Lab members are highlighted in boldface;
* junior authors with equal contribution;
† senior authors with equal contribution
[102] Miao J., Wu Y., Sun Z., Miao X., Lu T., Zhao J., Lu Q. (2024). Valid inference for machine learning-assisted GWAS. Submitted. (preprint available on medRxiv)
[101] Kweon H., Burik C., Ning Y., Ahlskog R., Xia C., Abner E., Bao Y., Bhatta L., Faquih T., Fisher P., Gelemanovic A., Giannelis A., Hottenga J., Jaan M., Khalili B., Lee Y., Li-Gao R., Myhre R., Palviainen T., Rietveld C., Teumer A., Verweij R., Willoughby E., Agerbo E., Bergmann S., Boomsma D., Borglum A., Brumpton B., Davies N., Gordon S., Johannesson M., Kaprio J., Kidd M., Kutalik Z., Kwong A., Lee J., Luik A., Magnus P., Marques-Vidal P., Martin N., Mook-Kanamori D., Mortensen P., Oskarsson S., Pedersen E., Polasek O., Rosendaal F., Smart M., Snieder H., Tonu E., van der Most P., Vollenweider P., Volzke H., Beauchamp J., DiPrete T., Linner R., Lu Q., Morris T., Okbay A., Harden K., Abdellaoui A., Hill W., de Vlaming R., Benjamin D., Koellinger P. (2024). Associations between common genetic variants and income provide insights about the socio-economic health gradient. Submitted. (preprint available on bioRxiv)
[100] Miao J.*, Miao X.*, Wu Y., Zhao J., Lu Q. (2024). Assumption-lean and data-adaptive post-prediction inference. Submitted. (preprint available on Arxiv)
[99] Zheng B., Lu Q., Fletcher J. (2024). Estimating causal effects of fertility on life course outcomes: evidence using a dyadic genetic instrumental variable approach. Submitted. (NBER working paper available)
[98] Fletcher J., Lu Q., Mazumder B., Song J. (2024). Understanding sibling correlations in education: molecular genetics and family background. Submitted.
[97] Zhao Z., Gruenloh T., Wu Y., Sun Z., Miao J., Wu Y., Song J., Lu Q. (2024). Optimizing and benchmarking polygenic risk scores with GWAS summary statistics. Submitted. (preprint available on bioRxiv)
[96] Li J., He Q., Wang Z., Lu Q. (2024). Enhancing the discriminatory power of ADHD and autism spectrum disorder polygenic scores in clinical and non-clinical samples. Submitted. (preprint available on medRxiv)
[95] Zhang Y., Cheng Y., Ye Y., Jiang W., Lu Q., Zhao H. (2024). Estimating genetic correlation jointly using individual-level and summary-level GWAS data. Submitted. (preprint available on bioRxiv)
[94] Chen J., You J., Zhao Z., Ni Z., Huang K., Wu Y., Fletcher J., Lu Q. (2024). Gamete simulation improves polygenic transmission disequilibrium analysis. Submitted. (preprint available on bioRxiv)
[93] Song J.*, Zheng F.*, Wu Y., Fletcher J.†, Lu Q.† (2024). Generating polygenic risk scores in large biobanks through cross-validation. Submitted.
[92] Miao J., Song G., Wu Y., Hu J., Wu Y., Basu S., Andrews J., Schaumberg K., Fletcher J., Schmitz L., Lu Q. (2024). Reimagining gene-environment interaction analysis for human complex traits. Submitted. (preprint available on bioRxiv)
[91] Wu Y.*, Sun Z.*, Zheng Q., Miao J., Dorn S., Mukherjee S., Fletcher J., Lu Q. (2024). Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease. Submitted. (preprint available on bioRxiv)
[90] Panyard D., Reus L., Ali M., Liu J., Deming Y., Lu Q., Kollmorgen G., Suridjan I., Wild N., Visser P., Bertram L., Zetterberg H., Blennow K., Gobom J., Western D., Sung Y., Carlsson C., Johnson S., Asthana S., Cruchaga C., Tijms B., Engelman C., Snyder M. (2024). Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease implicates mediation through GPX3. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, in press.
[89] Braun A., Lai H., Laxova A., Biller J., Hubertz E., Zhao Z., Lu Q., Muralic S., Brown D., Worthey E., Farrell P. (2024). Vitamin D status and variable responses to supplements depend in part on genetic factors in adults with cystic fibrosis. Journal of Cystic Fibrosis, in press.
[88] IGVF Consortium (2024). Deciphering the impact of genomic variation on function. Nature, in press.
[87] Miao J., Wu Y., Lu Q. (2024). Statistical methods for gene-environment interaction analysis. WIREs Computational Statistics, 16(1), e1635. (Review)
[86] Fletcher J., Wu Y., Li T., Lu Q. (2024). Interpreting polygenic score effects in sibling analysis. PLOS One, 19(2): e0282212.
[85] Xu Y., Sun Z., Jonaitis E., Deming Y., Lu Q., Johnson S., Engelman C. (2024). Apolipoprotein E moderates the association between non-APOE polygenic risk score for Alzheimer's disease and aging on preclinical cognitive function. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 20: 1063-1075.
[84] Furuya S., Zheng F., Lu Q.†, Fletcher J.† (2024). Separating scarring and selection effect of early life exposures with genetic data. Demography, in press.
[82] Furuya S., Liu J., Sun Z., Lu Q.†, Fletcher J.† (2023). Understanding internal migration: a research note providing an assessment of migration selection with genetic data. Demography, 60(6): 1631-1648.
[81] Furuya S., Liu J., Sun Z., Lu Q.†, Fletcher J.† (2023). The big (genetic) sort? A research note on migration patterns and their genetic imprint in the United Kingdom. Demography, 60(6): 1649-1664.
[80] He Q., Keding T., Zhang Q., Miao J., Russell J., Herringa R., Lu Q., Travers B., Li J. (2023). Neurogenetic mechanisms of risk for ADHD: examining associations of polygenic scores and brain volumes in a population cohort. Journal of Neurodevelopmental Disorders, 15, 30.
[79] Zheng B., Fletcher J., Song J., Lu Q. (2023). Analysis of sex-specific gene-by-cohort and genetic correlation-by-cohort interaction in educational and reproductive outcomes using the UK Biobank data. Journal of Health and Social Behavior, in press.
[78] Yan D.*, Hu B.*, Darst B., Mukherjee S., Kunkle B., Deming Y., Dumitrescu L., Wang Y., Naj A., Kuzma A., Zhao Y., Kang H., Johnson S., Cruchaga C., Hohman T., Crane P., Engelman C., Alzheimer's Disease Genetics Consortium (ADGC), Lu Q. (2023). Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife, 12: RP91360.
[77] Huang L., Lai H., Song J., Zhao Z., Lu Q., Murali S., Brown D., Worthey E., Farrell P. (2023). Impact of intrinsic and extrinsic risk factors on early-onset lung disease in cystic fibrosis. Pediatric Pulmonology, 58: 3071-3082.
[76] Dong R., Lu Q., Kang H., Suridjan I., Kollmorgen G., Wild N., Deming Y., Van Hulle C., Anderson R., Zetterberg H., Blennow K., Carlsson C., Asthana S., Johnson S., Engelman C. (2023). CSF metabolites associated with biomarkers of Alzheimer's disease pathology. Frontiers in Aging Neuroscience, 15: 1214932.
[75] Xu Y., Vasiljevic E., Deming Y., Jonaitis E., Koscik R., Van Hulle C., Lu Q., Carboni M., Kollmorgen G., Wild N., Carlsson C., Johnson S., Zetterberg H., Blennow K., Engelman C. (2023). Effect of pathway-specific polygenic risk scores for Alzheimer's disease (AD) on rate of change in cognitive function and AD-related biomarkers among asymptomatic individuals. Journal of Alzheimer's Disease, 94(4), 1587-1605.
[74] Ch'en P., Gold L., Lu Q., Ye T., Andrews J.†, Patel P.† (2023). Exploring risk factors for persistent neurocognitive sequelae after hospitalization for COVID-19. Annals of Clinical and Translational Neurology, 10: 1200-1208.
[73] Fletcher J., Wu Y., Zhao Z., Lu Q. (2023). The production of within-family inequality: insights and implications of integrating genetic data. PNAS Nexus, 2(4), pgad121.
[72] Dong R., Denier-Fields D., Van Hulle C., Kollmorgen G., Suridjan I., Wild N., Lu Q., Anderson R., Zetterberg H., Blennow K., Carlsson C., Johnson S., Engelman C. (2023). Identification of plasma metabolites associated with modifiable risk factors and endophenotypes reflecting Alzheimer's disease pathology. European Journal of Epidemiology, 38, 559-571.
[71] Pettigrew C., Nazarovs J., Soldan A., Singh V., Wang J., Hohman T., Dumitrescu L., Libby J., Kunkle B., Gross A., Johnson S., Lu Q., Engelman C., Masters C., Maruff P., Laws S., Morris J., Hassenstab J., Cruchaga C., Resnick S., Albert M. (2023). AD genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals. Alzheimer's Research & Therapy, 15, 66.
[70] Deming Y., Vasiljevic E., Morrow A., Miao J., Van Hulle C., Jonaitis E., Ma Y., Whitenack V., Kollmorgen G., Wild N., Suridjan I., Shaw L., Asthana S., Carlsson C., Johnson S., Zetterberg H., Blennow K., Bendlin B., Lu Q., Engelman C., the Alzheimer's Disease Neuroimaging Initiative (2023). Neuropathology-based APOE genetic risk score better quantifies Alzheimer's risk. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 19: 3406-3416.
[69] Song J., Zou Y., Wu Y., Miao J., Yu Z., Fletcher J., Lu Q. (2023). Decomposing heritability and genetic covariance by direct and indirect effect paths. PLOS Genetics, 19(1): e1010620.
[68] Miao J.*, Guo H.*, Song G., Zhao Z., Hou L.†, Lu Q.† (2023). Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics. Nature Communications, 14, 832.
[67] Amin V., Fletcher J., Lu Q., Song J. (2023). Re-examining the relationship between education and adult mental health in the UK: a research note. Economics of Education Review, 93, 102354.
[66] Panyard D., Deming Y., Darst B., Van Hulle C., Blennow K., Kollmorgen G., Suridjan I., Carlsson C., Johnson S., Asthana S., Engelman C.†, Lu Q.† (2023). Liver-specific polygenic risk score is associated with Alzheimer's disease diagnosis. Journal of Alzheimer's Disease, 92(2): 395-409.
[65] Zhang Y., Fletcher J., Lu Q., Song J. (2023). Gender differences in the association between parity and cognitive function: evidence from the UK Biobank. Social Science & Medicine, 320: 115649.
[63] Li J., Zhang Q., Wang Z., Lu Q. (2022). Research domain criteria (RDoC) mechanisms of transdiagnostic polygenic risk for trajectories of depression: from early adolescence to adulthood. Journal of Psychopathology and Clinical Science, 131(6), 567-574.
[62] Zheng B., Fletcher J., Zheng F., Lu Q. (2022). Gene-by-peer-environment interaction effects on cigarette, alcohol, and marijuana use among US high school students of european ancestry. Social Science & Medicine, 309: 115249.
[61] Lai H., Song J., Lu Q., Murali S., Gajapathy M., Wilk B., Brown D., Worthey E., Farrell P., the FIRST Study Group (2022). Genetic factors help explain the variable responses of young children with cystic fibrosis to vitamin D supplements. Clinical Nutrition ESPEN, 51: 367-376.
[60] Guo H., Hou L., Shi Y., Jin S., Zeng X., Li B., Lifton R., Brueckner M., Zhao H.†, Lu Q.† (2022). Quantifying concordant genetic effects of de novo mutations on multiple disorders. eLife, 11: e75551.
[59] Miao J., Lu Q. (2022). Identifying genetic loci associated with complex trait variability. Handbook of Statistical Bioinformatics (2nd Edition). Springer, Berlin, Heidelberg. (Book Chapter)
[58] Eissman J., Dumitrescu L., Mahoney E., Smith A., Mukherjee S., Lee M., Scollard P., Choi S., Bush W., Engelman C., Lu Q., Fardo D., Trittschuh E., Mez J., Kaczorowski C., Saucedo H., Widaman K., Buckley R., Properzi M., Mormino E., Yang H., Harrison T., Hedden T., Nho K., Andrews S., Tommet D., Hadad N., Sanders E., Ruderfer D., Gifford K., Zhong X., Raghavan N., Vardarajan B., Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer's Disease Genetics Consortium (ADGC), A4 Study Team, Pericak-Vance M., Farrer L., Wang L., Cruchaga C., Schellenberg G., Cox N., Haines J., Keene D., Saykin A., Larson E., Sperling R., Mayeux R., Cuccaro M., Bennett D., Schneider J., Crane P., Jefferson A., Hohman T. (2022). Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease. Brain, 145(7), 2541-2554.
[57] Dong R., Denier-Fields D., Lu Q., Suridjan I., Kollmorgen G., Wild N., Betthauser T., Carlsson C., Asthana S., Johnson S., Zetterberg H., Blennow K., Engelman C. (2022). Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer's disease biomarkers. Neurobiology of Aging, 117: 12-23.
[56] Wu Y., Furuya S., Wang Z., Nobles J., Fletcher J.†, Lu Q.† (2022). GWAS on birth year infant mortality rates provides evidence of recent natural selection. Proceedings of the National Academy of Sciences, 119(12): e2117312119. Accompanying commentary article
[55] Wang Y.*, Wu Y.*, Choi J.*, Allington G.*, Zhao S.*, Khanfar M.*, Yang K.*, Fu P., Wrubel M., Yu X., Mekbib K., Ocken J., Smith H., Shohfi J., Kahle K., Lu Q.†, Jin S.† (2022). Computational genomics in the era of precision medicine: applications to variant analysis and gene therapy. Journal of Personalized Medicine, 12(2), 175. (Review)
[54] Amin V., Fletcher J., Sun Z., Lu Q. (2022). Higher educational attainment is associated with longer telomeres in midlife: evidence from sibling comparisons in the UK Biobank. SSM - Population Health, 17: 101018.
[53] Schmitz L., Zhao W., Ratliff S., Goodwin J., Miao J., Lu Q., Guo X., Taylor K., Ding J., Liu Y., Levine M., Smith J. (2022). The socioeconomic gradient in epigenetic aging clocks: evidence from the Multi-ethnic Study of Atherosclerosis and the Health and Retirement Study. Epigenetics, 17(6), 589-611.
[52] Furuya S., Fletcher J., Zhao Z., Sun Z., Lu Q. (2022). Detecting genetic heterogeneities in response to trauma: the case of 9/11. SSM - Mental Health, 2: 100044.
[50] Zhang Y.*, Lu Q.*, Ye Y., Huang K., Liu W., Wu Y., Zhong X., Li B., Yu Z., Travers B., Werling D., Li J., Zhao H. (2021). SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology, 22, 262.
[49] Zhao Z.*, Yi Y.*, Song J., Wu Y., Zhong X., Lin Y., Hohman T., Fletcher J., Lu Q. (2021). PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biology, 22, 257.
[48] Fletcher J., Topping M., Zheng F., Lu Q. (2021). The effects of education on cognition in older age: evidence from genotyped siblings. Social Science & Medicine, 280: 114044.
[47] Hou L., Lu Q., Yang C., Zhao H. (2021). Special issue on genome wide association study. Quantitative Biology, 9(2): 105-106. (Editorial)
[46] Wu Y., Zhong X., Lin Y., Zhao Z., Chen J., Zheng B., Li J., Fletcher J., Lu Q. (2021). Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies. Proceedings of the National Academy of Sciences, 118(25): e2023184118. Accompanying commentary article
[45] Kundishora A.*, Peters S.*, Pinard A., Duran D., Panchagnula S., Barak T., Miyagishima D., Dong W., Dunbar A., Smith H., Ocken J., Nelson-Williams C., Haider S., Walker R., Li B., Zhao H., Thumkeo D., Marlier A., Duy P., Diab N., Reeves B., Sujijantarat N., Stratman A., Chen Y., Zhao S., Roszko I., Lu Q., Zhang B., Mane S., Castaldi C., Lopez-Giraldez F., Knight J., Bamshad M., Nickerson D., Geschwind D., Chen S., Storm P., Matouk C., Alper S., Smith E., Lifton R., Gunel M., Milewicz D., Jin S.†, Kahle K.† (2021). DIAPH1 variants in non-East Asian patients with sporadic moyamoya disease. JAMA Neurology, 78(8): 993-1003.
[44] Schmitz L., Goodwin J., Miao J., Lu Q., Conley D. (2021). The impact of late-career job loss and genetic risk on body mass index: evidence from variance polygenic scores. Scientific Reports, 11: 7647.
[43] Li M.*, Zeng X.*, Jin L.*, Jin S., Dong W., Brueckner M., Lifton R., Lu Q., Zhao H. (2021). Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease. Quantitative Biology, 9(2): 216-227.
[42] Guo H., Li J., Lu Q.†, Hou L.† (2021). Detecting local genetic correlations with scan statistics. Nature Communications, 12: 2033.
[41] Dong R., Darst B., Deming Y., Ma Y., Lu Q., Zetterberg H., Blennow K., Carlsson C., Johnson S., Asthana S., Engelman C. (2021). CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 13: e12167.
[40] Zhao Z., Song J., Wang T., Lu Q. (2021). Polygenic risk scores: effect estimation and model optimization. Quantitative Biology, 9(2): 133-140. (Review)
[39] Huang K., Wu Y., Shin J., Zheng Y., Siahpirani A., Lin Y., Ni Z., Chen J., You J., Keles S., Wang D., Roy S., Lu Q. (2021). Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLOS Genetics, 17(2): e1009309.
[38] Zhang Y.*, Cheng Y.*, Jiang W., Ye Y., Lu Q., Zhao H. (2021). Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics. Briefings in Bioinformatics, 22(5): bbaa442.
[37] Darst B., Huo Z., Jonaitis E., Koscik R., Clark L., Lu Q., Kremen W., Franz C., Rana B., Lyons M., Hogan K., Zhao J., Johnson S., Engelman C. (2021). Metabolites associated with early cognitive changes implicated in Alzheimer's disease. Journal of Alzheimer's Disease, 79(3): 1041-1054.
[36] Panyard D., Kim K., Darst B., Deming Y., Zhong X., Wu Y., Kang H., Carlsson C., Johnson S., Asthana S., Engelman C.†, Lu Q.† (2021). Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Communications Biology, 4: 63.
[34] Fletcher J., Lu Q. (2020). Health policy and genetic endowments: understanding sources of response to Minimum Legal Drinking Age laws. Health Economics, 30: 194-203.
[33] Liu W., Li M., Zhang W., Zhou G., Wu X., Wang J., Lu Q., Zhao H. (2020). Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology, 16(11): e1008315.
[32] Dong W.*, Jin S.*, Allocco A.*, Zeng X.*, Sheth A., Panchagnula S., Castonguay A., Lorenzo L., Islam B., Brindle G., Bachand K., Hu J., Sularz A., Gaillard J., Choi J., Dunbar A., Nelson-Williams C., Kiziltug E., Furey C., Conine S., Duy P., Kundishora A., Loring E., Li B., Lu Q., Zhou G., Liu W., Li X., Sierant M., Mane S., Castaldi C., Lopez-Giraldez F., Knight J., Sekula R., Simard M., Eskandar E., Gottschalk C., Moliterno J., Gunel M., Gerrard J., Dib-Hajj S., Waxman S., Barker F., Alper S., Chahine M., Haider S., De Koninck Y., Lifton R., Kahle K. (2020). Exome sequencing implicates impaired GABA signaling and neuronal ion transport in trigeminal neuralgia. iScience, 23(10): 101552.
[31] Jin S.*, Dong W.*, Kundishora A.*, Panchagnula S.*, Moreno-De-Luca A.*, Furey C., Allocco A., Walker R., Nelson-Williams C., Smith H., Dunbar A., Conine S., Lu Q., Zeng X., Sierant M., Knight J., Sullivan W., Duy P., DeSpenza T., Reeves B., Karimy J., Marlier A., Castaldi C., Tikhonoa I., Li B., Pena H., Broach J., Kabachelor E., Ssenyonga P., Hehnly C., Ge L., Keren B., Timberlake A., Goto J., Mangano F., Johnston J., Butler W., Warf B., Smith E., Schiff S., Limbrick D., Heuer G., Jackson E., Iskandar B., Mane S., Haider S., Guclu B., Bayri Y., Sahin Y., Duncan C., Apuzzo M., DiLuna M., Hoffman E., Sestan N., Ment L., Alper S., Bilguvar K., Geschwind D., Gunel M., Lifton R., Kahle K. (2020). Exome sequencing implicates genetic disruption of prenatal neuro-gliogenesis in sporadic congenital hydrocephalus. Nature Medicine, 26(11), 1754-1765.
[30] Greenberg J., Hilton E., Li J., Lu Q., Mailick M. (2020). The impact of parenting a child with serious mental illness: accounting for the parent's genetic vulnerability to mental illness. Journal of Family Psychology, 35(3), 417-422.
[29] Dumitrescu L., Mahoney E., Mukherjee S., Lee M., Bush W., Engelman C., Lu Q., Fardo D., Trittschuh E., Mez J., Kaczorowski C., Hernandez H., Widaman K., Buckley R., Properzi M., Mormino E., Yang H., Harrison T., Hedden T., Nho K., Andrews S., Tommet D., Hadad N., Sanders E., Ruderfer D., Gifford K., Moore A., Cambronero F., Zhong X., Raghavan N., Vardarajan B., The Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer's Disease Genetics Consortium (ADGC), Pericak-Vance M., Farrer L., Wang L., Cruchaga C., Schellenberg G., Cox N., Haines J., Keene C., Saykin A., Larson E., Sperling R., Mayeux R., Bennett D., Schneider J., Crane P., Jefferson A., Hohman T. (2020). Genetic variants and functional pathways associated with resilience to Alzheimer's disease. Brain, 143(8), 2561-2575.
[28] Jin S.*, Lewis S.*, Bakhtiari S.*, Zeng X.*, Sierant M., Shetty S., Nordlie S., Elie A., Corbett M., Norton B., van Eyk C., Haider S., Guida B., Magee H., Liu J., Pastore S., Vincent J., Brunstrom-Hernandez J., Papavasileiou A., Fahey M., Berry J., Harper K., Zhou C., Zhang J., Li B., Zhao H., Heim J., Webber D., Frank M., Xia L., Xu Y., Zhu D., Zhang B., Sheth A., Knight J., Castaldi C., Tikhonoa I., Lopez-Giraldez F., Keren B., Whalen S., Buratti J., Doummar D., Cho M., Retterer K., Millan F., Wang Y., Waugh J., Rodan L., Cohen J., Fatemi A., Lin A., Phillips J., Feyma T., MacLennan S., Vaughan S., Crompton K., Reid S., Reddihough D., Shang Q., Gao C., Novak I., Badawi N., Wilson Y., McIntyre S., Mane S., Wang X., Amor D., Zarnescu D., Lu Q., Xing Q.†, Zhu C.†, Bilguvar K.†, Padilla-Lopez S.†, Lifton R.†, Gecz J.†, MacLennan A.†, Kruer M.† (2020). Mutations disrupting neuritogenesis genes confer risk for cerebral palsy. Nature Genetics, 52(10), 1046-1056.
[27] Hatchell K., Lu Q., Mares J., Michos E., Wood A., Engelman C. (2020). Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake. Genetic Epidemiology, 44(2), 208-217.
[26] Harvey P., Sun N., Bigdeli T., Fanous A., Aslan M., Malhotra A., Lu Q., Hu Y., Li B., Chen Q., Mane S., Miller P., Rajeevan N., Sayward F., Cheung K., Li Y., Greenwood T., Gur R., Braff D., Consortium on the Genetics of Schizophrenia (COGS), Brophy M., Pyarajan S., Gleason T., Przygodszki R., O'Leary T., Muralidhar S., Gaziano M., Million Veteran Program (MVP), Huang G., Concato J., Zhao H., Siever L. (2020). Genome-wide association study of cognitive performance in US veterans with schizophrenia or bipolar disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 183(3), 181-194.
[24] Darst B., Lu Q., Johnson S., Engelman C. (2019). Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants. Genetic Epidemiology, 43(6), 657-674.
[23] Gelernter J., Sun N., Polimanti R., Pietrzak R., Levey D., Lu Q., Hu Y., Li B., Radhakrishnan K., Aslan M., Cheung K., Li Y., Rajeevan N., Sayward F., Harrington K., Chen Q., Cho K., Honerlaw J., Pyarajan S., Lencz T., Quaden R., Shi Y., Hunter-Zink H., Gaziano J., Kranzler H., Concato J., Zhao H., Stein M., on behalf of the Department of Veterans Affairs Cooperative Studies Program (#575B) and Million Veteran Program. (2019). Genomewide association study of maximum habitual alcohol intake in >140,000 US European- and African-American veterans yields novel risk loci. Biological Psychiatry, 86(5), 365-376.
[22] Gelernter J., Sun N., Polimanti R., Pietrzak R., Levey D., Bryois J., Lu Q., Hu Y., Li B., Radhakrishnan K., Aslan M., Cheung K., Li Y., Rajeevan N., Sayward F., Harrington K., Chen Q., Cho K., Pyarajan S., Sullivan P., Quaden R., Shi Y., Hunter-Zink H., Gaziano J., Concato J., Zhao H., Stein M., on behalf of the Department of Veterans Affairs Cooperative Studies Program (#575B) and Million Veteran Program. (2019). Genome-wide association study of posttraumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nature Neuroscience, 22(9), 1394-1401.
[21] Lu Q., Zhao H. (2019). Improving genetic association analysis through integration of functional annotations of the human genome. Handbook of Statistical Genetics (4th Edition). John Wiley & Sons. (Book Chapter)
[20] Hu B., Shen N., Li J., Kang H., Hong J., Fletcher J., Greenberg J., Mailick M., Lu Q. (2019). Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness. PLOS Genetics, 15(4): e1007973. Accompanying perspective article
[19] Li J., Hilton E., Lu Q., Hong J., Greenberg J., Mailick M. (2019). Validating psychosocial pathways of risk between neuroticism and late life depression using a polygenic score approach. Journal of Abnormal Psychology, 128(3), 200-211.
[18] Hu Y.*, Li M.*, Lu Q.*, Weng H., Wang J., Zekavat M., Yu Z., Li B., Gu J., Muchnik S., Shi Y., Kunkle B., Mukherjee S., Natarajan P., Naj A., Kuzma A., Zhao Y., Crane P., Alzheimer's Disease Genetics Consortium (ADGC), Lu H., Zhao H. (2019). A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics, 51(3), 568-576. Winner of the ASA 2020 Outstanding Statistical Application Award
[16] Furey C.*, Choi J.*, Jin S., Zeng X., Timberlake A., Nelson-Williams C., Mansuri M., Lu Q., Duran D., Panchagnula S., Alloco A., Karimy J., Khanna A., Gaillard J., DeSpenza T., Antwi P., Loring E., Butler W., Smith E., Warf B., Strahle J., Limbrick D., Storm P., Heuer G., Jackson E., Iskandar B., Johnston J., Tikhonova I., Castaldi C., Lopez-Giraldez F., Bjornson R., Knight J., Bilguvar K., Mane S., Alper S., Haider S., Guclu B., Bayri Y., Sahin Y., Duncan C., DiLuna M., Gunel M., Lifton R.†, Kahle K.† (2018). De novo mutation in genes regulating neural stem cell fate in human congenital hydrocephalus. Neuron, 99(2), 302-314.e4.
[15] Li B.*, Lu Q.*, Zhao H. (2018). An evaluation of non-coding genome annotation tools through enrichment analysis of fifteen genome-wide association studies. Briefings in Bioinformatics, 20(3), 995-1003.
[13] Jin S.*, Homsy J.*, Zaidi S.*, Lu Q., Morton S., DePalma S., Zeng X., Qi H., Chang W., Hung W., Sierant M., Haider S., Zhang J., Knight J., Bjornson R., Castaldi C., Tikhonoa I., Bilguvar K., Mane S., Sanders S., Mital S., Russell M., Gaynor W., Deanfield J., Giardini A., Porter G., Srivastava D., Lo C., Shen Y., Watkins S., Yandell M., Yost J., Tristani-Firouzi M., Newburger J., Roberts A., Kim R., Zhao H., Kaltman J., Goldmuntz E., Chung W., Seidman J., Gelb B., Seidman C.†, Lifton R.†, Brueckner M.† (2017). Contribution of rare transmitted and de novo variants among 2,871 congenital heart disease probands. Nature Genetics, 49(11), 1593-1601.
[12] Lu Q.*, Powles R.*, Abdallah S., Ou D., Wang Q., Hu Y., Lu Y., Liu W., Li B., Mukherjee S., Crane P., Zhao H. (2017). Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLOS Genetics, 13(7): e1006933.
[11] Hu Y., Lu Q., Liu W., Zhang Y., Li M., Zhao H. (2017). Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics, 13(6): e1006836.
[10] Hu Y.*, Lu Q.*, Powles R., Yao X., Yang C., Fang F., Xu X., Zhao H. (2017). Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology, 13(6): e1005589.
[9] Lu Q., Jin C., Sun J., Bowler R., Kechris K., Kaminski N., Zhao H. (2017). Post-GWAS prioritization through data integration provides novel insights on chronic obstructive pulmonary disease. Statistics in Biosciences, 9(2), 605-621.
[8] Li M., Foli Y., Liu Z., Wang G., Hu Y., Lu Q., Selvaraj S., Lam W., Paintsil E. (2017). High frequency of mitochondrial DNA mutations in HIV-infected treatment-experienced individuals. HIV Medicine, 18(1), 45-55.
[7] Zhao B., Lu Q., Cheng Y., Belcher J., Siew E., Leaf D., Body S., Fox A., Waikar S., Collard C., Thiessen-Philbrook H., Ikizler T., Ware L., Edelstein C., Garg A., Choi M., Schaub J., Zhao H., Lifton R., Parikh C. for the TRIBE-AKI Consortium. (2017). A genome-wide association study to identify single nucleotide polymorphisms for acute kidney injury. American Journal of Respiratory and Critical Care Medicine, 195(4), 482-490.
[5] Lu Q.*, Powles R.*, Wang Q., He B., Zhao H. (2016). Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12(4): e1005947.
[4] Lu Q., Yao X., Hu Y., Zhao H. (2016). GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics, 32(4), 542-548.
[2] Wang Q.*, Lu Q.*, Zhao H. (2015). A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Frontiers in Genetics, 6:149. (Review)
[1] Lu Q., Ren S., Lu M., Zhang Y., Zhu D., Zhang X., Li T. (2013). Computational prediction of associations between long non-coding RNAs and proteins. BMC Genomics, 14(1), 651.
2024
[103] Zhao Z., Yang X., Miao J., Dorn S., Barcellos S., Fletcher J., Lu Q. (2024). Controlling for polygenic genetic confounding in epidemiologic association studies. Submitted. (preprint available on bioRxiv)[102] Miao J., Wu Y., Sun Z., Miao X., Lu T., Zhao J., Lu Q. (2024). Valid inference for machine learning-assisted GWAS. Submitted. (preprint available on medRxiv)
[101] Kweon H., Burik C., Ning Y., Ahlskog R., Xia C., Abner E., Bao Y., Bhatta L., Faquih T., Fisher P., Gelemanovic A., Giannelis A., Hottenga J., Jaan M., Khalili B., Lee Y., Li-Gao R., Myhre R., Palviainen T., Rietveld C., Teumer A., Verweij R., Willoughby E., Agerbo E., Bergmann S., Boomsma D., Borglum A., Brumpton B., Davies N., Gordon S., Johannesson M., Kaprio J., Kidd M., Kutalik Z., Kwong A., Lee J., Luik A., Magnus P., Marques-Vidal P., Martin N., Mook-Kanamori D., Mortensen P., Oskarsson S., Pedersen E., Polasek O., Rosendaal F., Smart M., Snieder H., Tonu E., van der Most P., Vollenweider P., Volzke H., Beauchamp J., DiPrete T., Linner R., Lu Q., Morris T., Okbay A., Harden K., Abdellaoui A., Hill W., de Vlaming R., Benjamin D., Koellinger P. (2024). Associations between common genetic variants and income provide insights about the socio-economic health gradient. Submitted. (preprint available on bioRxiv)
[100] Miao J.*, Miao X.*, Wu Y., Zhao J., Lu Q. (2024). Assumption-lean and data-adaptive post-prediction inference. Submitted. (preprint available on Arxiv)
[99] Zheng B., Lu Q., Fletcher J. (2024). Estimating causal effects of fertility on life course outcomes: evidence using a dyadic genetic instrumental variable approach. Submitted. (NBER working paper available)
[98] Fletcher J., Lu Q., Mazumder B., Song J. (2024). Understanding sibling correlations in education: molecular genetics and family background. Submitted.
[97] Zhao Z., Gruenloh T., Wu Y., Sun Z., Miao J., Wu Y., Song J., Lu Q. (2024). Optimizing and benchmarking polygenic risk scores with GWAS summary statistics. Submitted. (preprint available on bioRxiv)
[96] Li J., He Q., Wang Z., Lu Q. (2024). Enhancing the discriminatory power of ADHD and autism spectrum disorder polygenic scores in clinical and non-clinical samples. Submitted. (preprint available on medRxiv)
[95] Zhang Y., Cheng Y., Ye Y., Jiang W., Lu Q., Zhao H. (2024). Estimating genetic correlation jointly using individual-level and summary-level GWAS data. Submitted. (preprint available on bioRxiv)
[94] Chen J., You J., Zhao Z., Ni Z., Huang K., Wu Y., Fletcher J., Lu Q. (2024). Gamete simulation improves polygenic transmission disequilibrium analysis. Submitted. (preprint available on bioRxiv)
[93] Song J.*, Zheng F.*, Wu Y., Fletcher J.†, Lu Q.† (2024). Generating polygenic risk scores in large biobanks through cross-validation. Submitted.
[92] Miao J., Song G., Wu Y., Hu J., Wu Y., Basu S., Andrews J., Schaumberg K., Fletcher J., Schmitz L., Lu Q. (2024). Reimagining gene-environment interaction analysis for human complex traits. Submitted. (preprint available on bioRxiv)
[91] Wu Y.*, Sun Z.*, Zheng Q., Miao J., Dorn S., Mukherjee S., Fletcher J., Lu Q. (2024). Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease. Submitted. (preprint available on bioRxiv)
[90] Panyard D., Reus L., Ali M., Liu J., Deming Y., Lu Q., Kollmorgen G., Suridjan I., Wild N., Visser P., Bertram L., Zetterberg H., Blennow K., Gobom J., Western D., Sung Y., Carlsson C., Johnson S., Asthana S., Cruchaga C., Tijms B., Engelman C., Snyder M. (2024). Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease implicates mediation through GPX3. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, in press.
[89] Braun A., Lai H., Laxova A., Biller J., Hubertz E., Zhao Z., Lu Q., Muralic S., Brown D., Worthey E., Farrell P. (2024). Vitamin D status and variable responses to supplements depend in part on genetic factors in adults with cystic fibrosis. Journal of Cystic Fibrosis, in press.
[88] IGVF Consortium (2024). Deciphering the impact of genomic variation on function. Nature, in press.
[87] Miao J., Wu Y., Lu Q. (2024). Statistical methods for gene-environment interaction analysis. WIREs Computational Statistics, 16(1), e1635. (Review)
[86] Fletcher J., Wu Y., Li T., Lu Q. (2024). Interpreting polygenic score effects in sibling analysis. PLOS One, 19(2): e0282212.
[85] Xu Y., Sun Z., Jonaitis E., Deming Y., Lu Q., Johnson S., Engelman C. (2024). Apolipoprotein E moderates the association between non-APOE polygenic risk score for Alzheimer's disease and aging on preclinical cognitive function. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 20: 1063-1075.
[84] Furuya S., Zheng F., Lu Q.†, Fletcher J.† (2024). Separating scarring and selection effect of early life exposures with genetic data. Demography, in press.
2023
[83] Elsayed N., Valenzuela R., Kitchner T., Le T., Mayer J., Tang Z., Bayanagari V., Lu Q., Aston P., Anantharaman K., Shukla S. (2023). Genetic risk score in multiple sclerosis is associated with unique gut microbiome. Scientific Reports, 13: 16269.[82] Furuya S., Liu J., Sun Z., Lu Q.†, Fletcher J.† (2023). Understanding internal migration: a research note providing an assessment of migration selection with genetic data. Demography, 60(6): 1631-1648.
[81] Furuya S., Liu J., Sun Z., Lu Q.†, Fletcher J.† (2023). The big (genetic) sort? A research note on migration patterns and their genetic imprint in the United Kingdom. Demography, 60(6): 1649-1664.
[80] He Q., Keding T., Zhang Q., Miao J., Russell J., Herringa R., Lu Q., Travers B., Li J. (2023). Neurogenetic mechanisms of risk for ADHD: examining associations of polygenic scores and brain volumes in a population cohort. Journal of Neurodevelopmental Disorders, 15, 30.
[79] Zheng B., Fletcher J., Song J., Lu Q. (2023). Analysis of sex-specific gene-by-cohort and genetic correlation-by-cohort interaction in educational and reproductive outcomes using the UK Biobank data. Journal of Health and Social Behavior, in press.
[78] Yan D.*, Hu B.*, Darst B., Mukherjee S., Kunkle B., Deming Y., Dumitrescu L., Wang Y., Naj A., Kuzma A., Zhao Y., Kang H., Johnson S., Cruchaga C., Hohman T., Crane P., Engelman C., Alzheimer's Disease Genetics Consortium (ADGC), Lu Q. (2023). Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife, 12: RP91360.
[77] Huang L., Lai H., Song J., Zhao Z., Lu Q., Murali S., Brown D., Worthey E., Farrell P. (2023). Impact of intrinsic and extrinsic risk factors on early-onset lung disease in cystic fibrosis. Pediatric Pulmonology, 58: 3071-3082.
[76] Dong R., Lu Q., Kang H., Suridjan I., Kollmorgen G., Wild N., Deming Y., Van Hulle C., Anderson R., Zetterberg H., Blennow K., Carlsson C., Asthana S., Johnson S., Engelman C. (2023). CSF metabolites associated with biomarkers of Alzheimer's disease pathology. Frontiers in Aging Neuroscience, 15: 1214932.
[75] Xu Y., Vasiljevic E., Deming Y., Jonaitis E., Koscik R., Van Hulle C., Lu Q., Carboni M., Kollmorgen G., Wild N., Carlsson C., Johnson S., Zetterberg H., Blennow K., Engelman C. (2023). Effect of pathway-specific polygenic risk scores for Alzheimer's disease (AD) on rate of change in cognitive function and AD-related biomarkers among asymptomatic individuals. Journal of Alzheimer's Disease, 94(4), 1587-1605.
[74] Ch'en P., Gold L., Lu Q., Ye T., Andrews J.†, Patel P.† (2023). Exploring risk factors for persistent neurocognitive sequelae after hospitalization for COVID-19. Annals of Clinical and Translational Neurology, 10: 1200-1208.
[73] Fletcher J., Wu Y., Zhao Z., Lu Q. (2023). The production of within-family inequality: insights and implications of integrating genetic data. PNAS Nexus, 2(4), pgad121.
[72] Dong R., Denier-Fields D., Van Hulle C., Kollmorgen G., Suridjan I., Wild N., Lu Q., Anderson R., Zetterberg H., Blennow K., Carlsson C., Johnson S., Engelman C. (2023). Identification of plasma metabolites associated with modifiable risk factors and endophenotypes reflecting Alzheimer's disease pathology. European Journal of Epidemiology, 38, 559-571.
[71] Pettigrew C., Nazarovs J., Soldan A., Singh V., Wang J., Hohman T., Dumitrescu L., Libby J., Kunkle B., Gross A., Johnson S., Lu Q., Engelman C., Masters C., Maruff P., Laws S., Morris J., Hassenstab J., Cruchaga C., Resnick S., Albert M. (2023). AD genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals. Alzheimer's Research & Therapy, 15, 66.
[70] Deming Y., Vasiljevic E., Morrow A., Miao J., Van Hulle C., Jonaitis E., Ma Y., Whitenack V., Kollmorgen G., Wild N., Suridjan I., Shaw L., Asthana S., Carlsson C., Johnson S., Zetterberg H., Blennow K., Bendlin B., Lu Q., Engelman C., the Alzheimer's Disease Neuroimaging Initiative (2023). Neuropathology-based APOE genetic risk score better quantifies Alzheimer's risk. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 19: 3406-3416.
[69] Song J., Zou Y., Wu Y., Miao J., Yu Z., Fletcher J., Lu Q. (2023). Decomposing heritability and genetic covariance by direct and indirect effect paths. PLOS Genetics, 19(1): e1010620.
[68] Miao J.*, Guo H.*, Song G., Zhao Z., Hou L.†, Lu Q.† (2023). Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics. Nature Communications, 14, 832.
[67] Amin V., Fletcher J., Lu Q., Song J. (2023). Re-examining the relationship between education and adult mental health in the UK: a research note. Economics of Education Review, 93, 102354.
[66] Panyard D., Deming Y., Darst B., Van Hulle C., Blennow K., Kollmorgen G., Suridjan I., Carlsson C., Johnson S., Asthana S., Engelman C.†, Lu Q.† (2023). Liver-specific polygenic risk score is associated with Alzheimer's disease diagnosis. Journal of Alzheimer's Disease, 92(2): 395-409.
[65] Zhang Y., Fletcher J., Lu Q., Song J. (2023). Gender differences in the association between parity and cognitive function: evidence from the UK Biobank. Social Science & Medicine, 320: 115649.
2022
[64] Miao J., Lin Y., Wu Y., Zheng B., Schmitz L., Fletcher J., Lu Q. (2022). A quantile integral linear model to quantify genetic effects on phenotypic variability. Proceedings of the National Academy of Sciences, 119(39): e2212959119. Winner of the 2022 ASA Section on Statistics in Genomics and Genetics Student Paper Award[63] Li J., Zhang Q., Wang Z., Lu Q. (2022). Research domain criteria (RDoC) mechanisms of transdiagnostic polygenic risk for trajectories of depression: from early adolescence to adulthood. Journal of Psychopathology and Clinical Science, 131(6), 567-574.
[62] Zheng B., Fletcher J., Zheng F., Lu Q. (2022). Gene-by-peer-environment interaction effects on cigarette, alcohol, and marijuana use among US high school students of european ancestry. Social Science & Medicine, 309: 115249.
[61] Lai H., Song J., Lu Q., Murali S., Gajapathy M., Wilk B., Brown D., Worthey E., Farrell P., the FIRST Study Group (2022). Genetic factors help explain the variable responses of young children with cystic fibrosis to vitamin D supplements. Clinical Nutrition ESPEN, 51: 367-376.
[60] Guo H., Hou L., Shi Y., Jin S., Zeng X., Li B., Lifton R., Brueckner M., Zhao H.†, Lu Q.† (2022). Quantifying concordant genetic effects of de novo mutations on multiple disorders. eLife, 11: e75551.
[59] Miao J., Lu Q. (2022). Identifying genetic loci associated with complex trait variability. Handbook of Statistical Bioinformatics (2nd Edition). Springer, Berlin, Heidelberg. (Book Chapter)
[58] Eissman J., Dumitrescu L., Mahoney E., Smith A., Mukherjee S., Lee M., Scollard P., Choi S., Bush W., Engelman C., Lu Q., Fardo D., Trittschuh E., Mez J., Kaczorowski C., Saucedo H., Widaman K., Buckley R., Properzi M., Mormino E., Yang H., Harrison T., Hedden T., Nho K., Andrews S., Tommet D., Hadad N., Sanders E., Ruderfer D., Gifford K., Zhong X., Raghavan N., Vardarajan B., Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer's Disease Genetics Consortium (ADGC), A4 Study Team, Pericak-Vance M., Farrer L., Wang L., Cruchaga C., Schellenberg G., Cox N., Haines J., Keene D., Saykin A., Larson E., Sperling R., Mayeux R., Cuccaro M., Bennett D., Schneider J., Crane P., Jefferson A., Hohman T. (2022). Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease. Brain, 145(7), 2541-2554.
[57] Dong R., Denier-Fields D., Lu Q., Suridjan I., Kollmorgen G., Wild N., Betthauser T., Carlsson C., Asthana S., Johnson S., Zetterberg H., Blennow K., Engelman C. (2022). Principal components from untargeted cerebrospinal fluid metabolomics associated with Alzheimer's disease biomarkers. Neurobiology of Aging, 117: 12-23.
[56] Wu Y., Furuya S., Wang Z., Nobles J., Fletcher J.†, Lu Q.† (2022). GWAS on birth year infant mortality rates provides evidence of recent natural selection. Proceedings of the National Academy of Sciences, 119(12): e2117312119. Accompanying commentary article
[55] Wang Y.*, Wu Y.*, Choi J.*, Allington G.*, Zhao S.*, Khanfar M.*, Yang K.*, Fu P., Wrubel M., Yu X., Mekbib K., Ocken J., Smith H., Shohfi J., Kahle K., Lu Q.†, Jin S.† (2022). Computational genomics in the era of precision medicine: applications to variant analysis and gene therapy. Journal of Personalized Medicine, 12(2), 175. (Review)
[54] Amin V., Fletcher J., Sun Z., Lu Q. (2022). Higher educational attainment is associated with longer telomeres in midlife: evidence from sibling comparisons in the UK Biobank. SSM - Population Health, 17: 101018.
[53] Schmitz L., Zhao W., Ratliff S., Goodwin J., Miao J., Lu Q., Guo X., Taylor K., Ding J., Liu Y., Levine M., Smith J. (2022). The socioeconomic gradient in epigenetic aging clocks: evidence from the Multi-ethnic Study of Atherosclerosis and the Health and Retirement Study. Epigenetics, 17(6), 589-611.
[52] Furuya S., Fletcher J., Zhao Z., Sun Z., Lu Q. (2022). Detecting genetic heterogeneities in response to trauma: the case of 9/11. SSM - Mental Health, 2: 100044.
2021
[51] Fletcher J., Wu Y., Lu Q. (2021). Polygenic screening: what's the use? eLife, 10: e73193. (Commentary Article)[50] Zhang Y.*, Lu Q.*, Ye Y., Huang K., Liu W., Wu Y., Zhong X., Li B., Yu Z., Travers B., Werling D., Li J., Zhao H. (2021). SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology, 22, 262.
[49] Zhao Z.*, Yi Y.*, Song J., Wu Y., Zhong X., Lin Y., Hohman T., Fletcher J., Lu Q. (2021). PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biology, 22, 257.
[48] Fletcher J., Topping M., Zheng F., Lu Q. (2021). The effects of education on cognition in older age: evidence from genotyped siblings. Social Science & Medicine, 280: 114044.
[47] Hou L., Lu Q., Yang C., Zhao H. (2021). Special issue on genome wide association study. Quantitative Biology, 9(2): 105-106. (Editorial)
[46] Wu Y., Zhong X., Lin Y., Zhao Z., Chen J., Zheng B., Li J., Fletcher J., Lu Q. (2021). Estimating genetic nurture with summary statistics of multi-generational genome-wide association studies. Proceedings of the National Academy of Sciences, 118(25): e2023184118. Accompanying commentary article
[45] Kundishora A.*, Peters S.*, Pinard A., Duran D., Panchagnula S., Barak T., Miyagishima D., Dong W., Dunbar A., Smith H., Ocken J., Nelson-Williams C., Haider S., Walker R., Li B., Zhao H., Thumkeo D., Marlier A., Duy P., Diab N., Reeves B., Sujijantarat N., Stratman A., Chen Y., Zhao S., Roszko I., Lu Q., Zhang B., Mane S., Castaldi C., Lopez-Giraldez F., Knight J., Bamshad M., Nickerson D., Geschwind D., Chen S., Storm P., Matouk C., Alper S., Smith E., Lifton R., Gunel M., Milewicz D., Jin S.†, Kahle K.† (2021). DIAPH1 variants in non-East Asian patients with sporadic moyamoya disease. JAMA Neurology, 78(8): 993-1003.
[44] Schmitz L., Goodwin J., Miao J., Lu Q., Conley D. (2021). The impact of late-career job loss and genetic risk on body mass index: evidence from variance polygenic scores. Scientific Reports, 11: 7647.
[43] Li M.*, Zeng X.*, Jin L.*, Jin S., Dong W., Brueckner M., Lifton R., Lu Q., Zhao H. (2021). Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease. Quantitative Biology, 9(2): 216-227.
[42] Guo H., Li J., Lu Q.†, Hou L.† (2021). Detecting local genetic correlations with scan statistics. Nature Communications, 12: 2033.
[41] Dong R., Darst B., Deming Y., Ma Y., Lu Q., Zetterberg H., Blennow K., Carlsson C., Johnson S., Asthana S., Engelman C. (2021). CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 13: e12167.
[40] Zhao Z., Song J., Wang T., Lu Q. (2021). Polygenic risk scores: effect estimation and model optimization. Quantitative Biology, 9(2): 133-140. (Review)
[39] Huang K., Wu Y., Shin J., Zheng Y., Siahpirani A., Lin Y., Ni Z., Chen J., You J., Keles S., Wang D., Roy S., Lu Q. (2021). Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLOS Genetics, 17(2): e1009309.
[38] Zhang Y.*, Cheng Y.*, Jiang W., Ye Y., Lu Q., Zhao H. (2021). Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics. Briefings in Bioinformatics, 22(5): bbaa442.
[37] Darst B., Huo Z., Jonaitis E., Koscik R., Clark L., Lu Q., Kremen W., Franz C., Rana B., Lyons M., Hogan K., Zhao J., Johnson S., Engelman C. (2021). Metabolites associated with early cognitive changes implicated in Alzheimer's disease. Journal of Alzheimer's Disease, 79(3): 1041-1054.
[36] Panyard D., Kim K., Darst B., Deming Y., Zhong X., Wu Y., Kang H., Carlsson C., Johnson S., Asthana S., Engelman C.†, Lu Q.† (2021). Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations. Communications Biology, 4: 63.
2020
[35] Bigdeli T., Fanous A., Li Y., Rajeevan N., Sayward F., Genovese G., Gupta R., Radakrishnan K., Malhotra A., Sun N., Lu Q., Hu Y., Li B., Chen Q., Mane S., Miller P., Cheung K., Gur R., Greenwood T., Braff D., Consortium on the Genetics of Schizophrenia (COGS), Achtyes E., Buckley P., Escamilla M., Lehrer D., Malaspina D., McCarroll S., Rapaport M., Vawter M., Pato M., Pato C., Genomic Psychiatry Cohort (GPC) Investigators, Zhao H., Kosten T., Brophy M., Pyarajan S., Shi Y., O'Leary T., Gleason T., Przygodszki R., Muralidhar S., Gaziano M., Million Veteran Program (MVP), Huang G., Concato J., Siever L., Aslan M., Harvey P. (2020). Genome-wide association studies of schizophrenia and bipolar disorder in a diverse cohort of United States veterans. Schizophrenia Bulletin, 47(2), 517-529.[34] Fletcher J., Lu Q. (2020). Health policy and genetic endowments: understanding sources of response to Minimum Legal Drinking Age laws. Health Economics, 30: 194-203.
[33] Liu W., Li M., Zhang W., Zhou G., Wu X., Wang J., Lu Q., Zhao H. (2020). Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology, 16(11): e1008315.
[32] Dong W.*, Jin S.*, Allocco A.*, Zeng X.*, Sheth A., Panchagnula S., Castonguay A., Lorenzo L., Islam B., Brindle G., Bachand K., Hu J., Sularz A., Gaillard J., Choi J., Dunbar A., Nelson-Williams C., Kiziltug E., Furey C., Conine S., Duy P., Kundishora A., Loring E., Li B., Lu Q., Zhou G., Liu W., Li X., Sierant M., Mane S., Castaldi C., Lopez-Giraldez F., Knight J., Sekula R., Simard M., Eskandar E., Gottschalk C., Moliterno J., Gunel M., Gerrard J., Dib-Hajj S., Waxman S., Barker F., Alper S., Chahine M., Haider S., De Koninck Y., Lifton R., Kahle K. (2020). Exome sequencing implicates impaired GABA signaling and neuronal ion transport in trigeminal neuralgia. iScience, 23(10): 101552.
[31] Jin S.*, Dong W.*, Kundishora A.*, Panchagnula S.*, Moreno-De-Luca A.*, Furey C., Allocco A., Walker R., Nelson-Williams C., Smith H., Dunbar A., Conine S., Lu Q., Zeng X., Sierant M., Knight J., Sullivan W., Duy P., DeSpenza T., Reeves B., Karimy J., Marlier A., Castaldi C., Tikhonoa I., Li B., Pena H., Broach J., Kabachelor E., Ssenyonga P., Hehnly C., Ge L., Keren B., Timberlake A., Goto J., Mangano F., Johnston J., Butler W., Warf B., Smith E., Schiff S., Limbrick D., Heuer G., Jackson E., Iskandar B., Mane S., Haider S., Guclu B., Bayri Y., Sahin Y., Duncan C., Apuzzo M., DiLuna M., Hoffman E., Sestan N., Ment L., Alper S., Bilguvar K., Geschwind D., Gunel M., Lifton R., Kahle K. (2020). Exome sequencing implicates genetic disruption of prenatal neuro-gliogenesis in sporadic congenital hydrocephalus. Nature Medicine, 26(11), 1754-1765.
[30] Greenberg J., Hilton E., Li J., Lu Q., Mailick M. (2020). The impact of parenting a child with serious mental illness: accounting for the parent's genetic vulnerability to mental illness. Journal of Family Psychology, 35(3), 417-422.
[29] Dumitrescu L., Mahoney E., Mukherjee S., Lee M., Bush W., Engelman C., Lu Q., Fardo D., Trittschuh E., Mez J., Kaczorowski C., Hernandez H., Widaman K., Buckley R., Properzi M., Mormino E., Yang H., Harrison T., Hedden T., Nho K., Andrews S., Tommet D., Hadad N., Sanders E., Ruderfer D., Gifford K., Moore A., Cambronero F., Zhong X., Raghavan N., Vardarajan B., The Alzheimer's Disease Neuroimaging Initiative (ADNI), Alzheimer's Disease Genetics Consortium (ADGC), Pericak-Vance M., Farrer L., Wang L., Cruchaga C., Schellenberg G., Cox N., Haines J., Keene C., Saykin A., Larson E., Sperling R., Mayeux R., Bennett D., Schneider J., Crane P., Jefferson A., Hohman T. (2020). Genetic variants and functional pathways associated with resilience to Alzheimer's disease. Brain, 143(8), 2561-2575.
[28] Jin S.*, Lewis S.*, Bakhtiari S.*, Zeng X.*, Sierant M., Shetty S., Nordlie S., Elie A., Corbett M., Norton B., van Eyk C., Haider S., Guida B., Magee H., Liu J., Pastore S., Vincent J., Brunstrom-Hernandez J., Papavasileiou A., Fahey M., Berry J., Harper K., Zhou C., Zhang J., Li B., Zhao H., Heim J., Webber D., Frank M., Xia L., Xu Y., Zhu D., Zhang B., Sheth A., Knight J., Castaldi C., Tikhonoa I., Lopez-Giraldez F., Keren B., Whalen S., Buratti J., Doummar D., Cho M., Retterer K., Millan F., Wang Y., Waugh J., Rodan L., Cohen J., Fatemi A., Lin A., Phillips J., Feyma T., MacLennan S., Vaughan S., Crompton K., Reid S., Reddihough D., Shang Q., Gao C., Novak I., Badawi N., Wilson Y., McIntyre S., Mane S., Wang X., Amor D., Zarnescu D., Lu Q., Xing Q.†, Zhu C.†, Bilguvar K.†, Padilla-Lopez S.†, Lifton R.†, Gecz J.†, MacLennan A.†, Kruer M.† (2020). Mutations disrupting neuritogenesis genes confer risk for cerebral palsy. Nature Genetics, 52(10), 1046-1056.
[27] Hatchell K., Lu Q., Mares J., Michos E., Wood A., Engelman C. (2020). Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake. Genetic Epidemiology, 44(2), 208-217.
[26] Harvey P., Sun N., Bigdeli T., Fanous A., Aslan M., Malhotra A., Lu Q., Hu Y., Li B., Chen Q., Mane S., Miller P., Rajeevan N., Sayward F., Cheung K., Li Y., Greenwood T., Gur R., Braff D., Consortium on the Genetics of Schizophrenia (COGS), Brophy M., Pyarajan S., Gleason T., Przygodszki R., O'Leary T., Muralidhar S., Gaziano M., Million Veteran Program (MVP), Huang G., Concato J., Zhao H., Siever L. (2020). Genome-wide association study of cognitive performance in US veterans with schizophrenia or bipolar disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 183(3), 181-194.
2019
[25] Hatchell K., Lu Q., Hebbring S., Michos E., Wood A., Engelman C. (2019). Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations. Human Genetics, 138(10), 1155-1169.[24] Darst B., Lu Q., Johnson S., Engelman C. (2019). Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer's risk factors among 1,111 cohort participants. Genetic Epidemiology, 43(6), 657-674.
[23] Gelernter J., Sun N., Polimanti R., Pietrzak R., Levey D., Lu Q., Hu Y., Li B., Radhakrishnan K., Aslan M., Cheung K., Li Y., Rajeevan N., Sayward F., Harrington K., Chen Q., Cho K., Honerlaw J., Pyarajan S., Lencz T., Quaden R., Shi Y., Hunter-Zink H., Gaziano J., Kranzler H., Concato J., Zhao H., Stein M., on behalf of the Department of Veterans Affairs Cooperative Studies Program (#575B) and Million Veteran Program. (2019). Genomewide association study of maximum habitual alcohol intake in >140,000 US European- and African-American veterans yields novel risk loci. Biological Psychiatry, 86(5), 365-376.
[22] Gelernter J., Sun N., Polimanti R., Pietrzak R., Levey D., Bryois J., Lu Q., Hu Y., Li B., Radhakrishnan K., Aslan M., Cheung K., Li Y., Rajeevan N., Sayward F., Harrington K., Chen Q., Cho K., Pyarajan S., Sullivan P., Quaden R., Shi Y., Hunter-Zink H., Gaziano J., Concato J., Zhao H., Stein M., on behalf of the Department of Veterans Affairs Cooperative Studies Program (#575B) and Million Veteran Program. (2019). Genome-wide association study of posttraumatic stress disorder reexperiencing symptoms in >165,000 US veterans. Nature Neuroscience, 22(9), 1394-1401.
[21] Lu Q., Zhao H. (2019). Improving genetic association analysis through integration of functional annotations of the human genome. Handbook of Statistical Genetics (4th Edition). John Wiley & Sons. (Book Chapter)
[20] Hu B., Shen N., Li J., Kang H., Hong J., Fletcher J., Greenberg J., Mailick M., Lu Q. (2019). Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness. PLOS Genetics, 15(4): e1007973. Accompanying perspective article
[19] Li J., Hilton E., Lu Q., Hong J., Greenberg J., Mailick M. (2019). Validating psychosocial pathways of risk between neuroticism and late life depression using a polygenic score approach. Journal of Abnormal Psychology, 128(3), 200-211.
[18] Hu Y.*, Li M.*, Lu Q.*, Weng H., Wang J., Zekavat M., Yu Z., Li B., Gu J., Muchnik S., Shi Y., Kunkle B., Mukherjee S., Natarajan P., Naj A., Kuzma A., Zhao Y., Crane P., Alzheimer's Disease Genetics Consortium (ADGC), Lu H., Zhao H. (2019). A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics, 51(3), 568-576. Winner of the ASA 2020 Outstanding Statistical Application Award
2018
[17] Duran D.*, Zeng X.*, Jin S.*, Choi J.*, Nelson-Williams C., Yatsula B., Gaillard J., Furey C., Lu Q., Timberlake A., Dong W., Sorscher M., Loring E., Klein J., Allocco A., Hunt A., Conine S., Karimy J., Youngblood M., Zhang J., DiLuna M., Matouk C., Mane S., Tikhonova I., Castaldi C., Lopez-Giraldez F., Knight J., Haider S., Soban M., Alper S., Komiyama M., Ducruet A., Zabramski J., Dardik A., Walcott B., Stapleton C., Aagaard-Kienitz B., Rodesch G., Jackson E., Smith E., Orbach D., Berenstein A., Bilguvar K., Vikkula M., Gunel M., Lifton R.†, Kahle K.† (2018). Mutations in chromatin modifiers and Ephrin signaling components in Vein of Galen malformation. Neuron, 101(3), 429-443.e4.[16] Furey C.*, Choi J.*, Jin S., Zeng X., Timberlake A., Nelson-Williams C., Mansuri M., Lu Q., Duran D., Panchagnula S., Alloco A., Karimy J., Khanna A., Gaillard J., DeSpenza T., Antwi P., Loring E., Butler W., Smith E., Warf B., Strahle J., Limbrick D., Storm P., Heuer G., Jackson E., Iskandar B., Johnston J., Tikhonova I., Castaldi C., Lopez-Giraldez F., Bjornson R., Knight J., Bilguvar K., Mane S., Alper S., Haider S., Guclu B., Bayri Y., Sahin Y., Duncan C., DiLuna M., Gunel M., Lifton R.†, Kahle K.† (2018). De novo mutation in genes regulating neural stem cell fate in human congenital hydrocephalus. Neuron, 99(2), 302-314.e4.
[15] Li B.*, Lu Q.*, Zhao H. (2018). An evaluation of non-coding genome annotation tools through enrichment analysis of fifteen genome-wide association studies. Briefings in Bioinformatics, 20(3), 995-1003.
2017
[14] Lu Q., Li B., Ou D., Erlendsdottir M., Powles R., Jiang T., Hu Y., Chang D., Jin C., Dai W., He Q., Liu Z., Mukherjee S., Crane P., Zhao H. (2017). A powerful approach to estimating annotation-stratified genetic covariance via GWAS summary statistics. The American Journal of Human Genetics, 101(6), 939-964.[13] Jin S.*, Homsy J.*, Zaidi S.*, Lu Q., Morton S., DePalma S., Zeng X., Qi H., Chang W., Hung W., Sierant M., Haider S., Zhang J., Knight J., Bjornson R., Castaldi C., Tikhonoa I., Bilguvar K., Mane S., Sanders S., Mital S., Russell M., Gaynor W., Deanfield J., Giardini A., Porter G., Srivastava D., Lo C., Shen Y., Watkins S., Yandell M., Yost J., Tristani-Firouzi M., Newburger J., Roberts A., Kim R., Zhao H., Kaltman J., Goldmuntz E., Chung W., Seidman J., Gelb B., Seidman C.†, Lifton R.†, Brueckner M.† (2017). Contribution of rare transmitted and de novo variants among 2,871 congenital heart disease probands. Nature Genetics, 49(11), 1593-1601.
[12] Lu Q.*, Powles R.*, Abdallah S., Ou D., Wang Q., Hu Y., Lu Y., Liu W., Li B., Mukherjee S., Crane P., Zhao H. (2017). Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLOS Genetics, 13(7): e1006933.
[11] Hu Y., Lu Q., Liu W., Zhang Y., Li M., Zhao H. (2017). Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics, 13(6): e1006836.
[10] Hu Y.*, Lu Q.*, Powles R., Yao X., Yang C., Fang F., Xu X., Zhao H. (2017). Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology, 13(6): e1005589.
[9] Lu Q., Jin C., Sun J., Bowler R., Kechris K., Kaminski N., Zhao H. (2017). Post-GWAS prioritization through data integration provides novel insights on chronic obstructive pulmonary disease. Statistics in Biosciences, 9(2), 605-621.
[8] Li M., Foli Y., Liu Z., Wang G., Hu Y., Lu Q., Selvaraj S., Lam W., Paintsil E. (2017). High frequency of mitochondrial DNA mutations in HIV-infected treatment-experienced individuals. HIV Medicine, 18(1), 45-55.
[7] Zhao B., Lu Q., Cheng Y., Belcher J., Siew E., Leaf D., Body S., Fox A., Waikar S., Collard C., Thiessen-Philbrook H., Ikizler T., Ware L., Edelstein C., Garg A., Choi M., Schaub J., Zhao H., Lifton R., Parikh C. for the TRIBE-AKI Consortium. (2017). A genome-wide association study to identify single nucleotide polymorphisms for acute kidney injury. American Journal of Respiratory and Critical Care Medicine, 195(4), 482-490.
2016
[6] Timberlake A., Choi J., Zaidi S., Lu Q., Nelson-Williams C., Brooks E., Bilguvar K., Tikhonova I., Mane S., Yang J., Sawh-Martinez R., Persing S., Zellner E., Loring E., Chuang C., Galm A., Hashim P., Steinbacher D., DiLuna M., Duncan C., Pelphrey K., Zhao H., Persing J., Lifton R. (2016). Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and common BMP2 alleles. eLife, 5: e20125.[5] Lu Q.*, Powles R.*, Wang Q., He B., Zhao H. (2016). Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12(4): e1005947.
[4] Lu Q., Yao X., Hu Y., Zhao H. (2016). GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics, 32(4), 542-548.
2013 – 2015
[3] Lu Q., Hu Y., Sun J., Cheng Y., Cheung K., Zhao H. (2015). A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Scientific Reports, 5, 10576.[2] Wang Q.*, Lu Q.*, Zhao H. (2015). A review of study designs and statistical methods for genomic epidemiology studies using next generation sequencing. Frontiers in Genetics, 6:149. (Review)
[1] Lu Q., Ren S., Lu M., Zhang Y., Zhu D., Zhang X., Li T. (2013). Computational prediction of associations between long non-coding RNAs and proteins. BMC Genomics, 14(1), 651.