I suggest running your variants through the VEP. You will get detailed information about the effects on genes. The default setting for up/downstream is 5kb but you can alter that to 100kb. If you're looking for variants associated with common disease, you might want to consider using the postgap plugin as well which will find variants in LD with your input variants and look all of them up in GTEx, which may give you more useful information about how these variants may affect gene expression.

You can use BEDOPS to solve this problem.

First, reformat your variants into a sorted BED file, which uses UCSC chromosome names:

$ tail -n+2 variants.txt | awk -v FS="\t" -v OFS="\t" '{ print "chr"$1, ($2 - 1), $2, $3 }' | sort-bed - > variants.bed

Second, get gene annotations, e.g. from Gencode, assuming human:

$ GENCODE_URL=ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_28/gencode.v28.basic.annotation.gff3.gz
$ wget -qO- "${GENCODE_URL}" | gunzip -c | awk '($3=="gene")' | gff2bed - | awk -v FS="\t" -v OFS="\t" '{ n=split($10,a,";"); p=a[4]; n=split(p,a,"="); $4=a[2]; print $0; }' | cut -f1-6 > genes.bed

Now you have variants and genes in sorted BED format, both using UCSC chromosome names.

If you want all genes within 100kb, you can use bedmap --range to associate the variants with genes overlapping within that window:

$ bedmap --echo --echo-map-id-uniq --range 100000 --delim '\t' variants.bed genes.bed > answer.bed

The file answer.bed will contain the variant position and the unique IDs of all genes overlapping a 200kb window, centered on the variant position — i.e., 100kb upstream and downstream.

The --echo-map-id-uniq option can be replaced with --echo-map, if you need the intervals or other metadata for overlapping genes; see the documentation for more detail.

This file can be read into R via read.table or similar, if needed.