conda 直接gatk并不完整，总会有问题，这里提供摸索后的conda安装解决方法

#准备
gatkcondaenv.yml, gatkPythonPackageArchive.zip 来自下列网址压缩包
https://github.com/broadinstitute/gatk/releases/download/4.2.6.1/gatk-4.2.6.1.zip

${your_path} = xxxxxx # 你自己控制的安装路径

#修改yml 文件
gatkcondaenv2  修改自 gatkcondaenv.yml, 添加gatk4 以及java 1.8
之前冲突可能来自 intel-openmp ，去掉版本限制，自动安装新版本

#conda 静默安装
nohup conda create -y -q -p ${your_path}/env/gatk4 --file gatkcondaenv2 -c conda-forge -c defaults  -c bioconda >gatk4.log 2>gatk4.err&
# 过程总安装软件版本在 gatk4.log 或 tmp.txt 中

# 进入环境
conda activate ${your_path}/env/gatk4

# 每次使用设置或者修改环境变量.bash_profile
export JAVA_HOME=${your_path}/env/gatk4
export PATH=$JAVA_HOME/bin:$PATH

#安装gatk 的python包
cd ${your_path}/env/tmp/python
7zzs x gatkPythonPackageArchive.zip
#unzip gatkPythonPackageArchive.zip

${your_path}/env/gatk4/bin/python3 setup_gcnvkernel.py install

${your_path}/env/gatk4/bin/python3 setup_vqsr_cnn.py install

# 安装完毕，可以测试，或退出环境
conda deactivate

gatkcondaenv2 文件内容

gatk4=4.2.6.1
java-1.8.0-openjdk-headless-cos7-s390x
python=3.6.10        # do not update
pip
mkl=2019.5           # MKL typically provides dramatic performance increases for theano, tensorflow, and other key dependencies
mkl-service=2.3.0
numpy=1.17.5         # do not update, this will break scipy=1.0.0
                                    #   verify that numpy is compiled against MKL (e.g., by checking *_mkl_info using numpy.show_config())
                                    #   and that it is used in tensorflow, theano, and other key dependencies
theano=1.0.4         # it is unlikely that new versions of theano will be released
                                    #   verify that this is using numpy compiled against MKL (e.g., by the presence of -lmkl_rt in theano.config.blas.ldflags)
defaults::tensorflow=1.15.0       # update only if absolutely necessary, as this may cause conflicts with other core dependencies
                                    #   verify that this is using numpy compiled against MKL (e.g., by checking tensorflow.pywrap_tensorflow.IsMklEnabled())
scipy=1.0.0          # do not update, this will break a scipy.misc.logsumexp import (deprecated in scipy=1.0.0) in pymc3=3.1
pymc3=3.1            # do not update, this will break gcnvkernel
h5py=2.10.0          # required by keras 2.2.4
keras=2.2.4          # updated from pip-installed 2.2.0, which caused various conflicts/clobbers of conda-installed packages
                                    #   conda-installed 2.2.4 appears to be the most recent version with a consistent API and without conflicts/clobbers
                                    #   if you wish to update, note that versions of conda-forge::keras after 2.2.5
                                    #   undesirably set the environment variable KERAS_BACKEND = theano by default
defaults::intel-openmp
scikit-learn=0.23.1
matplotlib=3.2.1
pandas=1.0.3

# core R dependencies; these should only be used for plotting and do not take precedence over core python dependencies!
r-base=3.6.2
r-data.table=1.12.8
r-dplyr=0.8.5
r-getopt=1.20.3
r-ggplot2=3.3.0
r-gplots=3.0.3
r-gsalib=2.1
r-optparse=1.6.4
r-backports=1.1.10

biopython=1.76
pyvcf=0.6.8
pysam=0.15.3