Molecular Genetics

Charney, E. (2008). Genes and ideologies. Perspectives on Politics, 6, 299 –319. doi:10.1017/S1537592708080626

GWAS

Curtis, D (2018). Clinical Relevance of Genome-Wide Polygenic Score may Be Less than Claimed. Preprints, 2018100171 (doi: 10.20944/preprints201810.0171.v3)

GCTA

Krishna Kumar, S., Feldman, M. W., Rehkopf, D. H., & Tuljapurkar, S. (2015). Limitations of GCTA as a solution to the missing heritability problem. Proceedings of the National Academy of Sciences, 113(1), E61–E70.doi:10.1073/pnas.1520109113

Kumar SK, Feldman MW, Rehkopf DH, Tuljapurkar S (2016). Response to commentary on “Limitations of GCTA as a solution to the missing heritability problem.” bioRxiv doi:10.1101/039594

GREML

Population Stratification

Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, Boyle EA, Zhang X, Racimo F (2019). Reduced signal for polygenic adaptation of height in UK Biobank. eLife.doi:10.7554/eLife.39725
Comment: Per Alexander Young, this demonstrates LD regression is not sufficient control for population stratification.

M Sohail, RM Maier, A Ganna, A Bloemendal, AR Martin, MC Turchin, CW Chiang, J Hirschhorn, MJ Daly, N Patterson, B Neale, I Mathieson, D Reich, SR Sunyaev (2019). Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8.doi:10.7554/eLife.39702

Kerminen S, Martin AR, Koskela J, Ruotsalainen SE, Havulinna AS, Surakka I, Palotie A, Perola M, Salomaa V, Daly MJ, Ripatti S, Pirinen M (2018). Geographic variation and bias in polygenic scores of complex diseases and traits in Finland. Preprints.doi:10.1101/485441

Barton N, Joachim H, Nordborg M (2019). Population Genetics: Why structure matters. eLife 8.doi:10.7554/eLife.45380.

Dandine-Roulland, C., Bellenguez, C., Debette, S., Amouyel, P., Génin, E., & Perdry, H. (2016). Accuracy of heritability estimations in presence of hidden population stratification. Scientific Reports, 6(1).doi:10.1038/srep26471

Sul, J. H., Martin, L. S., & Eskin, E. (2018). Population structure in genetic studies: Confounding factors and mixed models. PLOS Genetics, 14(12), e1007309.doi:10.1371/journal.pgen.1007309

Other Biases
Evans, L. M., Tahmasbi, R., Vrieze, S. I., Abecasis, G. R., Das, S., … Keller, M. C. (2018). Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nature Genetics, 50(5), 737–745. doi:10.1038/s41588-018-0108-x
Comment: It demonstrates estimates of heritability are highly sensitive to assumptions. This says that binning solves problems, but other studies demonstrate it fails.

Ruby, J. G., Wright, K. M., Rand, K. A., Kermany, A., Noto, K., Curtis, D., … Ball, C. (2018). Estimates of the Heritability of Human Longevity Are Substantially Inflated due to Assortative Mating. Genetics, 210(3), 1109–1124.doi:10.1534/genetics.118.301613

Persyn, E., Redon, R., Bellanger, L., & Dina, C. (2018). The impact of a fine-scale population stratification on rare variant association test results. PLOS ONE, 13(12), e0207677.doi:10.1371/journal.pone.0207677
Comment: Demonstrates that population stratification & other methodological issues are significantly compounded when associating rare variants

Heckerman, D., Gurdasani, D., Kadie, C., Pomilla, C., Carstensen, T., Martin, H., … Sandhu, M. S. (2016). Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proceedings of the National Academy of Sciences, 113(27), 7377–7382.doi:10.1073/pnas.1510497113
Comment: Shows linear mixed model overestimates heritability

Schweiger, R., Fisher, E., Weissbrod, O., Rahmani, E., Müller-Nurasyid, M., Kunze, S., … Halperin, E. (2018). Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests. Nature Communications, 9(1).doi:10.1038/s41467-018-07276-w
Comment: Shows p-values inflated & parametric violations substantially upwardly bias results.

Haworth S, Mitchell R, Corbin L, Wade KH, Dudding T, Budu-Aggrey A, Carslake D, Hermani G, Paternoster L, Smith GD, Davies N, Lawson DJ, J Timpson N (2019). Apparent latent structure within UK Biobank sample has implications for epidemiological analysis. Nature communications 10(1), 333.doi:10.1038/s41467-018-08219-1.

Rosenberg, N. A., Edge, M. D., Pritchard, J. K., & Feldman, M. W. (2018). Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences. Evolution, Medicine, and Public Health.doi:10.1093/emph/eoy036

Guang G, Meng-Jung L, Harris KM (2019). Socioeconomic and Genomic Roots of Verbal Ability. Preprints.doi:10.1101/544411
Comment: Demonstrate that standard social science predictor much stronger than PGS

Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J., & Lachance, J. (2018). Genetic disease risks can be misestimated across global populations. Genome Biology, 19(1). doi:10.1186/s13059-018-1561-7

Hartwig, F. P., Davies, N. M., & Davey Smith, G. (2018). Bias in Mendelian randomization due to assortative mating. Genetic Epidemiology.doi:10.1002/gepi.22138

Curtis, D. (2018). Polygenic risk score for schizophrenia is not strongly associated with the expression of specific genes or gene sets. Psychiatric Genetics, 1.doi:10.1097/ypg.0000000000000197
Comment: PGS scores break down when you test their expression

Curtis, D. (2018). Polygenic risk score for schizophrenia is more strongly associated with ancestry than with schizophrenia. Psychiatric Genetics, 28(5), 85–89.doi:10.1097/ypg.0000000000000206

Brumpton B, Sanderson E, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li Shuai, Chen WM, Bjørngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Smith GD, Åsvold BO, Hermani G, Davies NM (2019). Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. bioRxiv.doi:10.1101/602516
Comment: Shows that associations between height/BMI & educational attainment are products of associative mating. Will be similar for other phenotypical associations

Duncan LE, Shen H, Gelaye B, Ressler KJ, Feldman MW, Peterson RE, Dominogue BW (2018). Analysis of Polygenic Score Usage and Performance in Diverse Human Populations. bioRxiv.doi:10.1101/398396

Keyes, K. M., & Westreich, D. (2019). UK Biobank, big data, and the consequences of non-representativeness. The Lancet, 393(10178), 1297. doi:10.1016/s0140-6736(18)33067-8
Comment: Also see this.

Richardson, K. (2017). GWAS and cognitive abilities: Why correlations are inevitable and meaningless. EMBO Reports, 18(8), 1279–1283.doi:10.15252/embr.201744140

Holmes, J. B., Speed, D., Balding, D. J. (2019). Summary statistic analyses can mistake confounding bias for heritability. bioRxiv. 

Selection

Harris, R. B., Sackman, A., & Jensen, J. D. (2018). On the unfounded enthusiasm for soft selective sweeps II: Examining recent evidence from humans, flies, and viruses. PLOS Genetics, 14(12), e1007859.doi:10.1371/journal.pgen.1007859

Missing Heritability

Charney, E. (2014). Behavioural Genetics in the Postgenomics Era. eLS.doi:10.1002/9780470015902.a0025250 

Feldman, M. W., & Ramachandran, S. (2018). Missing compared to what? Revisiting heritability, genes and culture. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1743), 20170064.doi:10.1098/rstb.2017.0064 

Candidate Genes

Arango, C. (2017). Candidate gene associations studies in psychiatry: time to move forward. European Archives of Psychiatry and Clinical Neuroscience, 267(1), 1–2.doi:10.1007/s00406-016-0765-7 

Border R, Johnson EC, Evans LM, Smolen A, Berley N, Sullivan PF, Keller MC (2019). No Support for Historical Candidate Gene or Candidate Gene-by-Interaction Hypotheses for Major Depression Across Multiple Large Samples. American Journal of Psychiatry.doi:10.1176/appi.ajp.2018.18070881

Evan Charney and William English (2012), “Candidate Genes and Political Behavior,” American Political Science Review 106 (1).

Colhoun, H. M., McKeigue, P. M., & Smith, G. D. (2003). Problems of reporting genetic associations with complex outcomes. The Lancet, 361(9360), 865–872.doi:10.1016/s0140-6736(03)12715-8 

Duncan, L. E., & Keller, M. C. (2011). A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in Psychiatry. American Journal of Psychiatry, 168(10), 1041–1049.doi:10.1176/appi.ajp.2011.11020191 

Hirschhorn, J. N., Lohmueller, K., Byrne, E., & Hirschhorn, K. (2002). A comprehensive review of genetic association studies. Genetics in Medicine, 4(2), 45–61.doi:10.1097/00125817-200203000-00002 

Ioannidis, J. P. A., Ntzani, E. E., Trikalinos, T. A., & Contopoulos-Ioannidis, D. G. (2001). Replication validity of genetic association studies. Nature Genetics, 29(3), 306–309.doi:10.1038/ng749 

Van der Velden WJFM, Feuth T, Stevens WBC, Donnelly JP & Blijlevens NMA (2011). Issues in genetic association studies: limitations of statistical analysis and biological plausibility. Bone Marrow Transplantation (2011) 46, 906–907; doi:10.1038/bmt.2010.211