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Fig 1.

Overview of the protein structure guided local test (POINT).

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Table 1.

Counts of neighboring variants which contribute “significantly” to the focal rare variants in PLA2G7 for different values of c.

Neighboring variants ’s contributing ≥ 5% of the information to the focal variant m (i.e., neighboring variants with rℓm ≥ 0.05) are considered as “significant”.

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Fig 2.

PLA2G7 rare variant positions.

A: Rare variant locations on the protein tertiary structure. B: Corresponding Euclidean distance-based clustering of the variants.

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Fig 2 Expand

Table 2.

Selection performance of continuous-trait simulation with n = 2000 subjects.

Selection performance for single variant test (SVT), REBET, POINT test using local burden kernel (POINT-Burden), and POINT test using local linear kernel (POINT-Linear). The best performed methods (based on the composite F-measure) are shown in bold and the second best are shown in italic.

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Table 3.

Selection performance of binary trait simulation with n = 2000 subjects.

Selection performance for single variant test (SVT), scan statistic (SCAN), ADA, REBET, POINT test using local burden kernel (POINT-Burden), and POINT test using local linear kernel (POINT-Linear). The best performed methods (based on the composite F-measure) are shown in bold and the second best are shown in italic.

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Fig 3.

Information-borrowing map for PLA2G7 variant V279.

Information-borrowing map shows the amount of borrowing from neighboring variants for PLA2G7 variant V279 for different values of c, with darker color representing higher levels of contribution via the variant correlation matrix R.

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Fig 4.

PCSK9 rare variant positions.

A: Rare variant locations on the protein tertiary structure of PCSK9 binding with LDLR (shown in yellow). Promising variants (i.e., p-value<0.05) are shown in colored boxes, with variants found by both single variant test and POINT shown in red and variants found only by POINT shown in green and blue. B: Euclidean distance-based clustering of the variants.

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Fig 4 Expand

Table 4.

PCSK9 analysis results summary.

Results of PCSK9 analysis using the single variant test (SVT), REBET, and POINT (POINT-Burden). Promising variants are selected using the criterion of p-value< 0.05 and are shown in bold font.

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Fig 5.

Assessment of PCSK9-LDLR binding stability for the mutant sequences from the five POINT-identified loci using molecular dynamic simulations.

There are 8 mutant sequences observed in ACCORD samples, four of which have significant conformational fluctuation changes comparing to wildtype sequence: N157K, H553R, A443T+H391N, and A443T+N425S. P-values from Wilcoxon rank sum test of difference in RMSF are shown in parentheses.

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Fig 6.

POINT computational time with large number of resamples, i.e., 1e5, 5e5 and 1e6.

Average run time (in minutes) based on 10 replications for a single variant POINT test with 13 associated variants.

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