An efficient family‐based association test using multiple markers

Xu, Xin; Rakovski, Cyril; Xu, Xiping; Laird, Nan M.

doi:10.1002/gepi.20174

Cited by 40 publications

(48 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the S tests become two-sided tests (different from the ones implemented in FBAT ). In addition, we would like to note that equations 1, 2, 3, and 5 are identical to the ones proposed by Xu et al [13] .…”

Section: Methodsmentioning

confidence: 74%

“…For the readers' convenience, we recall the notion of Xu et al [13] and adopt all the equation given by the original authors. Let M be the total number of variants in the gene of interest.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the linear combination ( LC ) method, which also uses the beta-determined weights, has been previously implemented in the FBAT software for common variants. FBAT -LC has been demonstrated to be a powerful test for analyzing multiple markers using the estimated regression coefficients by single marker regression as the weights [13] .…”

mentioning

confidence: 99%

See 2 more Smart Citations

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Guo

Shugart

2012

Hum Hered

View full text Add to dashboard Cite

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.

show abstract

Section: Methodsmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Guo

Shugart

2012

Hum Hered

View full text Add to dashboard Cite

show abstract

“…In brief, the FBAT T LC linearly combined the single-marker test statistics with the use of data-driven weights into 1 global statistic and was shown to outperform the global haplotype FBAT and other multimarker tests in power to detect associations. 19 All FBATs were performed without use of an offset option and were based on additive model of inheritance, appropriate when genetic background of a disease is complex 20 but less powerful than recessive or codominant models to detect causal genetic recessive effects, in particular those exerted by rare alleles. 21 To estimate the relative risk of hypertension conferred by the most significant FGF1 polymorphism, we used Schaid's genotypic risk ratio test for familial association 22 available in the SIB-PAIR software.…”

Section: Discussionmentioning

confidence: 99%

Fibroblast Growth Factor 1 Gene and Hypertension

et al. 2007

View full text Add to dashboard Cite

Background-The distal portion of the long arm of chromosome 5 is linked to hypertension and contains functional candidate blood pressure-regulating genes. Methods and Results-Tightening the grid of microsatellite markers under this quantitative trait locus in the Silesian Hypertension Study (629 individuals from 207 Polish hypertensive families) provided enhanced support for linkage of this region to blood pressure (maximal Zϭ3.51, Pϭ0.0002). The fine mapping, comparative genomics, and functional prioritization identified fibroblast growth factor 1 gene (FGF1) as the positional candidate. Linkage disequilibrium mapping based on 51 single nucleotide polymorphisms spanning the locus showed no overlap between 3 independent haploblocks of FGF1 and the adjacent extragenic chromosomal regions. Single and multilocus family-based analysis revealed that genetic variation within FGF1 haploblock 1 was associated with hypertension and identified a common intronic single nucleotide polymorphism, rs152524, as the major driver of this association (Pϭ0.0026). Real-time quantitative polymerase chain reaction and Western blotting analysis of renal tissue obtained from subjects undergoing unilateral nephrectomy showed an increase in both mRNA and protein FGF1 expression in hypertensive patients compared with normotensive controls. Renal immunohistochemistry revealed that FGF1 was expressed exclusively within the glomerular endothelial and mesangial cells. Conclusions-Our data demonstrate that genetic variation within FGF1 cosegregates with elevated blood pressure in hypertensive families and that this association is likely to be mediated by upregulation of renal FGF1 expression. The results of our study will need to be replicated in other cohorts.

show abstract

“…This advantage cannot be achieved by the haplotype test or the smoothing procedure. The concept of the combining procedure also can be applied to the family-based association tests [34] . A program written in C++ to perform the T C test is available at http://www.biostat.harvard.edu/ ϳ fbat.…”

Section: Discussionmentioning

confidence: 99%

Combining Association Tests across Multiple Genetic Markers in Case-Control Studies

Zhou

Li²,

Xu³

2007

Hum Hered

Self Cite

View full text Add to dashboard Cite

In the search to detect genetic associations between complex traits and DNA variants, a practice is to select a subset of Single Nucleotide Polymorphisms (tag SNPs) in a gene or chromosomal region of interest. This allows study of untyped polymorphisms in this region through the phenomenon of linkage disequilibrium (LD). However, it is crucial in the analysis to utilize such multiple SNP markers efficiently. In this study, we present a robust testing approach (T_C) that combines single marker association test statistics or p values. This combination is based on the summation of single test statistics or p values, giving greater weight to those with lower p values. We compared the powers of T_C in identifying common trait loci, using tag SNPs within the same haplotype block that the trait loci reside, with competing published tests, in case-control settings. These competing tests included the Bonferroni procedure (T_B), the simple permutation procedure (T_P), the permutation procedure proposed by Hoh et al. (T_P-H) and its revised version using ‘deflated’ statistics (T_{P-H_def}), the traditional χ² procedure (T_CHI), the regression procedure (Hotelling T² test) (T_R) and the haplotype-based test (T_H). Results of these comparisons show that our proposed combining procedure (T_C) is preferred in all scenarios examined. We also apply this new test to a data set from a previously reported association study on airway responsiveness to methacholine.

show abstract

An efficient family‐based association test using multiple markers

Cited by 40 publications

References 21 publications

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

Fibroblast Growth Factor 1 Gene and Hypertension

Combining Association Tests across Multiple Genetic Markers in Case-Control Studies

Contact Info

Product

Resources

About