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Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls.

Lin WY, Liang YC - Sci Rep (2016)

Bottom Line: By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants.Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification.We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

View Article: PubMed Central - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

ABSTRACT
Detection of rare causal variants can help uncover the etiology of complex diseases. Recruiting case-parent trios is a popular study design in family-based studies. If researchers can obtain data from population controls, utilizing them in trio analyses can improve the power of methods. The transmission disequilibrium test (TDT) is a well-known method to analyze case-parent trio data. It has been extended to rare-variant association testing (abbreviated as "rvTDT"), with the flexibility to incorporate population controls. The rvTDT method is robust to population stratification. However, power loss may occur in the conditioning process. Here we propose a "conditioning adaptive combination of P-values method" (abbreviated as "conADA"), to analyze trios with/without unrelated controls. By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants. Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification. With regard to statistical methods for next-generation sequencing data analyses, validity may be hampered by population stratification, whereas power may be affected by the inclusion of neutral variants. We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

No MeSH data available.


Related in: MedlinePlus

Power comparison given the same source population for trios and controls (smaller proportion of causal variants).The figure shows the empirical power given the nominal significance level of 0.05. Top row: all causal variants were deleterious; bottom row: 50% of causal variants were deleterious and 50% were protective. The x-axis is the number of population controls, whereas the y-axis is the power. The study subjects (including trio members and population controls) comprised 0:100 (the left column, all were Africans), 20:80 (the second column), 50:50 (the middle column), 80:20 (the fourth column), and 100:0 (the right column, all were Europeans) ratios of Europeans to Africans, respectively.
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f2: Power comparison given the same source population for trios and controls (smaller proportion of causal variants).The figure shows the empirical power given the nominal significance level of 0.05. Top row: all causal variants were deleterious; bottom row: 50% of causal variants were deleterious and 50% were protective. The x-axis is the number of population controls, whereas the y-axis is the power. The study subjects (including trio members and population controls) comprised 0:100 (the left column, all were Africans), 20:80 (the second column), 50:50 (the middle column), 80:20 (the fourth column), and 100:0 (the right column, all were Europeans) ratios of Europeans to Africans, respectively.

Mentions: Given a smaller proportion of causal variants: In this scenario, we specified 25% of rare variants (with pooled MAF ≤ 0.01) as causal variants. Figure 2 presents the power of the seven conditioning approaches, and Supplemental Tables S7 (when all causal variants were deleterious) and S8 (when 50% of causal variants were deleterious and 50% were protective) show the power of all the 12 tests.


Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls.

Lin WY, Liang YC - Sci Rep (2016)

Power comparison given the same source population for trios and controls (smaller proportion of causal variants).The figure shows the empirical power given the nominal significance level of 0.05. Top row: all causal variants were deleterious; bottom row: 50% of causal variants were deleterious and 50% were protective. The x-axis is the number of population controls, whereas the y-axis is the power. The study subjects (including trio members and population controls) comprised 0:100 (the left column, all were Africans), 20:80 (the second column), 50:50 (the middle column), 80:20 (the fourth column), and 100:0 (the right column, all were Europeans) ratios of Europeans to Africans, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920030&req=5

f2: Power comparison given the same source population for trios and controls (smaller proportion of causal variants).The figure shows the empirical power given the nominal significance level of 0.05. Top row: all causal variants were deleterious; bottom row: 50% of causal variants were deleterious and 50% were protective. The x-axis is the number of population controls, whereas the y-axis is the power. The study subjects (including trio members and population controls) comprised 0:100 (the left column, all were Africans), 20:80 (the second column), 50:50 (the middle column), 80:20 (the fourth column), and 100:0 (the right column, all were Europeans) ratios of Europeans to Africans, respectively.
Mentions: Given a smaller proportion of causal variants: In this scenario, we specified 25% of rare variants (with pooled MAF ≤ 0.01) as causal variants. Figure 2 presents the power of the seven conditioning approaches, and Supplemental Tables S7 (when all causal variants were deleterious) and S8 (when 50% of causal variants were deleterious and 50% were protective) show the power of all the 12 tests.

Bottom Line: By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants.Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification.We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

View Article: PubMed Central - PubMed

Affiliation: Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

ABSTRACT
Detection of rare causal variants can help uncover the etiology of complex diseases. Recruiting case-parent trios is a popular study design in family-based studies. If researchers can obtain data from population controls, utilizing them in trio analyses can improve the power of methods. The transmission disequilibrium test (TDT) is a well-known method to analyze case-parent trio data. It has been extended to rare-variant association testing (abbreviated as "rvTDT"), with the flexibility to incorporate population controls. The rvTDT method is robust to population stratification. However, power loss may occur in the conditioning process. Here we propose a "conditioning adaptive combination of P-values method" (abbreviated as "conADA"), to analyze trios with/without unrelated controls. By first truncating the variants with larger P-values, we decrease the vulnerability of conADA to the inclusion of neutral variants. Moreover, because the test statistic is developed by conditioning on parental genotypes, conADA generates valid statistical inference in the presence of population stratification. With regard to statistical methods for next-generation sequencing data analyses, validity may be hampered by population stratification, whereas power may be affected by the inclusion of neutral variants. We recommend conADA for its robustness to these two factors (population stratification and the inclusion of neutral variants).

No MeSH data available.


Related in: MedlinePlus