@article {8666, title = {Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.}, journal = {Nature}, volume = {590}, year = {2021}, month = {2021 02}, pages = {290-299}, abstract = {

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400~million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400~million detected variants, 97\% have frequencies of less than 1\% and 46\% are singletons that are present in only one individual (53\% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01\%.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-03205-y}, author = {Taliun, Daniel and Harris, Daniel N and Kessler, Michael D and Carlson, Jedidiah and Szpiech, Zachary A and Torres, Raul and Taliun, Sarah A Gagliano and Corvelo, Andr{\'e} and Gogarten, Stephanie M and Kang, Hyun Min and Pitsillides, Achilleas N and LeFaive, Jonathon and Lee, Seung-Been and Tian, Xiaowen and Browning, Brian L and Das, Sayantan and Emde, Anne-Katrin and Clarke, Wayne E and Loesch, Douglas P and Shetty, Amol C and Blackwell, Thomas W and Smith, Albert V and Wong, Quenna and Liu, Xiaoming and Conomos, Matthew P and Bobo, Dean M and Aguet, Francois and Albert, Christine and Alonso, Alvaro and Ardlie, Kristin G and Arking, Dan E and Aslibekyan, Stella and Auer, Paul L and Barnard, John and Barr, R Graham and Barwick, Lucas and Becker, Lewis C and Beer, Rebecca L and Benjamin, Emelia J and Bielak, Lawrence F and Blangero, John and Boehnke, Michael and Bowden, Donald W and Brody, Jennifer A and Burchard, Esteban G and Cade, Brian E and Casella, James F and Chalazan, Brandon and Chasman, Daniel I and Chen, Yii-Der Ida and Cho, Michael H and Choi, Seung Hoan and Chung, Mina K and Clish, Clary B and Correa, Adolfo and Curran, Joanne E and Custer, Brian and Darbar, Dawood and Daya, Michelle and de Andrade, Mariza and DeMeo, Dawn L and Dutcher, Susan K and Ellinor, Patrick T and Emery, Leslie S and Eng, Celeste and Fatkin, Diane and Fingerlin, Tasha and Forer, Lukas and Fornage, Myriam and Franceschini, Nora and Fuchsberger, Christian and Fullerton, Stephanie M and Germer, Soren and Gladwin, Mark T and Gottlieb, Daniel J and Guo, Xiuqing and Hall, Michael E and He, Jiang and Heard-Costa, Nancy L and Heckbert, Susan R and Irvin, Marguerite R and Johnsen, Jill M and Johnson, Andrew D and Kaplan, Robert and Kardia, Sharon L R and Kelly, Tanika and Kelly, Shannon and Kenny, Eimear E and Kiel, Douglas P and Klemmer, Robert and Konkle, Barbara A and Kooperberg, Charles and K{\"o}ttgen, Anna and Lange, Leslie A and Lasky-Su, Jessica and Levy, Daniel and Lin, Xihong and Lin, Keng-Han and Liu, Chunyu and Loos, Ruth J F and Garman, Lori and Gerszten, Robert and Lubitz, Steven A and Lunetta, Kathryn L and Mak, Angel C Y and Manichaikul, Ani and Manning, Alisa K and Mathias, Rasika A and McManus, David D and McGarvey, Stephen T and Meigs, James B and Meyers, Deborah A and Mikulla, Julie L and Minear, Mollie A and Mitchell, Braxton D and Mohanty, Sanghamitra and Montasser, May E and Montgomery, Courtney and Morrison, Alanna C and Murabito, Joanne M and Natale, Andrea and Natarajan, Pradeep and Nelson, Sarah C and North, Kari E and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Pankratz, Nathan and Peloso, Gina M and Peyser, Patricia A and Pleiness, Jacob and Post, Wendy S and Psaty, Bruce M and Rao, D C and Redline, Susan and Reiner, Alexander P and Roden, Dan and Rotter, Jerome I and Ruczinski, Ingo and Sarnowski, Chloe and Schoenherr, Sebastian and Schwartz, David A and Seo, Jeong-Sun and Seshadri, Sudha and Sheehan, Vivien A and Sheu, Wayne H and Shoemaker, M Benjamin and Smith, Nicholas L and Smith, Jennifer A and Sotoodehnia, Nona and Stilp, Adrienne M and Tang, Weihong and Taylor, Kent D and Telen, Marilyn and Thornton, Timothy A and Tracy, Russell P and Van Den Berg, David J and Vasan, Ramachandran S and Viaud-Martinez, Karine A and Vrieze, Scott and Weeks, Daniel E and Weir, Bruce S and Weiss, Scott T and Weng, Lu-Chen and Willer, Cristen J and Zhang, Yingze and Zhao, Xutong and Arnett, Donna K and Ashley-Koch, Allison E and Barnes, Kathleen C and Boerwinkle, Eric and Gabriel, Stacey and Gibbs, Richard and Rice, Kenneth M and Rich, Stephen S and Silverman, Edwin K and Qasba, Pankaj and Gan, Weiniu and Papanicolaou, George J and Nickerson, Deborah A and Browning, Sharon R and Zody, Michael C and Z{\"o}llner, Sebastian and Wilson, James G and Cupples, L Adrienne and Laurie, Cathy C and Jaquish, Cashell E and Hernandez, Ryan D and O{\textquoteright}Connor, Timothy D and Abecasis, Goncalo R} } @article {8975, title = {Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.}, journal = {Am J Hum Genet}, volume = {109}, year = {2022}, month = {2022 01 06}, pages = {81-96}, abstract = {

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1\%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

}, keywords = {Alleles, Blood Glucose, Case-Control Studies, Computational Biology, Databases, Genetic, Diabetes Mellitus, Type 2, Exome, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Lipid Metabolism, Lipids, Liver, Molecular Sequence Annotation, Multifactorial Inheritance, Open Reading Frames, Phenotype, Polymorphism, Single Nucleotide}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.11.021}, author = {Hindy, George and Dornbos, Peter and Chaffin, Mark D and Liu, Dajiang J and Wang, Minxian and Selvaraj, Margaret Sunitha and Zhang, David and Park, Joseph and Aguilar-Salinas, Carlos A and Antonacci-Fulton, Lucinda and Ardissino, Diego and Arnett, Donna K and Aslibekyan, Stella and Atzmon, Gil and Ballantyne, Christie M and Barajas-Olmos, Francisco and Barzilai, Nir and Becker, Lewis C and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bonnycastle, Lori L and Bottinger, Erwin and Bowden, Donald W and Bown, Matthew J and Brody, Jennifer A and Broome, Jai G and Burtt, Noel P and Cade, Brian E and Centeno-Cruz, Federico and Chan, Edmund and Chang, Yi-Cheng and Chen, Yii-der I and Cheng, Ching-Yu and Choi, Won Jung and Chowdhury, Rajiv and Contreras-Cubas, Cecilia and C{\'o}rdova, Emilio J and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Danesh, John and de Vries, Paul S and DeFronzo, Ralph A and Doddapaneni, Harsha and Duggirala, Ravindranath and Dutcher, Susan K and Ellinor, Patrick T and Emery, Leslie S and Florez, Jose C and Fornage, Myriam and Freedman, Barry I and Fuster, Valentin and Garay-Sevilla, Ma Eugenia and Garc{\'\i}a-Ortiz, Humberto and Germer, Soren and Gibbs, Richard A and Gieger, Christian and Glaser, Benjamin and Gonzalez, Clicerio and Gonzalez-Villalpando, Maria Elena and Graff, Mariaelisa and Graham, Sarah E and Grarup, Niels and Groop, Leif C and Guo, Xiuqing and Gupta, Namrata and Han, Sohee and Hanis, Craig L and Hansen, Torben and He, Jiang and Heard-Costa, Nancy L and Hung, Yi-Jen and Hwang, Mi Yeong and Irvin, Marguerite R and Islas-Andrade, Sergio and Jarvik, Gail P and Kang, Hyun Min and Kardia, Sharon L R and Kelly, Tanika and Kenny, Eimear E and Khan, Alyna T and Kim, Bong-Jo and Kim, Ryan W and Kim, Young Jin and Koistinen, Heikki A and Kooperberg, Charles and Kuusisto, Johanna and Kwak, Soo Heon and Laakso, Markku and Lange, Leslie A and Lee, Jiwon and Lee, Juyoung and Lee, Seonwook and Lehman, Donna M and Lemaitre, Rozenn N and Linneberg, Allan and Liu, Jianjun and Loos, Ruth J F and Lubitz, Steven A and Lyssenko, Valeriya and Ma, Ronald C W and Martin, Lisa Warsinger and Mart{\'\i}nez-Hern{\'a}ndez, Ang{\'e}lica and Mathias, Rasika A and McGarvey, Stephen T and McPherson, Ruth and Meigs, James B and Meitinger, Thomas and Melander, Olle and Mendoza-Caamal, Elvia and Metcalf, Ginger A and Mi, Xuenan and Mohlke, Karen L and Montasser, May E and Moon, Jee-Young and Moreno-Macias, Hortensia and Morrison, Alanna C and Muzny, Donna M and Nelson, Sarah C and Nilsson, Peter M and O{\textquoteright}Connell, Jeffrey R and Orho-Melander, Marju and Orozco, Lorena and Palmer, Colin N A and Palmer, Nicholette D and Park, Cheol Joo and Park, Kyong Soo and Pedersen, Oluf and Peralta, Juan M and Peyser, Patricia A and Post, Wendy S and Preuss, Michael and Psaty, Bruce M and Qi, Qibin and Rao, D C and Redline, Susan and Reiner, Alexander P and Revilla-Monsalve, Cristina and Rich, Stephen S and Samani, Nilesh and Schunkert, Heribert and Schurmann, Claudia and Seo, Daekwan and Seo, Jeong-Sun and Sim, Xueling and Sladek, Rob and Small, Kerrin S and So, Wing Yee and Stilp, Adrienne M and Tai, E Shyong and Tam, Claudia H T and Taylor, Kent D and Teo, Yik Ying and Thameem, Farook and Tomlinson, Brian and Tsai, Michael Y and Tuomi, Tiinamaija and Tuomilehto, Jaakko and Tusi{\'e}-Luna, Teresa and Udler, Miriam S and van Dam, Rob M and Vasan, Ramachandran S and Viaud Martinez, Karine A and Wang, Fei Fei and Wang, Xuzhi and Watkins, Hugh and Weeks, Daniel E and Wilson, James G and Witte, Daniel R and Wong, Tien-Yin and Yanek, Lisa R and Kathiresan, Sekar and Rader, Daniel J and Rotter, Jerome I and Boehnke, Michael and McCarthy, Mark I and Willer, Cristen J and Natarajan, Pradeep and Flannick, Jason A and Khera, Amit V and Peloso, Gina M} } @article {9379, title = {Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 Jun 02}, pages = {3202}, abstract = {

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

}, keywords = {Blood Pressure, Ethnicity, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Population Health, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-023-38990-9}, author = {Kurniansyah, Nuzulul and Goodman, Matthew O and Khan, Alyna T and Wang, Jiongming and Feofanova, Elena and Bis, Joshua C and Wiggins, Kerri L and Huffman, Jennifer E and Kelly, Tanika and Elfassy, Tali and Guo, Xiuqing and Palmas, Walter and Lin, Henry J and Hwang, Shih-Jen and Gao, Yan and Young, Kendra and Kinney, Gregory L and Smith, Jennifer A and Yu, Bing and Liu, Simin and Wassertheil-Smoller, Sylvia and Manson, JoAnn E and Zhu, Xiaofeng and Chen, Yii-Der Ida and Lee, I-Te and Gu, C Charles and Lloyd-Jones, Donald M and Z{\"o}llner, Sebastian and Fornage, Myriam and Kooperberg, Charles and Correa, Adolfo and Psaty, Bruce M and Arnett, Donna K and Isasi, Carmen R and Rich, Stephen S and Kaplan, Robert C and Redline, Susan and Mitchell, Braxton D and Franceschini, Nora and Levy, Daniel and Rotter, Jerome I and Morrison, Alanna C and Sofer, Tamar} } @article {9586, title = {Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.}, journal = {medRxiv}, year = {2023}, month = {2023 Dec 14}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1\% to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8\% to 5.1\% (SBP) and 4.7\% to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

}, doi = {10.1101/2023.12.13.23299909}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchel, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Der Ida Chen, Yii and Sofer, Tamar} } @article {9543, title = {A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.}, journal = {bioRxiv}, year = {2023}, month = {2023 Nov 02}, 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 and an intergenic region on chromosome 1.

}, doi = {10.1101/2023.10.30.564764}, author = {Li, Xihao and Chen, Han and Selvaraj, Margaret Sunitha and Van Buren, Eric and Zhou, Hufeng and Wang, Yuxuan and Sun, Ryan and McCaw, Zachary R and Yu, Zhi and Arnett, Donna K and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Carson, April P and Carlson, Jenna C and Chami, Nathalie and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Fornage, Myriam and Franceschini, Nora and Freedman, Barry I and Gu, Charles and Heard-Costa, Nancy L and He, Jiang and Hou, Lifang and Hung, Yi-Jen and Irvin, Marguerite R and Kaplan, Robert C and Kardia, Sharon L R and Kelly, Tanika and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Minster, Ryan L and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura M and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Tiwari, Hemant and Vasan, Ramachandran S and Wang, Zhe and Yanek, Lisa R and Yu, Bing and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong} } @article {9702, title = {Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 May 30}, pages = {12436}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1\% (SBP) and 4.7 to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

}, keywords = {Blood Pressure, Female, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Humans, Hypertension, Machine Learning, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2045-2322}, doi = {10.1038/s41598-024-62945-9}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchell, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Ida Chen, Yii-Der and Sofer, Tamar} }