Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height

Hawkes, Gareth; Beaumont, Robin N; Li, Zilin; Mandla, Ravi; Li, Xihao; Albert, Christine M.; Arnett, Donna K.; Ashley-Koch, Allison E.; Ashrani, Aneel A.; Barnes, Kathleen C.; Boerwinkle, Eric; Brody, Jennifer A.; Carson, April P.; Chami, Nathalie; Chen, Yii-Der Ida; Chung, Mina K.; Curran, Joanne E.; Darbar, Dawood; Ellinor, Patrick T.; Fornage, Myrian; Gordeuk, Victor R.; Guo, Xiuqing; He, Jiang; Hwu, Chii-Min; Kalyani, Rita R.; Kaplan, Robert; Kardia, Sharon L.R.; Kooperberg, Charles; Loos, Ruth J.F.; Lubitz, Steven A.; Minster, Ryan L.; Mitchell, Braxton D.; Murabito, Joanne M.; Palmer, Nicholette D.; Psaty, Bruce M.; Redline, Susan; Benjamin Shoemaker, M.; Silverman, Edwin K.; Telen, Marilyn J.; Weiss, Scott T.; Yanek, Lisa R.; Zhou, Hufeng; ,; Liu, Ching-Ti; North, Kari E.; Justice, Anne E.; Locke, Jon; Owens, Nick; Murray, Anna; Patel, Kashyap; Frayling, Timothy M.; Wright, Caroline F.; Wood, Andrew R.; Lin, Xihong; Manning, Alisa; Weedon, Michael N.

doi:10.1101/2023.11.19.566520

Cited by 6 publications

(8 citation statements)

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“…Here, we leaned on CADD scores to focus on potentially deleterious variants. This genomic score is widely used in both coding and non-coding rare variant analysis 17,38,54 and here allows us to compare coding and non-coding association results within the same framework. However, exploring other scores such as ReMM, GERP and Eigen [55][56][57] for non-coding regions and AlphaMissense 58 for coding regions, constitute an open avenue of research, with deleteriousness or pathogenicity models continuing to be developed.…”

Section: Discussionmentioning

confidence: 99%

“…A few previous studies have proposed approaches to associate non-coding rare variants by aggregating variants in genomic sliding windows, functional annotations (e.g. enhancers) [47][48][49] and/or variant deleteriousness regions 17,38 . However, these approaches have been applied to only a limited number of traits (usually fewer than 10) or used smaller sample sizes (<35 000 samples).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, exonic regions represent only ~2% of the human genome, whereas up to 88% variants associated with complex traits and diseases through GWAS were found in the non-coding regulatory regions of the genome 14,15 . Yet, large-scale studies associating non-coding rare variation with phenotypes are scarce 16,17 . This is largely due to the requirement of WGS data in large cohorts and the challenging interpretation of non-coding variant effects 18 .…”

Section: Introductionmentioning

confidence: 99%

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Non-coding rare variant associations with blood traits on 166 740 UK Biobank genomes

Ribeiro,

Delaneau

2023

Preprint

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Large biobanks with whole-genome sequencing now enable the association of non-coding rare variants with complex human traits. Given that >98% of the genome is available for exploration, the selection of non-coding variants remains a critical yet unresolved challenge in these analyses. Here, we leverage knowledge of blood gene regulation and deleteriousness scores to select non-coding variants pertinent for association with blood-related traits. We leverage whole genome sequencing and 59 blood cell count and biomarker measurements for 166 740 UK Biobank samples to perform variant collapsing tests. We identified hundreds of gene-trait associations involving non-coding variants across the 59 traits. However, we demonstrate that the majority of these non-coding rare variant associations (i) reproduce associations known from common variant studies and (ii) are driven by linkage disequilibrium between nearby common and rare variants. This study underscores the prevailing challenges in rare variant analysis and the need for caution when interpreting non-coding rare variant association results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-coding rare variant associations with blood traits on 166 740 UK Biobank genomes

Ribeiro,

Delaneau

2023

Preprint

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“…UKB samples also underwent whole exome sequencing 7 (WES), which allows for characterization of the 2-3% exonic portion of the genome but omits nearly all non-coding variation and is limited in the detection of structural variants. Rare non-coding variation is known to contribute to human diseases and complex traits, although this remains relatively understudied 8–10 . This large-scale, deeply phenotyped WGS dataset brings enormous potential to expand our understanding of the role of rare non-coding variation in health and disease.…”

Section: Main Textmentioning

confidence: 99%

Whole-genome sequencing of half-a-million UK Biobank participants

Li,

Carss,

Halldorsson

et al. 2023

Preprint

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Summary ParagraphWhole-genome sequencing (WGS) provides a comprehensive view of the genome, enabling detection of coding and non-coding genetic variation, and surveying complex regions which are difficult to genotype. Here, we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping1and whole-exome sequencing (WES) efforts2,3. This advance deepens our understanding of how genetics influences disease biology and further enhances the value of this open resource for the study of human biology and health. Coupling this dataset with rich phenotypic data, we surveyed within- and cross-ancestry genomic associations with health-related phenotypes and identified novel genetic and clinical insights. While most genome-wide significant associations with disease traits were primarily observed in Europeans, we also identified strong or novel signals in individuals of African and Asian ancestries. Deeper capture of exonic variation in both coding and UTR sequences, strengthened and surfaced novel insights relative to WES analyses. This landmark dataset, representing the largest collection of WGS and available to the UK Biobank research community, will enable advances into our understanding of the human genome, and facilitate the discovery of new diagnostics, therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health.Abstract FigureGraphic summary.Framework of the WGS UKB study. This figure captures the flow of this manuscript. We start with the collection of patient samples by UK Biobank and followed by the strategy taken to perform WGS. We continue with quality control performed on GraphTyper and DRAGEN datasets, followed by variant calling of SNPs, in/dels, and structural variants (SV). Thereafter we defined the phenotypes (binary and quantitative) associated with SV, SNPs and at the gene level (rare variant analysis) and conclude with the definition of five ancestry groups and collective association effect as a cross-ancestry meta-analysis.

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“…Hawkes et al performed a whole genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank, TOPMed and All of Us. They identified non-coding sequences proximal to HMGA1 containing variants associated with a 4.83 cm taller height [4]. In the current study, we used UK Biobank data to examine the relationship of HMGA1 to height, risk, and prognosis of women with breast cancer.…”

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confidence: 99%

Tall women with breast cancer have poorer survival than short women

Lehrer,

Rheinstein

2024

Preprint

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BackgroundTall women are more likely to develop breast cancer (BC). High Mobility Group AT-Hook 1(HMGA1), an oncofetal protein, plays a role in the progression of breast cancer. Non-coding sequences proximal to HMGA1 contain variants associated with 4.83 cm taller height. In the current study, we used UK Biobank data to examine the relationship of HMGA1 to height, risk, and prognosis of women with breast cancer.MethodsOur analysis included all subjects with invasive BC that occurred either before or after participant enrollment and were recorded in the UK Biobank database using self-reported data and the International Classification of Diseases (ICD10, ICD9). We divided the subjects into three previously described three height groups: Short (< 155 cm), Medium (155 cm to 175 cm), Tall (> 175 cm). We analyzed the HMGA1 SNP rs41269028, a single nucleotide intron variant, C > T, minor allele frequency 0.044. SNP rs41269028 was previously evaluated in subjects with diabetes.ResultsHeight of 9583 women with BC homozygous for the HMGA1 SNP rs41269028 major allele was 162.29 cm ± 6.18. Height of 944 women with BC who were carriers or homozygotes (CT + TT) of the minor allele T was 162.88 cm ± 6.001. This difference was significant (p = 0.005). The effect of height group on survival was significant (p = 0.032, log rank test). Tall women had the poorest survival. The effect of HMGA1 SNP rs41269028 genotype on BC risk (p = 0.602) and survival (p = 0.439, log rank test) was insignificant.ConclusionWe conclude that HMGA1 influences height, but we were unable to demonstrate that HMGA1 is related to increased incidence or poor prognosis of tall women with breast cancer. We did find that tall women with breast cancer have poorer survival than short women. Our finding that tall women have a worse prognosis is important because it could help the oncologist decide, along with other prognostic factors, whether adjuvant therapy is warranted.

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Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height

Cited by 6 publications

References 58 publications

Non-coding rare variant associations with blood traits on 166 740 UK Biobank genomes

Non-coding rare variant associations with blood traits on 166 740 UK Biobank genomes

Whole-genome sequencing of half-a-million UK Biobank participants

Tall women with breast cancer have poorer survival than short women

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