Meta XWAS of HIV+ across sub-Saharan Africa

1 Authors

• Chirag J Patel
  • github: @chiragjp
  • web: www.chiragjpgroup.org
• Kajal Claypool
• Eric Chow
• Jake Chung
• Don Mai
• Jessie Chen
• Eran Bendavid
  • email: ebd[at]stanford[dot]edu

2 Github Repo and Summary Statistics

• https://github.com/chiragjp/dhs_hiv_meta

3 Meta-analytic associations

3.1 Nagelkerke R2 vs. -log10(pvalue)

## Warning: Removed 33 rows containing missing values (geom_point).

## Warning: Removed 24 rows containing missing values (geom_point).

Figure 3.1: -log10(pvalue) versus Nagelkerke R2. Each point is an average of the Nagelkerke R2 over a number of countries and the p-value is the meta-analytic association pvalue. Associations are stratified by the number of countries for comparison. Horizontal dotted line represents the Bonferroni level of significance. Vertical line represents the R2 threshold of 0.001.

3.2 Odds Ratio vs. -log10(pvalue)

Figure 3.2: -log10(pvalue) versus odds ratio. Each point is an meta-analytic odds ratio over a number of countries and the p-value is the meta-analytic association pvalue. Associations are stratified by the number of countries for comparison. Dotted line represents the Bonferroni level of significance.

3.3 Inter-survey heterogeneity of identified associations

## Warning: Removed 3 rows containing missing values (geom_point).

Figure 3.3: I2 vs. -log10(meta analytic pvalue); dotted line represents 50% I2

4 Describing the distribution of the associations

4.1 Summary Tables

## `summarise()` regrouping output by 'num_country_bin' (override with `.groups` argument)

gender	n	q_25_or	q_50_or	q_75_or	q_25_I2	q_50_I2	q_75_I2	q_25_r2	q_50_r2	q_75_r2
(0,1]
f	4543	1.20	1.62	3.69	0.00	0.00	0.00	8.56 × 10−5	3.58 × 10−4	1.17 × 10−3
m	4089	1.24	1.78	12.67	0.00	0.00	0.00	8.39 × 10−5	3.29 × 10−4	1.12 × 10−3
(1,10]
f	1702	1.19	1.67	136.40	0.00	62.10	99.01	2.56 × 10−4	6.30 × 10−4	1.39 × 10−3
m	1488	1.24	2.30	526.30	0.00	64.85	99.14	2.42 × 10−4	5.91 × 10−4	1.26 × 10−3
(10,20]
f	467	1.15	3.93	110.95	49.80	98.59	99.39	4.10 × 10−4	7.67 × 10−4	1.22 × 10−3
m	368	1.17	3.44	189.43	47.49	98.38	99.41	4.09 × 10−4	7.70 × 10−4	1.57 × 10−3
(20,30]
f	539	1.17	1.65	9.88	56.04	97.57	99.35	4.77 × 10−4	8.42 × 10−4	1.42 × 10−3
m	343	1.26	3.68	56.47	67.70	99.14	99.48	4.71 × 10−4	7.10 × 10−4	1.22 × 10−3

## `summarise()` regrouping output by 'num_country_bin' (override with `.groups` argument)

gender	n	q_25_or	q_50_or	q_75_or	q_25_I2	q_50_I2	q_75_I2	q_25_r2	q_50_r2	q_75_r2
(0,1]
f	209	2.69	871,167.69	3,311,394.30	0.00	0.00	0.00	1.64 × 10−3	3.22 × 10−3	7.64 × 10−3
m	236	1,016,942.20	3,344,900.40	6,751,671.86	0.00	0.00	0.00	1.60 × 10−3	2.50 × 10−3	5.86 × 10−3
(1,10]
f	39	1.55	1.98	14,451.18	0.00	10.48	67.59	1.68 × 10−3	2.63 × 10−3	4.80 × 10−3
m	38	2.31	443,747.77	2,182,770.88	0.00	0.60	86.23	1.28 × 10−3	1.84 × 10−3	3.54 × 10−3
(10,20]
f	35	1.34	1.51	1.92	21.63	51.69	73.08	1.25 × 10−3	1.94 × 10−3	3.20 × 10−3
m	34	1.39	1.56	1.90	17.25	50.51	61.30	1.73 × 10−3	2.27 × 10−3	5.17 × 10−3
(20,30]
f	90	1.36	1.57	1.77	48.98	65.01	75.69	1.72 × 10−3	2.61 × 10−3	3.76 × 10−3
m	36	1.26	1.48	1.87	59.49	64.83	82.21	1.47 × 10−3	2.42 × 10−3	3.14 × 10−3

## `summarise()` ungrouping output (override with `.groups` argument)

gender	n	q_25_or	q_50_or	q_75_or	q_25_I2	q_50_I2	q_75_I2	q_25_r2	q_50_r2	q_75_r2
f	31	1.42	1.57	1.82	57.99	69.43	75.01	2.03 × 10−3	2.93 × 10−3	4.02 × 10−3
m	16	1.20	1.39	1.62	59.49	60.60	75.39	1.72 × 10−3	2.51 × 10−3	6.01 × 10−3

4.2 ECDFs of effect sizes

## Warning: Removed 16 rows containing non-finite values (stat_ecdf).

5 Identified Associations

• R2 > 0.001 and p-value < 1e-6
• SIG==TRUE

## `summarise()` regrouping output by 'name' (override with `.groups` argument)

sig	n
f
FALSE	6878
TRUE	373
m
FALSE	5944
TRUE	344

6 Top Associations for females across 20-29 countries in Sub-Saharan Africa

Figure 6.1: Left most panel is the variable name and code name, 2nd panel from left is the percent of countries variable was identified, 3rd panel from left is the Nagelkerke R2 per country (red dot is the average; blue points are countries with OR > 1, dark blue are countries with OR < 1), 4th panel shows the odds ratios (overall meta-analytic estimate in the red dot), and 5th panel shows the I2 (heterogeneity).

6.1 Table of females across 20-29 countries in Sub-Saharan Africa

• num_country denotes the number of countries (max of 29) for the association; pct_sig_country includes the number of countries identified (pvalue < 1e-6 and R2 > 0.001)
• nlp denotes -log10(pvalue)

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_point).

7 Top Associations for males across 20-29 countries in Sub-Saharan Africa

7.1 Table of males across 20-29 countries in Sub-Saharan Africa

8 Concordance of Odds Ratios of males vs. females across Sub-Saharan Africa

• comparing the associations across countries by correlating their odds ratios in males versus females
• association has to occur in at least 2 countries
• Those labeled “Female” are identified in females (and not in males); those labeled with “Male” are those identified in males
• Those labeled “Male+Female” are identified in both males and females

Figure 8.1: Correlation of odds ratios in females vs. males

9 Country by country correlation for males

• correlate the associations that are assessed in all 29 countries between each of the countries

##       25%       50%       75% 
## 0.1029621 0.2047824 0.3064261

Figure 9.1: Pairwise correlations between associations from each country (in males)

10 Country by country correlation for females

##       25%       50%       75% 
## 0.1486389 0.2627353 0.3701838

Figure 10.1: Pairwise correlations between associations from each country (in males)