Two tidyverse ways.

Load the required libraries and read in the data.

library("dplyr")
library("readr")

data <- read_csv("input.csv")

You can use rowwise.

filtered_data <- data |>
  rowwise() |>
  filter(any(c_across(!Genes) > 2 | c_across(!Genes) < 0.2)) |>
  ungroup()

Or the more traditional data pivoting.

library("tidyr")

filtered_data <- data |>
  pivot_longer(!Genes) |>
  group_by(Genes) |>
  filter(any(value > 2 | value < 0.2)) |>
  ungroup() |>
  pivot_wider(names_from=name, values_from=value)

Using base R, check every value if they meet the condition, then subset

data[ which(rowSums(data[, -1 ] > 2 | data[, -1 ] < 0.2) > 0), ]