Summix2 is a suite of methods that detect and leverage substructure in genetic summary data. This package builds on Summix, a method that estimates and adjusts for substructure in genetic summary that was developed by the Hendricks Research Team at the University of Colorado Denver.

Find more details about Summix in our **manuscript published in the American Journal of Human Genetics**.

For individual function specifics in Summix2:

**summix** — fast forward to example

**adjAF** — fast forward to example

**summix_local** — fast forward to example

To install Summix2, ensure you are in the devel version of R- (to install in Windows click here). Start R (version “4.4”)-the devel version- and run the following commands:

```
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
#The following initializes usage of the Bioconductor development version of Summix2
BiocManager::install(version = "devel")
BiocManager::install("Summix")
```

The *summix()* function estimates mixture proportions of reference groups within genetic summary (allele frequency) data using sequential quadratic programming performed with the **slsqp() function** in the nloptr package.

Mandatory parameters are:

**data**: A data frame of the observed and reference group allele frequencies for N genetic variants.**reference**: A character vector of the column names for K reference groups.**observed**: A character value that is the column name for the observed group.

Optional parameters are:

**pi.start**: Numeric vector of length K containing the user’s initial guess for the reference group proportions. If not specified, this defaults to 1/K where K is the number of reference groups.**goodness.of.fit**: Default value is*TRUE*. If set as*FALSE*, the user will override the default goodness of fit measure and return the raw objective loss from*slsqp()*.**override_removeSmallRef**: Default value is*FALSE*. If set as*TRUE*, the user will override the automatic removal of reference groups with <1% global proportions - this is not recommended.

A data frame with the following columns:

**goodness.of.fit**: Scaled objective loss from*slsqp()*reflecting the fit of the reference data. Values between 0.5-1.5 are considered moderate fit and should be used with caution. Values greater than 1.5 indicate poor fit, and users should not perform further analyses using Summix2.**iterations**: The number of iterations for the SLSQP algorithm before best-fit reference group proportion estimates are found.**time**: The time in seconds before best-fit reference group mixture proportion estimations are found by the SLSQP algorithm.**filtered**: The number of genetic variants not used in the reference group mixture proportion estimation due to missing values.**K columns**of mixture proportions of reference groups input into the function.

The *adjAF()* function adjusts allele frequencies to match reference group substructure mixture proportions in a given target group or individual.

Mandatory parameters are:

**data**: A data frame containing the unadjusted allele frequency for the observed group and K reference group allele frequencies for N genetic variants.**reference**: A character vector of the column names for K reference groups.**observed**: A character value that is the column name for the observed group.**pi.target**: A numeric vector of the mixture proportions for K reference groups in the target group or individual.**pi.observed**: A numeric vector of the mixture proportions for K reference groups in the observed group.**N_reference**: A numeric vector of the sample sizes for each of the K reference groups that is in the same order as the reference parameter.**N_observed**: A numeric value of the sample size of the observed group.

Optional parameters are:

**adj_method**: User choice of method for the allele frequency adjustment; options*“average”*and*“effective”*are available. Defaults to*“average”*.**filter**: Sets adjusted allele frequencies equal to 1 if > 1, to 0 if > -.005 and < 0, and removes adjusted allele frequencies < -.005. Default is*TRUE*.

A data frame with the following columns:

**pi**: A table of input reference groups, pi.observed, and pi.target.**observed.data**: The name of the data column for the observed group from which the adjusted allele frequencies are estimated.**Nsnps**: The number of SNPs for which adjusted AF is estimated.**adjusted.AF**: A data frame of original data with an appended column of adjusted allele frequencies.**effective.sample.size**: The sample size of individuals effectively represented by the adjusted allele frequencies.

The *summix_local()* function estimates local ancestry mixture proportions in genetic summary data using the same *slspq()* functionality as *summix()*. *summix_local()* also performs a selection scan (optional) that identifies regions of selection along the given chromosome.

Mandatory parameters are:

**data**: A data frame of the observed group and reference group allele frequencies for N genetic variants on a single chromosome. Must contain a column specifying the genetic variant positions.**reference**: A character vector of the column names for K reference groups.**observed**: A character value that is the column name for the observed group.**position_col**: A character value that is the column name for the genetic variants positions. Default is*“POS”*.**maxStepSize**: A numeric value that defines the maximum gap in base pairs between two consecutive genetic variants within a given window. Default is 1000.

Optional parameters are:

**algorithm**: User choice of algorithm to define local ancestry blocks; options*“fastcatch”*and*“windows”*are available.*“windows”*uses a fixed window in a sliding windows algorithm.*“fastcatch”*allows dynamic window sizes. The*“fastcatch”*algorithm is recommended- though it is computationally slower. Default is*“fastcatch”*.**type**: User choice of how to define window size; options*“variants”*and*“bp”*are available where*“variants”*defines window size as the number of variants in a given window and*“bp”*defines window size as the number of base pairs in a given window. Default is*“variants”*.**override_fit**: Default is*FALSE*. If set as*TRUE*, the user will override the auto-stop of*summix_local()*that occurs if the global goodness of fit value is greater than 1.5 (indicating a poor fit of the reference data to the observed data).**override_removeSmallAnc**: Default is*FALSE*. If set as*TRUE*, the user will override the automatic removal of reference ancestries with <2% global proportions – this is not recommended.**selection_scan**: User option to perform a selection scan on the given chromosome. Default is*FALSE*. If set as*TRUE*, a test statistic will be calculated for each local ancestry block. Note: the user can expect extended computation time if this option is set as*TRUE*.

Conditional parameters are:

If **algorithm** = *“windows”*:

**windowOverlap**: A numeric value that defines the number of variants or the number of base pairs that overlap between the given sliding windows. Default is 200.

If **algorithm** = *“fastcatch”*:

**diffThreshold**: A numeric value that defines the percent difference threshold to mark the end of a local ancestry block. Default is 0.02.

If **type** = *“variants”*:

**maxVariants**: A numeric value that specifies the maximum number of genetic variants allowed to define a given window.

If **type** = *“bp”*:

**maxWindowSize**: A numeric value that defines the maximum allowed window size by the number of base pairs in a given window.

If **algorithm** = *“fastcatch”* and **type** = *“variants”*:

**minVariants**: A numeric value that specifies the minimum number of genetic variants allowed to define a given window.

If **algorithm** = *“fastcatch”* and **type** = *“bp”*:

**minWindowSize**: A numeric value that specifies the minimum number of base pairs allowed to define a given window.

If **selection_scan** = *TRUE*:

**NSimRef**: A numeric vector of the sample sizes for each of the K reference groups that is in the same order as the reference parameter. This is used in a simulation framework that calculates within local ancestry block standard error.

A data frame with a row for each local ancestry block and the following columns:

**goodness.of.fit**: Scaled objective loss from*slsqp()*reflecting the fit of the reference data. Values between 0.5-1.5 are considered moderate fit and should be used with caution. Values greater than 1.5 indicate poor fit, and users should not perform further analyses using Summix2.**iterations**: The number of iterations for the SLSQP algorithm before best-fit reference group mixture proportion estimations are found.**time**: The time in seconds before best-fit reference group mixture proportion estimations are found by the SLSQP algorithm.**filtered**: The number of genetic variants not used in the reference group mixture proportion estimation due to missing values.**K columns**of mixture proportions of reference ancestry in the given local ancestry block.**nSNPs**: The number of genetic variants in the given local ancestry block.

Additional Output if **selection_scan** = *TRUE*:

**K columns**of local ancestry test statistics for each reference ancestry in the given local ancestry block.**K columns**of p-values for each reference ancestry in the given local ancestry block. P-values calculated using the Student’s t-distribution with degrees of freedom=(nSNPs in the block)-1.

For quick runs of all demos, we suggest using the data saved within the Summix library called ancestryData.

The commands:

```
library(Summix)
# load the data
data("ancestryData")
# Estimate 5 reference ancestry proportion values for the gnomAD African/African American group
# using a starting guess of .2 for each ancestry proportion.
summix(data = ancestryData,
reference=c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
observed="gnomad_AF_afr",
pi.start = c(.2, .2, .2, .2, .2),
goodness.of.fit=TRUE)
#> goodness.of.fit iterations time filtered reference_AF_afr
#> 1 0.4853597 20 0.6608024 secs 0 0.812142
#> reference_AF_eur reference_AF_iam
#> 1 0.169953 0.017905
```

The commands:

```
library(Summix)
# load the data
data("ancestryData")
adjusted_data<-adjAF(data = ancestryData,
reference = c("reference_AF_afr", "reference_AF_eur"),
observed = "gnomad_AF_afr",
pi.target = c(1, 0),
pi.observed = c(.85, .15),
adj_method = 'average',
N_reference = c(704,741),
N_observed = 20744,
filter = TRUE)
#> [1] "Average fold change between observed and target group proportions is: 0.58"
#>
#>
#> [1] "Note: In this AF adjustment, 0 SNPs (with adjusted AF > -.005 & < 0) were rounded to 0. 0 SNPs (with adjusted AF > 1) were rounded to 1, and 0 SNPs (with adjusted AF <= -.005) were removed from the final results."
#>
#> [1] $pi
#> ref.group pi.observed pi.target
#> 1 reference_AF_afr 0.85 1
#> 2 reference_AF_eur 0.15 0
#>
#> [1] $observed.data
#> [1] "observed AF data to update: 'gnomad_AF_afr'"
#>
#> [1] $Nsnps
#> [1] 1000
#>
#>
#> [1] $effective.sample.size
#> [1] 17632
#>
#>
#> [1] "use $adjusted.AF$adjustedAF to see adjusted AF data"
#>
#>
#> [1] "Note: The accuracy of the AF adjustment is likely lower for rare variants (< .5%)."
print(adjusted_data$adjusted.AF[1:5,])
#> POS REF ALT CHROM reference_AF_afr reference_AF_eas reference_AF_eur
#> 1 31652001 T A chr22 0.040925268 0 0.000000000
#> 2 34509945 C G chr22 0.217971527 0 0.000000000
#> 3 34636589 CAA C chr22 0.181117576 0 0.001149425
#> 4 38889885 A AAG chr22 0.007117446 0 0.000000000
#> 5 49160931 G T chr22 0.064056997 0 0.000000000
#> reference_AF_iam reference_AF_sas gnomad_AF_afr adjustedAF
#> 1 0 0 0.04171490 0.045000811
#> 2 0 0 0.18774500 0.219423999
#> 3 0 0 0.15198300 0.179859133
#> 4 0 0 0.00422064 0.006041453
#> 5 0 0 0.05445710 0.064062087
```

The commands:

```
library(Summix)
# load the data
data("ancestryData")
results <- summix_local(data = ancestryData,
reference = c("reference_AF_afr",
"reference_AF_eas",
"reference_AF_eur",
"reference_AF_iam",
"reference_AF_sas"),
NSimRef = c(704,787,741,47,545),
observed="gnomad_AF_afr",
goodness.of.fit = T,
type = "variants",
algorithm = "fastcatch",
minVariants = 150,
maxVariants = 250,
maxStepSize = 1000,
diffThreshold = .02,
override_fit = F,
override_removeSmallAnc = TRUE,
selection_scan = F,
position_col = "POS")
#> [1] "Done getting LA proportions"
print(results$results)
#> Start_Pos End_Pos goodness.of.fit iterations time filtered
#> 1 10595784 19258643 1.2555376 10 0.2096086 0
#> 2 19258643 25252606 0.5018649 13 0.2035103 0
#> 3 25252606 30743600 0.2304807 11 0.2430234 0
#> 4 30743600 35846592 0.2933341 14 0.1931231 0
#> 5 35846592 42706228 0.5480859 14 0.2230997 0
#> 6 42706228 47902876 0.2634092 11 0.2055104 0
#> 7 47902876 50791970 0.2891929 10 0.2163279 0
#> reference_AF_afr reference_AF_eas reference_AF_eur reference_AF_iam
#> 1 0.809208 0.000000 0.146185 0.034417
#> 2 0.816933 0.000000 0.161511 0.021556
#> 3 0.805795 0.002730 0.160926 0.000000
#> 4 0.820812 0.002558 0.161353 0.015276
#> 5 0.806428 0.016357 0.157855 0.019360
#> 6 0.810130 0.004046 0.181798 0.004025
#> 7 0.811265 0.000000 0.148492 0.019896
#> reference_AF_sas nSNPs
#> 1 0.010189 150
#> 2 0.000000 149
#> 3 0.030550 149
#> 4 0.000000 149
#> 5 0.000000 149
#> 6 0.000000 149
#> 7 0.020347 104
```