Say you're working with hg19.

Grab SNP entries from NCBI and convert them to sorted BED with vcf2bed:

$ wget -qO- ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz \
    | gunzip -c - \
    | convert2bed --input=vcf --output=bed --sort-tmpdir=${PWD} - \
    > hg19.snp151.bed

Or use whatever subset or other source of SNPs desired, and use the command-line to turn it into a sorted BED file.

Grab gene annotations of interest (e.g., GENCODE) and filter for genes into a sorted BED with gff2bed:

$ wget -qO- ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_21/gencode.v21.annotation.gff3.gz \
    | gunzip -c - \
    | gff2bed - \
    | awk '$8=="gene"' - \
    > genes.bed

Say we define proximal promoters as a region 1kb upstream of the gene. We can process the file genes.bed per-strand and generate promoter regions:

$ awk '{ \
        if ($6=="+") { \
            print $1"\t"($2 - 1000)"\t"$2"\t"$4"\t"$5"\t"$6; \
        } \
        else { \
            print $1"\t"$3"\t"($3 + 1000)"\t"$4"\t"$5"\t"$6; \
        } \
    }' genes.bed \
    > promoters.bed

Finally, we map SNP IDs to promoters with bedmap:

$ bedmap --echo --echo-map-id-uniq --delim '\t' promoters.bed hg19.snp151.bed > snps_over_promoters.bed