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Meta-Analysis
. 2015 Mar 5;96(3):487-97.
doi: 10.1016/j.ajhg.2015.01.011.

Characterization of large structural genetic mosaicism in human autosomes

Mitchell J Machiela  1 , Weiyin Zhou  2 , Joshua N Sampson  1 , Michael C Dean  3 , Kevin B Jacobs  4 , Amanda Black  1 , Louise A Brinton  1 , I-Shou Chang  5 , Chu Chen  6 , Constance Chen  7 , Kexin Chen  8 , Linda S Cook  9 , Marta Crous Bou  10 , Immaculata De Vivo  10 , Jennifer Doherty  11 , Christine M Friedenreich  12 , Mia M Gaudet  13 , Christopher A Haiman  14 , Susan E Hankinson  15 , Patricia Hartge  1 , Brian E Henderson  14 , Yun-Chul Hong  16 , H Dean Hosgood 3rd  17 , Chao A Hsiung  18 , Wei Hu  1 , David J Hunter  19 , Lea Jessop  1 , Hee Nam Kim  20 , Yeul Hong Kim  21 , Young Tae Kim  22 , Robert Klein  23 , Peter Kraft  7 , Qing Lan  1 , Dongxin Lin  24 , Jianjun Liu  25 , Loic Le Marchand  26 , Xiaolin Liang  27 , Jolanta Lissowska  28 , Lingeng Lu  29 , Anthony M Magliocco  30 , Keitaro Matsuo  31 , Sara H Olson  27 , Irene Orlow  27 , Jae Yong Park  32 , Loreall Pooler  33 , Jennifer Prescott  10 , Radhai Rastogi  27 , Harvey A Risch  29 , Fredrick Schumacher  14 , Adeline Seow  34 , Veronica Wendy Setiawan  14 , Hongbing Shen  35 , Xin Sheng  33 , Min-Ho Shin  36 , Xiao-Ou Shu  37 , David VanDen Berg  14 , Jiu-Cun Wang  38 , Nicolas Wentzensen  1 , Maria Pik Wong  39 , Chen Wu  24 , Tangchun Wu  40 , Yi-Long Wu  41 , Lucy Xia  33 , Hannah P Yang  1 , Pan-Chyr Yang  42 , Wei Zheng  43 , Baosen Zhou  44 , Christian C Abnet  1 , Demetrius Albanes  1 , Melinda C Aldrich  45 , Christopher Amos  46 , Laufey T Amundadottir  1 , Sonja I Berndt  1 , William J Blot  47 , Cathryn H Bock  48 , Paige M Bracci  49 , Laurie Burdett  2 , Julie E Buring  50 , Mary A Butler  51 , Tania Carreón  51 , Nilanjan Chatterjee  1 , Charles C Chung  2 , Michael B Cook  1 , Michael Cullen  2 , Faith G Davis  52 , Ti Ding  53 , Eric J Duell  54 , Caroline G Epstein  1 , Jin-Hu Fan  55 , Jonine D Figueroa  1 , Joseph F Fraumeni Jr  1 , Neal D Freedman  1 , Charles S Fuchs  56 , Yu-Tang Gao  57 , Susan M Gapstur  13 , Ana Patiño-Garcia  58 , Montserrat Garcia-Closas  59 , J Michael Gaziano  60 , Graham G Giles  61 , Elizabeth M Gillanders  62 , Edward L Giovannucci  63 , Lynn Goldin  1 , Alisa M Goldstein  1 , Mark H Greene  1 , Goran Hallmans  64 , Curtis C Harris  65 , Roger Henriksson  66 , Elizabeth A Holly  49 , Robert N Hoover  1 , Nan Hu  1 , Amy Hutchinson  2 , Mazda Jenab  67 , Christoffer Johansen  68 , Kay-Tee Khaw  69 , Woon-Puay Koh  70 , Laurence N Kolonel  26 , Charles Kooperberg  6 , Vittorio Krogh  71 , Robert C Kurtz  72 , Andrea LaCroix  6 , Annelie Landgren  1 , Maria Teresa Landi  1 , Donghui Li  73 , Linda M Liao  1 , Nuria Malats  74 , Katherine A McGlynn  1 , Lorna H McNeill  75 , Robert R McWilliams  76 , Beatrice S Melin  66 , Lisa Mirabello  1 , Beata Peplonska  77 , Ulrike Peters  6 , Gloria M Petersen  78 , Ludmila Prokunina-Olsson  1 , Mark Purdue  1 , You-Lin Qiao  79 , Kari G Rabe  78 , Preetha Rajaraman  1 , Francisco X Real  80 , Elio Riboli  81 , Benjamín Rodríguez-Santiago  82 , Nathaniel Rothman  1 , Avima M Ruder  51 , Sharon A Savage  1 , Ann G Schwartz  48 , Kendra L Schwartz  83 , Howard D Sesso  50 , Gianluca Severi  84 , Debra T Silverman  1 , Margaret R Spitz  85 , Victoria L Stevens  13 , Rachael Stolzenberg-Solomon  1 , Daniel Stram  14 , Ze-Zhong Tang  53 , Philip R Taylor  1 , Lauren R Teras  13 , Geoffrey S Tobias  1 , Kala Viswanathan  86 , Sholom Wacholder  1 , Zhaoming Wang  2 , Stephanie J Weinstein  1 , William Wheeler  87 , Emily White  6 , John K Wiencke  88 , Brian M Wolpin  56 , Xifeng Wu  89 , Jay S Wunder  90 , Kai Yu  1 , Krista A Zanetti  62 , Anne Zeleniuch-Jacquotte  91 , Regina G Ziegler  1 , Mariza de Andrade  78 , Kathleen C Barnes  92 , Terri H Beaty  86 , Laura J Bierut  93 , Karl C Desch  94 , Kimberly F Doheny  95 , Bjarke Feenstra  96 , David Ginsburg  97 , John A Heit  98 , Jae H Kang  99 , Cecilia A Laurie  100 , Jun Z Li  101 , William L Lowe  102 , Mary L Marazita  103 , Mads Melbye  104 , Daniel B Mirel  105 , Jeffrey C Murray  106 , Sarah C Nelson  100 , Louis R Pasquale  107 , Kenneth Rice  100 , Janey L Wiggs  108 , Anastasia Wise  109 , Margaret Tucker  1 , Luis A Pérez-Jurado  110 , Cathy C Laurie  100 , Neil E Caporaso  1 , Meredith Yeager  2 , Stephen J Chanock  111
Affiliations
Meta-Analysis

Characterization of large structural genetic mosaicism in human autosomes

Mitchell J Machiela et al. Am J Hum Genet. .

Abstract

Analyses of genome-wide association study (GWAS) data have revealed that detectable genetic mosaicism involving large (>2 Mb) structural autosomal alterations occurs in a fraction of individuals. We present results for a set of 24,849 genotyped individuals (total GWAS set II [TGSII]) in whom 341 large autosomal abnormalities were observed in 168 (0.68%) individuals. Merging data from the new TGSII set with data from two prior reports (the Gene-Environment Association Studies and the total GWAS set I) generated a large dataset of 127,179 individuals; we then conducted a meta-analysis to investigate the patterns of detectable autosomal mosaicism (n = 1,315 events in 925 [0.73%] individuals). Restricting to events >2 Mb in size, we observed an increase in event frequency as event size decreased. The combined results underscore that the rate of detectable mosaicism increases with age (p value = 5.5 × 10(-31)) and is higher in men (p value = 0.002) but lower in participants of African ancestry (p value = 0.003). In a subset of 47 individuals from whom serial samples were collected up to 6 years apart, complex changes were noted over time and showed an overall increase in the proportion of mosaic cells as age increased. Our large combined sample allowed for a unique ability to characterize detectable genetic mosaicism involving large structural events and strengthens the emerging evidence of non-random erosion of the genome in the aging population.

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Figures

Figure 1
Figure 1
Genomic Locations of the Combined 1,334 Events Overall and by Cancer Status Green indicates mosaic copy gains, blue represents mosaic copy-neutral events, and red represents mosaic losses. (A) All 1,334 events from the combined GENEVA, TGSI, and TGSII analysis. (B) Events in cancer-free control individuals. (C) Events in individuals with solid tumors.
Figure 2
Figure 2
Proportion of Mosaic Individuals across 5-Year Age Groups in the Combined GENEVA, TGSI, and TGSII Dataset by Cancer Status Affected individuals are in red, and cancer-free control individuals are in blue. Error bars represent 95% CIs. An overall significant relationship in the proportion of individuals with mosaic events was observed with age (p value = 1.1×10−30).
Figure 3
Figure 3
Forest Plots of Associations with Clonal Mosaicism Associations between clonal mosaicism and (A) age-group, (B) gender, and (C) ancestry.
Figure 4
Figure 4
Relationship between Mosaic Event Size and Detected Rate Counts of detected mosaic events from the combined analysis are plotted in 10-Mb bin sizes. An inverse exponential trend, represented by the dotted line, was fit to the counts (R2 = 0.81). Fitted coefficients (β) and 95% CIs are displayed in the plot.
Figure 5
Figure 5
Event Size and Mosaic-Proportion Distribution across Copy-Number State Violin plots of combined sample event size (A) and mosaic proportion (B) in relationship to mosaic copy gains, copy-neutral uniparental disomies, and copy losses. Boxplots with white circles denoting the median and thick black boxes representing the interquartile range are encapsulated in kernel density plots of the distribution of event length. Numbers below and above the violin plots of mosaic proportion (B) indicate the minimum and maximum detected range observed for each respective event’s copy-number state.
Figure 6
Figure 6
Characterization of Changes in Mosaic Proportion with Age Mosaic proportion is stratified across event copy-number state (columns) and age category (x axis) from the combined analysis. Points represent estimates of the mean, and error bars indicate 95% CI around the mean. Best-fit regression lines are plotted with dotted lines, and p values are shown for slopes that are significantly different from 0.
Figure 7
Figure 7
Changes in Percentage of Mosaicism of Serial Samples over Time (A) Lines (red for copy loss, blue for copy neutral, and green for copy gain) connect DNA-collection time points (triangles represent males, and circles represent females) for each mosaic chromosome (n = 58) to track changes in the percentage of mosaicism with increasing age. (B) Events are plotted with zero origin and a mixed model for a subject with zero random-effect fit (solid black line), showing the estimated average change in mosaic proportion per year (1.44%, p value = 3.3 × 10−7).
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