Statistical Genetics Methods papers

These papers provide new methods, or (where noted) transferring statistical technology to genetics research. For applications see Publications.

• Li X, et al. A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. Nat Comput Sci. 2025 Feb;5(2):125-143. doi: 10.1038/s43588-024-00764-8. PMID: 39920506; NIHMSID:NIHMS2065948
• Huang YJ, Kurniansyah N, Levey DF, Gelernter J, Huffman JE, Cho K, Wilson PWF, Gottlieb DJ, Rice KM, Sofer T. A semi-empirical Bayes approach for calibrating weak instrumental bias in sex-specific Mendelian randomization studies. medRxiv. 2025 Jan 2;. doi: 10.1101/2025.01.02.25319889. PubMed PMID: 39802770; PubMed Central PMCID: PMC11722449
• Little A, et al. General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing with Related Individuals. Genet Epidemiol. 2025 Jan;49(1):e22610. doi: 10.1002/gepi.22610. PubMed PMID: 39812506
• Li X, et al. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nat Genet. 2023 Jan;55(1):154-164. doi: 10.1038/s41588-022-01225-6. PubMed PMID: 36564505.
• Li Z et al A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat Methods. 2022 Dec;19(12):1599-1611. doi: 10.1038/s41592-022-01640-x. PubMed PMID: 36303018; NIHMSID:NIHMS1862492.
• Sofer T et al. BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion. HGG Adv. 2021 Jul 8;2(3). doi: 10.1016/j.xhgg.2021.100040. PubMed PMID: 34337551; PubMed Central PMCID: PMC8321319.
• Sofer T, Zheng X, Laurie CA, Gogarten SM, Brody JA, Conomos MP, Bis JC, Thornton TA, Szpiro A, O'Connell JR, Lange EM, Gao Y, Cupples LA, Psaty BM, Rice KM. Variant-specific inflation factors for assessing population stratification at the phenotypic variance level. Nat Commun. 2021 Jun 9;12(1):3506. doi: 10.1038/s41467-021-23655-2. PubMed PMID: 34108454; PubMed Central PMCID: PMC8190158.
  This paper shows how non-constant variance (heteroskedasticity) of outcomes can invalidate some forms of cross-population analysis, and provides methods that correct this problem.
• Li et al (2020) Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet. Sep;52(9):969-983. doi: 10.1038/s41588-020-0676-4. PubMed PMID: 32839606; PubMed Central PMCID: PMC7483769.
• Gogarten SM, Sofer T, Chen H, Yu C, Brody JA, Thornton TA, Rice KM, Conomos MP. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics. 2019 Jul 22;. doi: 10.1093/bioinformatics/btz567. PubMed PMID: 31329242.
• Sofer T, Zheng X, Gogarten SM, Laurie CA, Grinde K, Shaffer JR, Shungin D, O'Connell JR, Durazo-Arvizo RA, Raffield L, Lange L, Musani S, Vasan RS, Cupples LA, Reiner AP; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Laurie CC, Rice KM. A fully adjusted two-stage procedure for rank-normalization in genetic association studies. Genet Epidemiol. 2019 Apr;43(3):263-275. doi: 10.1002/gepi.22188. Epub 2019 Jan 17. PMID: 30653739
  Inverse-Normal transformations are widely used in analysis of quantitative phenotypes – but this paper shows how making them work when covariate adjustment is also needed requires care. We propose methods that let these approaches work harmoniously.
• Chen H, Huffman JE, Brody JA, Wang C, Lee S, Li Z, Gogarten SM, Sofer T, Bielak LF, Bis JC, Blangero J, Bowler RP, Cade BE, Cho MH, Correa A, Curran JE, de Vries PS, Glahn DC, Guo X, Johnson AD, Kardia S, Kooperberg C, Lewis JP, Liu X, Mathias RA, Mitchell BD, O'Connell JR, Peyser PA, Post WS, Reiner AP, Rich SS, Rotter JI, Silverman EK, Smith JA, Vasan RS, Wilson JG, Yanek LR; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Hematology and Hemostasis Working Group, Redline S, Smith NL, Boerwinkle E, Borecki IB, Cupples LA, Laurie CC, Morrison AC, Rice KM, Lin X. Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies. Am J Hum Genet. 2019 Feb 7;104(2):260-274. doi: 10.1016/j.ajhg.2018.12.012. Epub 2019 Jan 10. PMID: 30639324
• Lumley T, Brody J, Peloso G, Morrison A, Rice K. FastSKAT: Sequence kernel association tests for very large sets of markers. Genetic epidemiology. 2018; NIHMSID: NIHMS972871 PMID: 29932245 PMCID: PMC6129408
  This paper (and the grant that supported it) transfers recent results in random matrix theory to genetic association work, speeding up calculations by orders of magnitude
• Sondhi A, Rice KM. Fast permutation tests and related methods, for association between rare variants and binary outcomes. Ann Hum Genet. 2017 Dec 18; PubMed PMID: 29250767.
• Brody JA, et al. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology. Nature genetics. 2017; 49(11):1560-1563. PMID: 29074945
• Sofer T, Heller R, Bogomolov M, Avery CL, Graff M, et al. A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL. Genet Epidemiol. 2017 Apr;41(3):251-258. PubMed PMID: 28090672; NIHMSID: NIHMS829142; PubMed Central PMCID: PMC5340573.
• Castaldi PJ, Cho MH, Liang L, Silverman EK, Hersh CP, et al. Screening for interaction effects in gene expression data. PLoS One. 2017;12(3):e0173847. PubMed PMID: 28301596; PubMed Central PMCID: PMC5354413
• Rich SS, Wang ZY, Sturcke A, Ziyabari L, Feolo M, O'Donnell CJ, Rice K, Bis JC, Psaty BM. Rapid evaluation of phenotypes, SNPs and results through the dbGaP CHARGE Summary Results site. Nature genetics. 2016; 48(7):702-3. PMID: 27350599
• Sitlani CM, Dupuis J, Rice KM, Sun F, Pitsillides AN, Cupples LA, Psaty BM. Genome-wide gene-environment interactions on quantitative traits using family data. European Journal of Human Genetics. 2016; 24(7):1022-8. PMID: 26626313 PMCID: PMC5070904
• Sung YJ, Winkler TW, Manning AK, Aschard H, Gudnason V, et al. An Empirical Comparison of Joint and Stratified Frameworks for Studying GxE Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene-Lifestyle Interactions Working Group. Genet Epidemiol. 2016 Jul;40(5):404-15. PubMed PMID: 27230302; NIHMSID: NIHMS781298; PubMed Central PMCID: PMC4911246.
• Chen H, Wang C, Conomos M, Stilp A, Li Z, Sofer T, Szpiro A, Chen W, Brehm J, Celedon J, Redline S, Papanicolaou G, Thornton T, Laurie C, Rice K, Lin X: Control for population structure and relatedness for binary traits in genetic association studies using logistic mixed models. American Journal of Human Genetics. 2016; 98(4):653-66. PMID: 27018471 PMCID: PMC4833218
• Wang S, Zhao JH, An P, Guo X, Jensen RA, Marten J, Huffman JE, Meidtner K, Boeing H, Campbell A, Rice KM, Scott RA, Yao J, Schulze MB, Wareham NJ, Borecki IB, Province MA, Rotter JI, Hayward C, Goodarzi MO, Meigs JB, Dupuis J. General Framework for Meta-Analysis of Haplotype Association Tests. Genetic epidemiology. 2016; 40(3):244-52. NIHMSID: NIHMS789332 PMID: 27027517 PMCID: PMC4869684
• Sitlani CM, Rice KM, Lumley T, McKnight B, Cupples LA, Avery CL, Noordam R, Stricker BH, Whitsel EA, Psaty BM. Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Stat Med. 2015 Jan 15;34(1):118-30. doi: 10.1002/sim.6323. Epub 2014 Oct 9. PubMed PMID: 25297442.
• Li S, Mukherjee B, Taylor JM, Rice KM, Wen X, Rice JD, Stringham HM, Boehnke M. The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits. Genet Epidemiol. 2014 Jul;38(5):416-29. doi: 10.1002/gepi.21810. Epub 2014 May 6. PubMed PMID: 24801060; PubMed Central PMCID: PMC4108593.
• Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, Zheng X, Crosslin DR, Levine D, Lumley T, Nelson SC, Rice K, Shen J, Swarnkar R, Weir BS, Laurie CC (2012) GWASTools: an R/Bioconductor package for quality control and analysis of Genome-Wide Association Studies. Bioinformatics. Oct 10. PMID:23052040
• Voorman A, Rice K, Lumley T. (2012) Fast computation for genome-wide association studies using boosted one-step statistics. Bioinformatics. Jul 15;28(14):1818-22. PMID: 22592383 PMCID: PMC3389774
• Voorman A, Lumley T, McKnight B, Rice K (2011) Behavior of QQ-Plots and Genomic Control in Studies of Gene-Environment Interaction. PloS ONE 6(5): e19416. doi:10.1371/journal.pone.0019416 PMID: 21589913 PMCID: PMC3093379
• Divers J, Redden DT, Rice KM, et al (2011) Comparing self-reported ethnicity to genetic background measures in the context of the Multi-Ethnic Study of Atherosclerosis (MESA) BMC Genetics 12(3) Article Number 28. PMID: 21375750 PMCID: PMC3068121
• Manning A, LaValley M, Liu C, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris T, Province M, Borecki I, Florez J, Meigs J, Cupples L, Dupuis J (2011) Meta-analysis of Gene-Environment interaction: joint estimation of SNP and SNP x Environment regression coefficients, Genetic Epidemiology, 35(1) 11-18. PMID: 21181894
• Buzkova P, Lumley R, Rice K (2011) Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions, Annals of Human Genetics, 75(1) 36-45. doi: 10.1111/j.1469-1809.2010.00572.x. PMID: 20384625
  This article uses testing theory to show how, contrary to several published claims, there is no exact permutation approach for testing certain hypotheses about interactions. Different approaches for better (but non-exact) permutation tests are also considered.
• Laurie C, Doheny K, Mirel D, et al (2010) Quality control and quality assurance in genotypic data for genome-wide association studies Genetic Epidemiology, 34(6) 591-602. PMID: 20718045, PMCID: PMC3061487
• Lumley T, Rice K (2010) Potential for Revealing Individual-Level Information in Genome-wide Association Studies. JAMA 303(7) 659-660. PMID: 20159874
  This commentary uses concepts from within-sample prediction, scaled up to genome-wide scale, to show how publishing summary results can “leak” individual-level phenotype information, potentially violating study participants’ informed consent.
• French, B; Lumley, T; Monks, SA; Rice, KM; Hindorff, LA; Reiner, AP; Psaty, BM (2006) Simple estimates of haplotype relative risks in case-control data. Genetic Epidemiology 30 (6): 485-494. PMID: 16755519
• Rice K, Holmans P. (2003) Allowing for genotyping error in analysis of unmatched case-control studies. Annals of Human Genetics, 67(2), 165-174. PMID: 12675691