Simple single-SNP regression class. More...

#include <MuGen.h>

Inheritance diagram for BetaGrpSnp:

Collaboration diagram for BetaGrpSnp:

Public Member Functions
	BetaGrpSnp ()
	Default constructor.

	BetaGrpSnp (const string &predFlNam, const string &outFlNam, const size_t &Ndat, const size_t &Npred, const size_t &d, const int &Nthr)
	Constructor with no replication and \(p\)-values. More...

	BetaGrpSnp (const string &predFlNam, const string &outFlNam, RanIndex &low, const size_t &Npred, const size_t &d, const int &Nthr)
	Constructor with replication and \(p\)-values. More...

	BetaGrpSnp (const string &predFlNam, const string &outFlNam, const size_t &Ndat, const size_t &Npred, const size_t &d, const int &Nthr, const double &prVar)
	Constructor with no replication and ABF. More...

	BetaGrpSnp (const string &predFlNam, const string &outFlNam, RanIndex &low, const size_t &Npred, const size_t &d, const int &Nthr, const double &prVar)
	Constructor with replication and ABF. More...

	~BetaGrpSnp ()
	Destructor.

virtual void	dump ()
	Dump results to the output file. More...

	BetaGrpSnp (const BetaGrpSnp &mG)
	Copy constructor. More...

BetaGrpSnp &	operator= (const BetaGrpSnp &mG)
	Assignment operator. More...

const gsl_matrix *	fMat () const
	Access adjusted fitted value matrix. More...

void	update (const Grp &dat, const SigmaI &SigIm)
	Response update function. More...

Public Member Functions inherited from MuGrp
	MuGrp ()
	Default constructor.

	MuGrp (RanIndex &low, const size_t &d)
	Deterministic zero-value constructor. More...

	MuGrp (const string &datFlNam, RanIndex &low, RanIndex &up, const size_t &d)
	Constructor with data from file. More...

	MuGrp (const string &datFlNam, RanIndex &up, const size_t &d)
	Constructor with data from file and no lower level. More...

	MuGrp (const vector< MVnorm * > &dat, RanIndex &low, RanIndex &up)
	Constructor with a vector of MVnorm pointers. More...

	MuGrp (const Grp &dat, RanIndex &low, RanIndex &up)
	Constructor with a Grp object. More...

	MuGrp (const vector< MVnorm * > &dat, RanIndex &low, RanIndex &up, const string &outFlNam)
	Constructor with a vector of MVnorm pointers and output file name. More...

	MuGrp (const Grp &dat, RanIndex &low, RanIndex &up, const string &outFlNam)
	Constructor with a Grp object and output file name. More...

	MuGrp (const Grp &dat, RanIndex &low)
	Deterministic mean constructor. More...

	MuGrp (const Grp &dat, const Qgrp &q, RanIndex &low)
	Deterministic weighted mean constructor. More...

	MuGrp (const gsl_matrix *dat)
	Deterministic constructor with a GSL matrix. More...

	MuGrp (const gsl_matrix *dat, RanIndex &low)
	Deterministic GSL matrix mean constructor. More...

	MuGrp (const gsl_matrix *dat, const Qgrp &q, RanIndex &low)
	Deterministic GSL matrix weighted mean constructor. More...

virtual	~MuGrp ()
	Destructor.

	MuGrp (const MuGrp &mG)
	Copy constructor. More...

	MuGrp (const Grp &g)
	Copy constructor. More...

MuGrp &	operator= (const MuGrp &mG)
	Assignemnt operator. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm)
	Student- \(t\) likelihood, improper prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const SigmaI &SigIp)
	Gaussian likelihood, 0-mean Gaussian prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const SigmaI &SigIp)
	Student- \(t\) likelihood, 0-mean Gaussian prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Qgrp &qPr, const SigmaI &SigIp)
	Gaussian likelihood, 0-mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Qgrp &qPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, 0-mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Grp &muPr, const SigmaI &SigIp)
	Gaussian likelihood, non-zero mean Gaussian prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Grp &muPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, non-zero mean Gaussian prior. More...

virtual void	update (const Grp &dat, const SigmaI &SigIm, const Grp &muPr, const Qgrp &qPr, const SigmaI &SigIp)
	Gaussian likelihood, non-zero mean Student- \(t\) prior. More...

virtual void	update (const Grp &dat, const Qgrp &q, const SigmaI &SigIm, const Grp &muPr, const Qgrp &qPr, const SigmaI &SigIp)
	Student- \(t\) likelihood, non-zero mean Student- \(t\) prior. More...

Public Member Functions inherited from Grp
virtual	~Grp ()
	Destructor.

virtual void	save ()
	Save to pre-specified file. More...

virtual void	save (const string &outFlNam)
	Save to file. More...

virtual void	save (const string &outMuFlNam, const string &outSigFlNam, const SigmaI &SigI)
	Joint save. More...

virtual void	save (const SigmaI &SigI)
	Save with inverse-covariance. More...

virtual void	save (const Grp &y, const SigmaI &SigI)
	Save with data and inverse-covariance. More...

void	mhlSave (const string &outFlNam, const SigmaI SigI)
	Save Mahalanobis distance. More...

const vector< MVnorm * > &	dataVec () const
	Get vector of row pointers. More...

virtual const gsl_matrix *	dMat () const
	Access the value matrix. More...

const size_t	Ndata () const
	Get number of rows. More...

const size_t	phenD () const
	Get number of traits. More...

virtual double	lnOddsRat (const Grp &y, const SigmaI &SigI, const size_t i) const
	Log-odds ratio. More...

const MVnorm *	operator[] (const size_t i) const
	Subscript operator. More...

MVnorm *	operator[] (const size_t i)
	Subscript operator. More...

virtual MuGrp	mean (RanIndex &grp)
	Group mean. More...

virtual const MuGrp	mean (RanIndex &grp) const
	Group mean. More...

virtual MuGrp	mean (RanIndex &grp, const Qgrp &q)
	Group weighted mean. More...

virtual const MuGrp	mean (RanIndex &grp, const Qgrp &q) const
	Group weighted mean. More...

void	center ()
	Center the value matrix. More...

Protected Attributes
gsl_matrix *	_Xmat
	SNP predictor matrix. More...

gsl_matrix *	_fakeFmat
	Matrix for addition operators. More...

gsl_matrix *	_Ystore
	Response storage. More...

size_t	_Npred
	Number of predictors (SNPs)

int	_nThr
	Number of threads.

double	_numSaves
	Number of saves. More...

double	_priorVar
	Prior variance. More...

string	_inPredFl
	Predictor (SNP) file name.

Protected Attributes inherited from Grp
vector< MVnorm * >	_theta
	Vector of pointers to value rows. More...

gsl_matrix *	_valueMat
	Value matrix. More...

RanIndex *	_lowLevel
	Lower level index. More...

RanIndex *	_upLevel
	Upper level index. More...

vector< gsl_rng * >	_rV
	Vector of PNG pointers. More...

string	_outFlNam
	Name of the output file.

Additional Inherited Members
Protected Member Functions inherited from Grp
	Grp ()

Detailed Description

Simple single-SNP regression class.

Implements single-marker GWAS. Each trait is treated separately, including the variances (i.e., \( \widehat{\sigma}^2_p = \widehat{\boldsymbol{\Sigma}}_{p,p} \)). However, in addition to the single-trait tests an extra Hotelling-type mutivariate association test is performed. This multivariate test reflects the distance of the whole vector of trait effects to zero, potentially increasing the power to detect pleiotropic SNPs with moderate effects on mutiple traits. The saved value matrix thus has one extra column for this test. The output is either \( -\log_{10}p \), (although this statistic is not strictly a frequentist \(p\)-value, it performs very similarly in simulations), or Wakefield's [wakefield07] approximation of \( -ln BF \) (log-Bayes factor ratio). In the latter case, the user can set the prior variance manually. The SNP regression is performed on the point estimate of a response, as described in documentation of the update() and dump() functions. The latter can be, say, a residual of a mixed-model type GEBV estimate done to control population structure.

Constructor & Destructor Documentation

◆ BetaGrpSnp() [1/5]

BetaGrpSnp::BetaGrpSnp	(	const string &	predFlNam,
		const string &	outFlNam,
		const size_t &	Ndat,
		const size_t &	Npred,
		const size_t &	d,
		const int &	Nthr
	)

Constructor with no replication and \(p\)-values.

Initializes the object with no replication (i.e., the number of rows in the predictor is the same as in the response) and leaves the prior variance for SNP regressions at zero, thereby resulting in a dump of \( -\log_{10}p \) values.

Parameters

[in]	string&	predictor file name
[in]	string&	output file name
[in]	size_t&	number of rows in the response matrix
[in]	size_t&	number of predictors
[in]	size_t&	number of traits
[in]	int&	number of threads

◆ BetaGrpSnp() [2/5]

BetaGrpSnp::BetaGrpSnp	(	const string &	predFlNam,
		const string &	outFlNam,
		RanIndex &	low,
		const size_t &	Npred,
		const size_t &	d,
		const int &	Nthr
	)

Constructor with replication and \(p\)-values.

Initializes the object with replication, encoded with the given index, and leaves the prior variance for SNP regressions at zero, thereby resulting in a dump of \( -\log_{10}p \) values.

Parameters

[in]	string&	predictor file name
[in]	string&	output file name
[in]	RanIndex&	replicate index
[in]	size_t&	number of predictors
[in]	size_t&	number of traits
[in]	int&	number of threads

◆ BetaGrpSnp() [3/5]

BetaGrpSnp::BetaGrpSnp	(	const string &	predFlNam,
		const string &	outFlNam,
		const size_t &	Ndat,
		const size_t &	Npred,
		const size_t &	d,
		const int &	Nthr,
		const double &	prVar
	)

Constructor with no replication and ABF.

Initializes the object with no replication (i.e., the number of rows in the predictor is the same as in the response) and sets the prior variance for SNP regressions to the given value, therefore dumping Wakefield's log of approximate Bayes factor (ABF) ratios to the output file.

Parameters

[in]	string&	predictor file name
[in]	string&	output file name
[in]	size_t&	number of rows in the response matrix
[in]	size_t&	number of predictors
[in]	size_t&	number of traits
[in]	int&	number of threads
[in]	double&	prior variance

◆ BetaGrpSnp() [4/5]

BetaGrpSnp::BetaGrpSnp	(	const string &	predFlNam,
		const string &	outFlNam,
		RanIndex &	low,
		const size_t &	Npred,
		const size_t &	d,
		const int &	Nthr,
		const double &	prVar
	)

Constructor with replication and ABF.

Initializes the object with replication, encoded with the given index, and sets the prior variance for SNP regressions to the given value, therefore dumping Wakefield's log of approximate Bayes factor (ABF) ratios to the output file.

Parameters

[in]	string&	predictor file name
[in]	string&	output file name
[in]	RanIndex&	replicate index
[in]	size_t&	number of predictors
[in]	size_t&	number of traits
[in]	int&	number of threads
[in]	double&	prior variance

◆ BetaGrpSnp() [5/5]

BetaGrpSnp::BetaGrpSnp ( const BetaGrpSnp & mG )

Copy constructor.

Parameters

[in] BetaGrpSnp& object to be copied

Member Function Documentation

◆ dump()

void BetaGrpSnp::dump ( )

virtual

Dump results to the output file.

The predictor (SNP) file is read by this function, and the single-predictor regression (i.e., each predictor is tested separately, ignoring all others) is performed. The results are saved to the pre-specified output file.

Reimplemented from Grp.

Reimplemented in BetaGrpPSR, and BetaGrpSnpCV.

◆ fMat()

const gsl_matrix* BetaGrpSnp::fMat ( ) const

inlinevirtual

Access adjusted fitted value matrix.

Returns: gsl_matrix* pointer to a zero-valued matrix

Reimplemented from Grp.

◆ operator=()

BetaGrpSnp & BetaGrpSnp::operator= ( const BetaGrpSnp & mG )

Assignment operator.

Parameters

[in] BetaGrpSnp& object to be copied

Returns: BetaGrpSnp& target object

Member Data Documentation

◆ _fakeFmat

gsl_matrix* BetaGrpSnp::_fakeFmat

protected

Matrix for addition operators.

This matrix is addressed by fMat() and set to all zero values, so that if this object is in an addition/subtraction operator things don't break. Because this object operates outside of the regular Markov chains, on the residuals of the model, it ordinarily makes no sense to add or subtract it from anything.

◆ _numSaves

double BetaGrpSnp::_numSaves

protected

Number of saves.

Number of times the response samples have been saved.

◆ _priorVar

double BetaGrpSnp::_priorVar

protected

Prior variance.

Prior variance for Wakefield's [wakefield07] ABF calculation (Wakefield's notation for it is \(W\)). If it zero, \( -\log_{10}p \) are calculated.

◆ _Xmat

gsl_matrix* BetaGrpSnp::_Xmat

protected

SNP predictor matrix.

Read in only at the end of the run in the dump() function.

◆ _Ystore

gsl_matrix* BetaGrpSnp::_Ystore

protected

Response storage.

MCMC samples from a response variable (representing a residual of the model) are stored here and divided by the total number of saves at the end before the regression is performed.

The documentation for this class was generated from the following files:

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ BetaGrpSnp() [1/5]

◆ BetaGrpSnp() [2/5]

◆ BetaGrpSnp() [3/5]

◆ BetaGrpSnp() [4/5]

◆ BetaGrpSnp() [5/5]

Member Function Documentation

◆ dump()

◆ fMat()

◆ operator=()

Member Data Documentation

◆ _fakeFmat

◆ _numSaves

◆ _priorVar

◆ _Xmat

◆ _Ystore