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Meta-Analysis
. 2023 Jan 26;15(1):7.
doi: 10.1186/s13073-022-01152-5.

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Stefanie H Mueller  1 Alvina G Lai  1 Maria Valkovskaya  2 Kyriaki Michailidou  3   4   5 Manjeet K Bolla  5 Qin Wang  5 Joe Dennis  5 Michael Lush  5 Zomoruda Abu-Ful  6 Thomas U Ahearn  7 Irene L Andrulis  8   9 Hoda Anton-Culver  10 Natalia N Antonenkova  11 Volker Arndt  12 Kristan J Aronson  13 Annelie Augustinsson  14 Thais Baert  15 Laura E Beane Freeman  7 Matthias W Beckmann  16 Sabine Behrens  17 Javier Benitez  18   19 Marina Bermisheva  20 Carl Blomqvist  21   22 Natalia V Bogdanova  11   23   24 Stig E Bojesen  25   26   27 Bernardo Bonanni  28 Hermann Brenner  12   29   30 Sara Y Brucker  31 Saundra S Buys  32 Jose E Castelao  33 Tsun L Chan  34   35 Jenny Chang-Claude  17   36 Stephen J Chanock  7 Ji-Yeob Choi  37   38   39 Wendy K Chung  40 NBCS CollaboratorsSarah V Colonna  32 CTS ConsortiumSten Cornelissen  41 Fergus J Couch  42 Kamila Czene  43 Mary B Daly  44 Peter Devilee  45   46 Thilo Dörk  24 Laure Dossus  47 Miriam Dwek  48 Diana M Eccles  49 Arif B Ekici  50 A Heather Eliassen  51   52   53 Christoph Engel  54   55 D Gareth Evans  56   57 Peter A Fasching  16   58 Olivia Fletcher  59 Henrik Flyger  60 Manuela Gago-Dominguez  61   62 Yu-Tang Gao  63 Montserrat García-Closas  7 José A García-Sáenz  64 Jeanine Genkinger  65 Aleksandra Gentry-Maharaj  66 Felix Grassmann  43   67 Pascal Guénel  68 Melanie Gündert  69   70   71 Lothar Haeberle  16 Eric Hahnen  72   73 Christopher A Haiman  74 Niclas Håkansson  75 Per Hall  43   76 Elaine F Harkness  77   78   79 Patricia A Harrington  80 Jaana M Hartikainen  81   82 Mikael Hartman  83   84 Alexander Hein  16 Weang-Kee Ho  85   86 Maartje J Hooning  87 Reiner Hoppe  88   89 John L Hopper  90 Richard S Houlston  91 Anthony Howell  92 David J Hunter  52   93 Dezheng Huo  94 ABCTB InvestigatorsHidemi Ito  95   96 Motoki Iwasaki  97 Anna Jakubowska  98   99 Wolfgang Janni  100 Esther M John  101   102 Michael E Jones  91 Audrey Jung  17 Rudolf Kaaks  17 Daehee Kang  38   103 Elza K Khusnutdinova  20   104 Sung-Won Kim  105 Cari M Kitahara  106 Stella Koutros  7 Peter Kraft  52   107 Vessela N Kristensen  108   109 Katerina Kubelka-Sabit  110 Allison W Kurian  101   102 Ava Kwong  34   111   112 James V Lacey  113   114 Diether Lambrechts  115   116 Loic Le Marchand  117 Jingmei Li  118 Martha Linet  106 Wing-Yee Lo  88   89 Jirong Long  119 Artitaya Lophatananon  120 Arto Mannermaa  81   82   121 Mehdi Manoochehri  122 Sara Margolin  76   123 Keitaro Matsuo  96   124 Dimitrios Mavroudis  125 Usha Menon  66 Kenneth Muir  120 Rachel A Murphy  126   127 Heli Nevanlinna  128 William G Newman  56   57 Dieter Niederacher  129 Katie M O'Brien  130 Nadia Obi  131 Kenneth Offit  132   133 Olufunmilayo I Olopade  94 Andrew F Olshan  134 Håkan Olsson  14 Sue K Park  38   103   135 Alpa V Patel  136 Achal Patel  134 Charles M Perou  137 Julian Peto  138 Paul D P Pharoah  5   80 Dijana Plaseska-Karanfilska  139 Nadege Presneau  48 Brigitte Rack  100 Paolo Radice  140 Dhanya Ramachandran  24 Muhammad U Rashid  122   141 Gad Rennert  6 Atocha Romero  142 Kathryn J Ruddy  143 Matthias Ruebner  16 Emmanouil Saloustros  144 Dale P Sandler  130 Elinor J Sawyer  145 Marjanka K Schmidt  41   146 Rita K Schmutzler  72   73   147 Michael O Schneider  16 Christopher Scott  148 Mitul Shah  80 Priyanka Sharma  149 Chen-Yang Shen  150   151 Xiao-Ou Shu  119 Jacques Simard  152 Harald Surowy  69   70 Rulla M Tamimi  52   153 William J Tapper  49 Jack A Taylor  130   154 Soo Hwang Teo  86   155 Lauren R Teras  136 Amanda E Toland  156 Rob A E M Tollenaar  157 Diana Torres  122   158 Gabriela Torres-Mejía  159 Melissa A Troester  134 Thérèse Truong  68 Celine M Vachon  160 Joseph Vijai  132   133 Clarice R Weinberg  161 Camilla Wendt  123 Robert Winqvist  162   163 Alicja Wolk  75   164 Anna H Wu  74 Taiki Yamaji  97 Xiaohong R Yang  7 Jyh-Cherng Yu  165 Wei Zheng  119 Argyrios Ziogas  10 Elad Ziv  166 Alison M Dunning  80 Douglas F Easton  5   80 Harry Hemingway  1   167   168   169 Ute Hamann #  122 Karoline B Kuchenbaecker #  170   171
Collaborators, Affiliations
Meta-Analysis

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Stefanie H Mueller et al. Genome Med. .

Abstract

Background: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes.

Methods: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry.

Results: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C.

Conclusions: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.

Keywords: Breast cancer susceptibility; Diverse ancestry; Gene regulation; Genome-wide association study; Rare variants.

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Conflict of interest statement

S.H.M. became an employee of Boehringer Ingelheim after completing this study. Matthias W. Beckmann conducts research funded by Amgen, Novartis, and Pfizer. P.A.F. conducts research funded by Amgen, Novartis, and Pfizer. He received Honoraria from Roche, Novartis, and Pfizer. R.A.M. is a consultant for Pharmavite. A.W.K. received research funding from Myriad Genetics for an unrelated project (funding dates 2017–2019).

Figures

Fig. 1
Fig. 1
Study design. A Breast cancer patients and control individuals included in this study originate from 33 different study center countries, and comprise samples of African, Asian, European, or Latin American and Hispanic ancestry. B The mummy implemented extended SKAT-O analysis includes variants located in coding regions with an extended window and variants located in linked regulatory regions. Regulatory regions were identified based on overlap with genetic range of coding features or based on presence of gene-specific eQTLs in GTEx data in those regulatory regions
Fig. 2
Fig. 2
Regional Plot the FMNL3 Gene on Chromosome 12. Regional plots for the breast cancer association of FMNL3 at 12q13.12. A Depiction of coding regions of all coding genes (data retrieved from Ensembl biomart hg38) within the chromosomal region with FMNL3 highlighted in blue. B Variants included in the aggregation test, plotted according to their chromosomal position and analysis weight. Highlighted in blue are variants exclusively present in the analysis of samples of diverse ancestry. C Single-marker association results based on the same samples [10], with blue solid line denoting P-value for meta-analysis of all cohorts for gene of interest (P = 1.24 × 10−5) in this study and blue dashed line denoting unadjusted P-value for all-European meta-analysis (P = 6.11 × 10−6)

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