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A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies.

Journal of the American Statistical Association

Liu Z, Lin X.
PMID: 31564761
J Am Stat Assoc. 2019;114(527):975-990. doi: 10.1080/01621459.2018.1513363. Epub 2019 Feb 26.

Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high-dimensional, has been proposed to analyze multiple correlated phenotypes. It...

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Liu Z, Lin X. A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies. J Am Stat Assoc. 2019;114(527):975-990doi: 10.1080/01621459.2018.1513363.
Liu, Z., & Lin, X. (2019). A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies. Journal of the American Statistical Association, 114(527), 975-990. https://doi.org/10.1080/01621459.2018.1513363
Liu, Zhonghua, and Lin, Xihong. "A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies." Journal of the American Statistical Association vol. 114,527 (2019): 975-990. doi: https://doi.org/10.1080/01621459.2018.1513363
Liu Z, Lin X. A Geometric Perspective on the Power of Principal Component Association Tests in Multiple Phenotype Studies. J Am Stat Assoc. 2019;114(527):975-990. doi: 10.1080/01621459.2018.1513363. Epub 2019 Feb 26. PMID: 31564761; PMCID: PMC6764517.
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Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans.

American journal of respiratory and critical care medicine

Cade BE, Chen H, Stilp AM, Gleason KJ, Sofer T, Ancoli-Israel S, Arens R, Bell GI, Below JE, Bjonnes AC, Chun S, Conomos MP, Evans DS, Johnson WC, Frazier-Wood AC, Lane JM, Larkin EK, Loredo JS, Post WS, Ramos AR, Rice K, Rotter JI, Shah NA, Stone KL, Taylor KD, Thornton TA, Tranah GJ, Wang C, Zee PC, Hanis CL, Sunyaev SR, Patel SR, Laurie CC, Zhu X, Saxena R, Lin X, Redline S.
PMID: 26977737
Am J Respir Crit Care Med. 2016 Oct 01;194(7):886-897. doi: 10.1164/rccm.201512-2431OC.

RATIONALE: Obstructive sleep apnea is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. Although there is strong clinical and epidemiologic evidence supporting the importance of genetic factors in influencing obstructive sleep apnea, its...

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Cade BE, Chen H, Stilp AM, et al. Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans. Am J Respir Crit Care Med. 2016;194(7):886-897doi: 10.1164/rccm.201512-2431OC.
Cade, B. E., Chen, H., Stilp, A. M., Gleason, K. J., Sofer, T., Ancoli-Israel, S., Arens, R., Bell, G. I., Below, J. E., Bjonnes, A. C., Chun, S., Conomos, M. P., Evans, D. S., Johnson, W. C., Frazier-Wood, A. C., Lane, J. M., Larkin, E. K., Loredo, J. S., Post, W. S., Ramos, A. R., Rice, K., Rotter, J. I., Shah, N. A., Stone, K. L., Taylor, K. D., Thornton, T. A., Tranah, G. J., Wang, C., Zee, P. C., Hanis, C. L., Sunyaev, S. R., Patel, S. R., Laurie, C. C., Zhu, X., Saxena, R., Lin, X., & Redline, S. (2016). Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans. American journal of respiratory and critical care medicine, 194(7), 886-897. https://doi.org/10.1164/rccm.201512-2431OC
Cade, Brian E, et al. "Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans." American journal of respiratory and critical care medicine vol. 194,7 (2016): 886-897. doi: https://doi.org/10.1164/rccm.201512-2431OC
Cade BE, Chen H, Stilp AM, Gleason KJ, Sofer T, Ancoli-Israel S, Arens R, Bell GI, Below JE, Bjonnes AC, Chun S, Conomos MP, Evans DS, Johnson WC, Frazier-Wood AC, Lane JM, Larkin EK, Loredo JS, Post WS, Ramos AR, Rice K, Rotter JI, Shah NA, Stone KL, Taylor KD, Thornton TA, Tranah GJ, Wang C, Zee PC, Hanis CL, Sunyaev SR, Patel SR, Laurie CC, Zhu X, Saxena R, Lin X, Redline S. Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans. Am J Respir Crit Care Med. 2016 Oct 01;194(7):886-897. doi: 10.1164/rccm.201512-2431OC. PMID: 26977737; PMCID: PMC5074655.
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HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION.

Annals of statistics

Mukherjee R, Pillai NS, Lin X.
PMID: 26246645
Ann Stat. 2015 Feb;43(1):352-381. doi: 10.1214/14-AOS1279.

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing...

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Mukherjee R, Pillai NS, Lin X. HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION. Ann Stat. 2015;43(1):352-381doi: 10.1214/14-AOS1279.
Mukherjee, R., Pillai, N. S., & Lin, X. (2015). HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION. Annals of statistics, 43(1), 352-381. https://doi.org/10.1214/14-AOS1279
Mukherjee, Rajarshi, et al. "HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION." Annals of statistics vol. 43,1 (2015): 352-381. doi: https://doi.org/10.1214/14-AOS1279
Mukherjee R, Pillai NS, Lin X. HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION. Ann Stat. 2015 Feb;43(1):352-381. doi: 10.1214/14-AOS1279. PMID: 26246645; PMCID: PMC4522432.
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Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer.

Journal of the American Statistical Association

Sun R, Lin X.
PMID: 33041403
J Am Stat Assoc. 2020;115(531):1079-1091. doi: 10.1080/01621459.2019.1660170. Epub 2019 Oct 16.

Studying the effects of groups of single nucleotide polymorphisms (SNPs), as in a gene, genetic pathway, or network, can provide novel insight into complex diseases like breast cancer, uncovering new genetic associations and augmenting the information that can be...

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Sun R, Lin X. Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer. J Am Stat Assoc. 2019;115(531):1079-1091doi: 10.1080/01621459.2019.1660170.
Sun, R., & Lin, X. (2020). Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer. Journal of the American Statistical Association, 115(531), 1079-1091. https://doi.org/10.1080/01621459.2019.1660170
Sun, Ryan, and Lin, Xihong. "Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer." Journal of the American Statistical Association vol. 115,531 (2020): 1079-1091. doi: https://doi.org/10.1080/01621459.2019.1660170
Sun R, Lin X. Genetic Variant Set-Based Tests Using the Generalized Berk-Jones Statistic with Application to a Genome-Wide Association Study of Breast Cancer. J Am Stat Assoc. 2020;115(531):1079-1091. doi: 10.1080/01621459.2019.1660170. Epub 2019 Oct 16. PMID: 33041403; PMCID: PMC7539682.
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Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations.

Journal of the American Statistical Association

Wang L, Rotnitzky A, Lin X.
PMID: 23144520
J Am Stat Assoc. 2010 Sep;105(491):1135-1146. doi: 10.1198/jasa.2010.tm08463. Epub 2012 Jan 01.

We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating...

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Wang L, Rotnitzky A, Lin X. Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations. J Am Stat Assoc. 2012;105(491):1135-1146doi: 10.1198/jasa.2010.tm08463.
Wang, L., Rotnitzky, A., & Lin, X. (2010). Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations. Journal of the American Statistical Association, 105(491), 1135-1146. https://doi.org/10.1198/jasa.2010.tm08463
Wang, Lu, et al. "Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations." Journal of the American Statistical Association vol. 105,491 (2010): 1135-1146. doi: https://doi.org/10.1198/jasa.2010.tm08463
Wang L, Rotnitzky A, Lin X. Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations. J Am Stat Assoc. 2010 Sep;105(491):1135-1146. doi: 10.1198/jasa.2010.tm08463. Epub 2012 Jan 01. PMID: 23144520; PMCID: PMC3491912.
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Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity.

International journal of obesity (2005)

Liu Y, Zhang X, Lee J, Smelser D, Cade B, Chen H, Zhou H, Kirchner HL, Lin X, Mukherjee S, Hillman D, Liu CT, Redline S, Sofer T.
PMID: 33907307
Int J Obes (Lond). 2021 Jul;45(7):1532-1541. doi: 10.1038/s41366-021-00817-2. Epub 2021 Apr 27.

BACKGROUND/OBJECTIVES: Neck circumference, an index of upper airway fat, has been suggested to be an important measure of body-fat distribution with unique associations with health outcomes such as obstructive sleep apnea and metabolic disease. This study aims to study...

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Liu Y, Zhang X, Lee J, et al. Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity. Int J Obes (Lond). 2021;45(7):1532-1541doi: 10.1038/s41366-021-00817-2.
Liu, Y., Zhang, X., Lee, J., Smelser, D., Cade, B., Chen, H., Zhou, H., Kirchner, H. L., Lin, X., Mukherjee, S., Hillman, D., Liu, C. T., Redline, S., & Sofer, T. (2021). Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity. International journal of obesity (2005), 45(7), 1532-1541. https://doi.org/10.1038/s41366-021-00817-2
Liu, Yaowu, et al. "Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity." International journal of obesity (2005) vol. 45,7 (2021): 1532-1541. doi: https://doi.org/10.1038/s41366-021-00817-2
Liu Y, Zhang X, Lee J, Smelser D, Cade B, Chen H, Zhou H, Kirchner HL, Lin X, Mukherjee S, Hillman D, Liu CT, Redline S, Sofer T. Genome-wide association study of neck circumference identifies sex-specific loci independent of generalized adiposity. Int J Obes (Lond). 2021 Jul;45(7):1532-1541. doi: 10.1038/s41366-021-00817-2. Epub 2021 Apr 27. PMID: 33907307; PMCID: PMC8236408.
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