Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

<div><p>High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.</p></div

Additional file 1: of Low agreement between modified-Schwartz and CKD-EPI eGFR in young adults: a retrospective longitudinal cohort study

Estimated Glomerular Filtration Rate by Each Equation Overall and for Relevant Subgroups. These figures describe trends in eGFR over time based upon the CKD-EPI equation (dashed line) or the Schwartz equation (solid line). Panel A) describes the overall population trajectory, while Panel B) depicts separate trajectories for each third of height Z-score. Panel C) depicts separate trajectories for glomerular and non-glomerular CKD, panel D) shows separate trajectories for males and females, and panel E) shows separate trajectories for African American and non-African American participants. (TIF 618 kb

Additional file 2: of Low agreement between modified-Schwartz and CKD-EPI eGFR in young adults: a retrospective longitudinal cohort study

Subject Aggregated Bland-Altman Plots by Age Group. These Bland-Altman plots depict the agreement between our two measurements within age intervals, with each panel from A) to D) representing a separate age group. The closer the central dashed line representing the mean to 0 within each plot, the better the agreement within that age group. Unlike the figure in the main text, each individual can only contribute one dot to the plot collapsed across all creatinine measurements. (TIF 193 kb

Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits-1

<p><b>Copyright information:</b></p><p>Taken from "Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S85</p><p>BMC Proceedings 2007;1(Suppl 1):S85-S85.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367462.</p><p></p

Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits-2

En two transcripts and -values are displayed in the outside box along with the bivariate LOD scores. We found two potential networks of regulatory genes among 15 co-expressed transcripts on the 4q28.2 to 4q31.1 region. Five transcripts did not have significant genetic correlation with any other transcript and are not included in this graph.<p><b>Copyright information:</b></p><p>Taken from "Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S85</p><p>BMC Proceedings 2007;1(Suppl 1):S85-S85.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367462.</p><p></p

Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits-0

En two transcripts and -values are displayed in the outside box along with the bivariate LOD scores. We found two potential networks of regulatory genes among 15 co-expressed transcripts on the 4q28.2 to 4q31.1 region. Five transcripts did not have significant genetic correlation with any other transcript and are not included in this graph.<p><b>Copyright information:</b></p><p>Taken from "Comparison of strategies for identification of regulatory quantitative trait loci of transcript expression traits"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S85</p><p>BMC Proceedings 2007;1(Suppl 1):S85-S85.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367462.</p><p></p

Genes whose expression levels are associated with rs1859023.

<p>Genes whose expression levels are associated with rs1859023.</p

Regional plot (ARIC) of rs1859023 association with incident CHD and LD in the region arround rs1859023 (YRI) [<b>22]</b>, [23<b> </b>].

<p>Regional plot (ARIC) of rs1859023 association with incident CHD and LD in the region arround rs1859023 (YRI) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002199#pgen.1002199-Pruim1" target="_blank">[<b>22]</b></a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002199#pgen.1002199-Johnson1" target="_blank">[23<b> </b>]</a>.</p

Baseline characteristics of the ARIC sample.

<p>Baseline characteristics of the ARIC sample.</p

The MAP (and SBP) Amerindian admixture mapping region on chromosome 6.

<p>The left panel provides the admixture mapping results as two lines, with the blue line that crosses the genome-wide significance threshold (horizontal grey dashed line) representing results from the primary analysis and the other, green line, representing the results from the conditional analysis, and the association results in the same region as circles. Lines and points are given as -log(<i>p</i>-value, 10) against genomic positions. Filled triangles correspond to the SNPs used in the conditional analysis. The right panel provides the ancestry-specific effect allele frequencies (EAF) for each of the SNPs used in the conditional analysis, as estimated by ASAFE applied on the HCHS/SOL data set [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188400#pone.0188400.ref030" target="_blank">30</a>].</p