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1. Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization

Author

Cheng, Luyao, Wang, Hui, Zheng, Siqi, Chen, Yafeng, Huang, Rongjie, Zhang, Qinglin, Chen, Qian, and Li, Xihao

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Speaker diarization, the process of segmenting an audio stream or transcribed speech content into homogenous partitions based on speaker identity, plays a crucial role in the interpretation and analysis of human speech. Most existing speaker diarization systems rely exclusively on unimodal acoustic information, making the task particularly challenging due to the innate ambiguities of audio signals. Recent studies have made tremendous efforts towards audio-visual or audio-semantic modeling to enhance performance. However, even the incorporation of up to two modalities often falls short in addressing the complexities of spontaneous and unstructured conversations. To exploit more meaningful dialogue patterns, we propose a novel multimodal approach that jointly utilizes audio, visual, and semantic cues to enhance speaker diarization. Our method elegantly formulates the multimodal modeling as a constrained optimization problem. First, we build insights into the visual connections among active speakers and the semantic interactions within spoken content, thereby establishing abundant pairwise constraints. Then we introduce a joint pairwise constraint propagation algorithm to cluster speakers based on these visual and semantic constraints. This integration effectively leverages the complementary strengths of different modalities, refining the affinity estimation between individual speaker embeddings. Extensive experiments conducted on multiple multimodal datasets demonstrate that our approach consistently outperforms state-of-the-art speaker diarization methods.

Published
2024

2. 3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization

Author

Chen, Yafeng, Zheng, Siqi, Wang, Hui, Cheng, Luyao, Zhu, Tinglong, Huang, Rongjie, Deng, Chong, Chen, Qian, Zhang, Shiliang, Wang, Wen, and Li, Xihao

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined strengths of acoustic, semantic, and visual data, seamlessly fusing these modalities to offer robust speaker recognition capabilities. The acoustic module extracts speaker embeddings from acoustic features, employing both fully-supervised and self-supervised learning approaches. The semantic module leverages advanced language models to comprehend the substance and context of spoken language, thereby augmenting the system's proficiency in distinguishing speakers through linguistic patterns. The visual module applies image processing technologies to scrutinize facial features, which bolsters the precision of speaker diarization in multi-speaker environments. Collectively, these modules empower the 3D-Speaker-Toolkit to achieve substantially improved accuracy and reliability in speaker-related tasks. With 3D-Speaker-Toolkit, we establish a new benchmark for multimodal speaker analysis. The toolkit also includes a handful of open-source state-of-the-art models and a large-scale dataset containing over 10,000 speakers. The toolkit is publicly available at https://github.com/modelscope/3D-Speaker.

Published
2024

4. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Author

Wang, Yuxuan, Selvaraj, Margaret, Li, Xihao, Li, Zilin, Holdcraft, Jacob, Arnett, Donna, Bis, Joshua, Blangero, John, Boerwinkle, Eric, Bowden, Donald, Cade, Brian, Carlson, Jenna, Carson, April, Chen, Yii-Der, Curran, Joanne, de Vries, Paul, Dutcher, Susan, Ellinor, Patrick, Floyd, James, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, Guo, Xiuqing, He, Jiang, Heard-Costa, Nancy, Hildalgo, Bertha, Hou, Lifang, Irvin, Marguerite, Joehanes, Roby, Kaplan, Robert, Kardia, Sharon, Kelly, Tanika, Kim, Ryan, Kooperberg, Charles, Kral, Brian, Levy, Daniel, Li, Changwei, Liu, Chunyu, Lloyd-Jone, Don, Loos, Ruth, Mahaney, Michael, Martin, Lisa, Mathias, Rasika, Minster, Ryan, Mitchell, Braxton, Montasser, May, Morrison, Alanna, Murabito, Joanne, Naseri, Take, OConnell, Jeffrey, Palmer, Nicholette, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Rao, Dabeeru, Redline, Susan, Reiner, Alexander, Rich, Stephen, Ruepena, Muagututia, Sheu, Wayne, Smith, Jennifer, Smith, Albert, Tiwari, Hemant, Tsai, Michael, Viaud-Martinez, Karine, Wang, Zhe, Yanek, Lisa, Zhao, Wei, Lin, Xihong, Natarajan, Pradeep, Peloso, Gina, and Rotter, Jerome

Subjects
association, blood lipid, cholesterol, lncRNA, rare variants, whole-genome sequencing, Humans, RNA, Long Noncoding, Genome-Wide Association Study, Precision Medicine, Whole Genome Sequencing, Lipids, Polymorphism, Single Nucleotide
Abstract

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Published
2023

5. Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Author

Feofanova, Elena V, Brown, Michael R, Alkis, Taryn, Manuel, Astrid M, Li, Xihao, Tahir, Usman A, Li, Zilin, Mendez, Kevin M, Kelly, Rachel S, Qi, Qibin, Chen, Han, Larson, Martin G, Lemaitre, Rozenn N, Morrison, Alanna C, Grieser, Charles, Wong, Kari E, Gersztern, Robert E, Zhao, Zhongming, Lasky-Su, Jessica, and Yu, Bing

Subjects
Genetics, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Good Health and Well Being, Humans, Quantitative Trait Loci, Ethnicity, Metabolome, Genome-Wide Association Study, Polymorphism, Single Nucleotide, NHLBI Trans-Omics for Precision Medicine
Abstract

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Published
2023

6. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Author

Li, Xihao, Quick, Corbin, Zhou, Hufeng, Gaynor, Sheila M, Liu, Yaowu, Chen, Han, Selvaraj, Margaret Sunitha, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Haessler, Jeffrey, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Manichaikul, Ani, Martin, Lisa W, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Sitlani, Colleen M, Smith, Jennifer A, Taylor, Kent D, Vasan, Ramachandran S, Willer, Cristen J, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Natarajan, Pradeep, Peloso, Gina M, Li, Zilin, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Generic health relevance, Good Health and Well Being, Genome-Wide Association Study, Whole Genome Sequencing, Exome Sequencing, Phenotype, Lipids, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Published
2023

7. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Author

Li, Zilin, Li, Xihao, Zhou, Hufeng, Gaynor, Sheila M, Selvaraj, Margaret Sunitha, Arapoglou, Theodore, Quick, Corbin, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Blackwell, Thomas W, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Franceschini, Nora, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Lin, Bridget M, Manichaikul, Ani, Manning, Alisa K, Martin, Lisa W, Mathias, Rasika A, Meigs, James B, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Taylor, Kent D, Taub, Margaret A, Vasan, Ramachandran S, Weeks, Daniel E, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Willer, Cristen J, Natarajan, Pradeep, Peloso, Gina M, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Generic health relevance, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Genome, Phenotype, Genetic Variation, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Published
2022

8. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer.

Author

Byun, Jinyoung, Han, Younghun, Li, Yafang, Xia, Jun, Long, Erping, Choi, Jiyeon, Xiao, Xiangjun, Zhu, Meng, Zhou, Wen, Sun, Ryan, Bossé, Yohan, Song, Zhuoyi, Schwartz, Ann, Lusk, Christine, Rafnar, Thorunn, Stefansson, Kari, Zhang, Tongwu, Zhao, Wei, Pettit, Rowland W, Liu, Yanhong, Li, Xihao, Zhou, Hufeng, Walsh, Kyle M, Gorlov, Ivan, Gorlova, Olga, Zhu, Dakai, Rosenberg, Susan M, Pinney, Susan, Bailey-Wilson, Joan E, Mandal, Diptasri, de Andrade, Mariza, Gaba, Colette, Willey, James C, You, Ming, Anderson, Marshall, Wiencke, John K, Albanes, Demetrius, Lam, Stephan, Tardon, Adonina, Chen, Chu, Goodman, Gary, Bojeson, Stig, Brenner, Hermann, Landi, Maria Teresa, Chanock, Stephen J, Johansson, Mattias, Muley, Thomas, Risch, Angela, Wichmann, H-Erich, Bickeböller, Heike, Christiani, David C, Rennert, Gad, Arnold, Susanne, Field, John K, Shete, Sanjay, Le Marchand, Loic, Melander, Olle, Brunnstrom, Hans, Liu, Geoffrey, Andrew, Angeline S, Kiemeney, Lambertus A, Shen, Hongbing, Zienolddiny, Shanbeh, Grankvist, Kjell, Johansson, Mikael, Caporaso, Neil, Cox, Angela, Hong, Yun-Chul, Yuan, Jian-Min, Lazarus, Philip, Schabath, Matthew B, Aldrich, Melinda C, Patel, Alpa, Lan, Qing, Rothman, Nathaniel, Taylor, Fiona, Kachuri, Linda, Witte, John S, Sakoda, Lori C, Spitz, Margaret, Brennan, Paul, Lin, Xihong, McKay, James, Hung, Rayjean J, and Amos, Christopher I

Subjects
Humans, Lung Neoplasms, Genetic Predisposition to Disease, RNA-Binding Proteins, DNA-Binding Proteins, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Human Genome, Cancer, Genetics, Lung, 2.1 Biological and endogenous factors, Aetiology, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Published
2022

12. Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Author

Selvaraj, Margaret Sunitha, Li, Xihao, Li, Zilin, Pampana, Akhil, Zhang, David Y, Park, Joseph, Aslibekyan, Stella, Bis, Joshua C, Brody, Jennifer A, Cade, Brian E, Chuang, Lee-Ming, Chung, Ren-Hua, Curran, Joanne E, de las Fuentes, Lisa, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Graff, Mariaelisa, Guo, Xiuqing, Heard-Costa, Nancy, Hidalgo, Bertha, Hwu, Chii-Min, Irvin, Marguerite R, Kelly, Tanika N, Kral, Brian G, Lange, Leslie, Li, Xiaohui, Lisa, Martin, Lubitz, Steven A, Manichaikul, Ani W, Michael, Preuss, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Reupena, Muagututia S, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Tracy, Russell P, Tsai, Michael Y, Wang, Zhe, Wang, Yuxuan, Bao, Wei, Wilkins, John T, Yanek, Lisa R, Zhao, Wei, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Chen, Yii-Der Ida, Correa, Adolfo, Cupples, L Adrienne, Dutcher, Susan K, Ellinor, Patrick T, Fornage, Myriam, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, He, Jiang, Kaplan, Robert C, Kardia, Sharon LR, Kim, Ryan, Kooperberg, Charles, Loos, Ruth JF, Viaud-Martinez, Karine A, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Nickerson, Deborah, North, Kari E, Psaty, Bruce M, Redline, Susan, Reiner, Alexander P, Vasan, Ramachandran S, Rich, Stephen S, Willer, Cristen, Rotter, Jerome I, Rader, Daniel J, Lin, Xihong, Peloso, Gina M, and Natarajan, Pradeep

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Biotechnology, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Alleles, Cholesterol, LDL, Genome-Wide Association Study, Humans, Lipids, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Published
2022

13. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.

Author

Li, Xihao, Li, Zilin, Zhou, Hufeng, Gaynor, Sheila M, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Aslibekyan, Stella, Ballantyne, Christie M, Bielak, Lawrence F, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Broome, Jai G, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Freedman, Barry I, Guo, Xiuqing, Hindy, George, Irvin, Marguerite R, Kardia, Sharon LR, Kathiresan, Sekar, Khan, Alyna T, Kooperberg, Charles L, Laurie, Cathy C, Liu, X Shirley, Mahaney, Michael C, Manichaikul, Ani W, Martin, Lisa W, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Moore, Jill E, Morrison, Alanna C, O'Connell, Jeffrey R, Palmer, Nicholette D, Pampana, Akhil, Peralta, Juan M, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Tiwari, Hemant K, Tsai, Michael Y, Vasan, Ramachandran S, Wang, Fei Fei, Weeks, Daniel E, Weng, Zhiping, Wilson, James G, Yanek, Lisa R, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Neale, Benjamin M, Sunyaev, Shamil R, Abecasis, Gonçalo R, Rotter, Jerome I, Willer, Cristen J, Peloso, Gina M, Natarajan, Pradeep, and Lin, Xihong

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Humans, Genetic Predisposition to Disease, Phenotype, Genome, Models, Genetic, Computer Simulation, Cholesterol, LDL, Genetic Variation, Genome-Wide Association Study, Molecular Sequence Annotation, Whole Genome Sequencing, Models, Genetic, Cholesterol, LDL, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Published
2020

14. Population scale whole genome sequencing provides novel insights into cardiometabolic health

Author

Zhao, Yajie, primary, Lockhart, Sam, additional, Liu, Jimmy, additional, Li, Xihao, additional, Cortes, Adrian, additional, Hua, Xing, additional, Gardner, Eugene J., additional, Kentistou, Katherine A., additional, Lo, Yancy, additional, Davitte, Jonathan, additional, Savage, David B., additional, Buser-Doepner, Carolyn, additional, Ong, Ken K., additional, Zhang, Haoyu, additional, Scott, Robert, additional, O'Rahilly, Stephen, additional, and Perry, John R.B., additional

Published
2024
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15. MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric

Author

Sun, Quan, primary, Yang, Yingxi, additional, Rosen, Jonathan D., additional, Chen, Jiawen, additional, Li, Xihao, additional, Guan, Wyliena, additional, Jiang, Min-Zhi, additional, Wen, Jia, additional, Pace, Rhonda G., additional, Blackman, Scott M., additional, Bamshad, Michael J., additional, Gibson, Ronald L., additional, Cutting, Garry R., additional, O’Neal, Wanda K., additional, Knowles, Michael R., additional, Kooperberg, Charles, additional, Reiner, Alexander P., additional, Raffield, Laura M., additional, Carson, April P., additional, Rich, Stephen S., additional, Rotter, Jerome I., additional, Loos, Ruth J.F., additional, Kenny, Eimear, additional, Jaeger, Byron C., additional, Min, Yuan-I, additional, Fuchsberger, Christian, additional, and Li, Yun, additional

Published
2024
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16. MagicalRsq-X:A cross-cohort transferable genotype imputation quality metric

Author

Sun, Quan, Yang, Yingxi, Rosen, Jonathan D, Chen, Jiawen, Li, Xihao, Guan, Wyliena, Jiang, Min-Zhi, Wen, Jia, Pace, Rhonda G, Blackman, Scott M, Bamshad, Michael J, Gibson, Ronald L, Cutting, Garry R, O'Neal, Wanda K, Knowles, Michael R, Kooperberg, Charles, Reiner, Alexander P, Raffield, Laura M, Carson, April P., Rich, Stephen S., Rotter, Jerome I., Loos, Ruth J F, Kenny, Eimear, Jaeger, Byron C, Min, Yuan-I, Fuchsberger, Christian, Li, Yun, Sun, Quan, Yang, Yingxi, Rosen, Jonathan D, Chen, Jiawen, Li, Xihao, Guan, Wyliena, Jiang, Min-Zhi, Wen, Jia, Pace, Rhonda G, Blackman, Scott M, Bamshad, Michael J, Gibson, Ronald L, Cutting, Garry R, O'Neal, Wanda K, Knowles, Michael R, Kooperberg, Charles, Reiner, Alexander P, Raffield, Laura M, Carson, April P., Rich, Stephen S., Rotter, Jerome I., Loos, Ruth J F, Kenny, Eimear, Jaeger, Byron C, Min, Yuan-I, Fuchsberger, Christian, and Li, Yun

Abstract

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Published
2024

17. Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Author

Jiang, Min-Zhi, Gaynor, Sheila M, Li, Xihao, Buren, Eric Van, Stilp, Adrienne, Buth, Erin, Wang, Fei Fei, Manansala, Regina, Gogarten, Stephanie M, Li, Zilin, Polfus, Linda M, Salimi, Shabnam, Bis, Joshua C, Pankratz, Nathan, Yanek, Lisa R, Durda, Peter, Tracy, Russell P, Rich, Stephen S, Rotter, Jerome I, and Mitchell, Braxton D

Published
2024
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19. Iterative Basis Update for Ordered Statistics Decoding of Linear Block Codes

Author

Li, Xihao, Chen, Wenhao, Chen, Li, and Zhang, Huazi

Abstract

Ordered statistics decoding (OSD) requires Gaussian elimination (GE) for obtaining the most reliable independent positions (MRIPs) of the received vector. However, GE has cubic time complexity. Its sequential nature also introduces high latency in practice. To address this, the letter proposes an iterative basis update (IBU) to reduce complexity and latency of GE. Specifically, the IBU utilizes a systematic generator matrix (SGM) whose identity submatrix corresponds to a symbol position set. This set will then be iteratively updated for obtaining the MRIPs. For cyclic codes, the IBU can be further simplified by selecting multiple symbol position sets for the SGM. Our analysis and simulations show that the proposed IBU can significantly facilitate OSD without compromising decoding performance.

Published
2024
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20. Structure-Preserving Reduction of Finite-Difference Time-Domain Equations with Controllable Stability Beyond the CFL Limit

Author

Li, Xihao, Sarris, Costas D., and Triverio, Piero

Subjects
Computer Science - Computational Engineering, Finance, and Science, Physics - Computational Physics
Abstract

The timestep of the Finite-Difference Time-Domain method (FDTD) is constrained by the stability limit known as the Courant-Friedrichs-Lewy (CFL) condition. This limit can make FDTD simulations quite time consuming for structures containing small geometrical details. Several methods have been proposed in the literature to extend the CFL limit, including implicit FDTD methods and filtering techniques. In this paper, we propose a novel approach which combines model order reduction and a perturbation algorithm to accelerate FDTD simulations beyond the CFL barrier. We compare the proposed algorithm against existing implicit and explicit CFL extension techniques, demonstrating increased accuracy and performance on a large number of test cases, including resonant cavities, a waveguide structure, a focusing metascreen and a microstrip filter.

Published
2014
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24. A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Author

Li, Xihao, Chen, Han, Selvaraj, Margaret Sunitha, Van Buren, Eric, Zhou, Hufeng, Wang, Yuxuan, Sun, Ryan, McCaw, Zachary R, Yu, Zhi, Arnett, Donna K, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Carson, April P, Carlson, Jenna C, Chami, Nathalie, Chen, Yii-Der Ida, Curran, Joanne E, de Vries, Paul S, Fornage, Myriam, Franceschini, Nora, Freedman, Barry I, Gu, Charles, Heard-Costa, Nancy L, He, Jiang, Hou, Lifang, Hung, Yi-Jen, Irvin, Marguerite R, Kaplan, Robert C, Kardia, Sharon L R, Kelly, Tanika, Konigsberg, Iain, Kooperberg, Charles, Kral, Brian G, Li, Changwei, Loos, Ruth J F, Mahaney, Michael C, Martin, Lisa W, Mathias, Rasika A, Minster, Ryan L, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Li, Xihao, Chen, Han, Selvaraj, Margaret Sunitha, Van Buren, Eric, Zhou, Hufeng, Wang, Yuxuan, Sun, Ryan, McCaw, Zachary R, Yu, Zhi, Arnett, Donna K, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Carson, April P, Carlson, Jenna C, Chami, Nathalie, Chen, Yii-Der Ida, Curran, Joanne E, de Vries, Paul S, Fornage, Myriam, Franceschini, Nora, Freedman, Barry I, Gu, Charles, Heard-Costa, Nancy L, He, Jiang, Hou, Lifang, Hung, Yi-Jen, Irvin, Marguerite R, Kaplan, Robert C, Kardia, Sharon L R, Kelly, Tanika, Konigsberg, Iain, Kooperberg, Charles, Kral, Brian G, Li, Changwei, Loos, Ruth J F, Mahaney, Michael C, Martin, Lisa W, Mathias, Rasika A, Minster, Ryan L, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, and Raffield, Laura M

Abstract

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Published
2023

32. FAVOR: Functional Annotation of Variants Online Resource and Annotator for Variation across the Human Genome

Author

Zhou, Hufeng, primary, Arapoglou, Theodore, additional, Li, Xihao, additional, Li, Zilin, additional, Zheng, Xiuwen, additional, Moore, Jill E, additional, Asok, Abhijith, additional, Kumar, Sushant, additional, Blue, Elizabeth E, additional, Buyske, Steve, additional, Cox, Nancy, additional, Felsenfeld, Adam, additional, Gerstein, Mark, additional, Kenny, Eimear, additional, Li, Bingshan, additional, Matise, Tara, additional, Philippakis, Anthony, additional, Rehm, Heidi, additional, Sofia, Heidi J., additional, Neale, Benjamin, additional, Snyder, Grace, additional, Weng, Zhiping, additional, Sunyaev, Shamil, additional, and Lin, Xihong, additional

Published
2022
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38. Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer

Author

Sun, Ryan, Xu, Miao, Li, Xihao, Gaynor, Sheila, Zhou, Hufeng, Li, Zilin, Bosse, Yohan, Lam, Stephen, Tsao, Ming-Sound, Tardon, Adonina, Chen, Chu, Doherty, Jennifer, Goodman, Gary, Bojesen, Stig E., Landi, Maria T., Johansson, Mattias, Field, John K., Bickeboeller, Heike, Wichmann, H-Erich, Risch, Angela, Rennert, Gadi, Arnold, Suzanne, Wu, Xifeng, Melander, Olle, Brunnstrom, Hans, Le Marchand, Loic, Liu, Geoffrey, Andrew, Angeline, Duell, Eric, Kiemeney, Lambertus A., Shen, Hongbing, Haugen, Aage, Johansson, Mikael, Grankvist, Kjell, Caporaso, Neil, Woll, Penella, Dawn Teare, M., Scelo, Ghislaine, Hong, Yun-Chul, Yuan, Jian-Min, Lazarus, Philip, Schabath, Matthew B., Aldrich, Melinda C., Albanes, Demetrios, Mak, Raymond, Barbie, David, Brennan, Paul, Hung, Rayjean J., Amos, Christopher I., Christiani, David C., Lin, Xihong, Sun, Ryan, Xu, Miao, Li, Xihao, Gaynor, Sheila, Zhou, Hufeng, Li, Zilin, Bosse, Yohan, Lam, Stephen, Tsao, Ming-Sound, Tardon, Adonina, Chen, Chu, Doherty, Jennifer, Goodman, Gary, Bojesen, Stig E., Landi, Maria T., Johansson, Mattias, Field, John K., Bickeboeller, Heike, Wichmann, H-Erich, Risch, Angela, Rennert, Gadi, Arnold, Suzanne, Wu, Xifeng, Melander, Olle, Brunnstrom, Hans, Le Marchand, Loic, Liu, Geoffrey, Andrew, Angeline, Duell, Eric, Kiemeney, Lambertus A., Shen, Hongbing, Haugen, Aage, Johansson, Mikael, Grankvist, Kjell, Caporaso, Neil, Woll, Penella, Dawn Teare, M., Scelo, Ghislaine, Hong, Yun-Chul, Yuan, Jian-Min, Lazarus, Philip, Schabath, Matthew B., Aldrich, Melinda C., Albanes, Demetrios, Mak, Raymond, Barbie, David, Brennan, Paul, Hung, Rayjean J., Amos, Christopher I., Christiani, David C., and Lin, Xihong

Abstract

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1 beta pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-kappa B signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

Published
2021
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39. Supplemental Material, sj-pdf-1-tct-10.1177_15330338211033064 - Surgical Survival Benefits With Different Metastatic Patterns for Stage IV Extrathoracic Metastatic Non-Small Cell Lung Cancer: A SEER-Based Study

Author

Chao, Ce, Qian, Yongxiang, Li, Xihao, Sang, Chen, Wang, Bin, and Zhang, Xiao-ying

Subjects
110320 Radiology and Organ Imaging, FOS: Clinical medicine, Biochemistry, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental Material, sj-pdf-1-tct-10.1177_15330338211033064 for Surgical Survival Benefits With Different Metastatic Patterns for Stage IV Extrathoracic Metastatic Non-Small Cell Lung Cancer: A SEER-Based Study by Ce Chao, Yongxiang Qian, Xihao Li, Chen Sang, Bin Wang and Xiao-ying Zhang in Technology in Cancer Research & Treatment

Published
2021
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47. Integration of multiomic annotation data to prioritize and characterize inflammation and immune‐related risk variants in squamous cell lung cancer

Author

Sun, Ryan, primary, Xu, Miao, additional, Li, Xihao, additional, Gaynor, Sheila, additional, Zhou, Hufeng, additional, Li, Zilin, additional, Bossé, Yohan, additional, Lam, Stephen, additional, Tsao, Ming‐Sound, additional, Tardon, Adonina, additional, Chen, Chu, additional, Doherty, Jennifer, additional, Goodman, Gary, additional, Bojesen, Stig E., additional, Landi, Maria T., additional, Johansson, Mattias, additional, Field, John K., additional, Bickeböller, Heike, additional, Wichmann, H‐Erich, additional, Risch, Angela, additional, Rennert, Gadi, additional, Arnold, Suzanne, additional, Wu, Xifeng, additional, Melander, Olle, additional, Brunnström, Hans, additional, Le Marchand, Loic, additional, Liu, Geoffrey, additional, Andrew, Angeline, additional, Duell, Eric, additional, Kiemeney, Lambertus A., additional, Shen, Hongbing, additional, Haugen, Aage, additional, Johansson, Mikael, additional, Grankvist, Kjell, additional, Caporaso, Neil, additional, Woll, Penella, additional, Dawn Teare, M., additional, Scelo, Ghislaine, additional, Hong, Yun‐Chul, additional, Yuan, Jian‐Min, additional, Lazarus, Philip, additional, Schabath, Matthew B., additional, Aldrich, Melinda C., additional, Albanes, Demetrios, additional, Mak, Raymond, additional, Barbie, David, additional, Brennan, Paul, additional, Hung, Rayjean J., additional, Amos, Christopher I., additional, Christiani, David C., additional, and Lin, Xihong, additional

Published
2020
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49. Hot Tearing of 9Cr3Co3W Heat-Resistant Steel during Solidification

Author

Zhong Honggang, Li Xihao, Zhai Qijie, Bin Liu, Tieming Wu, Biao Wang, and Yunhu Zhang

Subjects
lcsh:TN1-997, Heat resistant, Materials science, heat-resistant steel, Metals and Alloys, Hot spot (veterinary medicine), 02 engineering and technology, 021001 nanoscience & nanotechnology, medicine.disease_cause, 020501 mining & metallurgy, Stress (mechanics), linear contraction, 0205 materials engineering, hot tearing, Casting (metalworking), Mold, numerical simulation, Tearing, medicine, Linear contraction, General Materials Science, Composite material, 0210 nano-technology, lcsh:Mining engineering. Metallurgy
Abstract

The linear contraction and hot tearing behavior of 9Cr3Co3W heat-resistant steel during solidification were studied by combining in-situ casting experiments and the corresponding numerical simulations. The linear contraction, stress and temperature of the samples were detected during solidification. The results show that the linear contraction of 9Cr3Co3W heat-resistant steel was 1.7% in the sand mold. Meanwhile, the critical temperature and stress of the hot tearing that occurred at the hot spot were 1304 &deg, C and 0.72 MPa, respectively. The occurrence of hot tearing was observed at the tip of secondary dendrites along the primary dendritic arms, where the solute elements were significantly enriched at the final solidification.

Published
2018

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