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. 2018 Oct 12;111(4):350–364. doi: 10.1093/jnci/djy132

Table 8.

Height or body mass index (BMI) single-nucleotide polymorphisms (SNPs) statistically significantly associated (P < .05) with breast cancer risk in CIMBA

Rsid Chromosome Position Nearest gene Reference allele in CIMBA Effect allele in CIMBA Effect allele frequency in CIMBA Imputation quality* Association with breast cancer in CIMBA
HR† SE P
Height
 rs10744956 15 51 269 629 AP4E1 A G 0.80 0.98 0.096 0.023 3 × 10-5
 rs7740107 6 130 374 461 L3MBTL3 T A 0.75 1 0.081 0.021 1 × 10-4
 rs10995319 10 52 762 887 PRKG1 T C 0.23 0.96 0.075 0.022 6 × 10-4
 rs8058684 16 53 515 118 RBL2 G A 0.32 1 0.061 0.019 .001
 rs11049611 12 28 600 244 CCDC91 C T 0.28 1 −0.065 0.020 .002
 rs8103992 19 19 665 643 PBX4 A C 0.78 0.98 −0.064 0.022 .004
 rs11618507 13 30 172 751 SLC7A1 G T 0.20 1 0.061 0.023 .007
 rs11244750 10 127 673 877 FANK1 C T 0.35 0.83 0.055 0.021 .009
 rs7701414 5 131 585 958 PDLIM4 A G 0.46 1 0.047 0.018 .01
 rs7733195 5 172 994 624 FAM44B G A 0.37 1 0.049 0.019 .01
 rs2306694 12 56 680 636 CS A G 0.06 1 0.096 0.038 .01
 rs6435143 2 203 194 256 NOP5/NOP A C 0.56 0.84 0.050 0.020 .01
 rs2284746 1 17 306 675 MFAP2 C G 0.50 0.84 −0.049 0.020 .01
 rs1797625 3 112 826 415 C3orf17 A T 0.36 0.91 −0.049 0.020 .01
 rs10495098 1 218 516 310 TGFB2 G T 0.41 0.84 −0.051 0.021 .01
 rs301901 5 37 046 626 NIPBL A G 0.45 0.93 −0.046 0.019 .02
 rs42039 7 92 244 422 CDK6 C T 0.26 1 0.051 0.021 .02
 rs1576900 9 18 629 792 ADAMTSL1 G A 0.30 0.91 0.048 0.020 .02
 rs891088 19 7 184 762 INSR A G 0.27 0.83 0.052 0.022 .02
 rs273945 7 137 611 566 CREB3L2 A C 0.58 1 −0.042 0.018 .02
 rs2682587 19 44 082 429 XRCC1 C A 0.18 1 0.053 0.023 .02
 rs1257763 9 96 893 945 PTPDC1 A G 0.96 0.72 −0.118 0.053 .03
 rs2888877 7 92 228 400 CDK6 T C 0.79 0.96 −0.050 0.022 .03
 rs8042424 15 101 762 539 CHSY1 C T 0.24 0.82 −0.052 0.024 .03
 rs7716219 5 54 955 071 SLC38A9 T C 0.70 0.97 −0.044 0.020 .03
 rs7727731 5 64 674 446 ADAMTS6 C T 0.12 0.67 −0.076 0.035 .03
 rs16964211 15 51 530 495 CYP19A1 G A 0.06 0.99 −0.085 0.040 .03
 rs11880992 19 2 176 403 DOT1L G A 0.42 0.95 −0.040 0.019 .03
 rs2302580 4 8 608 634 CPZ C T 0.44 1 −0.040 0.019 .03
 rs12538407 7 23 521 316 IGF2BP3 A G 0.39 0.99 0.040 0.019 .03
 rs2300921 3 185 651 001 SFRS10 T C 0.41 0.98 0.041 0.019 .03
 rs897080 2 44 774 202 C2orf34 C T 0.79 0.90 −0.050 0.024 .03
 rs4357716 11 69 163 161 MYEOV C T 0.14 1 0.055 0.026 .03
 rs11659752 18 77 222 862 NFATC1 T G 0.30 0.82 −0.045 0.021 .04
 rs2166898 2 121 612 659 GLI2 G A 0.17 0.55 0.067 0.032 .04
 rs6020202 20 48 634 821 SNAI1 G A 0.24 1 0.045 0.022 .04
 rs3760318 17 29 247 715 CENTA2 G A 0.37 1 −0.039 0.019 .04
 rs6746356 2 174 815 898 SP3 A C 0.24 0.79 0.050 0.024 .04
 rs9428104 1 118 855 587 SPAG17 A G 0.75 1 0.044 0.021 .04
 rs2834442 21 35 690 786 KCNE2 T A 0.66 0.98 −0.040 0.019 .04
 rs1546391 3 114 697 457 ZBTB20 C G 0.09 0.97 −0.069 0.033 .04
 rs12926008 16 2 488 211 CCNF T C 0.65 0.53 0.054 0.026 .04
 rs2123731 19 4 929 473 UHRF1 A G 0.27 0.70 −0.049 0.024 .04
 rs2289195 2 25 463 483 DNMT3A G A 0.41 0.65 0.046 0.023 .04
 rs16834765 1 32 371 442 PTP4A2 C T 0.05 0.82 0.091 0.045 .04
 rs10958476 8 57 095 808 PLAG1 T C 0.20 1 0.045 0.022 .045
 rs17369123 1 172 355 841 DNM3 C T 0.16 0.98 0.049 0.025 .046
 rs1415701 6 130 345 835 L3MBTL3 G A 0.26 1 0.041 0.021 .047
 rs11152213 18 57 852 948 MC4R A C 0.23 0.99 −0.043 0.022 .047
 rs4802134 19 38 346 685 SIPA1L3 A G 0.75 1 −0.041 0.021 .049
BMI
 rs13107325 4 103 188 709 SLC39A8 C T 0.09 0.76 0.101 0.037 .007
 rs10182181 2 25 150 296 ADCY3 A G 0.45 0.64 −0.056 0.023 .02
 rs7903146 10 114 758 349 TCF7L2 C T 0.30 1 0.044 0.020 .03
 rs9925964 16 31 129 895 KAT8 A G 0.39 0.99 −0.040 0.019 .03
 rs4740619 9 15 634 326 C9orf93 T C 0.46 0.97 0.040 0.019 .03
 rs1558902 16 53 803 574 FTO T A 0.42 1.00 −0.038 0.018 .04
 rs2207139 6 50 845 490 TFAP2B A G 0.16 0.99 0.049 0.024 .04
*

Imputation quality of 1 indicates genotyped SNPs. Rsid = Reference SNP cluster ID; CIMBA = Consortium of Investigators of Modifiers of BRCA1/2; HR = log hazard ratio; SE = standard error.

Per-allele association with breast cancer was adjusted for principal components, birth cohort, menopausal status, age at menopause, country of enrollment, and mutation status in weighted Cox models.

P values were calculated using weighted Cox models. All P values are two-sided.