Characteristics of the insulin/IGF-1 signaling pathway proteins:
Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage
PIK3R2 5296 19 18,125,016 18,142,343 17.33 5 66.67%
PIK3R3 8503 1 46,278,399 46,371,295 92.90 10 60.00%
PP2A PPP2R5B 5526 11 64,448,756 64,458,523 9.77 3 37.50%
Protor-1 (mTORC2) PRR5 55615 22
43,443,091
43,512,225
69.13
32
50.00%
PTEN PTEN 5728 10
89,613,175
89,718,512
105.34
8
47.06%
PTP1B PTPN1 5770 20
48,560,298
48,634,493
74.20
17
44.44%
p130Rb2 RBL2 5934 16
52,025,852
52,083,061
57.21
3
33.33%
RICTOR (mTORC2) RICTOR 253260 5
38,973,780
39,110,258
136.48
9
30.77%
SCP2 SCP2 6342 1
53,165,536
53,289,870
124.33
18
50.00%
SGK1 SGK1 6446 6
134,532,077 
134,680,889 
148.81 
38 
46.75%
SGK2 SGK2 10110 20
41,621,100 
41,647,687 
26.59 
9 
34.62%
SIRT1 SIRT1 23411 10
69,314,433 
69,348,152 
33.72 
4 
33.33%
SIRT2 SIRT2 22933 19
44,061,040 
44,082,201 
21.16 
7 
38.89%
SIRT3 SIRT3 23410 11
205,030 
226,362 
21.33 
17 
60.00%
SKP2 SKP2 6502 5
36,187,946 
36,219,904 
31.96 
15 
51.72%
SOCS1 SOCS1 8651 16
11,255,775
11,257,540
1.77
4
50.00%
SOCS3 SOCS3 9021 17
73,864,457
73,867,753
3.30
4
50.00%
MnSOD SOD2 664SNPs Coverage TPP1 (shelterin) ACD 65057 16 66,248,916 66,252,219 3.30 2 50.00% Dyskerin (telomerase) DKC1 1736 X 153,644,225 153,659,158 14.93 1 NA GAR1 (telomerase) GAR1 54433 4 110,956,115 110,965,342 9.23 1 14.29% NHP2 (telomerase) NHP2 55651 5 177,509,072 177,513,567 4.50 2 33.33% NOP10 (telomerase) NOP10 55505 15 32,421,209 32,422,654 1.45 7 45.45% POT1 (shelterin) POT1 25913 7 124,249,676-124,357,273-107.60-25-55.56% TP1 (telomerase) TEP1 -7011-14-19903666-19951419-47.75-21-40.00% TERC (telomerase) TERC -7012-3-170965092-170965542-0.45-1-25.00% TRF1 (shelterin) TERF1 -7013-8-74083651-74122541-38.89-10-60.00% TRF2 (shelterin) TERF2 -7014-16-67946965-67977375-30.41-6-57.14% RAP1 (shelterin) TERF2IP -54386 -16 -74239136 -74248842 -9.71 -4 -50.00% TERT (telomerase) TERT -7015-5-1306287-1348162-41.88-7-41.18% TIN2 (shelterin) TINF2 26277 14 23,778,691 23,781,720 3.03 1 14.29% Total 88 Chr Chromosome position of the gene according to NCBI Build 36, Start (bp) start position of the gene according to NCBI Build 36, End (bp) end position of the gene according to NCBI Build 36, Coverage coverage of genes based on Phased data HapMap II release 22 CEU, NA not available.
 
Characteristics of the eye and hair color pathway proteins: Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage ASIP ASIP 434 20 32,311,832 32,320,809 8.98 5 50.00% HERC2 HERC2 8924 15 26,029,783 26,240,890 211.11 9 41.67% IRF4 IRF4 3662 6 336,739-356443-19.70-14-65.00% MC1R MC1R 4157 16 88,511,788-88,514,886-3.10-3-33.33% OCA2 OCA2 4948 15 25,673,616-26,018,053-344.44-82-58.00% SLC24A4 SLC24A4 123041 14 91,858,678 -92,037,578 -178.90-62-53.68% SLC45A2 SLC45A2 51151 5 33,980,478-34,020,537-40.06-15-44.83% TYR TYR 7299 11 88,550,688 -88,668,575 -117.89-22-56.00% TYRP1 TYRP1 7306 9 12,683,386 -12,700,266 -16.88-11-50.00% Total 223 Chr Chromosome position of the gene according to NCBI Build 36, Start (bp) start position of the gene according to NCBI Build 36, End (bp) end position of the gene according to NCBI Build 36, Coverage coverage of genes based on Phased data HapMap II release 22 CEU would explain the scattered association in the IIS pathway. Abnormalities in growth and/or increased mortality (www.informatics.jax.org; Blake et al., 2011), indicate that these genes are indeed responsible for the growth andthat the pathway as a whole is associated with the trait, rather than relying on a single SNP. This makes SNP set analysis a valuable tool for identifying complex genetic associations. However, it is important to carefully select the pathways and methods used in such analyses, as different approaches can yield contradictory results. In this study, we focused on the IIS and TM pathways and used several different pathway tests to ensure robustness of our findings. We also chose a fixed window of 10 kb for SNP set analysis to take into account regulatory regions surrounding the genes, while acknowledging that this approach may miss some functional SNPs. Our study highlights the potential of SNP set analysis as a complementary approach to single SNP analysis in GWAS, particularly for identifying associations with complex genetic pathways.The combined effect of SNPs within a gene can overcome this problem. Therefore, replication of SNP set analysis is assumed to be more reproducible between genotyping platforms and populations (Luo et al., 2010; Wang et al., 2010). To support these assumptions, our findings should be replicated in other cohorts.

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