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Sas/genetics 9.1.3 User/'s Guide

Chapter 1
What’s New in SAS/Genetics 9.0, 9.1,
and 9.1.3
Chapter Contents
OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
ACCOMMODATING NEW DATA FORMATS . . . . . . . . . . . . . . . .
3
ALLELE PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
CASECONTROL PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . .
4
FAMILY PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
HAPLOTYPE PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . .
6
HTSNP PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
INBREED PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
PSMOOTH PROCEDURE . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
2
Chapter 1. What’s New in SAS/Genetics 9.0, 9.1, and 9.1.3
Chapter 1
What’s New in SAS/Genetics 9.0, 9.1,
and 9.1.3
Overview
SAS/Genetics includes several new procedures:
• the experimental HTSNP procedure for selecting a subset of SNPs that identify
9.1
groups of haplotypes that minimize within-group diversity
• the INBREED procedure for estimating covariance and/or inbreeding coeffi-
9.1
cients for pedigrees
New features have been added to the SAS/Genetics procedures:
• ALLELE
• CASECONTROL
• FAMILY
• HAPLOTYPE
• PSMOOTH
including options that accommodate new data formats.
Accommodating New Data Formats
There are several new options available for analyzing data in different formats.
9.1
The GENOCOL and DELIMITER= options have been added to four procedures:
ALLELE, CASECONTROL, FAMILY, and HAPLOTYPE. The GENOCOL option
enables you to use columns containing marker genotypes instead of a pair of columns
containing the two alleles that comprise the genotype. You can specify the delimiter
that is used to separate the two alleles with the DELIMITER= option. In addition, the
experimental options TALL, MARKER=, and INDIV= can be used collectively for
data in a “tall-skinny” format in the ALLELE, CASECONTROL, and HAPLOTYPE
procedures. Data sets in this format contain a marker identifier and individual identi-
fier, along with one variable containing the marker genotypes or two columns contain-
ing marker alleles. See the individual procedures’ Syntax sections for more details
about these new options.
4
Chapter 1. What’s New in SAS/Genetics 9.0, 9.1, and 9.1.3
ALLELE Procedure
9.1
The new options ALLELEMIN=, GENOMIN=, and HAPLOMIN= enable you to
specify the minimum estimated frequency for an allele, genotype, or haplotype, re-
spectively, to be included in its corresponding ODS table. By default, any allele,
genotype, or haplotype that occurs at least once in the sample is included in the re-
spective table. These options can be used to reduce the size of the ODS tables, or
alternatively, the GENOMIN= or HAPLOMIN= options can be set to 0 to view all
possible genotypes or haplotypes, not just those that are observed.
Some enhancements have been made in SAS 9.1.3 that improve the performance of
calculating linkage disequilibrium (LD) statistics for a large number of marker pairs.
These include:
• more efficient usage of memory when calculating LD measures
• a new LOGNOTE option to request notes indicating the status of the LD cal-
culations be printed to the log
• a new WITH statement that gives an alternative way of specifying pairs of
markers to analyze for LD and enables the partitioning of these calculations
Beginning in SAS 9.1.3, columns containing counts for alleles, genotypes, and, when
HAPLO=GIVEN, haplotypes are included in the corresponding ODS table in addi-
tion to the relative frequencies that have always been displayed. Cosmetic improve-
ments to the ODS tables in the SAS listing make these tables easier to read.
Also new, the HAPLO=NONEHWD option can be used to request that the CLD test
statistic for biallelic markers be adjusted for Hardy-Weinberg disequilibrium (Weir
1996). This adjustment is also made in the correlation coefficient when requested to
be displayed in the “LD Measures” table.
CASECONTROL Procedure
9.1
The new NULLSNPS= option enables you to specify SNPs to be used in calculating
the variance inflation factor for genomic control. By default, if VIF is specified,
the variables in the VAR statement are used, but this new option provides a way of
using particular SNPs, separate from those being tested for association and which
are assumed to have no association with the TRAIT variable, for genomic control
(Bacanu, Devlin, and Roeder 2000).
9.1
You can request that approximations of exact p-values for the case-control tests be
reported in place of the asymptotic chi-square p-values (Westfall and Young 1993).
The new PERMS= option indicates the number of permutations to be used for a
Monte Carlo estimate of each exact p-value, and the random seed can be provided in
the new SEED= option.
9.1
The OUTSTAT= data set includes two new columns: NumTrait1 and NumTrait2,
where the values 1 and 2 are replaced by the two values of the TRAIT variable.
FAMILY Procedure
5
These columns contain the number of genotyped individuals with each trait value for
each marker.
Beginning in SAS 9.1.3, the STRATA statement is available for specifying variables
that define strata in the sample. A Cochran-Mantel-Haenszel statistic (Agresti 1990)
is used to adjust for these categorical variables, enabling you to stratify your analysis
on the basis of gender or treatment for example, or to perform a nested or matched
case-control study.
Also new to SAS 9.1.3 is an OR option to have allele odds ratios and their confidence
intervals, with the level specified in the ALPHA= option, included in the output data
set for biallelic markers.
FAMILY Procedure
The new “Family Summary” ODS table displays information about each family in the
9.1
data set at each of the markers. This includes the number of parents genotyped, the
number of affected children, the number of unaffected children, as well as an error
code indicating what type of, if any, Mendelian inconsistencies occur in a nuclear
family’s genotypes at each marker. The new SHOWALL option can be used to display
this information for all families at each marker. By default, only those families with
a genotype error are included in the table for the marker(s) where the error occurs.
The new “Description of Error Codes” ODS table provides descriptions of the nu-
9.1
merical error codes used in the “Family Summary” table.
Approximations of exact p-values can now be requested in place of the asymptotic
9.1
chi-square p-values for the TDT, S-TDT, SDT, and combined S-TDT and SDT using
the PERMS= option. The number specified indicates the number of permutations to
be used in the Monte Carlo procedure for estimating exact p-values. You can provide
the random seed used for the permutations in the new SEED= option.
The multiallelic SDT and multiallelic combined SDT/TDT are now implemented as
9.1
described by Czika and Berry (2002).
Analysis of X-linked markers is facilitated in SAS 9.1.3 by the new XLVAR state-
ment. Markers listed in this statement can be tested for linkage and, under appropri-
ate conditions, association with a binary trait using the X-linked versions of the TDT,
S-TDT, combined S-TDT, and RC-TDT (Horvath, Laird, and Knapp 2000).
With the OUTQ= option, new to SAS 9.1.3 as well, you can create an output data
set containing the pair of allelic transmission scores (Abecasis, Cookson, and Cardon
2000) for each marker allele. These scores can then be used to perform family-based
tests for binary or quantitative traits in nuclear families or even extended pedigrees.
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Chapter 1. What’s New in SAS/Genetics 9.0, 9.1, and 9.1.3
HAPLOTYPE Procedure
9.1
The EST=EM | STEPEM option enables you to specify whether you would like hap-
lotype frequencies to be estimated using the original EM algorithm or the new step-
wise EM algorithm (Clayton 2002b). When EST=STEPEM is specified, a cutoff to
be used for trimming the set of haplotypes before adding an additional locus can be
given in the new STEPTRIM= option.
9.1
The ID statement enables variables from the input data set to be included in the OUT=
data set created by PROC HAPLOTYPE in addition to or instead of the –ID– vari-
able, a unique numeric identifier assigned to each individual by the procedure.
The option EST=BAYESIAN is experimental in SAS 9.1.3 and can be used to request
a Bayesian approach to the estimation of haplotype frequencies. Some new options
associated with only this estimation method are BURNIN=, INTERVAL=, THETA=,
and TOTALRUN=, all experimental as well.
HTSNP Procedure
9.1
The experimental HTSNP procedure implements search algorithms for identifying
a subset of SNPs called haplotype tag SNPs (htSNPs) (Johnson et al. 2001) that
capture much of the linkage disequilibrium and haplotype diversity among common
haplotypes.
Beginning in SAS 9.1.3, PROC HTSNP contains an option CRITERION= for select-
ing the measure to be used for determining the best set(s) of haplotype-tagging SNPs
(htSNPs). The possible values for this option are PDE (Clayton 2002a), the default
and the measure previously available, and RSQH, which implements the R2h measure
of Stram et al. (2003). Additionally, the best sets of htSNPs and their criterion mea-
sure are now displayed automatically in the ODS table “Evaluation of htSNPs” so the
OUTSTAT= option is no longer offered.
INBREED Procedure
9.1
The INBREED procedure is now included in SAS/Genetics in addition to SAS/STAT
where it originated. This procedure calculates the covariance or inbreeding coeffi-
cients for pedigrees either by treating the population as a single generation or by per-
forming separate analyses on each generation. You can also opt to have inbreeding
and covariance coefficients averaged within each gender category.
References
7
PSMOOTH Procedure
The new option TPM implements the truncated product method (Zaykin et al. 2002)
9.1
for smoothing p-values over windows of markers. The TAU= option, also new, can be
used in conjunction with the TPM option to specify the value of τ at which p-values
are truncated.
The Benjamini and Hochberg (1995) method of adjusting p-values to control the false
discovery rate (FDR) is available in SAS 9.1.3 with the ADJUST=FDR option.
References
Abecasis, G.R., Cookson, W.O.C., and Cardon, L.R. (2000), “Pedigree Tests
of Transmission Disequilibrium,” European Journal of Human Genetics, 8,
545–551.
Agresti, A. (1990), Categorical Data Analysis, New York: John Wiley & Sons, Inc.
Bacanu, S-A., Devlin, B., and Roeder, K. (2000), “The Power of Genomic Control,”
American Journal of Human Genetics, 66, 1933–1944.
Benjamini, Y. and Hochberg, Y. (1995), “Controlling the False Discovery Rate: A
Practical and Powerful Approach to Multiple Testing,” Journal of the Royal
Statistical Society, Series B, 57, 289–300.
Clayton, D. (2002a), “Choosing a Set of Haplotype Tagging SNPs from a Larger Set
of Diallelic Loci,” [http://www-gene.cimr.cam.ac.uk/clayton/software/
stata/htSNP/htsnp.pdf].
Clayton, D. (2002b), “SNPHAP: A Program for Estimating Frequencies of Large
Haplotypes of SNPs,” [http://www-gene.cimr.cam.ac.uk/clayton/software/
snphap.txt].
Czika, W. and Berry, J.J. (2002), “Using All Alleles in the Multiallelic Versions of
the SDT and Combined SDT/TDT,” American Journal of Human Genetics, 71,
1235–1236.
Horvath, S., Laird, N.M., and Knapp, M. (2000), “The Transmission/Disequilibrium
Test and Parental-Genotype Reconstruction for X-Chromosomal Markers,”
American Journal of Human Genetics, 66, 1161–1167.
Johnson, G.C.L. et al. (2001), “Haplotype Tagging for the Identification of Common
Disease Genes,” Nature Genetics, 29, 233–237.
Stram, D.O., Haiman, C.A., Hirschhorn, D.A., Kolonel, L.N., Henderson, B.E., and
Pike, M.C. (2003), “Choosing Haplotype-Tagging SNPs Based on Unphased
Genotype Data Using a Preliminary Sample of Unrelated Subjects with an
Example from the Multiethnic Cohort Study,” Human Heredity, 55, 27–36.
Weir, B.S. (1996), Genetic Data Analysis II, Sunderland, MA: Sinauer Associates,
Inc.
Westfall, P.H. and Young, S.S. (1993), Resampling-based Multiple Testing, New
York: John Wiley & Sons, Inc.
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Chapter 1. What’s New in SAS/Genetics 9.0, 9.1, and 9.1.3
Zaykin, D.V., Zhivotovsky, L.A., Westfall, P.H., and Weir, B.S. (2002), “Truncated
Product Method for Combining P -values,” Genetic Epidemiology, 22, 170–185.