Fuller, C.W. et al. The challenges of sequencing by synthesis. Nat. Biotechnol. 27, 1013–1023 (2009).
Rusk, N. & Kiermer, V. Primer: Sequencing—the next generation. Nat. Methods 5, 15 (2008).
Metzker, M.L. Sequencing technologies the next generation. Nat. Rev. Genet. 11, 31–46 (2010).
Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).
Clarke, J. et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nat. Nanotechnol. 4, 265–270 (2009).
Ng, S.B. et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat. Genet. 42, 790–793 (2010).
Teer, J.K. & Mullikin, J.C. Exome sequencing: the sweet spot before whole genomes. Hum. Mol. Genet. 19, R145–R151 (2010).
Hedges, D.J. et al. Comparison of three targeted enrichment strategies on the SOLiD sequencing platform. PLoS ONE 6, e18595 (2011).
Ng, S.B. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).
Pierce, S.B. et al. Am. Mutations in the DBP-deficiency protein HSD17B4 cause ovarian dysgenesis, hearing loss, and ataxia of Perrault Syndrome. J. Hum. Genet. 87, 282–288 (2010).
Krawitz, P.M. et al. Identity-by-descent filtering of exome sequence data identifies PIGV mutations in hyperphosphatasia mental retardation syndrome. Nat. Genet. 42, 827–829 (2010).
Wang, J.L. et al. TGM6 identified as a novel causative gene of spinocerebellar ataxias using exome sequencing. Brain. 133, 3510–3518 (2010).
Ng, S.B., Nickerson, D.A., Bamshad, M.J. & Shendure, J. Massively parallel sequencing and rare disease. Hum. Mol. Genet. 19, R119–R124 (2010).
Musunuru, K. et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N. Engl. J. Med. 363, 2220–2227 (2010).
Hoischen, A. et al. De novo mutations of SETBP1 cause Schinzel-Giedion syndrome. Nat. Genet. 42, 483–485 (2010).
Zhao, Q. et al. Systematic detection of putative tumor suppressor genes through the combined use of exome and transcriptome sequencing. Genome Biol. 11, R114 (2010).
Wei, X. et al. Exome sequencing identifies GRIN2A as frequently mutated in melanoma. Nat. Genet. 43, 442–446 (2011).
Varela, I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011).
Agrawal, N. et al. Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154–1157 (2011).
Chang, H. et al. Exome sequencing reveals comprehensive genomic alterations across eight cancer cell lines. PLoS ONE 6, e21097 (2011).
Cohen, J.C. et al. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 305, 869–872 (2004).
Ji, W. et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592–599 (2008).
Johansen, C.T. et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat. Genet. 42, 684–687 (2010).
Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J.A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).
Ahituv, N. et al. Medical sequencing at the extremes of human body mass. Am. J. Hum. Genet. 80, 779–791 (2007).
Romeo, S. et al. Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans. J. Clin. Invest. 119, 70–79 (2009).
Pritchard, J.K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001).
Pritchard, J.K. & Cox, N. J. The allelic architecture of human disease genes: common disease–common variant...or not? Hum. Mol. Genet. 11, 2417–2423 (2002).
Kryukov, G.V., Pennacchio, L.A. & Sunyaev, S.R. Most rare missense alleles are deleterious in humans: implications for complex disease and association studies. Am. J. Hum. Genet. 80, 727–739 (2007).
Kryukov, G.V., Shpunt, A., Stamatoyannopoulos, J.A. & Sunyaev, S.R. Power of deep, all-exon resequencing for discovery of human trait genes. Proc. Natl. Acad. Sci. USA 106, 3871–3876 (2009).
Boyko, A.R. et al. Assessing the evolutionary impact of amino acid mutations in the human genome. PLoS Genet. 4, e1000083 (2008).
Williamson, S.H. et al. Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc. Natl. Acad. Sci. USA 102, 7882–7887 (2005).
Eyre-Walker, A., Woolfit, M. & Phelps, T. The distribution of fitness effects of new deleterious amino acid mutations in humans. Genetics 173, 891–900 (2006).
Yampolsky, L.Y., Kondrashov, F.A. & Kondrashov, A.S. Distribution of the strength of selection against amino acid replacements in human proteins. Hum. Mol. Genet. 14, 3191–3201 (2005).
Fay, J.C., Wyckoff, G.J. & Wu, C.-I. Positive and negative selection on the human genome. Genetics 158, 1227–1234 (2001).
Nachman, M.W. & Crowell, S.L. Estimate of the mutation rate per nucleotide in humans. Genetics 156, 297–304 (2000).
Kondrashov, A.S. Direct estimates of human per nucleotide mutation rates at 20 loci causing Mendelian diseases. Hum. Mutat. 21, 12–27 (2003).
Roach, J.C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).
Xue, Y. et al. Human Y chromosome base-substitution mutation rate measured by direct sequencing in a deep-rooting pedigree. Curr. Biol. 19, 1453–1457 (2009).
The HIV Controllers Study. The major genetic determinants of HIV-1 control affect HLA class I peptide presentation. Science 330, 1551–1557 (2010).
Ewens, W.J. The sampling theory of selectively neutral alleles. Theor. Popul. Biol. 3, 87–112 (1972).
Kimura, M. Molecular evolutionary clock and the neutral theory. J. Mol. Evol. 26, 24–33 (1987).
Marth, G.T., Czabarka, E., Murvai, J. & Sherry, S.T. The allele frequency spectrum in genome-wide human variation data reveals signals of differential demographic history in three large world populations. Genetics 166, 351–372 (2004).
Coventry, A. et al. Deep resequencing reveals excess rare recent variants consistent with explosive population growth. Nat. Commun. 1, 131 (2010).
Li, Y. et al. Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants. Nat. Genet. 42, 969–972 (2010).
Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
Halushka, M.K. et al. Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat. Genet. 22, 239–247 (1999).
Cargill, M. et al. Characterization of single-nucleotide polymorphisms in coding regions of human genes. Nat. Genet. 22, 231–238 (1999).
Bustamante, C.D. et al. Natural selection on protein-coding genes in the human genome. Nature 437, 1153–1157 (2005).
Sunyaev, S., Ramensky, V. & Bork, P. Towards a structural basis of human non-synonymous single nucleotide polymorphisms. Trends Genet. 16, 198–200 (2000).
Sunyaev, S. et al. Prediction of deleterious human alleles. Hum. Mol. Genet. 10, 591–597 (2001).
McKenna, A. et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Hellmann, I. et al. Selection on human genes as revealed by comparisons to chimpanzee cDNA. Genome Res. 13, 831–837 (2003).
MacArthur, D.G. & Tyler-Smith, C. Loss-of-function variants in the genomes of healthy humans. Hum. Mol. Genet. 19, R125–R130 (2010).
Purcell, S., Cherny, S.S. & Sham, P.C. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150 (2003).
Li, B. & Leal, S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).
Madsen, B.E. & Browning, S.R. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5, e1000384 (2009).
Liu, D.J. & Leal, S.M. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet. 6, e1001156 (2010).
Price, A.L. et al. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86, 832–838 (2010).
Bansal, V., Libiger, O., Torkamani, A. & Schork, N.J. Statistical analysis strategies for association studies involving rare variants. Nat. Rev. Genet. 11, 773–785 (2010).
Asimit, J. & Zeggini, E. Rare variant association analysis methods for complex traits. Annu. Rev. Genet. 44, 293–308 (2010).
Basu, S. & Pan, W. Comparison of statistical tests for disease association with rare variants. Genet. Epidemiol. 35, 606–619 (2011).
Stitziel, N.O., Kiezun, A. & Sunyaev, S.R. Computational and statistical approaches to analyzing variants identified by exome sequencing. Genome Biol. 12, 227 (2011).
Wu, M.C. et al. Rare variant association testing for sequencing data using the sequence kernel association test (SKAT). Am. J. Hum. Genet. 89, 82–93 (2011).
Neale, B.M. et al. Testing for an unusual distribution of rare variants. PLoS Genet. 7, e1001322 (2011).
Kotowski, I.K. et al. A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. Am. J. Hum. Genet. 78, 410–422 (2006).
Hoffmann, T.J., Marini, N.J. & Witte, J.S. Comprehensive approach to analyzing rare genetic variants. PLoS ONE 5, e13584 (2010).
Ionita-Laza, I., Buxbaum, J.D., Laird, N.M. & Lange, C. A new testing strategy to identify rare variants with either risk or protective effect on disease. PLoS Genet. 7, e1001289 (2011).
Tavtigian, S.V. et al. Rare, evolutionarily unlikely missense substitutions in ATM confer increased risk of breast cancer. Am. J. Hum. Genet. 85, 427–446 (2009).
Sul, J.H., Han, B., He, D. & Eskin, E. An optimal weighted aggregated association test for identification of rare variants involved in common diseases. Genetics 188, 181–188 (2011).
Sul, J.H., Han, B. & Eskin, E. Increasing power of groupwise association test with likelihood ratio test. in Research in Computational Molecular Biology, Lecture Notes in Computer Science Vol. 6577/2011 452–467 (Springer, Berlin/Heidelberg, 2011).
Cooper, G.M. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005).
Cooper, G.M. et al. Single-nucleotide evolutionary constraint scores highlight disease-causing mutations. Nat. Methods 7, 250–251 (2010).
Ng, P.C. & Henikoff, S. Predicting the effects of amino acid substitutions on protein function. Annu. Rev. Genomics Hum. Genet. 7, 61–80 (2006).
Jordan, D.M., Ramensky, V.E. & Sunyaev, S.R. Human allelic variation: perspective from protein function, structure, and evolution. Curr. Opin. Struct. Biol. 20, 342–350 (2010).
Thusberg, J., Olatubosun, A. & Vihinen, M. Performance of mutation pathogenicity prediction methods on missense variants. Hum. Mutat. 32, 358–368 (2011).
Cooper, G.M. & Shendure, J. Needles in stacks of needles: finding disease-causing variants in a wealth of genomic data. Nat. Rev. Genet. 12, 628–640 (2011).
Hicks, S., Wheeler, D.A., Plon, S.E. & Kimmel, M. Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed. Hum. Mutat. 32, 661–668 (2011).
Stephens, M. & Balding, D.J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).
Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).
Saxena, R. et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).
Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).
Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010).
Lipman, P.J. et al. On the follow-up of genome-wide association studies: an overall test for the most promising SNPs. Genet. Epidemiol. 35, 303–309 (2011).
Price, A.L., Zaitlen, N.A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).
Pritchard, J.K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).
Keinan, A., Mullikin, J.C., Patterson, N. & Reich, D. Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans. Nat. Genet. 39, 1251–1255 (2007).
Alexander, D.H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
Li, H. & Durbin, R. ast and accurate short read alignment with Burrows Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Holsinger, K.E. & Weir, B.S. Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat. Rev. Genet. 10, 639–650 (2009).
Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98–101 (2008).
Clayton, D.G. et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat. Genet. 37, 1243–1246 (2005).