetes and the Onset Age Among 22,650 Young Finnish Twin Pairs: A Nationwide Follow-Up Study
      Diabetes, 52/2003, pages 1052-1055 DOI: 10.2337/diabetes.52.4.1052chrome_reader_mode
    28. Marjorie E. Marenberg, Neil Risch, Lisa F. Berkman, Birgitta Floderus, Ulf de Faire,
      Genetic Susceptibility to Death from Coronary Heart Disease in a Study of Twins
      The New England Journal of Medicine, 330/1994, pages 1041-1046 DOI: 10.1056/nejm199404143301503chrome_reader_mode
    29. J Sofaer,
      Crohn's disease: the genetic contribution.
    30. S Harney, B.P. Wordsworth,
      Genetic epidemiology of rheumatoid arthritis
      Tissue Antigens, 60/2002, pages 465-473 DOI: 10.1034/j.1399-0039.2002.600601.xchrome_reader_mode
    31. N Craddock, V Khodel, P van Eerdewegh, And T Reich
      Mathematical limits of multilocus models: the genetic transmission of bipolar disorder.
      The American Journal of Human Genetics, 57/1995, page 690–702 chrome_reader_mode
    32. Swapan Kumar Das, Steven C Elbein
      The Genetic Basis of Type 2 Diabetes
      Cellscience, 2/2006, page 100–131 chrome_reader_mode
    Commentslink

    Create a Matters account to leave a comment.