Advertisement

Human Genetics

, Volume 131, Issue 10, pp 1555–1563 | Cite as

The role of large pedigrees in an era of high-throughput sequencing

  • Ellen M. Wijsman
Review Paper

Abstract

Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.

Keywords

Markov Chain Monte Carlo Rare Variant Complex Trait Mendelian Disorder Large Pedigree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Supported by NIH grants GM046255, AG005136, HD054562, HD055782, MH092367, and AG039700.

References

  1. Abecasis GR, Wigginton JE (2005) Handling marker–marker linkage disequilibrium: pedigree analysis with clustered markers. Am J Hum Genet 77:754–767PubMedCrossRefGoogle Scholar
  2. Almasy L, Blangero J (2004) Exploring positional candidate genes: linkage conditional on measured genotype. Behav Genet 34:173–177PubMedCrossRefGoogle Scholar
  3. Amberger J, Bocchini C, Hamosh A (2011) A new face and new challenges for Online Mendelian Inheritance in man (OMIM (R)). Hum Mutat 32:564–567PubMedCrossRefGoogle Scholar
  4. Bailey-Wilson JE, Wilson AF (2011) Linkage analysis in the next-generation sequencing era. Hum Hered 72:228–236PubMedCrossRefGoogle Scholar
  5. Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40:695–701PubMedCrossRefGoogle Scholar
  6. Boehnke M (1994) Limits of resolution of genetic linkage studies: implications for positional cloning of human disease genes. Am J Hum Genet 55:379–390PubMedGoogle Scholar
  7. Boehnke M, Cox N (1997) Accurate inference of relationships in sib-pair linkage studies. Am J Hum Genet 61:423–429PubMedCrossRefGoogle Scholar
  8. Botstein D, Risch N (2003) Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet 33:228–237PubMedCrossRefGoogle Scholar
  9. Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331PubMedGoogle Scholar
  10. Bowden DW, An SS, Palmer ND, Brown WM, Norris JM, Haffner SM, Hawkins GA, Guo X, Rotter JI, Chen YDI, Wagenknecht LE, Langefeld CD (2010) Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS family study. Hum Mol Genet 19:4112–4120PubMedCrossRefGoogle Scholar
  11. Buetow K (1991) Influence of aberrant observations on high-resolution linkage analysis outcomes. Am J Hum Genet 49:985–994PubMedGoogle Scholar
  12. Burdick JT, Chen WM, Abecasis GR, Cheung VG (2006) In silico method for inferring genotypes in pedigrees. Nat Genet 38:1002–1004PubMedCrossRefGoogle Scholar
  13. Calafell F, Almasy L, Sabater-Lleal M, Buil A, Mordillo C, Ramirez-Soriano A, Sikora M, Souto JC, Blangero J, Fontcuberta J, Soria JM (2010) Sequence variation and genetic evolution at the human F12 locus: mapping quantitative trait nucleotides that influence FXII plasma levels. Hum Mol Genet 19:517–525PubMedCrossRefGoogle Scholar
  14. Cheung CYK, Thompson EA, Wijsman EM (2010) In silico genotype imputation on large pedigrees. Genet Epidemiol 34:919Google Scholar
  15. Cheung CYK, Wijsman EM, Thompson EA (2011) Detection of genotype errors in dense markers on large pedigrees. International Congress of Human Genetics, MontrealGoogle Scholar
  16. Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH (2004) Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 305:869–872PubMedCrossRefGoogle Scholar
  17. Collins FS (1991) Of needles and haystacks—finding human-disease genes by positional cloning. Clin Res 39:615–623PubMedGoogle Scholar
  18. Collins FS, Guyer MS, Chakravarti A (1997) Variations on a theme: cataloging human DNA sequence variation. Science 278:1580–1581PubMedCrossRefGoogle Scholar
  19. Cruchaga C, Chakraverty S, Mayo K, Vallania FL, Mitra RD, Faber K, Williamson J, Bird T, Diaz-Arrastia R, Foroud TM, Boeve BF, Graff-Radford NR, St Jean P, Lawson M, Ehm MG, Mayeux R, Goate AM (2012) Rare variants in APP, PSEN1 and PSEN2 increase risk for AD in late-onset Alzheimer’s disease families. PLoS One 7:e31039PubMedCrossRefGoogle Scholar
  20. Daw EW, Heath SC, Wijsman EM (1999) Multipoint oligogenic analysis of age-at-onset data with applications to Alzheimer’s disease pedigrees. Am J Hum Genet 64:839–851PubMedCrossRefGoogle Scholar
  21. Ehm MG, Kimmel M, Cottingham RW (1996) Error detection for genetic data, using likelihood methods. Am J Hum Genet 58:225–234PubMedGoogle Scholar
  22. Elston RC, Stewart J (1971) A general model for the genetic analysis of pedigree data. Hum Hered 21:523–542PubMedCrossRefGoogle Scholar
  23. Erlich Y, Edvardson S, Hodges E, Zenvirt S, Thekkat P, Shaag A, Dor T, Hannon GJ, Elpeleg O (2011) Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res 21:658–664PubMedCrossRefGoogle Scholar
  24. Flint J, Mackay TFC (2009) Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res 19:723–733PubMedCrossRefGoogle Scholar
  25. Gagnon F, Roslin NM, Lemire M (2011) Successful identification of rare variants using oligogenic segregation analysis as a prioritizing tool for whole-exome sequencing studies. BMC Proc 5:S11PubMedCrossRefGoogle Scholar
  26. Gerrish A, Russo G, Richards A, Moskvina V, Ivanov D, Harold D, Sims R, Abraham R, Hollingworth PCJ, Hamshere M, Pahwa JS, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Johnston JA, Holmes C, Mann D, Smith AD, Love S, Kehoe PG, Hardy J, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Kölsch H, Heun R, Schürmann B, Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frölich L, Hampel H, Hüll M, Rujescu D, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Davies G, Harris SE, Starr JM, Deary IJ, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Mühleisen TW, Nöthen MM, Moebus S, Jöckel KH, Klopp N, Wichmann HE, Carrasquillo MM, Pankratz VS, Younkin SG, Jones L, Holmans PA, O’Donovan MC, Owen MJ, Williams J (2012) The role of variation at AβPP, PSEN1, PSEN2, and MAPT in late onset Alzheimer’s disease. J Alzheimers Dis 28:377–387PubMedGoogle Scholar
  27. Gorlov IP, Gorlova OY, Frazier ML, Spitz MR, Amos CI (2011) Evolutionary evidence of the effect of rare variants on disease etiology. Clin Genet 79:199–206PubMedCrossRefGoogle Scholar
  28. Haldane JBS, Smith CAB (1947) A new estimate of the linkage between the genes for colour-blindness and haemophilia in man. Ann Eugen 14:10–31PubMedGoogle Scholar
  29. Heath SC (1997) Markov Chain Monte Carlo segregation and linkage analysis for oligogenic models. Am J Hum Genet 61:748–760PubMedCrossRefGoogle Scholar
  30. Hedin CR, Stagg AJ, Whelan K, Lindsay JO (2012) Family studies in Crohn’s disease: new horizons in understanding disease pathogenesis, risk and prevention. Gut 61:311–318PubMedCrossRefGoogle Scholar
  31. Hershberger RE, Morales A, Siegfried JD (2010) Clinical and genetic issues in dilated cardiomyopathy: a review for genetics professionals. Genet Med 12:655–667PubMedCrossRefGoogle Scholar
  32. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Nat Acad Sci USA 106:9362–9367PubMedCrossRefGoogle Scholar
  33. Hokanson JE, Brunzell JD, Jarvik GP, Wijsman EM, Austin MA (1999) Linkage of low-density lipoprotein size to the lipoprotein lipase gene in heterozygous lipoprotein lipase deficiency. Am J Hum Genet 64:608–618PubMedCrossRefGoogle Scholar
  34. Huntington’s Disease Collaborative Research Group (1993) A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell 72:971–983CrossRefGoogle Scholar
  35. Ionita-Laza I, Ottman R (2011) Study designs for identification of rare disease variants in complex diseases: the utility of family-based designs. Genetics 189:1061–U500Google Scholar
  36. Jaquish CE (2007) The Framingham Heart Study, on its way to becoming the gold standard for cardiovascular genetic epidemiology? BMC Med Genet 8:63Google Scholar
  37. Jarvik GP, Brunzell JD, Austin MA, Krauss RM, Motulsky AG, Wijsman EM (1994) Genetic predictors of FCHL in four large pedigrees: influence of ApoB level major locus predicted genotype and LDL subclass phenotype. Arterioscler Thromb Vasc Biol 14:1687–1694CrossRefGoogle Scholar
  38. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308:385–389PubMedCrossRefGoogle Scholar
  39. Koepke H, Thompson EA (2010) Efficient testing operations on dynamic graph structures using strong hash functions. Department of Statistics, technical reports. University of Washington, SeattleGoogle Scholar
  40. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, Cucchiara AJ, Savage PJ, Howard BV (1990) The Strong Heart-Study—a study of cardiovascular-disease in American–Indians—design and methods. Am J Epidemiol 132:1141–1155PubMedGoogle Scholar
  41. Leibon G, Rockmore DN, Pollak MR (2008) A SNP streak model for the identification of genetic regions identical-by-descent. Stat Appl Genet Mol Biol 7:16Google Scholar
  42. Leigh SEA, Foster AH, Whittall RA, Hubbart CS, Humphries SE (2008) Update and analysis of the University College London low density lipoprotein receptor familial hypercholesterolemia database. Ann Hum Genet 72:485–498PubMedCrossRefGoogle Scholar
  43. Levy-Lahad E, Wasco W, Poorkaj P, Romano DM, Oshima J, Pettingell WH, Yu CE, Jondro PD, Schmidt SD, Wang K, Crowley AC, Fu YH, Guenette SY, Galas D, Nemens E, Wijsman EM, Bird TD, Schellenberg GD, Tanzi RE (1995) Candidate gene for the chromosome 1 familial Alzheimer’s disease locus. Science 269:973–977PubMedCrossRefGoogle Scholar
  44. Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10:387–406PubMedCrossRefGoogle Scholar
  45. Manolio TA, Brooks LD, Collins FS (2008) A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118:1590–1605PubMedCrossRefGoogle Scholar
  46. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM (2009) Finding the missing heritability of complex diseases. Nature 461:747–753PubMedCrossRefGoogle Scholar
  47. Marchani EE, Wijsman EM (2011) Estimation and visualization of identity-by-descent within pedigrees simplifies interpretation of complex trait analysis. Hum Hered 72:289–297PubMedCrossRefGoogle Scholar
  48. Markus B, Birk OS, Geiger D (2011) Integration of SNP genotyping confidence scores in IBD inference. Bioinformatics 27:2880–2887PubMedCrossRefGoogle Scholar
  49. Matise TC, Chen F, Chen WW, De la Vega FM, Hansen M, He CS, Hyland FCL, Kennedy GC, Kong XY, Murray SS, Ziegle JS, Stewart WCL, Buyske S (2007) A second-generation combined linkage-physical map of the human genome. Genome Res 17:1783–1786PubMedCrossRefGoogle Scholar
  50. McClellan J, King MC (2010) Genetic heterogeneity in human disease. Cell 141:210–217PubMedCrossRefGoogle Scholar
  51. Miki Y, Swensen J, Shattuckeidens D, Futreal PA, Harshman K, Tavtigian S, Liu QY, Cochran C, Bennett LM, Ding W, Bell R, Rosenthal J, Hussey C, Tran T, McClure M, Frye C, Hattier T, Phelps R, Haugenstrano A, Katcher H, Yakumo K, Gholami Z, Shaffer D, Stone S, Bayer S, Wray C, Bogden R, Dayananth P, Ward J, Tonin P, Narod S, Bristow PK, Norris FH, Helvering L, Morrison P, Rosteck P, Lai M, Barrett JC, Lewis C, Neuhausen S, Cannonalbright L, Goldgar D, Wiseman R, Kamb A, Skolnick MH (1994) A strong candidate for the breast and ovarian-cancer susceptibility gene BRCA1. Science 266:66–71PubMedCrossRefGoogle Scholar
  52. Morris JC (2011) Dominantly Inherited Alzheimer Network (DIAN): registry characteristics and biomarker findings. Neurology 76:A416–A416Google Scholar
  53. Morton NE (1955) Sequential tests for the detection of linkage. Am J Hum Genet 7:277–318PubMedGoogle Scholar
  54. Mukhopadhyay N, Buxbaum SG, Weeks DE (2004) Comparative study of multipoint methods for genotype error detection. Hum Hered 58:175–189PubMedCrossRefGoogle Scholar
  55. Musunuru K, Pirruccello JP, Do R, Peloso GM, Guiducci C, Sougnez C, Garimella KV, Fisher S, Abreu J, Barry AJ, Fennell T, Banks E, Ambrogio L, Cibulskis K, Kernytsky A, Gonzalez E, Rudzicz N, Engert JC, DePristo MA, Daly MJ, Cohen JC, Hobbs HH, Altshuler D, Schonfeld G, Gabriel SB, Yue P, Kathiresan S (2010) Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N Engl J Med 363:2220–2227PubMedCrossRefGoogle Scholar
  56. Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, Bamshad M, Nickerson DA, Shendure J (2009) Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461:272–U153Google Scholar
  57. Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA, Shendure J, Bamshad MJ (2010) Exome sequencing identifies the cause of a mendelian disorder. Nat Genet 42:30–U41Google Scholar
  58. Norton N, Li DX, Rieder MJ, Siegfried JD, Rampersaud E, Zuchner S, Mangos S, Gonzalez-Quintana J, Wang LB, McGee S, Reiser J, Martin E, Nickerson DA, Hershberger RE (2011) Genome-wide studies of copy number variation and exome sequencing identify rare variants in BAG3 as a cause of dilated cardiomyopathy. Am J Hum Genet 88:273–282PubMedCrossRefGoogle Scholar
  59. Ott J (1974) Estimation of the recombination fraction in human pedigrees: efficient computation of the likelihood for human linkage studies. Am J Hum Genet 26:588–597PubMedGoogle Scholar
  60. Ott J (1992) Strategies for characterizing highly polymorphic markers in human gene mapping. Am J Hum Genet 51:283–290PubMedGoogle Scholar
  61. Pajukanta P, Allayee H, Krass KL, Kuraishy A, Soro A, Lilja HE, Mar R, Taskinen MR, Nuotio I, Laakso M, Rotter JI, de Bruin TWA, Cantor RM, Lusis AJ, Peltonen L (2003) Combined analysis of genome scans of Dutch and Finnish families reveals a susceptibility locus for high-density lipoprotein cholesterol on chromosome 16q. Am J Hum Genet 72:903–917PubMedCrossRefGoogle Scholar
  62. Penrose LS (1935) The detection of autosomal linkage in data which consist of pairs of brothers and sisters of unspecified parentage. Ann Eugen 6:133–138Google Scholar
  63. Raskind WH, Matsushita M, Peter B, Biberston J, Wolff J, Lipe H, Burbank R, Bird TD (2009) Familial dyskinesia and facial myokymia (FDFM): follow-up of a large family and linkage to chromosome 3p21–3q21. Am J Med Genet Part B Neuropsychiat Genet 150B:570–574CrossRefGoogle Scholar
  64. Raskind WH, Chen YZ, Matsushita MM, Robertson PD, Rieder M, Girirajan S, Lipe H, Eichler EE, Nickerson DA, Bird TD (2011) Linkage and single exome analyses identify ADCY5 as the gene for familial dyskinesia with facial myokymia. Int Congr Hum Genet, MontrealGoogle Scholar
  65. Regalado ES, Guo DC, Villamizar C, Avidan N, Gilchrist D, McGillivray B, Clarke L, Bernier F, Santos-Cortez RL, Leal SM, Bertoli-Avella AM, Shendure J, Rieder MJ, Nickerson DA, Milewicz DM (2011) Exome sequencing identifies SMAD3 mutations as a cause of familial thoracic aortic aneurysm and dissection with intracranial and other arterial aneurysms. Circ Res 109:680–U220Google Scholar
  66. Riordan JR, Rommens JM, Kerem B-S, Alon N, Rozmahel R, Grzelczak Z, Zielenski J, Lok S, Plavsic N, Chou J-L, Drumm ML, Iannuzzi MC, Collins FS, Tsui L-C (1989) Identification of the cystic fibrosis gene: cloning and characterization of complementary DNA. Science 245:1066–1073Google Scholar
  67. Risch N (2000) Searching for genetic determinants in the new millennium. Nature 405:847–856PubMedCrossRefGoogle Scholar
  68. Roach JC, Glusman G, Smit AFA, Huff CD, Hubley R, Shannon PT, Rowen L, Pant KP, Goodman N, Bamshad M, Shendure J, Drmanac R, Jorde LB, Hood L, Galas DJ (2010) Analysis of genetic inheritance in a family Quartet by whole-genome sequencing. Science 328:636–639PubMedCrossRefGoogle Scholar
  69. Roeder K, Bacanu SA, Wasserman L, Devlin B (2006) Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet 78:243–252PubMedCrossRefGoogle Scholar
  70. Rosenthal EA, Ronald J, Rothstein J, Rajagopalan R, Ranchalis J, Wolfbauer G, Albers JJ, Brunzell JD, Motulsky AG, Rieder MJ, Nickerson DA, Wijsman EM, Jarvik GP (2011) Linkage and association of phospholipid transfer protein activity to LASS4. J Lipid Res 52:1837–1846Google Scholar
  71. Sanna S, Li BS, Mulas A, Sidore C, Kang HM, Jackson AU, Piras MG, Usala G, Maninchedda G, Sassu A, Serra F, Palmas MA, Wood WH, Njolstad I, Laakso M, Hveem K, Tuomilehto J, Lakka TA, Rauramaa R, Boehnke M, Cucca F, Uda M, Schlessinger D, Nagaraja R, Abecasis GR (2011) Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability. Plos Genet 7:e1002198Google Scholar
  72. Sherrington R, Rogaev EI, Liang Y, Rogaeva EA, Levesque G, Ikeda M, Chi H, Lin C, Li G, Holman K, Tsuda T, Mar L, Foncin JF, Bruni AC, Montesi MP, Sorbi S, Rainero I, Pinessi L, Nee L, Chumakov I, Pollen D, Brookes A, Sanseau P, Polinsky RJ, Wasco W, Dasilva HAR, Haines JL, Pericak-Vance MA, Tanzi RE, Roses AD, Fraser PE, Rommens JM, St George-Hyslop PH (1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375:754–760PubMedCrossRefGoogle Scholar
  73. Sieh W, Yu C-E, Bird TD, Schellenberg GD, Wijsman EM (2007) Accounting for linkage disequilibrium among markers in linkage analysis: impact of haplotype frequency estimation and molecular haplotypes for a gene in a candidate region for Alzheimer’s disease. Hum Hered 63:26–34PubMedCrossRefGoogle Scholar
  74. Sillanpaa MJ, Auranen K (2004) Replication in genetic studies of complex traits. Ann Hum Genet 68:646–657PubMedCrossRefGoogle Scholar
  75. Simpson CL, Justice CM, Krishnan M, Wojciechowski R, Sung H, Cai J, Green T, Lewis D, Behneman D, Wilson AF, Bailey-Wilson JE (2011) Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data. BMC Proc 5:S83PubMedCrossRefGoogle Scholar
  76. Smith KR, Bromhead CJ, Hildebrand MS, Shearer AE, Lockhart PJ, Najmabadi H, Leventer RJ, McGillivray G, Amor DJ, Smith RJ, Bahlo M (2011) Reducing the exome search space for Mendelian diseases using genetic linkage analysis of exome genotypes. Genome Biol 12:R85Google Scholar
  77. Sobel E, Lange K (1993) Metropolis sampling in pedigree analysis. Stat Methods Med Res 2:263–282Google Scholar
  78. Sobel E, Lange K (1996) Descent graphs in pedigree analysis: applications to haplotyping, location scores, and marker-sharing statistics. Am J Hum Genet 58:1323–1337PubMedGoogle Scholar
  79. Sobel E, Sengul H, Weeks DE (2001) Multipoint estimation of identity-by-descent probabilities at arbitrary positions among marker loci on general pedigrees. Hum Hered 52:121–131PubMedCrossRefGoogle Scholar
  80. Southgate L, Machado RD, Snape KM, Primeau M, Dafou D, Ruddy DM, Branney PA, Fisher M, Lee GJ, Simpson MA, He Y, Bradshaw TY, Blaumeiser B, Winship WS, Reardon W, Maher ER, FitzPatrick DR, Wuyts W, Zenker M, Lamarche-Vane N, Trembath RC (2011) Gain-of-function mutations of ARHGAP31, a Cdc42/Rac1 GTPase regulator, cause syndromic cutis aplasia and limb anomalies. Am J Hum Genet 88:574–585PubMedCrossRefGoogle Scholar
  81. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K, Liang LM, Nemesh J, Park JH, Gustafsson S, Kilpelanen TO, Yang JA, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li SX, Moffatt MF, Myers RH, Narisu N, Perry JRB, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi JX, Thiering E, Alavere H, Alibrandi MTS, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AIF, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA et al (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42:937–U53Google Scholar
  82. Sturtevant AH (1913) The linear arrangement of six sex-linked factors in Drosophila as shown by their mode of association. J Exp Zool 14:43–59CrossRefGoogle Scholar
  83. Thoday JM (1961) Location of polygenes. Nature 191:368–370CrossRefGoogle Scholar
  84. Thomas A (2007) Towards linkage analysis with markers in linkage disequilibrium by graphical modelling. Hum Hered 64:16–26PubMedCrossRefGoogle Scholar
  85. Thomas A, Gutin A, Abkevich V, Bansal A (2000) Multilocus linkage analysis by blocked Gibbs sampling. Stat Comput 10:259–269CrossRefGoogle Scholar
  86. Thomas A, Camp NJ, Farnham JM, Allen-Brady K, Cannon-Albright LA (2008) Shared genomic segment analysis. Mapping disease predisposition genes in extended pedigrees using SNP genotype assays. Ann Hum Genet 72:279–287PubMedCrossRefGoogle Scholar
  87. Thompson EA (1994) Monte Carlo likelihood in the genetic mapping of complex traits. Philos Trans R Soc B 344:345–351CrossRefGoogle Scholar
  88. Thompson EA (2005) MCMC in the analysis of genetic data on pedigrees. In: Kendall WS, Wang JS, Liang F (eds) Markov chain Monte Carlo: innovations and applications. World Scientific Publishing Company, SingaporeGoogle Scholar
  89. Thompson EA (2011) The structure of genetic linkage data: from LIPED to 1M SNPs. Hum Hered 71:86–96PubMedCrossRefGoogle Scholar
  90. Tong LP, Thompson E (2008) Multilocus lod scores in large pedigrees: combination of exact and approximate calculations. Hum Hered 65:142–153PubMedCrossRefGoogle Scholar
  91. Varilo T, Peltonen L (2004) Isolates and their potential use in complex gene mapping efforts—commentary. Curr Opin Genet Dev 14:316–323PubMedCrossRefGoogle Scholar
  92. Wang JL, Yang X, Xia K, Hu ZM, Weng L, Jin X, Jiang H, Zhang P, Shen L, Guo JF, Li N, Li YR, Lei LF, Zhou J, Du J, Zhou YF, Pan Q, Wang J, Wang J, Li RQ, Tang BS (2010) TGM6 identified as a novel causative gene of spinocerebellar ataxias using exome sequencing. Brain 133:3510–3518PubMedCrossRefGoogle Scholar
  93. Weedon MN, Hastings R, Caswell R, Xie WJ, Paszkiewicz K, Antoniadi T, Williams M, King C, Greenhalgh L, Newbury-Ecob R, Ellard S (2011) Exome sequencing identifies a DYNC1H1 mutation in a large pedigree with dominant axonal Charcot-Marie-Tooth disease. Am J Hum Genet 89:308–312PubMedCrossRefGoogle Scholar
  94. Wijsman EM (1987) A deductive method of haplotype analysis in pedigrees. Am J Hum Genet 41:356–373PubMedGoogle Scholar
  95. Wijsman EM, Amos CI (1997) Genetic analysis of simulated oligogenic traits in nuclear and extended pedigrees: summary of GAW10 contributions. Genet Epidemiol 14:719–735PubMedCrossRefGoogle Scholar
  96. Wijsman EM, Rothstein JH, Thompson EA (2006) Multipoint linkage analysis with many multiallelic or dense diallelic markers: MCMC provides practical approaches for genome scans on general pedigrees. Am J Hum Genet 79:846–858PubMedCrossRefGoogle Scholar
  97. Wijsman EM, Sung YJ, Buil A (2007) Summary of GAW15: group 9 linkage analysis of the CEPH expression data. Genet Epidemiol 31:S75–S85PubMedCrossRefGoogle Scholar
  98. Wijsman EM, Rothstein JH, Igo RP, Brunzell JD, Motulsky AG, Jarvik GP (2010) Linkage and association analyses identify a candidate region for apoB level on chromosome 4q32.3 in FCHL families. Hum Genet 127:705–719PubMedCrossRefGoogle Scholar
  99. Wilcox MA, Pugh EW, Zhang H, Zhong X, Levinson DF, Kennedy GC, Wijsman EM (2005) Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: presentation groups 1, 2, and 3. Genet Epidemiol 29(Suppl 1):S7–S28PubMedCrossRefGoogle Scholar
  100. Wilson AF, Ziegler A (2011) Lessons learned from Genetic Analysis Workshop 17: transitioning from genome-wide association studies to whole-genome statistical genetic analysis. Genet Epidemiol 35:S107–S114PubMedCrossRefGoogle Scholar
  101. Wright AF, Carothers AD, Pirastu M (1999) Population choice in mapping genes for complex diseases. Nat Genet 23:397–404PubMedCrossRefGoogle Scholar
  102. Yazbek SN, Buchner DA, Geisinger JM, Burrage LC, Spiezio SH, Zentner GE, Hsieh CW, Scacheri PC, Croniger CM, Nadeau JH (2011) Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis. Genome Res 21:1065–1073PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  2. 2.Division of Medical Genetics, Department of MedicineUniversity of WashingtonSeattleUSA
  3. 3.Department of Genome SciencesUniversity of WashingtonSeattleUSA

Personalised recommendations