A Comprehensive Package for Exact Statistical Genetic Analysis of Qualitative and Quantitative Traits
The Mendel software program performs likelihood-based statistical analysis to solve a variety of genetic problems. Implementations are included for all common, and several novel, statistical genetic tests. Data sets can consist of qualitative or quantitative traits, pedigree or population samples, limited loci or dense SNPs. Where appropriate, the analysis will use either the Elston-Stewart or Lander-Green-Kruglyak algorithms, whichever is more efficient for each individual pedigree.
Mendel includes options to:
- order and map a set of markers along a chromosome;
- use parametric linkage methods to localize a gene on a fixed marker map;
- estimate pedigree haplotypes;
- use non-parametric linkage (NPL) methods to localize a gene on a fixed marker map;
- identify potential genotyping errors;
- correctly estimate allele frequencies even in presence of pedigrees;
- perform genetic risk calculations;
- test for allelic association via the gamete-competition model;
- test for paternity or other pedigree relationships;
- estimate kinship coefficients conditional on marker data;
- identify deviations from Hardy-Weinberg and linkage equilibrium;
- test for association in unrelated using permutation tests;
- test for allelic association via the TDT;
- estimate trait genotype penetrances;
- estimate ethnic composition using pedigree data;
- combine alleles within a marker;
- simulate genetic data by gene dropping;
- combine loci to form non-codominant super-loci;
- localize QTLs, including X-linked QTLs, using variance component analysis;
- perform association analysis on quantitative traits;
- trim pedigrees to a core group and all necessary connecting relatives;
- test for association given linkage;
- impute dense SNP genotypes and haplotypes;
- perform GWAS: dense SNP association testing;
- convert between text-based and binary data files;
- perform maternal-fetal genotype (MFG) incompatibility tests;
- analyze QTLs in inbred strains;
- simulate trait values incorporating major loci and various models;
- perform pedigree-based GWAS (SNP association testing);
- test MFG interactions on quantitative traits using likelihood ratio tests;
- test MFG interactions on quantitative traits using very fast score tests.
Contact and Citation Information
Any comments, suggestions, and questions regarding Mendel are welcome at email@example.com.
Lange K, Papp JC, Sinsheimer JS, Sripracha R, Zhou H, Sobel EM (2013) Mendel: The Swiss army knife of genetic analysis programs. Bioinformatics 29:1568-1570.
New Features in Mendel Version 16
In version 16 of Mendel, the expiration date has been removed. Also, two new analysis options have been added, both quantitative versions of the Maternal-Fetal Genotype (MFG) incompatibility test. These are QMFG LRT (Analysis Option 30) that performs a Likelihood Ratio Test (LRT), and QMFG Score (Analysis Option 31) that uses the Score test instead of the LRT. The score test algorithm is much more efficient than the LRT, while the LRT is slightly more accurate. The QMFG Score Option can easily handle dense genome-wide SNP data sets. (There was no public version 15 of Mendel.)
New Features in Mendel Version 14
The main change in this version of Mendel is faster computation in many of the analysis options, dramatically faster for GWAS (Analysis Option 24), Ped-GWAS (Analysis Option 29), and Kinship Estimation (Analysis Option 10). This was predominantly obtained through parallelization. If desired, the user can limit or turn off this parallelization by setting the new keyword MAX_ THREADS to the number of processors Mendel should use. The default is to use all available processors.
In addition to making Ped-GWAS and Kinship Estimation much faster, they are also now much easier to use. For example, now simply setting the value of the keyword KINSHIP_ SOURCE in the control file determines if the global kinship coefficients are estimated via (1) only the explicit pedigree structures in the input file, (2) by comparing the SNP genotypes of pairs of individuals within pedigrees, which is the default, or (3) comparing the SNP genotypes of all pairs of individuals in the data set. If the kinship coefficients are set to be derived from SNP genotypes, the number of SNPs used can be set via the keyword SNP_ SAMPLING_ INCREMENT. The default value of 5 implies 20% of the SNPs are used; to use 100% of the SNPS, set this keyword to the value 1. The user can also trivially set overall minimum and maximum numbers of SNPS to use. Finally, the user can use the new keyword KINSHIP_ METHOD to choose between the Method of Moments (MoM) and the Genetic Relationship Matrix (GRM) algorithms for converting SNP genotypes to global kinship coefficients. On a standard laptop computer the association analysis of 1 million SNPs and 1000 related individuals often takes less than 5 minutes and requires about 1 GB of RAM.
Many more minor additions are also included in this new version of Mendel. For example, the new keywords MIN_ MAF and MAX_ MAF allow one to easily set bounds on the minor allele frequencies of the SNPs allowed to be included in the analysis. Also, Pedigree Trimming (Option 21) has a new sub-option that simply combines all individuals into one pedigree, keeping all stated relationships intact.
The v14.2 minor revision of Mendel significantly simplified and decreased the computation time for Ped-GWAS (Analysis Option 29) and Kinship Estimation (Analysis Option 10). For the Ped-GWAS Option, when you list several quantitative traits, each is analyzed separately, unless the new keyword MULTIVARIATE_ANALYSIS is set to true. This makes performing many univariate analyses much faster, since the data needs be initialized and the kinship coefficients estimated only once. Also, a new shorthand "all traits" can be used if all variables should be analyzed or a particular predictor should be used for all traits.
In version 14.4 of Mendel, the default penalty function used in penalized regression in GWAS (Analysis Option 24) has been changed from the classic lasso to the minimax concave penalty (MCP). The MCP escapes the over-shrinkage of the lasso, and thus should improve model selection by better avoiding false positives. To force Mendel to use the lasso penalty instead of MCP, set the new keyword LASSO_PENALTY to true.
In the v14.5 minor revision of Mendel, importing Plink format files is improved.
New Features in Mendel Version 13
This new version has significantly expanded several existing options and add one entirely new analysis option. The new Ped-GWAS analysis option provides extremely fast GWAS analysis on extended pedigrees, nuclear families, unrelated individuals, or any combination thereof. It can handle univariate or multivariate quantitative traits with missing data, and allows for covariate adjustment, including correction for population stratification. Pedigree kinships can either be explicitly provided or automatically estimated from dense markers. SNPs can be analyzed under codominant (additive), dominant, or recessive models.
The Ethnic Admixture analysis option has been expanded to rank markers by their informativeness to distinguish subpopulations. This option can now also perform very efficient principal component analysis (PCA). These two new features can be used with either text-based or binary data files.
The Kinship analysis option has also been expanded to binary files. It can now quickly estimate SNP-based global and local kinship coefficients on dense genomewide data. It can also use the SNP-based global estimates to cluster individuals and thus construct pedigree groupings without pre-existing relationship information.
The input data formats are now more flexible. For example, PLINK binary data sets (.fam, .bin, and .bed files) are now accepted (see the Mendel Documentation for details). The SNP definition file can now include allele names. Analogous to the SNP subset file, there is now a sample subset file to easily change which subset of individuals should be included in an analysis. Also, pedigree files without the twin field or without any pedigree fields can be read directly. These options are set using the new keyword INPUT_ FORMAT.
Many smaller improvements were also implemented. For example, missing SNP genotypes are now replaced with random "average" genotypes when missing data is not allowed in an analysis. Each selection of a random genotype is based on the allele frequencies within the data set. The GWAS output has been expanded and an issue with the Q-Q plot data is fixed. Also in the GWAS analysis option, one can now set specific values for the LASSO tuning constants using the command TUNING_ CONSTANT, if one wishes to override Mendel's automatic adjustments.
In version 13.2 the analysis to rank markers by their informativeness to distinguish subpopulations, has been extended to allow non-uniform population contributions. There are also a few minor bug fixes and other improvements.
Mendel Software License Agreement
Please read and specify that you agree or disagree at the bottom of this document.
Name of software being licensed: MENDEL
THIS IS A LEGAL AGREEMENT BETWEEN YOU (EITHER AS AN INDIVIDUAL OR ENTITY) AND KENNETH L. LANGE. BY INSTALLING THIS SOFTWARE, YOU ARE CONSENTING TO BE BOUND BY AND ARE BECOMING A PARTY TO THIS AGREEMENT. IF YOU DO NOT AGREE TO ALL OF THE TERMS OF THIS AGREEMENT, DO NOT INSTALL MENDEL.
THIS IS A PERPETUAL LICENSE.
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USE: Use is permitted on single-user computers or multi-user computational clusters, by the LICENSEE or authorized LICENSEE's colleagues within the same institution. LICENSEE agrees to provide KENNETH LANGE with a report of any errors encountered in LICENSEE'S installed MENDEL systems.
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TERMINATION: This license will terminate automatically if LICENSEE fails to comply with the limitations described herein. On termination, LICENSEE must destroy all copies of the Software and Documentation.
ACKNOWLEDGEMENT: LICENSEE agrees to make a good faith effort to ensure that for all LICENSEE'S installed MENDEL systems, all users of these MENDEL systems reference MENDEL in every publication that employs results generated by MENDEL. See the bottom of every summary output file generated by MENDEL for the preferred citations to use.